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Risk and aversion in the integrated assessment of climate change

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  • Crost, Benjamin
  • Traeger, Christian P.

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 Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number qt1562s275.

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Date of creation: 01 Nov 2011
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Handle: RePEc:cdl:agrebk:qt1562s275

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Related research

Keywords: climate change; uncertainty; integrated assessment; risk aversion; intertemporal substitution; recursive utility; dynamic programming; Agricultural and Resource Economics; Agriculture; Agriculture Operations; and Related Sciences; Natural Resources and Conservation;

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References

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  1. 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.
  2. Campbell, John, 1996. "Understanding Risk and Return," Scholarly Articles 3153293, Harvard University Department of Economics.
  3. 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.
  4. 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.
  5. 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.
  6. Minh Ha-Duong & Nicolas Treich, 2004. "Risk aversion, intergenerational equity and climate change," Post-Print halshs-00000680, HAL.
  7. 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.
  8. Weil, Philippe, 1990. "Nonexpected Utility in Macroeconomics," The Quarterly Journal of Economics, MIT Press, vol. 105(1), pages 29-42, February.
  9. Manne, Alan S. & Richels, Richard G. & Wigley, Tom M. L., 2004. "Moving Beyond Concentrations: The Challenge of Limiting Temperature Change," Working paper 531, Regulation2point0.
  10. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
  11. 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.
  12. 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).
  13. 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. 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.
  2. Mort Webster & Nidhi Santen & Panos Parpas, 2011. "An Approximate Dynamic Programming Framework for Modeling Global Climate Policy under Decision-Dependent Uncertainty," Working Papers 1118, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
  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. 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.

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