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The Climate Change Learning Curve

The key element in the tension between those who believe climate change is an issue and those who do not is essentially the question of whether we are merely in a long period of shock-induced above average temperatures or if we have led to this increase in temperatures by anthropogenic carbon emissions. The model proposed in this paper allows for a model in which we weigh observations on temperature against the potential that these are generated by a combination of uncertain parameters; namely the coefficient of autoregression and the sensitivity of temperature change to atmospheric carbon levels. This paper shows that, contrary to predictions in the literature that we can resolve uncertainty very quickly, the time to learn may be on the order of thousands of years when uncertainty surrounds two parameters in the law of motion for temperature. When the learning model is embedded in an optimal policy growth model, policy decisions are found to be affected by the prior mean but not the variance. A new solution algorithm which relies on randomization and least squares approximation is applied to solve the value function in the model.

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File URL: http://www.hec.ca/iea/cahiers/2004/iea0403_ale.pdf
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Paper provided by HEC Montréal, Institut d'économie appliquée in its series Cahiers de recherche with number 04-03.

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Length: 27 pages
Date of creation: Apr 2004
Date of revision:
Handle: RePEc:iea:carech:0403
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  1. Gollier & Jullien & Treich, 2000. "Scientific progress and irreversibility : an economic interpretation of the Precautionary principle," Working Papers 156240, Institut National de la Recherche Agronomique, France.
  2. Epstein, Larry G, 1980. "Decision Making and the Temporal Resolution of Uncertainty," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(2), pages 269-83, June.
  3. Jose-Victor Rios-Rull, 1997. "Computation of equilibria in heterogeneous agent models," Staff Report 231, Federal Reserve Bank of Minneapolis.
  4. Kolstad, Charles D. & Kelly, David L. & Mitchell, Glenn, 1999. "Adjustment Costs from Environmental Change Induced by Incomplete Information and Learning," University of California at Santa Barbara, Economics Working Paper Series qt9mx119gc, Department of Economics, UC Santa Barbara.
  5. Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-50, May.
  6. Reiter, Michael, 1999. "Solving higher-dimensional continuous-time stochastic control problems by value function regression," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1329-1353, September.
  7. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, June.
  8. 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.
  9. Michael P. Keane & Kenneth I. Wolpin, 1994. "The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence," Staff Report 181, Federal Reserve Bank of Minneapolis.
  10. Alan Manne & Richard Richels, 1992. "Buying Greenhouse Insurance: The Economic Costs of CO2 Emission Limits," MIT Press Books, The MIT Press, edition 1, volume 1, number 026213280x, June.
  11. Cyert, Richard M & DeGroot, Morris H, 1974. "Rational Expectations and Bayesian Analysis," Journal of Political Economy, University of Chicago Press, vol. 82(3), pages 521-36, May/June.
  12. 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.
  13. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
  14. Kolstad, Charles D., 1996. "Learning and Stock Effects in Environmental Regulation: The Case of Greenhouse Gas Emissions," Journal of Environmental Economics and Management, Elsevier, vol. 31(1), pages 1-18, July.
  15. Pizer, William A., 1999. "The optimal choice of climate change policy in the presence of uncertainty," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 255-287, August.
  16. Kelly, David L & Kolstad, Charles D, 2001. "Solving Infinite Horizon Growth Models with an Environmental Sector," Computational Economics, Society for Computational Economics, vol. 18(2), pages 217-31, October.
  17. Maddison, David, 1995. "A cost-benefit analysis of slowing climate change," Energy Policy, Elsevier, vol. 23(4-5), pages 337-346.
  18. Kelly, David L. & Kolstad, Charles D. & Mitchell, Glenn T., 2005. "Adjustment costs from environmental change," Journal of Environmental Economics and Management, Elsevier, vol. 50(3), pages 468-495, November.
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