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Provedor de dados:  AgEcon
País:  United States
Título:  Bayesian Learning and the Regulation of Greenhouse Gas Emissions
Autores:  Karp, Larry S.
Zhang, Jiangfeng
Data:  2008-05-07
Ano:  2001
Palavras-chave:  Climate change
Uncertainty
Bayesian learning
Asymmetric information
Choice of instruments
Dynamic optimization
Environmental Economics and Policy
Research Methods/ Statistical Methods
Cll
C6l
D8
H2l
Q28
Resumo:  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.
Tipo:  Working or Discussion Paper
Idioma:  Inglês
Identificador:  29622

http://purl.umn.edu/6214
Relação:  University of California, Berkeley>Department of Agricultural and Resource Economics>CUDARE Working Papers
CUDARE Working Paper
926
Formato:  41
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