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Climate Change and the Irreversibility Effect – Combining Expected Utility and MaxiMin

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  • Andreas Lange

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Abstract

This paper analyzes decisions on emissions of a stock pollutant under uncertaintyin a two period model. Decisions are based on a weighted average of expected utility (EU) and the MaxiMin criterion. I first show that more weight on the worst case (less weight on EU) may lead to increased first period emissions. The effect of learning possibilities on emissions is not clear in general, but depends qualitatively on the weight given to MaxiMin: For the quadratic utility case, considering prospective learning increases today's abatement effort, i.e. the “irreversibility effect” holds, if the weight on EU is small. This contrasts standard results on the irreversibility effect for EU which translates to small weights on MaxiMin. There is, however, the possibility of a negative value of learning. It is shown that the irreversibility effect holds if and only if thevalue of learning is negative. Consequences for the applicability of generalized EU-MaxiMin are discussed. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Andreas Lange, 2003. "Climate Change and the Irreversibility Effect – Combining Expected Utility and MaxiMin," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 25(4), pages 417-434, August.
  • Handle: RePEc:kap:enreec:v:25:y:2003:i:4:p:417-434
    DOI: 10.1023/A:1025054716419
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    References listed on IDEAS

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    Cited by:

    1. LANGE Andreas & TREICH Nicolas, 2007. "Uncertainty, Learning and Ambiguity in Economic Models on Climate Policy: Some Classical Results and New Directions," LERNA Working Papers 07.16.237, LERNA, University of Toulouse.
    2. Treich, Nicolas, 2010. "The value of a statistical life under ambiguity aversion," Journal of Environmental Economics and Management, Elsevier, vol. 59(1), pages 15-26, January.
    3. David McInerney & Robert Lempert & Klaus Keller, 2012. "What are robust strategies in the face of uncertain climate threshold responses?," Climatic Change, Springer, vol. 112(3), pages 547-568, June.
    4. Iverson, Terrence, 2012. "Communicating Trade-offs amid Controversial Science: Decision Support for Climate Policy," Ecological Economics, Elsevier, vol. 77(C), pages 74-90.
    5. Clemens Löffler & Thomas Pfeiffer & Georg Schneider, 2013. "The irreversibility effect and agency conflicts," Theory and Decision, Springer, vol. 74(2), pages 219-239, February.
    6. Peterson, Sonja, 2006. "Uncertainty and economic analysis of climate change: a survey of approaches and findings," Open Access Publications from Kiel Institute for the World Economy 3778, Kiel Institute for the World Economy (IfW).
    7. 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.
    8. A. Lopez & E. Suckling & F. Otto & A. Lorenz & D. Rowlands & M. Allen, 2015. "Towards a typology for constrained climate model forecasts," Climatic Change, Springer, vol. 132(1), pages 15-29, September.
    9. Camilla Froyn, 2005. "Decision Criteria, Scientific Uncertainty, and the Globalwarming Controversy," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 10(2), pages 183-211, April.
    10. Peterson, Sonja, 2004. "The contribution of economics to the analysis of climate change and uncertainty: a survey of approaches and findings," Kiel Working Papers 1212, Kiel Institute for the World Economy (IfW).

    More about this item

    Keywords

    irreversibility; learning; MaxiMin; uncertainty;

    JEL classification:

    • H4 - Public Economics - - Publicly Provided Goods
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D60 - Microeconomics - - Welfare Economics - - - General

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