Bayesian learning and the regulation of greenhouse gas emissions
AbstractWe 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper 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.
Length: 41 pages
Date of creation: 2001
Date of revision:
Contact details of provider:
Postal: 207 Giannini Hall #3310, Berkeley, CA 94720-3310
Phone: (510) 642-3345
Fax: (510) 643-8911
Web page: http://areweb.berkeley.edu/library/Main/CUDARE
More information through EDIRC
Postal: University of California, Giannini Foundation of Agricultural Economics Library, 248 Giannini Hall #3310, Berkeley CA 94720-3310
Other versions of this item:
- Karp, Larry & Zhang, Jiangfeng, 2001. "Bayesian Learning and the Regulation of Greenhouse Gas Emissions," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2fr0783c, Department of Agricultural & Resource Economics, UC Berkeley.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- Kolstad, Charles D., 1996. "Fundamental irreversibilities in stock externalities," Journal of Public Economics, Elsevier, vol. 60(2), pages 221-233, May.
- 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.
- Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
- Chichilnisky, G. & Heal, G., 1993.
"Global Environmental Risks,"
1993_03, Columbia University, Department of Economics.
- Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-50, May.
- 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.
- repec:fth:coluec:645 is not listed on IDEAS
- 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.
- 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.
- Judd, K.L., 1992.
"Projection Methods for Saving Aggregate Growth Models,"
e-92-7, Hoover Institution, Stanford University.
- Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
- 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.
- 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.
- 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.
- Hoel, Michael & Karp, Larry, 2002.
"Taxes versus quotas for a stock pollutant,"
Resource and Energy Economics,
Elsevier, vol. 24(4), pages 367-384, November.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jeff Cole).
If references are entirely missing, you can add them using this form.