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Optimal Policy under Uncertainty and Learning about Climate Change: A Stochastic Dominance Approach

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  • ERIN BAKER

Abstract

Global climate change presents a classic problem of decision making under uncertainty with learning. We provide stochastic dominance theorems that provide new insights into when abatement and investment into low carbon technology should increase in risk. We show that R&D into low‐carbon technologies and near‐term abatement are in some sense opposites in terms of risk. Abatement provides insurance against the possibility of major catastrophes; R&D provides insurance against the possibility that climate change is marginally worse than average. We extend our results to the comparative statics of learning.

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  • Erin Baker, 2009. "Optimal Policy under Uncertainty and Learning about Climate Change: A Stochastic Dominance Approach," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 11(5), pages 721-747, October.
    • Handle: RePEc:bla:jpbect:v:11:y:2009:i:5:p:721-747
    DOI: 10.1111/j.1467-9779.2009.01427.x
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    Cited by:

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    2. Shittu, Ekundayo & Kamdem, Bruno G. & Weigelt, Carmen, 2019. "Heterogeneities in energy technological learning: Evidence from the U.S. electricity industry," Energy Policy, Elsevier, vol. 132(C), pages 1034-1049.
    3. Christian Gollier & Miles S. Kimball, 2018. "New methods in the classical economics of uncertainty: comparing risks," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 5-23, May.
    4. Erin Baker & Olaitan Olaleye & Lara Aleluia Reis, 2015. "Decision Frameworks and the Investment in R&D," Working Papers 2015.42, Fondazione Eni Enrico Mattei.
    5. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    6. Agbo, Maxime, 2014. "Strategic exploitation with learning and heterogeneous beliefs," Journal of Environmental Economics and Management, Elsevier, vol. 67(2), pages 126-140.
    7. Chang, Charles W., 2014. "DICESC: Optimal Policy in a Stochastic Control Framework," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170831, Agricultural and Applied Economics Association.
    8. Baker, Erin & Olaleye, Olaitan & Aleluia Reis, Lara, 2015. "Decision frameworks and the investment in R&D," Energy Policy, Elsevier, vol. 80(C), pages 275-285.

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