Climate change and optimal energy technology R&D policy. (English) Zbl 1215.91062

Summary: Public policy response to global climate change presents a classic problem of decision making under uncertainty. Theoretical work has shown that explicitly accounting for uncertainty and learning in climate change can have a large impact on optimal policy, especially technology policy. However, theory also shows that the specific impacts of uncertainty are ambiguous. In this paper, we provide a framework that combines economics and decision analysis to implement probabilistic data on energy technology research and development (R&D) policy in response to global climate change. We find that, given a budget constraint, the composition of the optimal R&D portfolio is highly diversified and robust to risk in climate damages. The overall optimal investment into technical change, however, does depend (in a non-monotonic way) on the risk in climate damages. Finally, we show that in order to properly value R&D, abatement must be included as a recourse decision.


91B76 Environmental economics (natural resource models, harvesting, pollution, etc.)
91B32 Resource and cost allocation (including fair division, apportionment, etc.)
90C15 Stochastic programming


Full Text: DOI


[1] Apostolakis, G., The concept of probability in safety assessments of technological systems, Science, 250, 1359-1364 (1990)
[2] Baker, E., Increasing risk and increasing informativeness: Equivalence theorems, Operations Research, 54, 26-36 (2006) · Zbl 1167.91333
[3] Baker, E., Uncertainty and learning in climate change, Journal of Public Economic Theory, 11, 721-747 (2009)
[4] Baker, E.; Adu-Bonnah, K., Investment in risky R&D programs in the face of climate uncertainty, Energy Economics, 30, 465-486 (2008)
[5] Baker, E.; Shittu, E., Profit-maximizing R&D in response to a random carbon tax, Resource and Energy Economics, 28, 160-180 (2006)
[6] Baker, E.; Shittu, E., Uncertainty and endogenous technical change in climate policy models, Energy Economics, 30 (2008)
[7] Baker, E.; Clarke, L.; Weyant, J., Optimal technology R&D in the face of climate uncertainty, Climatic Change, 78 (2006)
[9] Baker, E.; Chon, H.; Keisler, J., Advanced solar R&D: Combining economic analysis with expert elicitations to inform climate policy, Energy Economics, 31, S37-S49 (2009)
[10] Baker, E.; Chon, H.; Keisler, J., Carbon capture and storage: Combining expert elicitations with economic analysis to inform climate policy, Climatic Change, 96, 3, 379 (2009)
[11] Blanford, G. J., R&D investment strategy for climate change: A numerical study, Energy Economics, 31, S27-S36 (2009)
[14] Bosetti, V.; Gilotte, L., The impact of carbon capture and storage on overall mitigation policy, Climate Policy, 7, 3-12 (2007)
[17] Clarke, L.; Weyant, J.; Birky, A., On the sources of technological advance: Assessing the evidence, Energy Economics, 28, 5-6, 579-595 (2006)
[19] Clarke, L.; Weyant, J.; Edmonds, J., On the sources of technological advance: What do the models assume?, Energy Economics, 30, 409-424 (2008)
[20] Clemen, R.; Kwit, R., The value of decision analysis at Eastman Kodak Company, 1990-1999, Interfaces, 31, 74-92 (2001)
[23] Farzin, Y. H.; Kort, P., Pollution abatement investment when environmental regulation is uncertain, Journal of Public Economic Theory, 2, 183-212 (2000)
[25] Goeschl, T.; Perino, G., On backstops and boomerangs: Environmental R&D under technological uncertainty, Energy Economics, 31, 437, 800-809 (2009)
[27] Grubb, M.; Kohler, J.; Anderson, D., Induced technical change in energy and environmental modeling: Analytic approaches and policy implications, Annual Review of Energy and the Environment, 27, 271-308 (2002)
[29] Hoffert, M. I.; Caldeira, K.; Jain, A. K., Energy implications of future stabilization of atmospheric \(CO_2\) content, Nature, 395, 881-884 (1998)
[30] Howard, R. A., Decision analysis: Practice and promise, Management Science, 34, 679-695 (1988)
[31] Howard, R. A.; Matheson, J. E.; North, D. W., The decision to seed hurricanes, Science, 176, 1191-1202 (1972)
[32] Kleywegt, A.; Shapiro, A.; De-Mello, T., The sample average approximation method for stochastic discrete optimization, SIAM Journal on Optimization, 12, 2, 479-502 (2002) · Zbl 0991.90090
[33] Linderoth, J.; Shapiro, A.; Wright, S., The empirical behavior of sampling methods for stochastic programming, Annals of Operations Research, 142, 1, 215-241 (2006) · Zbl 1122.90391
[34] Loschel, A., Technological change, energy consumption, and the costs of environmental policy in energy-economy-environment modeling, International Journal of Energy Technology and Policy, 2, 3, 250-261 (2004)
[35] Morgan, M. G.; Keith, D. W., Subjective judgments by climate experts, Environmental Science and Technology, 29, A468-A476 (1995)
[37] Nemhauser, G. L.; Wolsey, L. A., Integer and Combinatorial Optimization (1999), Wiley-Interscience: Wiley-Interscience New York, NY, USA · Zbl 0469.90052
[38] Nordhaus, W. D., Expert opinion on climatic change, American Scientist, 82, 45-51 (1994)
[40] Nordhaus, W., A Question of Balance: Weighing the Options on Global Warming Policies (2008), Yale University Press
[41] Peerenboom, J. P.; Buehring, W. A.; Joseph, T. W., Selecting a portfolio of environmental-programs for a synthetic fuels facility, Operations Research, 37, 5, 689-699 (1989)
[42] Pizer, W. A.; Popp, D., Endogenizing technological change: Matching empirical evidence to modeling needs, Energy Economics, 30, 2754-2770 (2008)
[43] Popp, D., ENTICE-BR: The effects of backstop technology R&D on climate policy models, Energy Economics, 28, 188-222 (2006)
[44] Rothschild, M.; Stiglitz, J., Increasing risk. I: A definition, Journal of Economic Theory, 2, 225-243 (1970)
[45] Shapiro, A., Inference of statistical bounds for multistage stochastic programming problems, Mathematical Methods of Operations Research, 58, 57-68 (2003) · Zbl 1116.90384
[46] Sharpe, P.; Keelin, T., How Smithkline Beecham makes better resource-allocation decisions, Harvard Business Review, 76, 2, 45-57 (1998)
[47] Sue-Wing, I., Representing induced technological change in models for climate policy analysis, Energy Economics, 28, 539-562 (2006)
[48] Tol, R., The damage costs of climate change toward more comprehensive calculations, Environmental and Resource Economics, 5, 4, 353-374 (1995)
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