Understanding the Complexity of Technology Transitions

Transcription

1 Understanding the Complexity of Technology Transitions Arnulf Grubler IIASA and Yale STEM-FORMAS Seminar Eskilstuna March

2 Main EnergyTransitions: A History of Technology Non-commercial commercial Renewable fossil post-fossil? Rural urban South North South Low exergy higher exergy (H:C ratio ) Improved efficiency/productivity Conversion deepening (e.g. electrification) Increasing supply/demand density Desulfurization, Decarbonization

3 2 Grand SHARES IN Technology PRIMARY & Infrastructure ENERGY Transitions Oil/Gas 0% 100% 20% 80% Measuring supply, but driven by TC in end-use (steam engines, cars, aircraft..) 40% % 60% % 80% 20% % 0% Coal 20% % 1850 historical 60% 0% 80% 100% Renewables/Nuclear

4 Decarbonization of Energy: Evolutionary Envelope of Multiple Transitions 35 Carbon intensity of: 30 wood = 29.9 gc/mj coal = 25.8 oil = gas =

5 Minds More Creative than Models Great Transitions (agriculture industry; work pleasure; renewable C fossil C hydrogen): few anticipated, yet fewer modeled Decarbonization research milestones: --Replicate past (Ausubel et al., 1988) --Design quantified normative futures (FFES, 1994) --Integration qualitative-quantitative transition scenarios (SRES, 2000)

6 40% Oil/Gas 0% 100% 20% 80% Oil & gas forever A1 60% Path Dependent Futures in IIASA-WEC and IPCC SRES Scenarios Coal 100% 0% 80% 60% % A2 40% A1F1 Return to coal A2 B 60% B2 B1 Muddling through 1850 historical A1B 40% A3 C 20% A1T Grand transition 0% 80% 100% Renewables/Nuclear

7 1.6 Decarbonization Scenarios Global Carbon Dioxide Intensity of Energy (index, 1990=1) % 75% 0.8 Median % 0.2 Source: IPCC SRES, %

8 Modeling Difficulties Dominance of equilibrium (CGE, I-O) Chronic difficulties to capture structural change Ignorance of uncertainty and surprise Feedbacks ignored or underestimated Productivity growth as manna from heaven Technology: treated as exogenous Σ: Dominance of dumb farmer or business-as-usual scenarios

9 I think you should be more explicit here in step two

10 The black box of Technology: Stages, Actors, Feedbacks funding Public Sector incentives, standards, regulation, subsidies, taxes Disembodied Technology (Knowledge) Learning Basic R&D Applied R&D Demonstration Product / Technology Push Market / Demand Pull Niche markets Diffusion Embodied Technology (plant, equipment,..) funding Private Sector investments, knowledge and market spillovers

11 Technological Uncertainties (modeled at IIASA) Innovation feasibility Existence of increasing returns to adoption Diffusion environment (demand growth, capital stock turnover) Complementary technologies and infrastructures Innovation and adoption policy support

12 Technological Uncertainty 1: Patented but non-functional smoke-spark arrestors Basalla, 1988.

13 Technological Uncertainties 2: Technology cost declines (push) and market growth (pull) 1.5 Nuclear Reactors France % 1.5 Cost index ($/kw) % interval 90% interval mean (115 case studies): -20% per doubling PVs Japan % Number of doublings (installed capacity) 0.0

14 Induced Technological Change Inducement 1: Knowledge (generation, spillovers, trade) Inducement factor 2: Diffusion environment (economic, social, regulatory) Uncertainty 1: outcomes of R&D and investment strategies ( learning ) Uncertainty 2: market environment incl. demand, environmental and social constraints, etc.

15 A Simple Multi-agent Model 5 regions Energy trade For each region: Demand Resource availability, costs Technology agents Technology demand/ supply Local, technology specific Regional (infrastructures, supply portfolios) Global (extraction, transport, portfolios) Costs Increasing scale Increasing breath of technology portfolio C-tax Model actors and their interactions. Zagged lines= stochastic variables

16 Global Decarbonization History and Future in 4 Models of Increasing Treatment of Uncertainty (1) No uncertainty static technology (2) Uncertainty in demand, resources, costs 0.75 Tons C/toe (3) = (2) + uncertain C - tax (4) Full uncertainty (incl. techn. learning)

17 Summary of IIASA Modeling Endogenous technological change through anticipation of (uncertain) increasing returns Multi-agent, spatial heterogeneous change First policy variables included (e.g. taxes) Great Technology Transition: Continued decarbonization only under full uncertainty model But: Many more driving forces of humbling complexity Info:

18 Technology Transitions: What have we learned? Importance of: uncertainty, increasing returns, path dependency, heterogeneity Policy implications: -- more innovation not less -- earlier experimentation not later -- smaller rather than lumpy investments -- supply demand integration (R&D and deployment incentives) -- importance of spillovers (across technologies, regions) risk hedging via portfolios

19 Remaining Challenges Technology spillovers across sectors Proliferation of scenarios (even with optimal hedging strategies) Computational limits for simultaneous treatment of full technological uncertainty and multiple agents Treatment of myopic behavior Representation of barriers to change (institutions, politics) Social embedding: Resistance, feedbacks ( take back effects), behavioral surprises (e.g. SUVs)

20 3 Challenges Models Provide no Answer (yet) Dealing with social push (away) (e.g. nuclear) and pull factors (e.g. cell-phones) Reconciling long-term technology needs with short-term disincentives (declining R&D, privatization myopia ) value of innovation and optimal risk hedging technology portfolios now exist, but no actors to implement them to overcome innovation cost-benefit externality

21 Optimal Diffusion of Fuel Cells Under Full Technological Uncertainty (Δt = 50 yrs) EUR NAM Uses innovation (after costs decline) REF ASIA Mtoe 4000 ROW Pays for innovation (R&D and expensive first niche markets)

22 Needing More Technology not Less vs. US - Decline in Energy R&D and Innovation Patents Granted Funds for Energy R&D Patents Granted Energy R&D (Billions 1996$) Year Source: Kammen and Nemet, 2005.