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Risk and aversion in the integrated assessment of climate change

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Author Info

  • Crost, Benjamin
  • Traeger, Christian P.

    ()
    (University of California, Berkeley. Dept of agricultural and resource economics)

Abstract

We analyze the impact of damage uncertainty on optimal mitigation policies in the integrated assessment of climate change. Usually, these models analyzeuncertainty by averaging deterministic paths. In contrast, we build a consistentmodel deriving optimal policy rules under persistent uncertainty. For this purpose,we construct a close relative of the DICE model in a recursive dynamic programming framework. Our recursive approach allows us to disentangle effects of risk, risk aversion, and aversion to intertemporal substitution. We analyze different ways how damage uncertainty can affect the DICE equations. We compare the optimal policies to those resulting from the wide-spread ex-ante uncertainty approach averaging deterministic paths.

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Bibliographic Info

Paper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 1104R.

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Length: 41 pages
Date of creation: 2010
Date of revision: Jul 2011
Handle: RePEc:are:cudare:1104r

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Postal: University of California, Giannini Foundation of Agricultural Economics Library, 248 Giannini Hall #3310, Berkeley CA 94720-3310
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Related research

Keywords: climate change; uncertainty; integrated assessment; risk aversion; intertemporal substitution; recursive utility; dynamic programming;

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References

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  1. Christian P. Traeger, 2009. "Recent Developments in the Intertemporal Modeling of Uncertainty," Annual Review of Resource Economics, Annual Reviews, vol. 1(1), pages 261-285, 09.
  2. Manne, Alan S. & Richels, Richard G. & Wigley, Tom M. L., 2004. "Moving Beyond Concentrations: The Challenge of Limiting Temperature Change," Working paper 531, Regulation2point0.
  3. Ravi Bansal & Amir Yaron, 2004. "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles," Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, 08.
  4. Karp, Larry & Zhang, Jiangfeng, 2006. "Regulation with anticipated learning about environmental damages," Journal of Environmental Economics and Management, Elsevier, vol. 51(3), pages 259-279, May.
  5. John Y. Campbell, 1995. "Understanding Risk and Return," Harvard Institute of Economic Research Working Papers 1711, Harvard - Institute of Economic Research.
  6. Weil, Philippe, 1990. "Nonexpected Utility in Macroeconomics," The Quarterly Journal of Economics, MIT Press, vol. 105(1), pages 29-42, February.
  7. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
  8. Ackerman, Frank & Stanton, Elizabeth A. & Bueno, Ramón, 2010. "Fat tails, exponents, extreme uncertainty: Simulating catastrophe in DICE," Ecological Economics, Elsevier, vol. 69(8), pages 1657-1665, June.
  9. Minh Ha-Duong & Nicolas Treich, 2004. "Risk aversion, intergenerational equity and climate change," Post-Print halshs-00000680, HAL.
  10. Tol, Richard S. J. & Anthoff, David, 2010. "Climate Policy under Fat-Tailed Risk: An Application of FUND," Papers WP348, Economic and Social Research Institute (ESRI).
  11. Andrew J. Leach, 2004. "The Climate Change Learning Curve," Cahiers de recherche 04-03, HEC Montréal, Institut d'économie appliquée.
  12. Keller, Klaus & Bolker, Benjamin M. & Bradford, D.F.David F., 2004. "Uncertain climate thresholds and optimal economic growth," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 723-741, July.
  13. Baker, Erin & Shittu, Ekundayo, 2008. "Uncertainty and endogenous technical change in climate policy models," Energy Economics, Elsevier, vol. 30(6), pages 2817-2828, November.
  14. Traeger, Christian P., 2010. "Intertemporal risk aversion – or – wouldn’t it be nice to tell whether Robinson Crusoe is risk averse?," CUDARE Working Paper Series 1102, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
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Citations

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Cited by:
  1. Svenja Hector, 2013. "Accounting for Different Uncertainties: Implications for Climate Investments?," Working Papers 2013.107, Fondazione Eni Enrico Mattei.
  2. Derek M. Lemoine & Christian P. Traeger, 2012. "Tipping Points and Ambiguity in the Economics of Climate Change," NBER Working Papers 18230, National Bureau of Economic Research, Inc.
  3. Traeger, Christian, 2012. "A 4-stated DICE: quantitatively addressing uncertainty effects in climate change," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6jx2p7fv, Department of Agricultural & Resource Economics, UC Berkeley.
  4. Mort Webster & Nidhi Santen & Panos Parpas, 2012. "An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty," Computational Management Science, Springer, vol. 9(3), pages 339-362, August.
  5. Kopp, Robert E. & Mignone, Bryan K., 2012. "The US government's social cost of carbon estimates after their first two years: Pathways for improvement," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 6(15), pages 1-41.
  6. Yongyang Cai & Kenneth L. Judd & Thomas S. Lontzek, 2013. "The Social Cost of Stochastic and Irreversible Climate Change," NBER Working Papers 18704, National Bureau of Economic Research, Inc.
  7. Liu, Liqun, 2012. "Inferring the rate of pure time preference under uncertainty," Ecological Economics, Elsevier, vol. 74(C), pages 27-33.
  8. Svenja Hector(), . "Accounting for Different Uncertainties: Implications for Climate Investments?," Working Papers ETH-RC-13-007, ETH Zurich, Chair of Systems Design.

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