Emanuele MassettiSchool of Public PolicyGeorgia Institute of TechnologyNovember 27 2018
William Nordhausrsquo 2018 Nobel Prize in EconomicsIntegrating Climate Change into Long-Run Macroeconomic Analysis
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
bull 1972 UN Conference on the Human Environment (Stockholm)
Forecasts of Ending Prosperity
The limits to growth 1972
1972-1974 Natural Resources and Long-Term Economic Growth
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
bull 1972 UN Conference on the Human Environment (Stockholm)
Forecasts of Ending Prosperity
The limits to growth 1972
1972-1974 Natural Resources and Long-Term Economic Growth
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
bull 1972 UN Conference on the Human Environment (Stockholm)
Forecasts of Ending Prosperity
The limits to growth 1972
1972-1974 Natural Resources and Long-Term Economic Growth
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
bull 1972 UN Conference on the Human Environment (Stockholm)
Forecasts of Ending Prosperity
The limits to growth 1972
1972-1974 Natural Resources and Long-Term Economic Growth
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
1970rsquos End of The Growth Miraclebull Economic growth slows down
bull Trade-offs between economic growth and the environment
bull 1972 publication of the ldquoLimits to Growthrdquo
bull 1972 UN Conference on the Human Environment (Stockholm)
Forecasts of Ending Prosperity
The limits to growth 1972
1972-1974 Natural Resources and Long-Term Economic Growth
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
Forecasts of Ending Prosperity
The limits to growth 1972
1972-1974 Natural Resources and Long-Term Economic Growth
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
1972-1974 Natural Resources and Long-Term Economic Growth
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
bull Not a single relationship or variable is from actual data or empirical studiesbull Simulation data not subjected to empirical validation are void of meaningbull Lack of true multidisciplinary efforts
1973 Sharp Critique of the Limits to Growth
If there is sufficient substitutability between producible and non-producible resources and if the price system is functioning adequately the inevitable collapse predicted by Forrester will be avoided
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
1975 Economic Growth Meets Climate Changebull 1974-1975 IIASA Energy
ProgramldquoMy own first serious research on global warming started when I spent a year in Vienna at IIASA []rdquo
ldquoResearch on global warming is deeply interdisciplinary and IIASA at that time was uniquely able to foster interdisciplinary work It was here that I published my first (and I believe the first) economic model of global warming as an IIASA working paper in 1975rdquo
httpwwwiiasaacatwebhomeaboutalumniNews181008-nordhaushtml
[]when he was a research scholar in Austria doing energy research and happened to share an office with a climatologist who told him This is where energy research is going to be going Nordhaus remembers I said Well OK tell me about it And thats how it started
Despite some scientific interest in climate change at the time the topic was zero on the intellectual Kelvin scale in economics Nordhaus says There was nothing at that pointldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
bull Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoOne persistent concern has been thatmans economic activities would reach a scale
where the global climate would be significantly affectedrdquo
ldquoIt appears that the uncontrolled path will lead to very large increases in temperature in the coming decades taking the climate outside of any temperature pattern
observed in the last 100000 yearsrdquo
The Carbon Dioxide Problem
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
bull GHG emissions are an ldquoexternalityrdquobull The cost of increasing GHGs concentrations mostly falls on other individuals
bull Externalities are a market failurebull The polluter does not ldquoownrdquo the full consequences of releasing emissionsbull Free-lunchbull Excessive level of emissions
bull Carbon-dioxide emissions are the ldquoperfectrdquo externalitybull Global effectsbull Long-lasting effects
The Carbon Dioxide Problem
Economic Externalitiesbull Demand function as a measure of
the gross benefit from consumption
bull 119875119875119864119864 private cos tbull 119876119876119864119864 market equilibrium
bull 119890119890 external damage of one unit of gas oline
bull P s ocial cos t
bull Triangle abc meas ures welfare los sbull A tax equal to e res tores efficiency
Price
Gasoline
D
Q 119876119876119864119864
b
a
c119875119875119864119864e
P=119875119875119864119864+e
bull Marginal costbenefit costbenefit of one additional unit of consumptioninvestment
bull Economic efficiency (optimality) is the fundamental principle in economic analysis
bull Marginal cost = marginal benefit
bull The cost of reducing carbon-dioxide emissions by one additional unit must be equal to the benefit not releasing that unit from the atmosphere
bull This is a very complex problem that requires knowingbull Costsbull Benefits (avoided damages)bull In a dynamic long-run global setting
Economic Efficiency
bull Economic efficiency selects the ldquooptimalrdquo (efficient) level of concentrations in the atmosphere
bull Cost-effectiveness is less ambitiousbull Choose a desired level of concentrationsbull Achieve this goal at the least cost
bull Economic efficiency implies cost -effectiveness but not vice versa
Cost-Effectiveness
A Simple Integrated Assessment Model
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull Pioneering modeling of interactions between economic growth and climate change
bull A carbon-circulation model to track carbon -dioxide concentrationbull A climate model that describes climate change as function of concentrationsbull An economic model in which output is produced using capital labor and
energy
bull Solving a model with three different integrated systems was a challenge
bull Use of computer simulations instead of analytical solutions
bull We call this framework Integrated Assessment Modelbull Main advantage consistent scenarios for economic energy and climate
systems
This Changed the Way in Which Models are Used
Stabilization
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
bull The first model in 1977 studies cost-effective solutions
bull No damage functions
bull Efficiency cannot be established
Nordhaus W D (1977) Economic growth and climate the carbon dioxide problem The American Economic Review 67(1) 341-346
ldquoSubject to the limitations of the techniques used here we can be relatively optimistic about the technical feasibility of a carbon dioxide control strategyrdquo
Optimism with Reservations
The two central questions that remain to be answered are
ldquoHow costly are the projected changes in (or the uncertainties about) the climate likely to be and therefore to what level of control should we aspire ldquo
ldquoHow can we reasonably hope to negotiate an international control strategy among the several nations with widely divergent interestsrdquo
bull 1975-1977 the Provisional Modelbull A detailed energy sector derived from previous workbull No feedback of the climate on the economybull Path of economic growth is givenbull Study of cost of reducing emissions
bull 1994 the Dynamic Integrated Climate Economy (DICE) modelbull Still used today and updated regularlybull Integrated in an optimal (efficient) growth frameworkbull Impacts of the climate system on the economybull Analysis of optimal (efficient) climate policy what emissions should be
The DICE Model
bull 1996 The Regional Integrated Climate Economy (RICE) modelbull Multi -regional modelbull Regions can choose to cooperate by considering damages to other regions
caused by own emissions (Cooperative solution)bull Regions can behave non-cooperatively by ignoring damages to other regions
(Nash equilibrium)
bull Opens the possibility to test with simulations the growing literature on non-cooperative game theory applied to climate change
bull The cooperative solution solves the climate problem bull Analysis of efficient distribution of mitigation effort
The RICE Model
Example of Ouput
Nordhaus (2018)
1 Natural res ources and environmental pollutions in the context of long-run economic growth
2 Climate change problem as an inter-generational inves tment and cons umption dilemma
3 Technological change as key driver of long-run growth
4 Economic efficiency as guiding principle
5 Central role of carbon prices
6 International cooperation for climate change
Six Important Take -Away Messages
ldquoHis calm approach has a t times infuria ted environmental activis ts who are dis mayed at the pace of global actionrdquo
What can I say about calm he replies when as ked about that
I like to think of the economics as a cool head in the service of a warm heart and thats my approach to thisldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
A Cool Head in the Service of a Warm Heart
The 2018 Fourth National Climate Assessment
Where we learn why keeping a cool head is important and where we develop intuitions on economic damages and discounting
ldquoA major scientific report issued by 13 federal agencies on Friday presents the starkest warnings
to date of the consequences of climate change for the United States predicting that if significant steps are not taken to rein in global warming the damage will knock as much as 10 percent off the size of the American economy by centuryrsquos endrdquo
ldquoThe report puts the most precise price tags to date on the cost to the United States economy of projected climate impacts $141 billion from heat-related deaths $118 billion from sea level rise and $32 billion from infrastructure damage by the end
of the century among othersrdquo
A Case of False Perceptions
bull Nowhere in the Summary Findings nor anywhere else in the text one can find estimates of damages equal to 10 of GDP in 2100
bull NCA4 estimates economic losses equal to a fraction of a percentage point in 2100
bull 10 losses come from bad reporting and less than ideal writing of the report (more on this later)
bull Unfortunately extreme emissions scenario (RCP 85) is used as business as usual scenario without understanding the underlying implications
NCA4 Report in Brief Ch 29 p 170
RCP 85 RCP 45Damages avoided
under RCP 45
Labor 15500$ 7440$ 48Extreme temperature mortality 14100$ 8178$ 58Coastal Property 11800$ 2596$ 22Air Quality 2600$ 806$ 31Roads 2000$ 1180$ 59Electricity Supply and Demand 900$ 567$ 63Inland Flooding 800$ 376$ 47Urban Drainage 600$ 156$ 26Rail 600$ 216$ 36Water Quality 500$ 175$ 35Coral Reef 400$ 048$ 12West Nile Virus 300$ 141$ 47Freshwater Fish 300$ 132$ 44Winter Recreation 200$ 021$ 11Bridges 100$ 048$ 48Munic And Industrial Water Supply 032$ 010$ 33Harmful Algal Blooms 020$ 009$ 45Alaska Infrastructure 017$ 009$ 53Shellfish 023$ 013$ 57Agriculture 001$ 000$ 11Aeroallergens 000$ 000$ 57Wildfire (011)$ 014$ -134Total 50783$ 22137$
NCA4 Report in Brief Ch 29 p 170
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8534043909869707861E-338038514202625843E-369671971569556694E-345497851530274204E-316359057302934971E-337529045096676796E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage2618954768450908723708089876421781229910571535489842466150258043088135708598130968293563287920328417332190398775689046
Annual Damages pp in 2090 ( of GDP pp)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 8565638172495556552E-356881867378053572E-362803280770209521E-31351572744336758E-242769596580643507E-398117030163692477E-3
GDP pp in 2090 counting for damages( GDP pp in2020)
SSP 1SSP 2SSP 3SSP 4SSP 5SSP 5SSP 5RCP 45RCP 45RCP 45RCP 45RCP 45RCP 85RCP 85 high damage4682041047050789840101403531018143231629896906817972050109244607409876589207356060136761634849517797169226364990794194
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 17860$ 65023$ 221$ 034 364 363RCP 45 damages SSP 2 38 26 17904$ 58195$ 221$ 038 325 324RCP 45 damages SSP 3 41 26 17236$ 31772$ 221$ 070 184 183RCP 45 damages SSP 4 38 25 17972$ 48654$ 221$ 045 271 269RCP 45 damages SSP 5 51 27 18193$ 135317$ 221$ 016 744 743RCP 85 damages SSP 5 51 51 18193$ 135317$ 508$ 038 744 741RCP 85 high damage SSP 5 51 51 18193$ 135317$ 13532$ 1000 744 669
USA
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 53040$ 139384$ 475$ 034 263 262RCP 45 damages SSP 2 38 26 53326$ 126908$ 483$ 038 238 237RCP 45 damages SSP 3 41 26 52424$ 121374$ 846$ 070 232 230RCP 45 damages SSP 4 38 25 53867$ 133451$ 607$ 045 248 247RCP 45 damages SSP 5 51 27 53048$ 189739$ 310$ 016 358 357RCP 85 damages SSP 5 51 51 53048$ 189739$ 712$ 038 358 356RCP 85 high damage SSP 5 51 51 53048$ 189739$ 18974$ 1000 358 322
Latin America
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
RCP SSPΔT (degC)
(baseline)ΔT (degC)
GDP in 2020(Billion 2015 $)
GDP in 2100(Billion 2015 $)
Damages in 2100(Billion 2015 $)
Annual Damages( GDP in 2100)
GDP in 2100( GDP in 2020)
GDP in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 9376$ 33725$ 221$ 066 360 357RCP 45 damages SSP 2 38 26 9269$ 38917$ 221$ 057 420 417RCP 45 damages SSP 3 41 26 9190$ 35247$ 221$ 063 384 381RCP 45 damages SSP 4 38 25 8946$ 16378$ 221$ 135 183 181RCP 45 damages SSP 5 51 27 9420$ 51758$ 221$ 043 549 547RCP 85 damages SSP 5 51 51 9420$ 51758$ 508$ 098 549 544RCP 85 high damage SSP 5 51 51 9420$ 51758$ 5176$ 1000 549 495
RCP SSPGDP pp in 2020
(2015 $)GDP pp in 2100
(2015 $)
Damages pp in 2100
(2015 $)
Annual Dam pp( GDP in 2100)
GDP pp in 2100( GDP in 2020)
GDP pp in 2100with damages
( GDP in 2020)
RCP 45 damages SSP 1 30 27 14797$ 69738$ 458$ 066 471 468RCP 45 damages SSP 2 38 26 14418$ 58150$ 331$ 057 403 401RCP 45 damages SSP 3 41 26 14030$ 32702$ 205$ 063 233 232RCP 45 damages SSP 4 38 25 13982$ 29058$ 393$ 135 208 205RCP 45 damages SSP 5 51 27 14936$ 114883$ 491$ 043 769 766RCP 85 damages SSP 5 51 51 14936$ 114883$ 1127$ 098 769 762RCP 85 high damage SSP 5 51 51 14936$ 114883$ 11488$ 1000 769 692
In Which World Would You Prefer Living
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
USA - GDP pp in 2090 counting for damages
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5 SSP 5 SSP 5
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 45damages
RCP 85damages
RCP 85 highdamage
Thou
sand
s
Latin America - GDP pp in 2090 counting for damages
Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 RCP 45 scenarios include cost of damages but do not include cost of mitigation for simplicity
NCA4 Chapter 29 Figure 293
NCA4 Report in Brief Ch 29
What Happenedbull Figure from one study in the literature not assessed by the NCA4 teambull This study assumes
bull No adaptation at allbull Questionable assumptions on growth effectsbull Model misspecification identified using weather shocks instead of climate averages
bull In fact the 10 figure is not explicitly endorsed and contradicts NCA4 own assessment
bull The figure is used to provide an example of how mitigation can reduce uncertainty around impacts (in a way that is not quite correct)
bull ldquoLooking at the economy as a whole mitigation can substantially reduce damages while also narrowing the uncertainty in potential adverse impacts (Figure 293)rdquo
bull Les s than ideal report writing amplified by confirmation bias in reporting
1 Technical progres s trade and s ound ins titutions main drivers of economic progres s
2 Climate change impacts appear to be of s econd-order importance
3 Careful balance of benefits and cos ts of climate mitigation cons idering all ris ks and uncerta inties
Three Important Take -Away Messages
A question of balance
Actually from an economic point of view its a pretty simple problemldquo
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
Efficient Abatement
ldquoProjecting impacts is the most difficult and has the greatest uncertainties of all the processesassociated with global warmingrdquo
bull Key role of adaptation optimal adjustments to minimize (maximize) harmful (beneficial) effects of climate change
bull Most important driver of adaptation is rising income per capita
ldquoHuman societies increasingly devote resources to insulate their lives and property from environmentalconditions as their incomes riserdquo
All citations from Nordhaus (2014) ldquoThe Climate Casinordquo Yale University Press
Climate Change Impacts and Adaptation
Adaptation potential
Extensively Managed Systems
Partially Managed Systems Unmanageable Systems
Most economic sectorsbull Manufacturingbull Health Care
Most human activitiesbull Sleepingbull Surfing the intenet
Vulnerable economic sectorsbull Agriculturebull Forestry
Nonmarket systemsbull Beaches and coastal
ecosystemsbull Wildfires
HurricanesSea-level riseWildlifeOcean acidification
Increasing challenges and potential failure for adaptation
bull A new literature finds large damages because it assumes there will be no adaptation
bull No effects of increased income on adaptation potential
bull Identification of random weather shocks not climate changebull In stark contrast with Nordhausrsquo work on climate damages
Why Losses up to 10 of the US Economy are Unlikely
ldquoIn estimating impacts people often confuse weather and climaterdquo Nordahus (2014) The Climate Casino Yale University Press p 75
Nordahus (1977) Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper No 443
bull One of the most controversial issues in climate change policy debate
bull $100 invested today at a real interest rate equal to 45 by 2218 grows tohellip
$ 665669bull How much are you willing to give up today to receive $ 665669 in
2218$ 10
Discounting
bull As individuals we discount future monetary benefitsbull Two reasons
bull We are impatientbull Pure rate of time preference (d)
bull If we think our income will grow $1 in the future counts les s than $1 todaybull How much we dis count depends on how advers e we are to inter-temporal income
inequality (e) and on the expected growth ra te of the economy (g)
bull The dis count ra te 119903119903 is s et by the following formula119903119903 = 119889119889 + 119892119892 times 119890119890
Discounting
bull Mitigation costs are upfrontbull Benefits of emission reductions come late in the century
bull Strong inertia of the climate systembull Increased warming
bull Discounting reduces the relative importance of benefits and suggests a moderate approach to emission reductions
bull The faster is economic growth the larger the emissions the larger the climate damages the larger the discount ratehellip
The Effect of Discounting on Climate Change Policy
Extreme Inertia of the Climate System
IPCC AR5 Figure SPM8 Panel dIPCC AR5 Figure SPM7 Panel a
The Stern Reviewbull Very large damages at the end
of the century
bull Very low discount rate (r)
bull Large emis s ion reductions are optimal s tarting from now
119903119903 = 119889119889 + 119892119892 times 119890119890
bull Observed values for 119903119903 and 119892119892 are equal to about 4 ndash 45 and 3 at global level
bull 119890119890 can be estimatedbull 119889119889 is determined as a residual approximately 15
bull In the Stern Review 119889119889 is chosen to be equal to 01 and 119890119890 is not adjusted to give observed values for 119903119903 and 119892119892
bull The Stern Review uses inconsistent assumptions
Nordhaus and the Sternrsquos Review of Climate Change
bull No discounting leads to a paradoxbull Future generations have infinite importancebull We would be crushed under the burden of an infinite number of descendants
bull Even if we treat all generations equal growing incomes for future generations are a good reason for discounting
bull Real problem is if income declinesbull However a declining global economy fixes the climate problem without need for
policy
bull The theoretical framework can accommodate many different assumptions what matters is consistency
Discounting Ethical Considerations
PolicyWe need to put a price on carbon so that when anyone anywhere anytime does something that puts carbon dioxide in the atmosphere theres a price tag on that he says
His colleagues say that inspiration mdashnow taken for granted mdashmakes Nordhaus a prime candidate for a Nobel Prize
httpswwwnprorg20140211271537401economist-says-best-climate-fix-a-tough-sell-but-worth-it
The Social Cost of Carbon
Regional Costs of Carbonbull Strong incentives to adopt a
patchwork of policies
bull Domestic damages a fraction of global damages
bull US SCC uses global damagesbull Unclear legal frameworkbull Can be challenged in courts
Catastrophic Thresholdsbull Optimal policy
bull SCC calculated assuming optimal mitigation
bull No policybull SCC calculated assuming
business as usualbull Contradiction SSC is in fact used
for mitigation
bull Threshold effects not dramatic if one considers the optimal policy SCC
bull A tale of two externalitiesbull A negative one GHG emissionsbull A positive one knowledge diffusion
bull A global carbon tax uniform across all sectors and countries is the efficient policy tool to fix the environmental externality
bull The carbon tax should not be substituted with an innovation subsidy
bull Subsidies to innovation are needed to fix the knowledge externalitybull However as all sectors suffer from this problem this should be part of
industrialRampD policies not climate change policy per se
Why a Carbon Tax and Not Subsidies
Conclusions
My personal final thoughtshellip
The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018
Ill Niklas Elmehed copy Nobel Media
William D Nordhaus
for integrating climate change into long-run macroeconomic analysis
Ill Niklas Elmehed copy Nobel Media
Paul M Romer
for integrating technological innovations into long-run macroeconomic analysis
MLA style The Prize in Economic Sciences 2018 NobelPrizeorg Nobel Media AB 2018 Mon 26 Nov 2018
bull Paul Romer has introduced endogenous technical change in growth models
bull Innovation new ideas are at the core of economic growthbull Innovation is the engine of growth and will determine the fate of future
generations
bull Climate change is going to catastrophic effects only if it will stop the engine of innovation
bull Widespread geo-political disruptionsbull Collapse of institutions that have supported economic progressbull A return to the Middle Ages as after the end of the Roman Empire
The Long-Run Effect of Climate Change on Growth
At some point you move from calm to concerned he says Im not at panic
but there are some pretty deep concerns about whats going on mdash particularly at the slow pace of the steps that countries are taking
to deal with climate changeldquo
https wwwnprorg 2014 02 11 271537401 economis t-s ays -bes t-climate-fix-a-tough-s ell-but-worth-it
emanuelemassettigatecheduThank you
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 | |||||||||||||||
World | GCAM4 - SSP4-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1233 | 1486 | 173 | 195 | 2126 | 2258 | 2373 | 2458 | 2519 |
R52ASIA | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3604628 | 65745524 | 102371581 | 137480975 | 166674622 | 190204504 | 206706472 | 217323222 | 222048637 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35945205 | 58984526 | 81302982 | 103558389 | 125074591 | 147165996 | 169164388 | 190918354 | 212705514 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 35927702 | 54784872 | 67096992 | 76075045 | 82717721 | 89370103 | 96205704 | 103242024 | 11119915 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3570474 | 58053348 | 78455387 | 95651664 | 108662997 | 118483384 | 124720614 | 128565984 | 131008689 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 11991359 | 18086203 | 3636331 | 72321873 | 121677631 | 171847641 | 218530222 | 26234224 | 300818828 | 334812895 | 363259663 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33654987 | 61320521 | 95341583 | 128262564 | 155988045 | 17874593 | 194597728 | 204541199 | 208947472 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33498114 | 54452939 | 75077409 | 95709609 | 116011442 | 137253424 | 157970139 | 17746993 | 196717235 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33492901 | 50344413 | 613146 | 69261803 | 75047545 | 81026581 | 86782532 | 91786282 | 98042933 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33412989 | 54185829 | 7292152 | 8880678 | 101244251 | 110983749 | 117442109 | 121536178 | 12372587 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33699018 | 63064456 | 104430806 | 145784533 | 182334611 | 21397759 | 238342599 | 255537122 | 27941963 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33794001 | 65730096 | 10979697 | 155219688 | 198024513 | 238139606 | 272742333 | 302214033 | 327739741 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 3387672 | 66771019 | 112282025 | 158928235 | 202673921 | 243756592 | 2798636 | 312054378 | 338102978 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 11240759 | 16925256 | 33873656 | 67136662 | 113226581 | 160464406 | 20471011 | 246497445 | 283093588 | 315241581 | 341872352 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8432697 | 12188832 | 17270085 | 22838099 | 28312167 | 33213262 | 37264892 | 40356046 | 42220437 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8505645 | 11694895 | 15205641 | 19164199 | 23585221 | 28399502 | 33579753 | 39041147 | 4477846 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8601791 | 11376482 | 13693784 | 15894361 | 18119786 | 20472851 | 23002234 | 25600006 | 28384309 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8431843 | 11377274 | 14487364 | 17600329 | 20778471 | 23839544 | 26698565 | 29365842 | 31922239 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4806589 | 5834355 | 8422957 | 12826044 | 19632068 | 27492448 | 35809732 | 44049976 | 51894167 | 59252605 | 65673133 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8009924 | 11479699 | 16123894 | 21154884 | 26040597 | 30456825 | 33913856 | 36408961 | 38082056 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8074261 | 10943178 | 14076268 | 17560821 | 21398665 | 25516543 | 29726621 | 34124677 | 38326956 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8163403 | 10699506 | 1280348 | 14808566 | 16659626 | 18542564 | 2060046 | 22512503 | 24529737 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 80055 | 10744625 | 13597914 | 16412004 | 19288618 | 22021223 | 2445564 | 26592971 | 28380151 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8011422 | 11690489 | 17485373 | 23573321 | 30207085 | 36980353 | 44822631 | 51655075 | 56484946 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 7998249 | 11937 | 18024642 | 24955095 | 32106176 | 38825437 | 45249894 | 51433103 | 56652766 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8004829 | 12027522 | 1820116 | 25268883 | 32799072 | 40215537 | 47588549 | 54252216 | 60552558 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 4692556 | 5606778 | 8023368 | 12103439 | 1836751 | 25558349 | 33130769 | 40603197 | 47707455 | 54333997 | 60058554 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 4227022 | 5338265 | 8439864 | 14008705 | 23219774 | 36530051 | 54754792 | 77800013 | 103785693 | 131235499 | 157911128 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 8519829 | 13420345 | 20070996 | 29235147 | 42121866 | 59963545 | 83277091 | 112474569 | 147428895 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 4227022 | 5338229 | 850674 | 1283939 | 17699773 | 22927465 | 289566 | 36553672 | 46023677 | 57397784 | 7071738 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 4227022 | 5338272 | 851065 | 13118625 | 18621883 | 2446839 | 30857832 | 38179946 | 46457724 | 55832394 | 66439294 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 4227022 | 5338317 | 8553379 | 15374322 | 28319089 | 4782332 | 75411296 | 112087242 | 156553287 | 207998999 | 264023426 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6845954 | 11313819 | 18701998 | 29566721 | 44646066 | 6412169 | 86868898 | 111007333 | 13454348 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6922898 | 10721589 | 15909572 | 23007002 | 33233341 | 47984817 | 67774404 | 92655679 | 1224005 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6881027 | 10307069 | 14138931 | 18223792 | 22917721 | 28974381 | 36844776 | 45917529 | 56022791 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 6934812 | 10765858 | 15308234 | 19969849 | 25054779 | 30861971 | 37745962 | 45806155 | 54957606 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7051926 | 12050945 | 22118961 | 37162229 | 58716095 | 86743641 | 12010802 | 160216216 | 203508804 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7005296 | 12458003 | 22899035 | 38562545 | 61010359 | 91499632 | 128877523 | 171906362 | 219547897 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7006858 | 12600828 | 23138241 | 39089398 | 62092221 | 93318938 | 131892684 | 177429019 | 227838539 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3349468 | 4251611 | 7056202 | 12732927 | 23488305 | 39812812 | 63179874 | 94649676 | 133237702 | 178296752 | 227696461 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43210867 | 53825679 | 65720931 | 77095254 | 8820776 | 99684825 | 110293972 | 119998165 | 128845611 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42965126 | 51697388 | 60221529 | 68760878 | 77774706 | 87621345 | 97310158 | 106870773 | 116359057 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 42120154 | 48157196 | 52231825 | 54823791 | 56386012 | 57370376 | 57159051 | 56378353 | 55389016 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 427863 | 52627683 | 62993802 | 72307407 | 8119393 | 90011171 | 97492328 | 104080777 | 109937722 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 33168589 | 34694026 | 43961769 | 58444727 | 77733355 | 9999088 | 126701382 | 159818657 | 199079203 | 244479816 | 297055104 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43796523 | 54716423 | 66582038 | 77809894 | 88716484 | 99880495 | 110130686 | 119317458 | 127692544 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43549437 | 52385681 | 6090286 | 69213506 | 77759823 | 86835104 | 95429267 | 103499939 | 111551084 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 42634593 | 48757116 | 5279062 | 55098103 | 56184771 | 56555787 | 55399325 | 53428748 | 51271195 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 43351163 | 53568729 | 64117471 | 73485241 | 82340538 | 91033347 | 98114276 | 104152163 | 109629301 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44524915 | 58272326 | 76454936 | 97331749 | 122302721 | 153625779 | 191547396 | 233777139 | 278661288 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44571556 | 59070566 | 78240451 | 100211627 | 126411784 | 158547497 | 196943765 | 241430363 | 291808382 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44577173 | 59309323 | 78824514 | 101242629 | 127977833 | 161133695 | 200384277 | 245517759 | 297912865 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 33447573 | 35084337 | 44593089 | 59351119 | 78746698 | 101096491 | 127950481 | 161336286 | 201065534 | 24699144 | 300212785 | |||||||||||||||
R52REF | OECD Env-Growth - SSP1 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 428051 | 6413965 | 8875322 | 10602964 | 11895179 | 1304726 | 13549386 | 13803824 | 13878685 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4270468 | 600274 | 7677745 | 8984418 | 10331606 | 1191256 | 1330759 | 14642466 | 15973895 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4256867 | 5722059 | 6926806 | 7550697 | 8087909 | 899683 | 9980406 | 10970194 | 12023934 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4236323 | 599501 | 7869929 | 9173948 | 10288357 | 11412299 | 12117278 | 1260191 | 12916988 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | GDP|PPP | billion US$2005yr | 2343059 | 2854636 | 4312129 | 7063438 | 10752673 | 13750372 | 16385308 | 19086123 | 21136705 | 23022379 | 24743439 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5587201 | 8267917 | 11376487 | 13632005 | 1530977 | 16655582 | 17462944 | 17768973 | 17785676 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5610102 | 7715549 | 980588 | 11526458 | 13221169 | 15038627 | 16506182 | 17660106 | 19033703 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5631077 | 7440687 | 88704 | 9648683 | 10222076 | 11042689 | 11927139 | 12611781 | 13460178 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 556038 | 7755238 | 10030978 | 11596653 | 12891285 | 1420801 | 15128253 | 15617677 | 15873925 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | GDP|PPP | billion US$2005yr | 3119221 | 3748132 | 5643355 | 8199498 | 11745737 | 14342322 | 16226019 | 17350529 | 17439369 | 16916531 | 19291549 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5649 | 8780 | 12967 | 16538 | 19741 | 23048 | 25971 | 28747 | 30870 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5654 | 8960 | 13326 | 17129 | 20647 | 24107 | 26950 | 29287 | 31334 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 3119 | 3748 | 5677 | 9066 | 13598 | 17584 | 21240 | 24914 | 27882 | 30590 | 33059 | |||||||||||||||
R52ASIA | IIASA-WiC POP - SSP1 | Population | million | 3657 | 3939 | 4107 | 4168 | 4120 | 3974 | 3752 | 3471 | 3146 | 2796 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP2 | Population | million | 3657 | 3992 | 4249 | 4412 | 4478 | 4447 | 4338 | 4176 | 3976 | 3763 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP3 | Population | million | 3657118 | 4040365 | 4401393 | 469127 | 4936631 | 5128263 | 5282001 | 5429536 | 5584336 | 5750003 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP4 | Population | million | 3657118 | 3969432 | 4181312 | 4284616 | 4286939 | 418914 | 4013869 | 3792002 | 3548864 | 3310283 | ||||||||||||||||
R52ASIA | IIASA-WiC POP - SSP5 | Population | million | 3657118 | 3935871 | 4096622 | 4150685 | 4095737 | 3943777 | 371666 | 3434264 | 3110616 | 2763996 | ||||||||||||||||
R52ASIA | NCAR - SSP1 | Population | million | 3657007 | 3939383 | 4106989 | 416808 | 4120042 | 397432 | 3751486 | 3470705 | 3145825 | 2795457 | ||||||||||||||||
R52ASIA | NCAR - SSP2 | Population | million | 3657007 | 3991999 | 4248935 | 4411547 | 4478076 | 4447021 | 4338403 | 4176272 | 3975894 | 3762807 | ||||||||||||||||
R52ASIA | NCAR - SSP3 | Population | million | 3657007 | 4040245 | 4401258 | 4691122 | 4936473 | 5128093 | 5281822 | 5429349 | 558414 | 5749797 | ||||||||||||||||
R52ASIA | NCAR - SSP4 | Population | million | 3657007 | 3969316 | 4181191 | 4284494 | 4286817 | 4189019 | 4013749 | 3791881 | 3548739 | 3310152 | ||||||||||||||||
R52ASIA | NCAR - SSP5 | Population | million | 3657007 | 3935764 | 4096527 | 4150604 | 4095673 | 3943727 | 3716622 | 3434234 | 3110592 | 2763976 | ||||||||||||||||
R52ASIA | OECD Env-Growth - SSP1 | Population | million | 3447843 | 3632929 | 3914508 | 4081958 | 4143567 | 4096584 | 3952394 | 3731339 | 345244 | 3129593 | 2781358 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP2 | Population | million | 3447843 | 3632929 | 3966895 | 4223343 | 4386199 | 4453423 | 4423491 | 4316245 | 4155612 | 3956882 | 3745411 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP3 | Population | million | 3447843 | 3632929 | 4015063 | 4375274 | 4665118 | 4910785 | 5102857 | 5257184 | 5405221 | 5560369 | 5726244 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP4 | Population | million | 3447843 | 3632929 | 3944526 | 4156396 | 4260472 | 4264193 | 4168255 | 3995077 | 3775429 | 3534575 | 3298137 | |||||||||||||||
R52ASIA | OECD Env-Growth - SSP5 | Population | million | 3447843 | 3632929 | 391088 | 407147 | 4126036 | 4072134 | 3921694 | 3696351 | 3415835 | 3094227 | 2749751 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP1-45 | Population | million | 3425377 | 3607607 | 3888281 | 4054856 | 4115908 | 4068569 | 3924615 | 3705985 | 3429516 | 3108573 | 2761986 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP2-45 | Population | million | 3425377 | 3607599 | 3940946 | 4197052 | 4360073 | 4428035 | 4399568 | 4295996 | 4138476 | 3942095 | 3732192 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP3-45 | Population | million | 3425377 | 3607648 | 3989249 | 4349482 | 4640065 | 4887659 | 5082905 | 524243 | 5395241 | 5554682 | 5724032 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP4-45 | Population | million | 3425377 | 3607617 | 391868 | 4130522 | 42351 | 4239882 | 4145873 | 3976761 | 3760578 | 3522407 | 3287856 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-34 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-45 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-60 | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52ASIA | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 3425377 | 3607555 | 3884727 | 4044623 | 4098687 | 4043922 | 3892609 | 3668572 | 3389089 | 3067613 | 2722744 | |||||||||||||||
R52LAM | IIASA-WiC POP - SSP1 | Population | million | 585498 | 635158 | 665696 | 679091 | 675173 | 656177 | 626571 | 588621 | 541383 | 484805 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP2 | Population | million | 585498 | 644418 | 692076 | 724915 | 742425 | 744458 | 735763 | 718985 | 696277 | 670957 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP3 | Population | million | 585498 | 656624 | 730463 | 795681 | 854277 | 904629 | 949199 | 991717 | 1034749 | 108044 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP4 | Population | million | 585498 | 641327 | 68093 | 702484 | 706217 | 693918 | 670312 | 638825 | 602764 | 565294 | ||||||||||||||||
R52LAM | IIASA-WiC POP - SSP5 | Population | million | 585498 | 632205 | 656183 | 662686 | 652077 | 62698 | 593055 | 552988 | 505903 | 451684 | ||||||||||||||||
R52LAM | NCAR - SSP1 | Population | million | 584946 | 634535 | 665025 | 678389 | 674459 | 655465 | 625875 | 587954 | 540762 | 484247 | ||||||||||||||||
R52LAM | NCAR - SSP2 | Population | million | 584946 | 643783 | 691374 | 72416 | 741632 | 743638 | 734928 | 718151 | 695456 | 670158 | ||||||||||||||||
R52LAM | NCAR - SSP3 | Population | million | 584946 | 655987 | 729744 | 794891 | 853418 | 903698 | 9482 | 990651 | 1033618 | 1079245 | ||||||||||||||||
R52LAM | NCAR - SSP4 | Population | million | 584946 | 640693 | 680223 | 701716 | 705399 | 693053 | 669409 | 63789 | 601805 | 564317 | ||||||||||||||||
R52LAM | NCAR - SSP5 | Population | million | 584946 | 631581 | 655508 | 661979 | 651356 | 626261 | 592353 | 552317 | 505281 | 451127 | ||||||||||||||||
R52LAM | OECD Env-Growth - SSP1 | Population | million | 551382 | 584363 | 633933 | 664413 | 677771 | 67385 | 654875 | 625306 | 587415 | 540258 | 483792 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP2 | Population | million | 551382 | 584363 | 64318 | 690752 | 723528 | 740992 | 742999 | 73429 | 717507 | 694805 | 669507 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP3 | Population | million | 551382 | 584363 | 655382 | 729108 | 794229 | 852734 | 90298 | 947433 | 98982 | 1032712 | 1078262 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP4 | Population | million | 551382 | 584363 | 640094 | 679617 | 70111 | 704804 | 692479 | 668855 | 637358 | 601293 | 563835 | |||||||||||||||
R52LAM | OECD Env-Growth - SSP5 | Population | million | 551382 | 584363 | 630979 | 654894 | 661366 | 65075 | 625674 | 591793 | 551793 | 504802 | 450701 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-45 | Population | million | 547752 | 588219 | 637677 | 667959 | 681063 | 676845 | 657552 | 627673 | 589468 | 542012 | 485253 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-45 | Population | million | 547752 | 588219 | 646948 | 694365 | 726921 | 744117 | 745835 | 736856 | 719838 | 696937 | 671468 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-45 | Population | million | 547752 | 588219 | 659259 | 733028 | 798124 | 856569 | 906737 | 951125 | 993479 | 1036369 | 1081953 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-45 | Population | million | 547752 | 588219 | 643842 | 683161 | 704379 | 707753 | 69509 | 671149 | 639362 | 603041 | 565352 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-34 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-45 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-60 | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 547752 | 588219 | 634653 | 658238 | 664339 | 653326 | 627853 | 593612 | 55328 | 506006 | 451661 | |||||||||||||||
R52MAF | IIASA-WiC POP - SSP1 | Population | million | 1236928 | 1495816 | 1732218 | 1941444 | 2103629 | 2212372 | 2272426 | 2282524 | 2246459 | 2169876 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP2 | Population | million | 1236928 | 1528712 | 1828247 | 2121338 | 2390465 | 2614227 | 2792625 | 2924044 | 3009171 | 3053661 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP3 | Population | million | 1236928 | 1558226 | 1932255 | 2334104 | 2758073 | 3173604 | 3565845 | 3940216 | 4291641 | 4614374 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP4 | Population | million | 1236928 | 1550503 | 1905537 | 2279562 | 2664562 | 3032346 | 3370214 | 3682413 | 3965229 | 4216239 | ||||||||||||||||
R52MAF | IIASA-WiC POP - SSP5 | Population | million | 1236928 | 1495037 | 1727703 | 1931144 | 2085466 | 2184894 | 2236945 | 2241965 | 2204461 | 2130369 | ||||||||||||||||
R52MAF | NCAR - SSP1 | Population | million | 1236723 | 1495559 | 1731913 | 1941094 | 2103244 | 2211963 | 2272003 | 2282097 | 224604 | 2169476 | ||||||||||||||||
R52MAF | NCAR - SSP2 | Population | million | 1236723 | 152845 | 1827924 | 2120951 | 2390022 | 2613735 | 2792092 | 2923481 | 3008585 | 3053063 | ||||||||||||||||
R52MAF | NCAR - SSP3 | Population | million | 1236723 | 1557959 | 1931912 | 2333676 | 2757559 | 3173005 | 3565161 | 393945 | 4290795 | 4613452 | ||||||||||||||||
R52MAF | NCAR - SSP4 | Population | million | 1236723 | 1550236 | 1905193 | 2279132 | 2664047 | 3031745 | 3369529 | 3681646 | 3964384 | 421532 | ||||||||||||||||
R52MAF | NCAR - SSP5 | Population | million | 1236723 | 149478 | 1727398 | 1930794 | 2085081 | 2184485 | 2236522 | 2241538 | 2204041 | 2129969 | ||||||||||||||||
R52MAF | OECD Env-Growth - SSP1 | Population | million | 10996 | 1235882 | 149483 | 1731549 | 1942657 | 2108064 | 2220583 | 2284445 | 2297938 | 2264684 | 2190655 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP2 | Population | million | 10996 | 1235882 | 1528472 | 1829211 | 2124694 | 2398462 | 2627732 | 281199 | 2948993 | 3038862 | 3087567 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP3 | Population | million | 10996 | 1235882 | 1558967 | 1935319 | 2341474 | 2773338 | 3198771 | 3602876 | 3990159 | 4354039 | 4690362 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP4 | Population | million | 10996 | 1235882 | 1551456 | 190899 | 2287954 | 2681935 | 3061488 | 3413914 | 3742382 | 4041818 | 431174 | |||||||||||||||
R52MAF | OECD Env-Growth - SSP5 | Population | million | 10996 | 1235882 | 1494124 | 1726853 | 1931922 | 2089236 | 2192344 | 2248163 | 2256566 | 2221891 | 2150397 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP1-45 | Population | million | 1053865 | 1185914 | 1440302 | 1673269 | 188181 | 2045901 | 2158538 | 2224024 | 2240546 | 2211295 | 2141847 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP2-45 | Population | million | 1053865 | 1185914 | 147386 | 1770825 | 2063648 | 2335628 | 25639 | 2748169 | 2886313 | 2978064 | 3029284 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP3-45 | Population | million | 1053865 | 1185914 | 150515 | 1878536 | 2282304 | 2711991 | 3135438 | 3537663 | 3923136 | 428511 | 4619555 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP4-45 | Population | million | 1053865 | 1185914 | 1497512 | 1852745 | 2231111 | 2625927 | 3007296 | 3362611 | 3694914 | 3998697 | 4273267 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-34 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-45 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-60 | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52MAF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1053865 | 1185914 | 1439186 | 1667308 | 1868946 | 2024107 | 2126668 | 2183761 | 2195149 | 216467 | 2098103 | |||||||||||||||
R52OECD | IIASA-WiC POP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | IIASA-WiC POP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | NCAR - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | ||||||||||||||||
R52OECD | NCAR - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | ||||||||||||||||
R52OECD | NCAR - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | ||||||||||||||||
R52OECD | NCAR - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | ||||||||||||||||
R52OECD | NCAR - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | ||||||||||||||||
R52OECD | OECD Env-Growth - SSP1 | Population | million | 108027 | 1112816 | 1170812 | 1221811 | 1265611 | 1300793 | 1326057 | 1336259 | 1328746 | 1298543 | 1249539 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP2 | Population | million | 108027 | 1112816 | 1168272 | 1214964 | 1251277 | 1278596 | 129859 | 130807 | 1304502 | 1294628 | 1271887 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP3 | Population | million | 108027 | 1112816 | 1150695 | 1157022 | 1143355 | 1114097 | 1072847 | 1023985 | 97217 | 918872 | 864458 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP4 | Population | million | 108027 | 1112816 | 1159505 | 119234 | 1207352 | 1207425 | 1193164 | 1164225 | 1121433 | 1064313 | 999853 | |||||||||||||||
R52OECD | OECD Env-Growth - SSP5 | Population | million | 108027 | 1112816 | 1186803 | 1274906 | 1365219 | 1461241 | 1561975 | 1654081 | 1733015 | 1792534 | 183223 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP1-45 | Population | million | 1078216 | 1115751 | 1172902 | 1224176 | 1268055 | 1303407 | 1328893 | 1339128 | 1331304 | 1300972 | 1252152 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP2-45 | Population | million | 1078216 | 1115751 | 1168944 | 1213106 | 1246415 | 1270493 | 1287739 | 1294998 | 1291945 | 1278645 | 1254705 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP3-45 | Population | million | 1078216 | 1115751 | 1148288 | 1146754 | 1124433 | 1085088 | 1033218 | 974186 | 911927 | 847984 | 783102 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP4-45 | Population | million | 1078216 | 1115751 | 1161578 | 1188797 | 1200299 | 1195732 | 1177828 | 1146089 | 1101256 | 1044286 | 977118 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-34 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-45 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-60 | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52OECD | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 1078216 | 1115751 | 1189716 | 1279771 | 1371869 | 1469842 | 1572674 | 1666547 | 1746681 | 180748 | 1848673 | |||||||||||||||
R52REF | IIASA-WiC POP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | IIASA-WiC POP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | NCAR - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | ||||||||||||||||
R52REF | NCAR - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | ||||||||||||||||
R52REF | NCAR - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | ||||||||||||||||
R52REF | NCAR - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | ||||||||||||||||
R52REF | NCAR - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | ||||||||||||||||
R52REF | OECD Env-Growth - SSP1 | Population | million | 277852 | 278932 | 279383 | 27541 | 270167 | 262159 | 2509 | 237079 | 220243 | 201508 | 182278 | |||||||||||||||
R52REF | OECD Env-Growth - SSP2 | Population | million | 277852 | 278932 | 28141 | 280774 | 279327 | 277279 | 273316 | 267548 | 259979 | 251078 | 241573 | |||||||||||||||
R52REF | OECD Env-Growth - SSP3 | Population | million | 277852 | 278932 | 2831 | 284819 | 285857 | 289034 | 29267 | 297235 | 304139 | 312217 | 320287 | |||||||||||||||
R52REF | OECD Env-Growth - SSP4 | Population | million | 277852 | 278932 | 279045 | 274178 | 267118 | 257406 | 245038 | 230197 | 213184 | 195327 | 177406 | |||||||||||||||
R52REF | OECD Env-Growth - SSP5 | Population | million | 277852 | 278932 | 279371 | 276335 | 272513 | 26561 | 25537 | 242151 | 225253 | 20607 | 186141 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP1-45 | Population | million | 369882 | 371196 | 37862 | 379044 | 376178 | 368056 | 354318 | 336108 | 3132 | 287162 | 259638 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP2-45 | Population | million | 369882 | 371196 | 381922 | 388072 | 39151 | 392329 | 389131 | 382317 | 372088 | 359558 | 346048 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP3-45 | Population | million | 369882 | 371196 | 385324 | 396987 | 406552 | 417834 | 429067 | 440674 | 455178 | 471626 | 488504 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP4-45 | Population | million | 369882 | 371196 | 379373 | 380266 | 376408 | 367301 | 353019 | 33401 | 311189 | 286707 | 261821 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-34 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-45 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-60 | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 | |||||||||||||||
R52REF | WITCH-GLOBIOM - SSP5-Baseline | Population | million | 369882 | 371196 | 378581 | 37998 | 378684 | 371899 | 3594 | 341919 | 318995 | 292489 | 264197 |
R52ASIA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 17887275 | 42435773 | 71710642 | 95268134 | 113949165 | 12831403 | 138081602 | 145018794 | 148885386 | 148277118 | 207 | R52ASIA | IIASA GDP - SSP1 | Population | million | 3552221 | 3824018 | 3983406 | 4039278 | 3989456 | 3844983 | 3625402 | 3349539 | 3031105 | 2688315 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 17887275 | 41816092 | 68459967 | 89250208 | 10503012 | 118703707 | 13049634 | 140326987 | 148834701 | 156479181 | 229 | R52ASIA | IIASA GDP - SSP2 | Population | million | 3552221 | 3874631 | 4118917 | 4270331 | 4327794 | 4289746 | 4175973 | 4010215 | 3807699 | 3594048 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 17887275 | 41306044 | 63983678 | 7986611 | 89728975 | 956956 | 100010712 | 104273128 | 10921917 | 115469091 | 180 | R52ASIA | IIASA GDP - SSP3 | Population | million | 3552221 | 392075 | 4263785 | 4535996 | 4762864 | 4935479 | 5070678 | 5199666 | 5336255 | 5485513 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 17887275 | 40643076 | 60401336 | 71572862 | 75886881 | 75828838 | 73699884 | 70659769 | 67271554 | 64067622 | 106 | R52ASIA | IIASA GDP - SSP4 | Population | million | 3552221 | 385172 | 4049882 | 4140192 | 4129704 | 401985 | 3833234 | 3600165 | 3346476 | 3098792 | |||||||||||||||||||||||||||||||
R52ASIA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 17887275 | 42797877 | 75094232 | 105582211 | 134371813 | 161389641 | 187905446 | 212898442 | 234070642 | 249123465 | 332 | R52ASIA | IIASA GDP - SSP5 | Population | million | 3552221 | 3820945 | 3974794 | 4025153 | 3970183 | 3821362 | 3599076 | 3322636 | 3005826 | 2666518 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 5389115 | 789069 | 10832294 | 14167386 | 17728745 | 20729615 | 23193418 | 25431733 | 2729915 | 28382673 | 262 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 5389115 | 7800882 | 10472261 | 13306322 | 16764518 | 20331035 | 23489927 | 26558818 | 29647701 | 32751857 | 313 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 5389115 | 7734567 | 10093639 | 1250959 | 15125354 | 17867025 | 20522854 | 23273754 | 26248297 | 29663845 | 294 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 5389115 | 7528771 | 9205111 | 10518339 | 11747019 | 12781922 | 13383782 | 13673661 | 13771099 | 13783844 | 150 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | |||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 5389115 | 7927554 | 1116679 | 15219827 | 19893451 | 24535793 | 29384254 | 34550792 | 39529262 | 43558671 | 390 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 4343618 | 7057456 | 11141629 | 16931006 | 24159438 | 32480198 | 4140371 | 50589882 | 59488923 | 67524281 | 606 | R52MAF | IIASA GDP - SSP1 | Population | million | 1203486 | 1451811 | 1679939 | 1882238 | 2039389 | 2145117 | 2204099 | 2215024 | 2181287 | 2108354 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 4343618 | 6900299 | 10387325 | 15250288 | 21294808 | 2833998 | 36265499 | 45099974 | 54929129 | 65481321 | 630 | R52MAF | IIASA GDP - SSP2 | Population | million | 1203486 | 1483808 | 1773243 | 2057147 | 2318352 | 2535941 | 2710211 | 28395 | 2923995 | 296929 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 4343618 | 6736476 | 9288049 | 12311627 | 156563 | 19162867 | 22843334 | 26677718 | 30792793 | 35321468 | 380 | R52MAF | IIASA GDP - SSP3 | Population | million | 1203486 | 1513423 | 1876229 | 2267083 | 2680176 | 3085656 | 346907 | 38354 | 4179637 | 4496542 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 4343618 | 6617775 | 9006137 | 12020901 | 15623573 | 19702673 | 24123163 | 29010987 | 34547866 | 40853145 | 454 | R52MAF | IIASA GDP - SSP4 | Population | million | 1203486 | 1505219 | 1848661 | 2211677 | 2586293 | 2945067 | 3275823 | 3582471 | 3861043 | 4109203 | |||||||||||||||||||||||||||||||
R52MAF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 4343618 | 7143953 | 11832027 | 19255039 | 29489955 | 4254033 | 58240959 | 76204019 | 96817218 | 120611076 | 1019 | R52MAF | IIASA GDP - SSP5 | Population | million | 1203486 | 1450314 | 1673746 | 1869415 | 2018122 | 2114139 | 216492 | 2170831 | 2135897 | 2065866 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 34491016 | 42001186 | 53866725 | 66777877 | 79756317 | 93017318 | 106424264 | 119522595 | 131933255 | 14320435 | 266 | R52OECD | IIASA GDP - SSP1 | Population | million | 1110847 | 1168644 | 1219494 | 1263177 | 1298306 | 1323565 | 1333802 | 1326366 | 129629 | 1247453 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 34491016 | 41976377 | 53022888 | 6436675 | 75330184 | 86238376 | 97442677 | 108507805 | 119350483 | 12980375 | 245 | R52OECD | IIASA GDP - SSP2 | Population | million | 1110847 | 1166076 | 1212567 | 1248698 | 127588 | 1295772 | 1305175 | 1303763 | 1291641 | 1268893 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 34491016 | 38394627 | 42562597 | 46589207 | 50219608 | 53373252 | 56219732 | 58646262 | 60565151 | 62075003 | 146 | R52OECD | IIASA GDP - SSP3 | Population | million | 1110847 | 1148477 | 1154534 | 1140595 | 1111069 | 1069541 | 1020383 | 968244 | 914605 | 85983 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 34491016 | 42039576 | 52419949 | 61946691 | 70441665 | 78280162 | 85592483 | 91944371 | 97063887 | 100889415 | 192 | R52OECD | IIASA GDP - SSP4 | Population | million | 1110847 | 1159425 | 1189945 | 1205209 | 1204711 | 119034 | 1161309 | 1118443 | 1062881 | 996767 | |||||||||||||||||||||||||||||||
R52OECD | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 34491016 | 42708013 | 57865633 | 77519438 | 100812559 | 128437342 | 161048549 | 198126382 | 239507723 | 285269924 | 493 | R52OECD | IIASA GDP - SSP5 | Population | million | 1110847 | 1184636 | 1272595 | 1362801 | 1458772 | 1559507 | 1651655 | 1730677 | 1790331 | 1830201 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 2904979 | 4934046 | 6903555 | 806563 | 8683524 | 9334621 | 10155339 | 10958426 | 1163614 | 12178292 | 176 | R52REF | IIASA GDP - SSP1 | Population | million | 278933 | 279383 | 275409 | 270167 | 262159 | 250901 | 237081 | 220244 | 201507 | 182279 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 2904979 | 4970751 | 6831307 | 8003802 | 8842083 | 9691796 | 10707472 | 1178314 | 12871603 | 14026054 | 205 | R52REF | IIASA GDP - SSP2 | Population | million | 278933 | 281411 | 280774 | 279328 | 277278 | 273314 | 267547 | 25998 | 251078 | 241573 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 2904979 | 4952233 | 6706144 | 7778524 | 8612847 | 9490652 | 10589526 | 11976309 | 13653327 | 15629631 | 233 | R52REF | IIASA GDP - SSP3 | Population | million | 278933 | 283102 | 284818 | 285858 | 289034 | 29267 | 297235 | 304137 | 312216 | 320286 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 2904979 | 4879401 | 6310894 | 6866536 | 7056888 | 7139413 | 724052 | 7341927 | 7427268 | 749914 | 119 | R52REF | IIASA GDP - SSP4 | Population | million | 278933 | 279045 | 274178 | 267118 | 257407 | 24504 | 230197 | 213184 | 195327 | 177405 | |||||||||||||||||||||||||||||||
R52REF | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 2904979 | 49811 | 7229577 | 8892638 | 10240554 | 11887538 | 13912984 | 16025643 | 18029388 | 19847257 | 275 | R52REF | IIASA GDP - SSP5 | Population | million | 278933 | 279373 | 276334 | 272512 | 265609 | 25537 | 242151 | 225253 | 206071 | 186141 | |||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 53891 | 78907 | 108323 | 141674 | 177287 | 207296 | 231934 | 254317 | 272992 | 283827 | R52LAM | IIASA GDP - SSP1 | Population | million | 584079 | 633637 | 664111 | 677472 | 673556 | 654592 | 625042 | 587172 | 54004 | 483597 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 53891 | 78009 | 104723 | 133063 | 167645 | 203310 | 234899 | 265588 | 296477 | 327519 | R52LAM | IIASA GDP - SSP2 | Population | million | 584079 | 642879 | 690439 | 723207 | 740675 | 742688 | 733987 | 717217 | 69453 | 669244 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 53891 | 77346 | 100936 | 125096 | 151254 | 178670 | 205229 | 232738 | 262483 | 296638 | R52LAM | IIASA GDP - SSP3 | Population | million | 584079 | 655074 | 728777 | 793878 | 852365 | 902598 | 94704 | 989418 | 1032299 | 1077832 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 53891 | 75288 | 92051 | 105183 | 117470 | 127819 | 133838 | 136737 | 137711 | 137838 | R52LAM | IIASA GDP - SSP4 | Population | million | 584079 | 639795 | 67931 | 700805 | 704508 | 692196 | 668592 | 637115 | 601075 | 563638 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 53891 | 79276 | 111668 | 152198 | 198935 | 245358 | 293843 | 345508 | 395293 | 435587 | R52LAM | IIASA GDP - SSP5 | Population | million | 584079 | 630685 | 654604 | 661078 | 650473 | 625414 | 591552 | 551574 | 504604 | 450525 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP1-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80320 | 115132 | 161882 | 212801 | 262482 | 306626 | 342974 | 370467 | 386710 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP2-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80979 | 110536 | 142813 | 179016 | 219210 | 262822 | 309717 | 359127 | 411034 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP3-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 81813 | 107572 | 128952 | 149075 | 169193 | 190283 | 212807 | 235758 | 260187 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP4-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80201 | 107540 | 136335 | 164976 | 193964 | 221604 | 247051 | 270350 | 292180 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R52LAM | WITCH-GLOBIOM - SSP5-Baseline | GDP|PPP | billion US$2005yr | 46926 | 56068 | 80234 | 121034 | 183675 | 255583 | 331308 | 406032 | 477075 | 543340 | 600586 |
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP1 | GDP|PPP | billion US$2005yr | 126323 | 150305 | 187647 | 230075 | 275830 | 325141 | 379089 | 437108 | 494632 | 547231 | USA | IIASA GDP - SSP1 | Population | million | 310384 | 336722 | 363686 | 388801 | 411058 | 431955 | 450962 | 465256 | 470602 | 466507 | IIASA GDP - SSP1 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP2 | GDP|PPP | billion US$2005yr | 126323 | 150680 | 186080 | 223989 | 263529 | 305623 | 351088 | 398080 | 444492 | 489763 | USA | IIASA GDP - SSP2 | Population | million | 310384 | 335751 | 361029 | 383233 | 402305 | 42093 | 437638 | 449545 | 456212 | 45856 | IIASA GDP - SSP2 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP3 | GDP|PPP | billion US$2005yr | 126323 | 145053 | 166387 | 185525 | 202994 | 219525 | 235566 | 249480 | 259960 | 267394 | USA | IIASA GDP - SSP3 | Population | million | 310384 | 328774 | 337347 | 338918 | 334232 | 326108 | 315313 | 300574 | 282232 | 261774 | IIASA GDP - SSP3 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP4 | GDP|PPP | billion US$2005yr | 126323 | 151249 | 185626 | 219251 | 252612 | 286996 | 322240 | 355685 | 384941 | 409467 | USA | IIASA GDP - SSP4 | Population | million | 310384 | 333634 | 353765 | 369196 | 379154 | 38615 | 389653 | 387129 | 378219 | 364584 | IIASA GDP - SSP4 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ||||||||||||||||||||||||||||||||||||||||||||||||
USA | IIASA GDP - SSP5 | GDP|PPP | billion US$2005yr | 126323 | 153108 | 203201 | 271493 | 357366 | 463523 | 595040 | 753206 | 935207 | 1138811 | USA | IIASA GDP - SSP5 | Population | million | 310384 | 342944 | 384823 | 428702 | 475715 | 527773 | 581139 | 63246 | 677321 | 713172 | IIASA GDP - SSP5 | GDPCapita | thousands | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | ERRORREF | |||||||||||||||||||||||||||||||||||||||||||||||
World | AIMCGE - SSP3-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 123 | 1522 | 1841 | 2063 | 2237 | 2362 | 2466 | 2554 | 2617 | |||||||||||||||
World | AIMCGE - SSP3-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0988 | 1226 | 1524 | 1864 | 2203 | 2551 | 2912 | 3284 | 3672 | 4071 | |||||||||||||||
World | GCAM4 - SSP4-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1237 | 1533 | 1874 | 2216 | 2546 | 2875 | 3201 | 3494 | 3758 | |||||||||||||||
World | IMAGE - SSP1-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1236 | 1509 | 1739 | 1944 | 2145 | 2336 | 2488 | 2596 | 2659 | |||||||||||||||
World | IMAGE - SSP1-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1238 | 1521 | 1783 | 2029 | 2253 | 247 | 268 | 2867 | 3026 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1243 | 1486 | 1742 | 1965 | 2152 | 2317 | 246 | 257 | 2634 | |||||||||||||||
World | MESSAGE-GLOBIOM - SSP2-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1241 | 1482 | 1759 | 2049 | 2351 | 2675 | 3015 | 338 | 3756 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-45 | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1259 | 1555 | 1857 | 2109 | 2292 | 2438 | 2556 | 2638 | 2683 | |||||||||||||||
World | REMIND-MAGPIE - SSP5-Baseline | Diagnostics|MAGICC6|Temperature|Global Mean | degC | 0913 | 0989 | 1266 | 1603 | 2013 | 248 | 2976 | 3504 | 405 | 4576 | 5052 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 9376 | $ 33725 | $ 221 | 066 | 360 | 357 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 9269 | $ 38917 | $ 221 | 057 | 420 | 417 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 9190 | $ 35247 | $ 221 | 063 | 384 | 381 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 8946 | $ 16378 | $ 221 | 135 | 183 | 181 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 9420 | $ 51758 | $ 221 | 043 | 549 | 547 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 508 | 098 | 549 | 544 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 9420 | $ 51758 | $ 5176 | 1000 | 549 | 495 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 14797 | $ 69738 | $ 458 | 066 | 471 | 468 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 14418 | $ 58150 | $ 331 | 057 | 403 | 401 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 14030 | $ 32702 | $ 205 | 063 | 233 | 232 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 13982 | $ 29058 | $ 393 | 135 | 208 | 205 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 14936 | $ 114883 | $ 491 | 043 | 769 | 766 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 1127 | 098 | 769 | 762 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 14936 | $ 114883 | $ 11488 | 1000 | 769 | 692 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
RCP | SSP | ΔT (degC)(baseline) | ΔT (degC) | GDP in 2020(Billion 2015 $) | GDP in 2100(Billion 2015 $) | Damages in 2100(Billion 2015 $) | Annual Damages( GDP in 2100) | GDP in 2100( GDP in 2020) | GDP in 2100with damages( GDP in 2020) | |||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 17860 | $ 65023 | $ 221 | 034 | 364 | 363 | 119 | ||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 17904 | $ 58195 | $ 221 | 038 | 325 | 324 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 17236 | $ 31772 | $ 221 | 070 | 184 | 183 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 17972 | $ 48654 | $ 221 | 045 | 271 | 269 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 18193 | $ 135317 | $ 221 | 016 | 744 | 743 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 508 | 038 | 744 | 741 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 18193 | $ 135317 | $ 13532 | 1000 | 744 | 669 | |||||||||||||
RCP | SSP | GDP pp in 2020(2015 $) | GDP pp in 2100(2015 $) | Damages pp in 2100(2015 $) | Annual Dam pp( GDP in 2100) | GDP pp in 2100( GDP in 2020) | GDP pp in 2100with damages( GDP in 2020) | |||||||||||||||
RCP 45 | SSP 1 | 30 | 27 | $ 53040 | $ 139384 | $ 475 | 034 | 263 | 262 | |||||||||||||
RCP 45 | SSP 2 | 38 | 26 | $ 53326 | $ 126908 | $ 483 | 038 | 238 | 237 | |||||||||||||
RCP 45 | SSP 3 | 41 | 26 | $ 52424 | $ 121374 | $ 846 | 070 | 232 | 230 | |||||||||||||
RCP 45 | SSP 4 | 38 | 25 | $ 53867 | $ 133451 | $ 607 | 045 | 248 | 247 | |||||||||||||
RCP 45 | SSP 5 | 51 | 27 | $ 53048 | $ 189739 | $ 310 | 016 | 358 | 357 | |||||||||||||
RCP 85 | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 712 | 038 | 358 | 356 | |||||||||||||
RCP 85 high damage | SSP 5 | 51 | 51 | $ 53048 | $ 189739 | $ 18974 | 1000 | 358 | 322 | |||||||||||||
1 Damages from FNCA (2018 Fig 292) High damage scenario from FNCA (2018 Fig 293) SSP scenarios from IIASA database IIASA GDP series Riahi et al (2017) shows that only the SSP 5 baseline scenarios of three models (AIMCGE REMIND-MAGPIE and WITCH-GLOBIOM) can reach the 85 Wm2 radiative forcing level by 2100 |
GDP in 2100(B) | Annual Damages (B) | Annual Damages ( GDP in 2090) | GDP in 2100( GDP in 2017) | GDP in 2100 with damages( GDP in 2017) | |||||||
RCP 45 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 221 | 11 | 100 | 99 | ||||||
1 annual growth rate | $ 39693 | $ 221 | 06 | 205 | 204 | ||||||
2 annual growth rate | $ 80685 | $ 221 | 03 | 416 | 415 | ||||||
RCP 85 1 | |||||||||||
0 annual growth rate | $ 19390 | $ 508 | 26 | 100 | 97 | ||||||
1 annual growth rate | $ 39693 | $ 508 | 13 | 205 | 202 | ||||||
2 annual growth rate | $ 80685 | $ 508 | 06 | 416 | 413 | ||||||
RCP 85 with extreme damages 2 | |||||||||||
0 annual growth rate | $ 19390 | $ 1939 | 100 | 100 | 80 | ||||||
1 annual growth rate | $ 39693 | $ 3969 | 100 | 205 | 184 | ||||||
2 annual growth rate | $ 80685 | $ 8068 | 100 | 416 | 396 | ||||||
1 FNCA (2018 Fig 292) 2 FNCA (2018 Fig 293) | |||||||||||
RCP 85 | RCP 45 | Damages avoided under RCP 45 | |||||
Labor | $ 15500 | $ 7440 | 48 | ||||
Extreme temperature mortality | $ 14100 | $ 8178 | 58 | ||||
Coastal Property | $ 11800 | $ 2596 | 22 | ||||
Air Quality | $ 2600 | $ 806 | 31 | ||||
Roads | $ 2000 | $ 1180 | 59 | ||||
Electricity Supply and Demand | $ 900 | $ 567 | 63 | ||||
Inland Flooding | $ 800 | $ 376 | 47 | ||||
Urban Drainage | $ 600 | $ 156 | 26 | ||||
Rail | $ 600 | $ 216 | 36 | ||||
Water Quality | $ 500 | $ 175 | 35 | ||||
Coral Reef | $ 400 | $ 048 | 12 | ||||
West Nile Virus | $ 300 | $ 141 | 47 | ||||
Freshwater Fish | $ 300 | $ 132 | 44 | ||||
Winter Recreation | $ 200 | $ 021 | 11 | ||||
Bridges | $ 100 | $ 048 | 48 | ||||
Munic And Industrial Water Supply | $ 032 | $ 010 | 33 | ||||
Harmful Algal Blooms | $ 020 | $ 009 | 45 | ||||
Alaska Infrastructure | $ 017 | $ 009 | 53 | ||||
Shellfish | $ 023 | $ 013 | 57 | ||||
Agriculture | $ 001 | $ 000 | 11 | ||||
Aeroallergens | $ 000 | $ 000 | 57 | ||||
Wildfire | $ (011) | $ 014 | -134 | ||||
Total | $ 50783 | $ 22137 |
Top related