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The Economics of Tail Events with an Application to Climate Change

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  • William D. Nordhaus

Abstract

From time to time, something occurs that is outside the range of what is normally expected. This is called a tail event in the sense that it is way out the tail of a probability distribution. This article considers the implications of tail events for economic policy and climate change economics. This issue has been analyzed by Martin Weitzman , who has proposed a dismal theorem. The theorem's general point is that under limited conditions concerning the structure of uncertainty and preferences, society has an indefinitely large expected loss from high-consequence, low-probability events and that standard economic analysis does not apply. The present article is intended to put the dismal theorem in context and examine the extent of its relevance with regard to climate change. There are indeed deep uncertainties about virtually every aspect of the natural and social sciences of climate change, and the only way these uncertainties can be resolved is through continued careful consideration and analysis of all data and theories. I conclude that tail events are important phenomena that require careful analysis and attention. At the same time, I find that there is no universal rule for determining when benefit--cost analysis should or should not be applied. (JEL: Q5, D8, D8) Copyright 2011, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/reep/rer004
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Bibliographic Info

Article provided by Association of Environmental and Resource Economists in its journal Review of Environmental Economics and Policy.

Volume (Year): 5 (2011)
Issue (Month): 2 (Summer)
Pages: 240-257

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Handle: RePEc:oup:renvpo:v:5:y:2011:i:2:p:240-257

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Cited by:
  1. Halkos, George, 2013. "Uncertainty in optimal pollution levels: Modeling the benefit area," MPRA Paper 47768, University Library of Munich, Germany.
  2. Horowitz, John & Lange, Andreas, 2014. "Cost–benefit analysis under uncertainty — A note on Weitzman's dismal theorem," Energy Economics, Elsevier, vol. 42(C), pages 201-203.
  3. Antony Millner, 2013. "On Welfare Frameworks and Catastrophic Climate Risks," CESifo Working Paper Series 4442, CESifo Group Munich.
  4. Richard S.J. Tol, 2012. "Targets for Global Climate Policy: An Overview," Working Paper Series 3712, Department of Economics, University of Sussex.
  5. Benjamin Jones & Michael Keen & Jon Strand, 2013. "Fiscal implications of climate change," International Tax and Public Finance, Springer, vol. 20(1), pages 29-70, February.
  6. William D. Nordhaus, 2011. "Integrated Economic and Climate Modeling," Cowles Foundation Discussion Papers 1839, Cowles Foundation for Research in Economics, Yale University.
  7. Iverson, Terrence, 2012. "Optimal Carbon Taxes with Non-Constant Time Preference," MPRA Paper 43264, University Library of Munich, Germany.
  8. Tack, Jesse, 2013. "A Nested Test for Common Yield Distributions with Applications to U.S. Corn," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(1), April.
  9. Tierney, Heather L.R., 2013. "Forecasting and Tracking Real-Time Data Revisions in Inflation Persistence," MPRA Paper 53374, University Library of Munich, Germany, revised Nov 2013.
  10. Noy, I, 2012. "Investing in Disaster Risk Reduction: A Global Fund," Working Paper Series 2390, Victoria University of Wellington, School of Economics and Finance.
  11. 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.
  12. 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.

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