文章基本信息

标题：Bayesian Learning and the Regulation of Greenhouse Gas Emissions
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作者：Karp, Larry S. ; Zhang, Jiangfeng
期刊名称：Journal of Food Distribution Research
印刷版ISSN：0047-245X
出版年度：2001
期号：SUPPL
出版社：Food Distribution Research Society
摘要：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.
关键词：Climate change;Uncertainty;Bayesian learning;Asymmetric information;Choice of instruments;Dynamic optimization