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Scientific Ambiguity and Climate Policy

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  • Antony Millner

    ()

  • Simon Dietz

    ()

  • Geoffrey Heal

Abstract

Economic evaluation of climate policy traditionally treats uncertainty by appealing to expected utility theory. Yet our knowledge of the impacts of climate policy may not be of sufficient quality to be described by unique probabilistic beliefs. In such circumstances, it has been argued that the axioms of expected utility theory may not be the correct standard of rationality. By contrast, several axiomatic frameworks have recently been proposed that account for ambiguous knowledge. In this paper, we apply static and dynamic versions of a smooth ambiguity model to climate mitigation policy. We obtain a general result on the comparative statics of optimal abatement and ambiguity aversion, and then extend our analysis to a more realistic, dynamic setting, where we introduce scientific ambiguity into the well-known DICE model of the climate-economy system. For policy-relevant exogenous mitigation policies, we show that the value of emissions abatement increases as ambiguity aversion increases, and that this ‘ambiguity premium’ can in some plausible cases be very large. In these cases the effect of ambiguity aversion on welfare is comparable to that of other much studied welfare parameters. Thus ambiguity aversion may be an important neglected aspect of climate change economics, and seems likely to provide another argument for strong abatement policy. Copyright Springer Science+Business Media Dordrecht 2013

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Bibliographic Info

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

Volume (Year): 55 (2013)
Issue (Month): 1 (May)
Pages: 21-46

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Handle: RePEc:kap:enreec:v:55:y:2013:i:1:p:21-46

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Web page: http://www.springerlink.com/link.asp?id=100263

Related research

Keywords: Climate change; Uncertainty; Ambiguity; Q54; D81;

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References

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Citations

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Cited by:
  1. Antony Millner & Raphael Calel & David Stainforth & George MacKerron, 2013. "Do probabilistic expert elicitations capture scientists’ uncertainty about climate change?," Climatic Change, Springer, vol. 116(2), pages 427-436, January.
  2. Geoffrey Heal & Antony Millner, 2013. "Uncertainty and Decision in Climate Change Economics," NBER Working Papers 18929, National Bureau of Economic Research, Inc.
  3. Simon Dietz & Anca N. Matei, 2013. "Is there space for agreement on climate change? A non-parametric approach to policy evaluation," Grantham Research Institute on Climate Change and the Environment Working Papers 136, Grantham Research Institute on Climate Change and the Environment.
  4. Osberghaus, Daniel, 2013. "Prospect theory, mitigation and adaptation to climate change," ZEW Discussion Papers 13-091, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  5. Hwang, In Chang & Reynes, Frederic & Tol, Richard, 2014. "The effect of learning on climate policy under fat-tailed uncertainty," MPRA Paper 53681, University Library of Munich, Germany.
  6. Foley, Duncan K. & Rezai, Armon & Taylor, Lance, 2013. "The social cost of carbon emissions: Seven propositions," Economics Letters, Elsevier, vol. 121(1), pages 90-97.

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