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Bayesian Learning and the Regulation of Greenhouse Gas Emissions

  • Karp, Larry
  • Zhang, Jiangfeng

We study the importance of anticipated learning - about both environmental damages and abatement costs - in determining the level and the method of controlling greenhouse gas emissions. We also compare active learning, passive learning, and parameter uncertainty without learning. Current beliefs about damages and abatement costs have an important effect on the optimal level of emissions, However, the optimal level of emissions is not sensitive either to the possibility of learning about damages. or to the type of learning (active or passive), Taxes dominate quotas, but by a small margin.

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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 qt2fr0783c.

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Date of creation: 01 Aug 2001
Date of revision:
Handle: RePEc:cdl:agrebk:qt2fr0783c
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  1. Roughgarden, Tim & Schneider, Stephen H., 1999. "Climate change policy: quantifying uncertainties for damages and optimal carbon taxes," Energy Policy, Elsevier, vol. 27(7), pages 415-429, July.
  2. Nordhaus, William D, 1991. "To Slow or Not to Slow: The Economics of the Greenhouse Effect," Economic Journal, Royal Economic Society, vol. 101(407), pages 920-37, July.
  3. Graciela Chichilnisky & Geoffrey Heal, 1993. "Global Environmental Risks," Journal of Economic Perspectives, American Economic Association, vol. 7(4), pages 65-86, Fall.
  4. Peter Kennedy, 1999. "Learning About Environmental Damage: Implications for Emissions Trading," Canadian Journal of Economics, Canadian Economics Association, vol. 32(5), pages 1313-1327, November.
  5. Karp, Larry & Costello, Christopher J, 2000. "Dynamic Quotas with Learning," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt88x3f17p, Department of Agricultural & Resource Economics, UC Berkeley.
  6. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
  7. Kolstad, Charles D., 1996. "Fundamental irreversibilities in stock externalities," Journal of Public Economics, Elsevier, vol. 60(2), pages 221-233, May.
  8. Larry Karp, Jiangfeng Zhang, 2001. "Regulating Global Climate Change with Bayesian Learning about Damages," Computing in Economics and Finance 2001 251, Society for Computational Economics.
  9. 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.
  10. Hoel, Michael & Karp, Larry, 2002. "Taxes versus quotas for a stock pollutant," Resource and Energy Economics, Elsevier, vol. 24(4), pages 367-384, November.
  11. John B. Taylor & Harald Uhlig, 1989. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," NBER Working Papers 3117, National Bureau of Economic Research, Inc.
  12. 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.
  13. Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-50, May.
  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.
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