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Climate Policy Under Fat-Tailed Risk: An Application of Dice

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

Abstract

Uncertainty plays a significant role in evaluating climate policy, and fat-tailed uncertainty may dominate policy advice. Should we make our utmost effort to prevent the arbitrarily large impacts of climate change under deep uncertainty? In order to answer to this question we propose an new way of investigating the impact of (fat-tailed) uncertainty on optimal climate policy: the curvature of carbon tax against the uncertainty. We find that the optimal carbon tax increases as the uncertainty about climate sensitivity increases, but it does not accelerate as implied by Weitzman's Dismal Theorem. We find the same result in a wide variety of sensitivity analyses. These results emphasize the importance of balancing of the costs and the benefits of climate policy, also under deep uncertainty.

Suggested Citation

  • Hwang, In Chang & Reynès, Frédéric & Tol, Richard S. J., 2011. "Climate Policy Under Fat-Tailed Risk: An Application of Dice," Papers WP403, Economic and Social Research Institute (ESRI).
  • Handle: RePEc:esr:wpaper:wp403
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    Cited by:

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    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.
    3. Ikefuji, Masako & Laeven, Roger J.A. & Magnus, Jan R. & Muris, Chris, 2020. "Expected utility and catastrophic risk in a stochastic economy–climate model," Journal of Econometrics, Elsevier, vol. 214(1), pages 110-129.
    4. Havranek, Tomas & Irsova, Zuzana & Janda, Karel & Zilberman, David, 2015. "Selective reporting and the social cost of carbon," Energy Economics, Elsevier, vol. 51(C), pages 394-406.
    5. 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.
    6. 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.
    7. Vogt-Schilb, Adrien & Meunier, Guy & Hallegatte, Stéphane, 2018. "When starting with the most expensive option makes sense: Optimal timing, cost and sectoral allocation of abatement investment," Journal of Environmental Economics and Management, Elsevier, vol. 88(C), pages 210-233.
    8. Delton B. Chen & Joel van der Beek & Jonathan Cloud, 2017. "Climate mitigation policy as a system solution: addressing the risk cost of carbon," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 7(3), pages 233-274, July.
    9. W. J. Wouter Botzen & Jeroen C. J. M. Van Den Bergh & Graciela Chichilnisky, 2018. "Climate Policy Without Intertemporal Dictatorship: Chichilnisky Criterion Versus Classical Utilitarianism In Dice," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 1-17, May.
    10. Giacomo Marangoni & Jonathan R. Lamontagne & Julianne D. Quinn & Patrick M. Reed & Klaus Keller, 2021. "Adaptive mitigation strategies hedge against extreme climate futures," Climatic Change, Springer, vol. 166(3), pages 1-17, June.
    11. Edilio Valentini & Paolo Vitale, 2019. "Optimal Climate Policy for a Pessimistic Social Planner," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(2), pages 411-443, February.
    12. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    13. 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.
    14. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
    15. W. Botzen & Jeroen Bergh, 2014. "Specifications of Social Welfare in Economic Studies of Climate Policy: Overview of Criteria and Related Policy Insights," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(1), pages 1-33, May.
    16. Jasper N. Meya & Ulrike Kornek & Kai Lessmann, 2018. "How empirical uncertainties influence the stability of climate coalitions," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 18(2), pages 175-198, April.
    17. Masako Ikefuji & Jan R. Magnus, 2020. "The perception of climate sensitivity: Revealing priors from posteriors," ISER Discussion Paper 1111, Institute of Social and Economic Research, Osaka University.
    18. Davidson, Marc D., 2014. "Zero discounting can compensate future generations for climate damage," Ecological Economics, Elsevier, vol. 105(C), pages 40-47.
    19. 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.
    20. Hwang, In Chang & Tol, Richard S.J. & Hofkes, Marjan W., 2016. "Fat-tailed risk about climate change and climate policy," Energy Policy, Elsevier, vol. 89(C), pages 25-35.
    21. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Tail-effect and the Role of Greenhouse Gas Emissions Control," Working Paper Series 6613, Department of Economics, University of Sussex Business School.

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    Keywords

    Climate policy/Policy/risk/uncertainty/impacts/Impacts of climate change/Climate change/taxes/cost;

    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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