Baker, Erin; Solak, Senay Climate change and optimal energy technology R&D policy. (English) Zbl 1215.91062 Eur. J. Oper. Res. 213, No. 2, 442-454 (2011). 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. Cited in 11 Documents MSC: 91B76 Environmental economics (natural resource models, harvesting, pollution, etc.) 91B32 Resource and cost allocation (including fair division, apportionment, etc.) 90C15 Stochastic programming Keywords:R&D portfolio; energy technology; climate change; stochastic programming; public policy Software:SUTIL PDF BibTeX XML Cite \textit{E. Baker} and \textit{S. Solak}, Eur. J. Oper. Res. 213, No. 2, 442--454 (2011; Zbl 1215.91062) Full Text: DOI References: [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) This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.