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Optimal climate policy when damages are unknown

Author

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  • Rudik, Ivan

    (Cornell University)

Abstract
Integrated assessment models (IAMs) are economists’ primary tool for analyzing the optimal carbon tax. Damage functions, which link temperature to economic impacts, have come under fire because of their assumptions that may be incorrect in significant, but a priori unknowable ways. Here I develop recursive IAM frameworks to model uncertainty, learning, and concern for misspecification about damages. I decompose the carbon tax into channels capturing state uncertainty, insurance motives, and precautionary saving. Damage learning improves ex ante welfare by $750 billion. If damage functions are misspecified and omit the potential for catastrophic damages, robust control may be beneficial ex post.

Suggested Citation

  • Rudik, Ivan, 2019. "Optimal climate policy when damages are unknown," SocArXiv nc43k, Center for Open Science.
    • Handle: RePEc:osf:socarx:nc43k
    DOI: 10.31219/osf.io/nc43k
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    References listed on IDEAS

    as
    1. Loic Berger & Massimo Marinacci, 2017. "Model Uncertainty in Climate Change Economics," Working Papers 616, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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    Cited by:

    1. Lint Barrage, 2019. "The Nobel Memorial Prize for William D. Nordhaus," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(3), pages 884-924, July.
    2. Samuel Jovan Okullo, 2020. "Determining the Social Cost of Carbon: Under Damage and Climate Sensitivity Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(1), pages 79-103, January.

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