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Fat Tails, Thin Tails, and Climate Change Policy

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  • Robert S. Pindyck

Abstract

Climate policy is complicated by the considerable compounded uncertainties over the costs and benefits of abatement. We don’t even know the probability distributions for future temperatures and impacts, making cost-benefit analysis based on expected values challenging to say the least. There are good reasons to think that those probability distributions are fat-tailed, which implies that if social welfare is based on the expectation of a CRRA utility function, we should be willing to sacrifice close to 100% of GDP to reduce GHG emissions. I argue that unbounded marginal utility makes little sense, and once we put a bound on marginal utility, this implication of fat tails goes away: Expected marginal utility will be finite even if the distribution for outcomes is fat-tailed. Furthermore, depending on the bound on marginal utility, the index of risk aversion, and the damage function, a thin-tailed distribution can yield a higher expected marginal utility (and thus a greater willingness to pay for abatement) than a fat-tailed one.

Suggested Citation

  • Robert S. Pindyck, 2010. "Fat Tails, Thin Tails, and Climate Change Policy," Working Papers 1012, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
  • Handle: RePEc:mee:wpaper:1012
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    References listed on IDEAS

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    1. Pindyck, Robert S., 2012. "Uncertain outcomes and climate change policy," Journal of Environmental Economics and Management, Elsevier, vol. 63(3), pages 289-303.
    2. Robert S. Pindyck, 2011. "Modeling the Impact of Warming in Climate Change Economics," NBER Chapters, in: The Economics of Climate Change: Adaptations Past and Present, pages 47-71, National Bureau of Economic Research, Inc.
    3. Stern,Nicholas, 2007. "The Economics of Climate Change," Cambridge Books, Cambridge University Press, number 9780521700801, May.
    4. Robert S. Pindyck & Neng Wang, 2013. "The Economic and Policy Consequences of Catastrophes," American Economic Journal: Economic Policy, American Economic Association, vol. 5(4), pages 306-339, November.
    5. Adam Daigneault & Steve Newbold, 2009. "Climate Response Uncertainty and the Unexpected Benefits of Greenhouse Gas Emissions Reductions," NCEE Working Paper Series 200806, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2009.
    6. Pindyck, Robert S., 2002. "Optimal timing problems in environmental economics," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1677-1697, August.
    7. Stephen Newbold & Adam Daigneault, 2009. "Climate Response Uncertainty and the Benefits of Greenhouse Gas Emissions Reductions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 44(3), pages 351-377, November.
    8. Geoffrey Heal & Bengt Kriström, 2002. "Uncertainty and Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 22(1), pages 3-39, June.
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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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