Bayesian Learning and the Regulation of Greenhouse Gas Emissions
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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- 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.
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- 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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"Global Environmental Risks,"
1993_03, Columbia University, Department of Economics.
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NBER Working Papers
3117, National Bureau of Economic Research, Inc.
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- 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.
- 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.
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