Data/hora: 04/06/2024 13:12:21

Provedor de dados: 	31	
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&gt;Department of Agricultural and Resource Economics&gt;CUDARE Working Papers

CUDARE Working Paper

926	
Formato: 	41