Transition to New Technologies (TNT) Program

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Aubrey Greene
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Applied Systems Analysis Professor Emeritus of Energy Economics

1 Transition to New Technologies (TNT) Program Nebojsa Nakicenovic Deputy Director General and Deputy CEO International Institute for Applied Systems Analysis Professor Emeritus of Energy Economics Vienna University of Technology UK Panel Visit IIASA, Laxenburg December 2014

2 Why study Technology? 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) 2

3 Transitions to New Technologies Point of departure Scenarios/impacts of diffusion of new technologies (clusters): ICT, transport, energy, and impact on environment (e.g. climate). Strategic Goal Furthering the understanding of patterns, dynamics, and constraints of technological change, and its drivers for global sustainability conditions. Research Goal Focusing 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) 3

Networks for Policy Relevant Research Major International Assessments: Global Energy

report) Global Fora: Sustainable Energy For All (SE4ALL) TNT provides methodological

national scale modeling to support the SE4ALL 2030 goals: Universal access to modern

International Council for Science (ICSU) Future Earth Initiative Sustainable Development

4 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

5 Global Access to Technologies (Lorenz Curves) Data: UN, ITU, World Bank,

6 Global Historic Primary Energy Transitions (changeover time t: years) Begin of energy policy focus: ts >2000 yrs Percent in Primary Energy traditional biomass coal t +130 yrs t -130 yrs t -80 yrs t +90 yrs modern fuels: oil, gas, electricity Source: Chapter 24: GEA,

7 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

The GEA ETIS Framework ACTORS & INSTITUTIONS shared expectations entrepreneurs / risk taking key Analysis & Modelling Roadmaps & Portfolios Technology Collaborations resource inputs Directable

8 The GEA ETIS Framework ACTORS & INSTITUTIONS shared expectations entrepreneurs / risk taking key Analysis & Modelling Roadmaps & Portfolios Technology Collaborations resource inputs Directable (Activities) generation KNOWLEDGE R,D&D (public $) Future Needs RESOURCES Non- Directable (Outputs) Market Formation learning Social Rates of Return public policy & leverage supply : end-use (relative effort) Learning Effects Diffusion Support performance cost Source: Chapter 24: GEA, 2012 TECHNOLOGY CHARACTERISTICS CLIMATE MITIGATION 8

9 Current Public ETIS Policy Leverage/Focus (policy-induced resource mobilization, billion US$2005) Source: Wilson et al. Nature Climate Change,

10 ETIS Case Studies Criteria for Case Study Selection Thematic / Meta analytic Supply Technologies End Use Technologies Single Context Comparative Context Current Historical Developed Country(s) Developing Country(s) Influential Public Policy 1 Energy Transitions X X X X X X X Systemic Knowledge Adoption & Use Actors & Institutions Resources 2 Technology Diffusion X X X X X X X 3 Assessment Metrics X 4 Technology Portfolios X X X X 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 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 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 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 Source: Chapter 24: GEA,

11 World Energy Technology Innovation Investments (Billion $) innovation market diffusion (RD&D) formation End-use & efficiency >> Fossil fuel supply >12 >> Nuclear > Renewables >12 ~20 >20 Electricity (Gen+T&D) >>1 ~ Other* >>4 <15 n.a. Total >50 < <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,

12 Knowledge Depreciation Rates (% per year) Degree of knowledge stock turnover (policy & human capital volatility) Low High Degree of technological obsolescence (rate of innovation) High Engineering PV Japan: designs US: OECD Wind US: 30% <5% nuclear R&D: 10% Aircraft, Liberty ships manufct. US: 40% Service industries: 95% Chemicals, Drugs: 15-20% Miscell. >20% 10 40% Computers: 32% Electrical, Machinery: 32-36% France breeder reactors: 50-60% empirical studies reviewed GEA Chap 24 (2012) and modeled R&D deprecation in US manufacturing (Hall, 2007) 12

13 Post Fossil Energy Supply Technologies Cost Trends Source: Grubler/Wilson: CUP,

14 Learning rates and cumulative experience (# of units produced/sold) for energy technologies category technology data for: cumulative production (units) learning # exp period rate energy Transitors World >1 10^ end-use DRAMs World >1 10^ Automobiles World >2 10^ Washing machines World >2 10^ ±9 Refrigerators World >2 10^ ±4 Dishwashers World >6 10^ ±7 Freezers (upright) World >6 10^ ±5 Freezers (chest) World >5 10^ ±2 Dryers World >3 10^ ±7 Hand-held calculators US >4 10^8 early 1970s 30 CF light bulbs US >4 10^ A/C & heat pumps US >1 10^ ±1 Air furnaces US >1 10^ ±3 Solar hot water heaters US >1 10^ average for end-use technologies 10^9 20 energy supply PV modules World >1 10^ Wind turbines World >1 10^ Heat pumps S, CH <1 10^ Gas turbines World >4 10^ Pulverized coal boilers World >6 10^ Hypropower plants OECD ~5 10^ Nuclear reactors US, France <1 10^ Ethanol Brazil <1 10^ Coal power plants OECD <1 10^ Coal power plants US <1 10^ Gas pipelines US <1 10^ Gas combined cycles OECD <1 10^ Hydrogen production (SRM) World >1 10^ LNG production World >1 10^ average for suppy technologies 8 average for supply, excluding nuclear 10^4 12 Source: Nature CC, 2012, S1 14

15 Market Size (normalized index, Core markets) vs Diffusion Speed ( t) of Energy Technologies CELL PHONES Source: Wilson, YSSP, E-Bikes & Cell Phones courtesy of IIASA Post-Doc Dr. Bento 15

16 Scaling patterns Past and Scenarios (IIASA GGI) (8 Scenarios: A2r/B1/B2 * base/670/480) 1.E+07 1.E+06 Cumulative Total Capacity (OECD): normalised K vs Δt ALL TECHS: HISTORICAL & SCENARIOS semi log SCENARIOS (All Techs) Historical (Core) Scenarios more conservative compared to past Normalised K (index) 1.E+05 1.E+04 1.E+03 1.E+02 1.E+01 1.E Δt (yrs) of cumulative total capacity Historical (Core) POWER only no WIND Expon. (SCENARIOS (All Techs)) Expon. (Historical (Core)) Expon. (Historical (Core) POWER only no WIND) Closer relationship for lumpy power techs Method adopted in IAM community for sce4nario validation Source: Wilson et al., Climatic Change,

Cumulative Experience and Learning: The Importance of granularity Learning rate (% cost change per doubling) 50.0 40.0 30.0 20.0 10.0 0.0 10.0 20.0 30.0 40.0 8 12 9 10 11 7 5 6 4 2 3 N M I J F L 50.

17 Cumulative Experience and Learning: The Importance of granularity Learning rate (% cost change per doubling) N M I J F L E+00 1.E+03 1.E+06 1.E+09 1.E+12 1.E+15 1.E+18 Cumulative # of units produced K G H D C E 1 B Mean of granular end use technologies: LR=20% CumProd= 10e9 Mean of lumpy supply technologies: LR=10% CumProd= 10e4 A A Transitors B DRAMs C Automobiles D Washing machines E Refrigerators F Dishwashers G Freezers (upright) H Freezers (chest) I Dryers J Calculators K CF light bulbs L A/C & heat pumps M Air furnaces N Solar hot water heaters 1 PV modules 2 Wind turbines 3 Heat pumps 4 Gas turbines 5 Pulverized coal boilers 6 Hypropower plants 7 Nuclear reactors 8 Ethanol 9 Coal power plants 10 Coal power plants 11 Gas pipelines 12 Gas combined cycles Source: Wilson et al, Nature CC, 2012

18 TNT Collaboration - Publications 18

19 TNT Resources Pioneering: 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) Energy & CO2 inventories Database Scaling Dynamics of Energy Technologies (SD-ET) on novel historical technology data Primary Final and Useful Energy Database (PFUDB) en.html Models: LSM2 Technological Growth & Substitution 19

The Evolution of Technological Complexity Agent-based simulation model of global energy system since 1800 Random walk model of invention discovery and stochastic combination with other technologies

20 The Evolution of Technological Complexity Agent-based simulation model of global energy system since 1800 Random walk model of invention discovery and stochastic combination with other technologies into energy chains 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 20

Transitions to Low-Carbon Economy

Transitions to Low-Carbon Economy Nebojsa Deputy Director General International Institute for Applied Systems Analysis Professor Emeritus of Energy Economics Vienna University of Technology Fordham University