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Bayesian learning and the regulation of greenhouse gas emissions

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Author Info

  • Karp, Larry S.

    ()
    (University of California, Berkeley. Dept of agricultural and resource economics and policy)

  • Zhang, Jiangfeng

Abstract

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.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Paper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 926.

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Length: 41 pages
Date of creation: 2001
Date of revision:
Handle: RePEc:are:cudare:926

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Related research

Keywords: abatement cost; air pollution; bayesian analysis; climate change; dynamic analysis; greenhouse effect; risk;

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References

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  1. John B. Taylor & Harald Uhlig, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," NBER Working Papers 3117, National Bureau of Economic Research, Inc.
  2. Graciela Chichilnisky & Geoffrey Heal, 1993. "Global Environmental Risks," Journal of Economic Perspectives, American Economic Association, vol. 7(4), pages 65-86, Fall.
  3. 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.
  4. repec:fth:coluec:645 is not listed on IDEAS
  5. Hoel, Michael & Karp, Larry, 2002. "Taxes versus quotas for a stock pollutant," Resource and Energy Economics, Elsevier, vol. 24(4), pages 367-384, November.
  6. 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.
  7. Karp, Larry S. & Costello, Christopher, 2000. "Dynamic quotas with learning," CUDARE Working Paper Series 914, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  8. 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.
  9. 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.
  10. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
  11. Kolstad, Charles D., 1996. "Fundamental irreversibilities in stock externalities," Journal of Public Economics, Elsevier, vol. 60(2), pages 221-233, May.
  12. 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.
  13. 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.
  14. Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-50, May.
  15. 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.
  16. 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.
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Cited by:
  1. Hoel, Michael & Karp, Larry, 2001. "Taxes versus Quotas for a Stock Pollutant," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt5fx9p7kf, Department of Agricultural & Resource Economics, UC Berkeley.

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