Transition to New Technologies (TNT) Program

Nebojsa NakicenovicDeputy Director General and Deputy CEOInternational Institute for Applied Systems AnalysisProfessor Emeritus of Energy EconomicsVienna University of Technology

UK Panel VisitIIASA, Laxenburg – 11-12 December 2014

Why study Technology?

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• Main mediator between humans and the environment

• Main source of productivity and welfare growth (development)

• Policy interest: “man-made resource”, but…– Change costly (investments!)– High uncertainty

(innovation and diffusion)– Large inertia for major transformations

(lock-in, path dependency)– Slow rates of change (systems/infrastructures)

Transitions to New Technologies

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• Point of departureScenarios/impacts of diffusion of new technologies (clusters): ICT, transport, energy, and impact on environment (e.g. climate).

• Strategic GoalFurthering the understanding of patterns, dynamics, and constraints of technological change, and its drivers for global sustainability conditions.

• Research GoalFocusing on the systemic aspects, understanding the evolution of entire technology systems.

• Research- Drivers beyond “black box”- Models (uncertainty, increasing returns, agents)- Heterogeneity (time, space)- Impacts (scenarios)- Synthesis (metastudies)

Networks for Policy Relevant Research• Major International Assessments:

– Global Energy Assessment (GEA) (coordination, CLAs for 4 chapters)– IPCC AR5 (4 chapters, synthesis report)

• Global Fora: – Sustainable Energy For All (SE4ALL)TNT provides methodological frameworks, policy advice on technology strategies, roadmaps, urbanization patterns, and national scale modeling to support the SE4ALL 2030 goals:

• Universal access to modern energy• Doubling energy efficiency improvement rates• Doubling the share of renewable energy

– International Council for Science (ICSU)

– Future Earth Initiative

– Sustainable Development Solutions Network (SDSN)

– Global Carbon Project (GCP)

– German Government’s Advisory Council on Global Change (WBGU)4

Data: UN, ITU, World Bank, 2010

Global Access to Technologies (Lorenz Curves)

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Global – Historic Primary Energy Transitions (changeover time ∆t: 80-130 years)

Source: Chapter 24: GEA, 2012

0

25

50

75

100

1850 1875 1900 1925 1950 1975 2000 2025

Perc

ent i

n Pr

imar

y En

ergy

traditional biomass

coal

modern fuels:oil, gas,electricity

0

25

50

75

100

1850 1875 1900 1925 1950 1975 2000 2025

Perc

ent i

n Pr

imar

y En

ergy

traditional biomass

coal

modern fuels:oil, gas,electricity

Begin of energy policy focus:∆ts >2000 yrs

∆t -130 yrs

∆t -80 yrs

∆t +90 yrs

∆t +130 yrs

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TNT Niche: Technology & Innovation Systems• Transformative change needs systemic understanding & policies on:

all innovation phases, processes, and energy systems components:– R&D, niche markets, diffusion, obsolescence– learning, actors/institutions, resources, technology (hard+soft-ware)

…and yet…

Important biases at all stages:– R&D: supply side bias (nuclear, fossil)– Niche markets: supply side bias (solar/wind)– Diffusion: huge distortions via fossil fuel subsidies– Obsolescence: “grandfathering” of old/”dirty”

• New framework for analysis and GEA policy design criteria: Energy Technology Innovation Systems (ETIS)

• Modeling endogenous evolution of technology systems• ABM of technological complexity• Technology “meta-studies”

(metrics & determinants of change, past and future scenarios)7

Market FormationR,D&D

(public $)Diffusion Support

Social Rates of Return

Analysis & Modelling

Future Needs

supply : end-use(relative effort)

ACTO

RS &

INST

ITUT

IONS

TECHNOLOGY CHARACTERISTICS

KNOWLEDGE

RESOURCES

learninggenerationsh

ared

expe

ctatio

nsen

trepr

eneu

rs / r

isk

taking cost

resourceinputs

public policy & leverage

performance

key

Roadmaps & Portfolios

Technology Collaborations

Learning Effects

Directable(Activities)

Non-Directable(Outputs) 8

The GEA ETIS Framework

Source: Chapter 24: GEA, 2012

CLIMATE MITIGATION

Current Public ETIS Policy Leverage/Focus(policy-induced resource mobilization, billion US$2005)

Source: Wilson et al. Nature Climate Change, 2012 9

         Criteria for Case Study Selection 

Them

atic / Meta‐analytic 

Supp

ly Techn

ologies 

End‐Use Techn

ologies 

Single Con

text 

Comparativ

e Co

ntext 

Curren

t 

Historical 

Develope

d Co

untry(s) 

Developing

 Cou

ntry(s) 

Influ

entia

l Pub

lic Policy 

System

ic  1  Energy Transitions  X  X  X    X    X  X  X   

2  Technology Diffusion  X  X  X    X    X  X  X   3  Assessment Metrics  X                   4  Technology Portfolios  X  X  X      X         

Know

ledge  5  Solar Water Heaters      X  X      X  X    X 

6  Heat Pumps      X    X  X  X  X    X 5  Knowledge Depreciation  X  X  X               6  Nuclear Power (France)    X    X      X  X     

Adop

tion 

& Use 

7  Solar Thermal Electricity (US)    X    X      X  X    X 8  Vehicle Efficiency     X  X    X  X  X    X 9  Hybrid Cars     X    X  X    X  X  X 10  Solar Photovoltaics    X      X  X  X  X    X 

Actors & 

Institu

tions  11  Wind Power    X      X  X  X  X  X  X 12  End‐Use Efficiency (Japan)      X  X    X  X  X    X 14  Rural Solar (Kenya)    X    X    X  X    X   15  Synfuels (US)    X    X      X  X     

Resources  13  Ethanol (Brazil)    X  X  X    X  X    X  X 

18  Global Financial Resources  X  X  X    X  X    X  X  X 19  R,D&D Investments (Emerging Economies)  X  X  X    X  X      X  X 20  Global End‐Use Investments  X    X    X  X    X  X   

ETIS Case Studies

10Source: Chapter 24: GEA, 2012

World Energy Technology Innovation Investments (Billion $)

innovation market diffusion(RD&D) formation

End-use & efficiency >>8 5 300-3500Fossil fuel supply >12 >>2 200-550Nuclear >10 0 3-8Renewables >12 ~20 >20Electricity (Gen+T&D) >>1 ~100 450-520Other* >>4 <15 n.a.Total >50 <150 1000 - <5000 non-OECD ~20 ~30 ~400 - ~1500 non-OECD share >40% <20% 40% - 30%

* hydrogen, fuel cells, other power & storage technologies, basic energy research

Source: Chapter 24: GEA, 2012 11

Knowledge Depreciation Rates (% per year)

Degree of technological obsolescence (rate of innovation)

Deg

ree

of k

now

ledg

e st

ock

turn

over

(pol

icy

& h

uman

cap

ital v

olat

ility

)

PV Japan:30%Wind US:

10%

Engineeringdesigns US:

<5%

Serviceindustries:

95%

Aircraft,Liberty shipsmanufct. US:

40%

Chemicals,Drugs:15-20%

Computers:32%

Electrical,Machinery:

32-36%Miscell.>20%

OECDnuclear R&D:

10 – 40%

Francebreeder reactors:50-60%

High

High

Low

12empirical studies reviewed GEA Chap 24 (2012) andmodeled R&D deprecation in US manufacturing (Hall, 2007)

Post Fossil Energy Supply Technologies Cost Trends

Source: Grubler/Wilson: CUP, 2013 13

Learning rates and cumulative experience (# of units produced/sold) for energy technologies

Source: Nature CC, 2012, S1

category technology data for: cumulative production (units)# exp period rate

energy Transitors World >1 10^18 1960-2010 40end-use DRAMs World >1 10^11 1975-2005 16 - 24

Automobiles World >2 10^9 1900-2005 9 - 14Washing machines World >2 10^9 1965-2008 33 ±9Refrigerators World >2 10^9 1964-2008 9 ±4Dishwashers World >6 10^8 1968-2007 27 ±7Freezers (upright) World >6 10^8 1970-2003 10 ±5Freezers (chest) World >5 10^8 1970-1998 8 ±2Dryers World >3 10^8 1969-2003 28 ±7Hand-held calculators US >4 10^8 early 1970s 30CF light bulbs US >4 10^8 1992-1998 16A/C & heat pumps US >1 10^8 1972-2009 18 ±1Air furnaces US >1 10^8 1953-2009 31 ±3Solar hot water heaters US >1 10^6 1974-2003 -3

average for end-use technologies 10^9 20energysupply PV modules World >1 10^10 1975-2009 18-24

Wind turbines World >1 10^5 1975-2009 10-17Heat pumps S, CH <1 10^5 1982-2008 2 - 21Gas turbines World >4 10^4 1958-1980 10-13Pulverized coal boilers World >6 10^3 1940-2000 6Hypropower plants OECD ~5 10^3 1975-1993 1Nuclear reactors US, France <1 10^3 1971-2000 -20 - -47Ethanol Brazil <1 10^3 1975-2009 21Coal power plants OECD <1 10^3 1975-1993 8Coal power plants US <1 10^3 1950-1982 1 - 6Gas pipelines US <1 10^3 1984-1997 4Gas combined cycles OECD <1 10^3 1981-1997 10Hydrogen production (SRM) World >1 10^2 1980-2005 27LNG production World >1 10^2 1980-2005 14

average for suppy technologies 8average for supply, excluding nuclear 1210^4

learning

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Market Size (normalized index, Core markets) vsDiffusion Speed (∆t) of Energy Technologies

Source: Wilson, YSSP, 2008. E-Bikes & Cell Phones courtesy of IIASA Post-Doc Dr. Bento

CELL PHONES

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Scaling patterns Past and Scenarios (IIASA GGI) (8 Scenarios: A2r/B1/B2 * base/670/480)

• Scenariosmore conservativecompared to past

• Closer relationship for “lumpy”power techs

• Method adopted in IAM community forsce4nario validation

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

0 25 50 75 100 125

Normalised

 K (ind

ex)

Δt (yrs) of cumulative total capacity

Cumulative Total Capacity (OECD): normalised K  vs  ΔtALL TECHS: HISTORICAL & SCENARIOS ‐ semi‐log 

SCENARIOS (All Techs)

Historical (Core)

Historical (Core) ‐POWER only ‐ no WIND

Expon. (SCENARIOS (All Techs))

Expon. (Historical (Core))

Expon. (Historical (Core) ‐ POWER only ‐no WIND)

Source: Wilson et al., Climatic Change, 2013 16

Cumulative Experience and Learning: The Importance of “granularity”

A

B

C

D

E

F

GH

IJ

KL

M

N

1

23

4

56

7

8

91011

12

‐50.0

‐40.0

‐30.0

‐20.0

‐10.0

0.0

10.0

20.0

30.0

40.0

50.0

1.E+00 1.E+03 1.E+06 1.E+09 1.E+12 1.E+15 1.E+18

Learning

 rate (%

 cost cha

nge pe

r dou

bling)

Cumulative # of units produced

A TransitorsB DRAMsC AutomobilesD Washing machinesE RefrigeratorsF DishwashersG Freezers (upright)H Freezers (chest)I DryersJ CalculatorsK CF light bulbsL A/C & heat pumpsM Air furnacesN Solar hot water heaters

1 PV modules2 Wind turbines3 Heat pumps4 Gas turbines5 Pulverized coal boilers6 Hypropower plants7 Nuclear reactors8 Ethanol9 Coal power plants

10 Coal power plants11 Gas pipelines12 Gas combined cycles

Mean of “granular”end use technologies:LR=20%CumProd= 10e9

Mean of “lumpy”supply technologies:LR=10%CumProd= 10e4

Source: Wilson et al, Nature CC, 2012

TNT Collaboration - Publications

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TNT ResourcesPioneering:• Uncertainty IR (Gritsevskyi/Grubler)• Supercomputer runs for tech uncertainty in CC (Nakicenovic/Gritsevskyi)• Agent-based modeling of tech complexity (Ma/Grubler/Nakicenovic/Brian Arthur)Collaboration/spin-offs:• Stochastic uncertainty modeling (w. ENE)• MCA multiple objectives (w. ENE)• Tech change in IAMs (w. ENE, RITE)• Web access/open source models (LSM)

On-line scenario and technology data bases• Scenario DBs IIASA GGI, GEA, IPCC-RCPs-SSPs (with ENE)

http://www.iiasa.ac.at/web-apps/ggi/GgiDbhttp://www.iiasa.ac.at/web-apps/tnt/RcpDb

• Energy & CO2 inventories Databasehttp://www.iiasa.ac.at/Research/TNT/WEB/Publications/Energy_Carbon_DataBase

• Scaling Dynamics of Energy Technologies (SD-ET) on novel historical technology datahttp://www.iiasa.ac.at/~gruebler/data.htm http://www.iiasa.ac.at/Research/TNT/WEB/Publications/Scaling_Dynamics_of_Energy_Technologies

• Primary Final and Useful Energy Database (PFUDB) http://www.iiasa.ac.at/web/home/research/researchPrograms/TransitionstoNewTechnologies/PFUDB.en.html

Models:• LSM2 Technological Growth & Substitution

http://www.iiasa.ac.at/Research/TNT/WEB/Software/LSM2 19

The Evolution of Technological Complexity

• Agent-based simulation model ofglobal energy system since 1800

• Random walk model of invention discovery and stochastic combinationwith other technologies into energychains and systems

• Evolutionary selection environment- uncertain increasing returns- market share gains f(rel. advantage)- externalities (stochastic C-tax)

• Evolution of complexity is function of learning rate and innovation impatience

• Complexity lock-in requires“gales of creative destruction”

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