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The effect of learning on climate policy under fat-tailed risk

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  • Hwang, In Chang
  • Reynès, Frédéric
  • Tol, Richard S.J.

Abstract

This paper investigates the effect of learning on climate policy under fat tailed risk about climate change. We construct an endogenous learning model with fat-tailed uncertainty about the equilibrium climate sensitivity. We find that a decision maker with a possibility of learning lowers efforts to reduce carbon emissions relative to the no-learning case. The larger the tail effect, the larger the counteracting learning effect because learning reduces the marginal benefit of emissions control compared to the case where there is no learning. The optimal decisions (summarized by the carbon tax level) in the learning case are less sensitive to the true value of the uncertain variable than the decisions in the uncertainty case. Learning lets uncertainty converge to the true value of the state in the sense that the variance approaches zero as information accumulates.

Suggested Citation

  • Hwang, In Chang & Reynès, Frédéric & Tol, Richard S.J., 2017. "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Elsevier, vol. 48(C), pages 1-18.
    • Handle: RePEc:eee:resene:v:48:y:2017:i:c:p:1-18
    DOI: 10.1016/j.reseneeco.2017.01.001
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    Cited by:

    1. 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.
    2. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Active Learning about Climate Change," Working Paper Series 6513, Department of Economics, University of Sussex Business School.
    3. 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.
    4. In Chang Hwang & Richard S. J. Tol & Marjan W. Hofkes, 2019. "Active Learning and Optimal Climate Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1237-1264, August.
    5. Gissela Landa Rivera & Paul Malliet & Aurélien Saussay & Frédéric Reynès, 2018. "The State of Applied Environmental Macroeconomics," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(3), pages 133-149.
    6. repec:hal:spmain:info:hdl:2441/3qbhmo3oe19bo8u5dc21qfic27 is not listed on IDEAS
    7. repec:spo:wpmain:info:hdl:2441/3qbhmo3oe19bo8u5dc21qfic27 is not listed on IDEAS
    8. Ahlvik, Lassi & Iho, Antti, 2018. "Optimal geoengineering experiments," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 148-168.
    9. Yongyang Cai, 2020. "The Role of Uncertainty in Controlling Climate Change," Papers 2003.01615, arXiv.org, revised Oct 2020.
    10. In Chang Hwang, 2017. "A Recursive Method for Solving a Climate–Economy Model: Value Function Iterations with Logarithmic Approximations," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 95-110, June.
    11. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    12. Ekholm, Tommi, 2018. "Climatic Cost-benefit Analysis Under Uncertainty and Learning on Climate Sensitivity and Damages," Ecological Economics, Elsevier, vol. 154(C), pages 99-106.
    13. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.

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    More about this item

    Keywords

    Climate policy; Fat tailed risk; Bayesian learning; Integrated assessment; Dynamic programming;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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