Economics and the Geosciences
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Transcript of Economics and the Geosciences
1
Economics and the Geosciences
William D. Nordhaus AAAS Annual MeetingsYale University February 18, 2011
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Outline of presentation
1. Economics and geography (GEcon)2. Economics and luminosity3. Integrated modeling of economics of climate
change (DICE/RICE)
3
The GEcon project
• Purpose is to develop matched geophysical and economic data at geophysically scaling
• Purposes:– Many processes are geophysically based (e.g.,
climate)– Much higher resolution (circa 100x): like Hubble
telescope– Can be matched with geophysical, environmental
data (climate, elevation, distance from coast or market, pollution, etc.)
Nordhaus, Macroeconomics and Geography, PNAS, 2007; Nordhaus and Chen,
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Derivation of Data Set
National or regional gross output,
population data
Regional (e.g., county)estimates of
output per capita
National and provincial GIS grid data (RIG, area, boundaries)
GPW grid cell estimates of
population, area, RIG
GEcon gross cellproduct (GCP)
data
Proportional allocation
from political to
geophysical boundaries
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Countries and grid cells for Europe
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Luminosity as a Proxy for Output
Xi ChenWilliam Nordhaus
Combining socioeconomic and luminosity data
Economic data on developing countries is very weak.
Question for this project: Can we use luminosity (nighttime lights) data as a proxy for standard accounting data for low-quality regions?
Allows use of regional GEcon data for rich regional data set.
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Key elements in evaluating luminosity as a proxy
The key elements in determining the value of a proxy are:1. The quality of the luminosity data2. The errors of measurement of the standard GDP
data3. The statistical relationship between luminosity and
GDP
The background paper shows the optimal weighting as a signal-extraction statistical problem.
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Chen and Nordhaus, The Value of Luminosity Data as a Proxy for Economic Statistics, NBER Working Paper, 2010
99
Problems illustrated for southern New England
Bleeding
Saturation
Stable lights and output by 1° x 1° grid cell (n = 14,287)
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Results on optimal weight on luminosity
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0.0
0.2
0.4
0.6
0.8
1.0
A B C D E
Opti
mal
frac
tiona
l wei
ght o
n lu
min
osity
Country statistical quality grade (A = best; E = worst)
All regions
Low-density regions
Chen and Nordhaus, in process.
Main Results
1. For most countries, luminosity is essentially useless as a proxy for GDP and output measures.
2. Possible information value in statistical basket cases.
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Economic Integrated Assessment (IA) Models
in Climate Change
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Integrated Assessment (IA) Models in Climate Change
What are IA models?These are models that include the full range of cause and effect in climate change (“end to end” modeling).
Major goals of IA models:Project trends in consistent manner Assess costs and benefits of climate policies Estimate the carbon price and efficient emissions
reductions for different goals
Nordhaus, “Copenhagen Accord,” PNAS, 2010.
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Fossil fuel usegenerates CO2
emissions
Carbon cycle: redistributes around
atmosphere, oceans, etc.
Climate system: change in radiative warming, precip,
ocean currents, sea level rise,…
Impacts on ecosystems,agriculture, diseases,
skiing, golfing, …
Measures to controlemissions (limits, taxes,
subsidies, …)
The emissions-climate-impacts-policy nexus:
The RICE-2010model
RICE-2010 model structure*
Economic module:- Standard economic production structure- GHG emissions are global externality - 12 regions, multiple periods
CO2/Climate module:
- Emissions = f(Q, carbon price, time)- Concentrations = g(emissions, diffusion)- Temperature change = h(GHG forcings, time lag)
- Economic damage = F(output, T, CO2, sea level rise)
* Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
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1. Baseline.
2. Economic cost-benefit “optimum.”
3. Limit to 2 °C.
4. Copenhagen Accord, all countries.
5. Copenhagen Accord, rich only.
Policy Scenarios for Analysis using the RICE-2010 model
Temperature profiles: RICE -2010
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0.0
1.0
2.0
3.0
4.0
5.0
6.0
2005 2025 2045 2065 2085 2105 2125 2145 2165 2185 2205
Glo
bal m
ean
tem
pera
ture
(deg
rees
C)
Optimal
Baseline
Lim T<2
Copen trade
Copen rich
Temperature
Source: Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
An interesting byproduct: CO2 shadow prices
Shadow prices (social costs) were discovered by developers of linear programming techniques (Kantorovich and Koopmans, Nobel 1974). Originally thought useful for central planning prices.
Today, useful because they reflect the marginal cost, or prices, of a constraint when efficiently imposed.
For example, IA models can calculate the price associated with the 2 °C temperature target as a byproduct of the economic models.
Can be used as guidelines for setting CO2 taxes or prices.
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Carbon prices for major scenarios from RICE-2010 model
20Source: Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
0
50
100
150
200
250
300
350
2005 2025 2045 2065 2085 2105
Car
bon
pri
ce (2
005
$ per
ton
CO
2)T < 2 °C
Kyoto Trade
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0
5
10
15
20
25
30
35
40
45
50
2005 2025
Car
bon
pri
ce (2
005
$ per
ton
CO
2)
T < 2 °C
Kyoto Trade
Where are we today?
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Actual equivalent global carbon price = $1 / tCO2
Source: Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
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A new scientific renaissance of social and
natural sciences?