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Fat-Tailed Uncertainty, Learning, And Climate Policy

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  • JOHN E. BISTLINE

    (Electric Power Research Institute, 3420 Hillview Ave, Palo Alto, CA 94304, USA;
    Steyer-Taylor Center for Energy Policy and Finance, Stanford University, Stanford, CA 94305, USA)

Abstract

Low-probability, high-impact risks are critical features of climate change economics; however, there are many unanswered policy and modeling questions about the implications of fat-tailed uncertainty. This paper examines the impact of fat-tailed uncertainty about the climate sensitivity on abatement decisions using a sequential decision-making framework. The results demonstrate how policy prescriptions from integrated assessment models are sensitive to the specifications of uncertainty, learning, and damages. Fat tails alone do not merit immediate and stringent mitigation but require strongly convex damages and slow learning. The analysis illustrates the potential value of midcourse corrections on reducing consumption risks imposed by uncertain damages from climate change and focuses attention on the dynamics of learning.

Suggested Citation

  • John E. Bistline, 2015. "Fat-Tailed Uncertainty, Learning, And Climate Policy," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-21.
    • Handle: RePEc:wsi:ccexxx:v:06:y:2015:i:02:n:s2010007815500098
    DOI: 10.1142/S2010007815500098
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    References listed on IDEAS

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    Cited by:

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    2. David Anthoff & Richard S. J. Tol, 2022. "Testing the Dismal Theorem," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 9(5), pages 885-920.

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