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The effect of learning on climate policy under fat-tailed uncertainty

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  • Hwang, In Chang
  • Reynes, Frederic
  • Tol, Richard

Abstract

We construct an endogenous (Bayesian) learning model with fat-tailed uncertainty on the equilibrium climate sensitivity and solve the model with stochastic dynamic programming. In our model a decision maker updates her belief on the climate sensitivity through temperature observations each time period and takes a course of action (carbon reductions) based on her belief. We find that the uncertainty is partially resolved over time, although the rate of learning is relatively slow, and the decision maker with a possibility of learning lowers the efforts to reduce carbon emissions relative to the no-learning case. The larger the tail effect, the larger the counteracting learning effect. Learning at least partly offsets the tail-effect of deep uncertainty. This is intuitive in that the decision maker fully utilizes the information revealed to reduce uncertainty, and thus she can make a decision contingent on the updated information. In addition, with various scenarios, we find that learning enables the economic agent to have less regrets for her past actions after the true value of the uncertain variable turns out to be different from the initial best guess. Furthermore the optimal decisions in the learning case are less sensitive to the true value of the uncertain variable than the decisions in the uncertainty case. The reason is that learning lets uncertainty converge to the true value of the state in the sense that the variance approaches 0 as information accumulates.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 53681.

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Date of creation: 13 Feb 2014
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Handle: RePEc:pra:mprapa:53681

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Keywords: Climate policy; deep uncertainty; Bayesian learning; integrated assessment; stochastic dynamic programming;

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  1. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-58, November.
  2. Mort Webster, 2002. "The Curious Role of "Learning" in Climate Policy: Should We Wait for More Data?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 97-119.
  3. Weitzman, Martin L., 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," Scholarly Articles 3693423, Harvard University Department of Economics.
  4. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
  5. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
  6. Lawrence J. Christiano & Jonas D.M. Fisher, 1994. "Algorithms for solving dynamic models with occasionally binding constraints," Working Paper Series, Macroeconomic Issues 94-6, Federal Reserve Bank of Chicago.
  7. Tol, Richard S.J., 2013. "Targets for global climate policy: An overview," Journal of Economic Dynamics and Control, Elsevier, vol. 37(5), pages 911-928.
  8. Marten, Alex L., 2011. "Transient temperature response modeling in IAMs: the effects of over simplification on the SCC," Economics Discussion Papers 2011-11, Kiel Institute for the World Economy.
  9. Grossman, Sanford J & Kihlstrom, Richard E & Mirman, Leonard J, 1977. "A Bayesian Approach to the Production of Information and Learning by Doing," Review of Economic Studies, Wiley Blackwell, vol. 44(3), pages 533-47, October.
  10. Ingham, Alan & Ma, Jie & Ulph, Alistair, 2007. "Climate change, mitigation and adaptation with uncertainty and learning," Energy Policy, Elsevier, vol. 35(11), pages 5354-5369, November.
  11. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models," Quantitative Economics, Econometric Society, vol. 2(2), pages 173-210, 07.
  12. Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," Discussion Papers dp-09-19, Resources For the Future.
  13. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
  14. Henry, Claude, 1974. "Investment Decisions Under Uncertainty: The "Irreversibility Effect."," American Economic Review, American Economic Association, vol. 64(6), pages 1006-12, December.
  15. 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.
  16. Mario J. Miranda & Paul L. Fackler, 2004. "Applied Computational Economics and Finance," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633094, December.
  17. J. Annan & J. Hargreaves, 2011. "On the generation and interpretation of probabilistic estimates of climate sensitivity," Climatic Change, Springer, vol. 104(3), pages 423-436, February.
  18. Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," Working Papers in Economics 354, University of Gothenburg, Department of Economics.
  19. Le Mao, Caroline, 2012. "Introduction," Histoire, économie & société, Editions NecPlus, vol. 2012(01), pages 3-6, May.
  20. Johanna Etner & Meglena Jeleva & Jean‐Marc Tallon, 2012. "Decision Theory Under Ambiguity," Journal of Economic Surveys, Wiley Blackwell, vol. 26(2), pages 234-270, 04.
  21. Weitzman, Martin L., 2012. "GHG Targets as Insurance Against Catastrophic Climate Damages," Scholarly Articles 11315435, Harvard University Department of Economics.
  22. 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.
  23. Derek Lemoine & Christian Traeger, 2014. "Watch Your Step: Optimal Policy in a Tipping Climate," American Economic Journal: Economic Policy, American Economic Association, vol. 6(1), pages 137-66, February.
  24. Lee Yong-Shik, 2012. "Introduction," The Law and Development Review, De Gruyter, vol. 5(2), pages 1-1, December.
  25. 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.
  26. Antony Millner & Simon Dietz & Geoffrey Heal, 2013. "Scientific Ambiguity and Climate Policy," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 55(1), pages 21-46, May.
  27. 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.
  28. Arrow, Kenneth J & Fisher, Anthony C, 1974. "Environmental Preservation, Uncertainty, and Irreversibility," The Quarterly Journal of Economics, MIT Press, vol. 88(2), pages 312-19, May.
  29. Martin L. Weitzman, 2012. "GHG Targets as Insurance Against Catastrophic Climate Damages," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 14(2), pages 221-244, 03.
  30. Kolstad, Charles D., 1996. "Fundamental irreversibilities in stock externalities," Journal of Public Economics, Elsevier, vol. 60(2), pages 221-233, May.
  31. Bartz, Sherry & Kelly, David L., 2008. "Economic growth and the environment: Theory and facts," Resource and Energy Economics, Elsevier, vol. 30(2), pages 115-149, May.
  32. Peck, Stephen C. & Teisberg, Thomas J., 1993. "Global warming uncertainties and the value of information: an analysis using CETA," Resource and Energy Economics, Elsevier, vol. 15(1), pages 71-97, March.
  33. William D. Nordhaus & David Popp, 1997. "What is the Value of Scientific Knowledge? An Application to Global Warming Using the PRICE Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-45.
  34. 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.
  35. Pindyck, Robert S., 2002. "Optimal timing problems in environmental economics," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1677-1697, August.
  36. Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-50, May.
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Cited by:
  1. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
  2. Hwang, In Chang, 2014. "A recursive method for solving a climate-economy model: value function iterations with logarithmic approximations," MPRA Paper 54782, University Library of Munich, Germany.

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