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A 4-stated DICE: quantitatively addressing uncertainty effects in climate change

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  • Traeger, Christian

Abstract

We introduce a version of the DICE-2007 model designed for uncertaintyanalysis. DICE is a wide-spread deterministic integrated assessment model of climatechange. However, climate change, long-term economic development, and theirinteractions are highly uncertain. A thorough empirical analysis of the effects ofuncertainty requires a recursive dynamic programming implementation of integratedassessment models. Such implementations are subject to the curse of dimensionality.Every increase in the dimension of the state space is paid for by a combinationof (exponentially) increasing processor time, lower quality of the value function andcontrol rules approximations, and reductions of the uncertainty domain. The paperpromotes a four stated recursive dynamic programming implementation of the DICEmodel. Our implementation solves the infinite planning horizon problem for an arbitrarytime step. Moreover, we present a closed form continuous time approximationto the exogenous (discretely and inductively defined) processes in DICE and presenta Bellman equation for DICE that disentangles risk attitude from the propensity tosmooth consumption over time.

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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 qt6jx2p7fv.

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Date of creation: 15 Dec 2012
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Handle: RePEc:cdl:agrebk:qt6jx2p7fv

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Keywords: Social and Behavioral Sciences; climate change; uncertainty; intergrated assessment; DICE; dynamic programming; risk aversion; interemporal substitution; recursive utility;

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References

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  1. Emi Nakamura & Jón Steinsson & Robert Barro & José Ursúa, 2010. "Crises and Recoveries in an Empirical Model of Consumption Disasters," NBER Working Papers 15920, National Bureau of Economic Research, Inc.
  2. Kelly, David L. & Kolstad, Charles D., 1999. "Solving Infinite Horizon Growth Models with an Environmental Sector," University of California at Santa Barbara, Economics Working Paper Series qt3hd4c4v3, Department of Economics, UC Santa Barbara.
  3. Lemoine, Derek M. & Traeger, Christian P., 2011. "Tipping points and ambiguity in the economics of climate change," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9nd591ww, Department of Agricultural & Resource Economics, UC Berkeley.
  4. Traeger, Christian P, 2008. "Why uncertainty matters - discounting under intertemporal risk aversion and ambiguity," CUDARE Working Paper Series 1092R2, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy, revised Jan 2012.
  5. John Y. Campbell, 1995. "Understanding Risk and Return," Harvard Institute of Economic Research Working Papers 1711, Harvard - Institute of Economic Research.
  6. Crost, Benjamin & Traeger, Christian P., 2011. "Risk and aversion in the integrated assessment of climate change," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt1562s275, Department of Agricultural & Resource Economics, UC Berkeley.
  7. Weil, Philippe, 1990. "Nonexpected Utility in Macroeconomics," The Quarterly Journal of Economics, MIT Press, vol. 105(1), pages 29-42, February.
  8. 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.
  9. 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.
  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. 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.
  12. 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.
  13. 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.
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Cited by:
  1. 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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