Characterizing long term technological change in IAMs · Characterizing long term technological...
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Characterizing long term technological change Prof. Gregory Nemet November 2015
Transcript of Characterizing long term technological change in IAMs · Characterizing long term technological...
Characterizing long term technological change
Prof. Gregory Nemet November 2015
Gregory Nemet— Long term technological change
Technological change: surprise and stationarity
Characteristics •Ex ante ignorance •Skewed outcomes •Pervasive spillovers •Combinatorial •Depreciating knowledge •Interaction w/ production •Public early
…private later
Drivers of smoothness • long lifetimes • risk aversion • incremental
improvement • aggregation
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Gregory Nemet— Long term technological change
80 years of PV prices
Gregory Nemet— Long term technological change
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80 years of PV prices
Gregory Nemet— Long term technological change
Missing perspectives on technological change
1. incentives in bureaucracies 2. knowledge dissemination 3. international cooperation 4. credibility of policy targets 5. public attitudes to novel technologies 6. characterization of the social returns to
innovation 7. near-term metrics to orient innovation toward
longer term human needs.
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Nemet, G. F., A. Grubler and C. Wilson (2015). "Re-orienting energy innovation to address human needs requires new social science." Nature Energy In review.
Gregory Nemet— Long term technological change
Appendix
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Gregory Nemet— Long term technological change
To what extent does aggregation smooth surprises
and disappointments?
• The closer you look
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Gregory Nemet— Long term technological change
x. Tech change: characteristics to address • Skewed outcomes • Diminishing returns • Crowding out • Technological opportunity • Public early…private later • Interactions with production • Depreciating knowledge • Small $s…vs deploy, taxes, damages
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Gregory Nemet— Long term technological change
x. Tech change: characteristics to address • The closer you look, the less predictable it
seems • Is there a benefit to not looking too closely? • Does aggregation enable smootheness?
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Gregory Nemet— Long term technological change
x. Tech change: drivers of smooth change • S-shaped adoption • Long lived assets • Risk aversion in adoption
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Gregory Nemet— Long term technological change
1. LEARNING CURVES
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Gregory Nemet— Long term technological change
1. Learning curves: Appeal and problems
Appeal: • Makes tech dynamic • Compact • Data availability • Some theory • Historical evidence • Goodness of fit
Two broad problems: 1.Uncertainty poorly
characterized
2.Omitted variable bias
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Gregory Nemet— Long term technological change
1. Learning curves: LR variation across technologies
Nemet, G. F. (2009). "Interim monitoring of cost dynamics for publicly supported energy technologies." Energy Policy 37(3): 825-835.
LRmedian=0.185 ±1σ=0.064 – 0.306
A small change in learning rate makes a difference.
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Gregory Nemet— Long term technological change
1. Learning curves: LR variation within technologies
Nemet, G. F. (2009). "Interim monitoring of cost dynamics for publicly supported energy technologies." Energy Policy 37(3): 825-835.
Learning rates for wind power (1981–2006) calculated for all periods >=10 years (n=153).
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Gregory Nemet— Long term technological change
1. Learning curves: Other factors affect performance
Other factors: 1. R&D-induced techΔ 2. Scale effects 3. Input costs 4. Diminishing returns 5. Knowledge
depreciation 6. Serendipitous
spillovers 7. Time
Implications: - LR bias
- error not random
- Forecasts biased
- Incentives wrong
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Gregory Nemet— Long term technological change
1. Learning curves: Two summary points
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1. If we are to depend on learning curves, we need to characterize reliability of resulting forecasts.
2. Need to more fully represent the
drivers of technological change.
Gregory Nemet— Long term technological change
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Nemet question to venture capitalist: How do you value the benefits of policy in your decisions to invest in start-up companies?
“We ignore it. What the government giveth, it can taketh away.’’ - Venture capitalist in energy sector
4. Incentives If policy commitments are not fully credible…
Gregory Nemet— Long term technological change
APPENDIX
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Gregory Nemet— Long term technological change
2. R&D: Demand curves for coal CCS
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Gregory Nemet— Long term technological change
Outcomes and conclusions • PDF of costs • Benefits to diversification
Next: • Use integrated assessment model • Effect of policy changes on:
– emissions, concentrations, – abatement costs
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Gregory Nemet— Long term technological change
4. Incentives How did solar get cheap?
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Gregory Nemet— Long term technological change
Summary of results
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Results: 1. Evidence that firms did learn from experience, 2. Firms learned from other firms’ experience 3. Clearer for operating performance than for
performance of new installations ...but... 4. rapid depreciation of knowledge from LbD 5. diminishing returns from LbD
6. Effects of policy varied:
• Capital cost incentive had negative effect • Production incentives had a mixed effect
Gregory Nemet— Long term technological change
Interpretation for policy
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Policy implications: • Spillovers exist so need demand-side policy
...even in the presence of pollution pricing. • Some evidence that performance incentives
really do make a difference • Codification to address knowledge depreciation? Open questions on policy implications: • Geographic extent of spillovers • LbD embodied in new technology? • Systemic benefits? e.g. grid operators • Firms are risk-averse? Unproven technology
Gregory Nemet— Long term technological change
1. Addressing energy problems requires changes in behavior and technology
2. Energy policy involves multiple objectives
3. Inertia in the energy system
4. Historical volatility in: policy, markets, and public interest
5. Technological change depends on expectations
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5 premises for energy policy analysis:
Gregory Nemet— Long term technological change
purpose • this workshop intends to strengthen current and future efforts to
analyze technological change and to introduce endogenous technology dynamics in large‐scale energy or integrated assessment models. In your session on "taking stock", we envisage you will present insights into the current status of research on issues related to cost dynamics, learning curves, R&D and innovation, such as:
• factors explaining cost dynamics along the learning curve • determinants of the pace of global innovation in energy technologies • other relevant insights to date
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