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ACTIVE LEARNING AND OPTIMAL CLIMATE POLICY
( In Chang Hwang ),( Richard S. J. Tol ),( Marjan W. Hofkes ) 한국환경경제학회·한국자원경제학회(구 한국환경경제학회) 2015 한국환경경제학회 학술발표논문집 Vol.2015 No.하계
This paper develops a climate-economy model with uncertainty, irreversibility and active learning. Whereas previous papers assume passive learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from research investment in improved observations. We restrict ourselves to improving observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker take improved decisions. The level of uncertainty decreases more rapidly in the active learning model with research investment than in the passive learning model only with temperature observations. As a result, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable.
Tail-effect and the role of greenhouse gas emissions control
( In Chang Hwang ),( Richard S. J. Tol ),( Marjan W. Hofkes ) 한국환경경제학회·한국자원경제학회(구 한국환경경제학회) 2014 한국환경경제학회 학술발표논문집 Vol.2014 No.하계
This paper investigates the role of emissions control on reducing the effect of fat-tailed uncertainty about climate change. Through a simple analysis on temperature distributions and some numerical simulations using the well-known Dynamic Integrated model of Climate and the Economy (DICE model), we find that the option for emissions control effectively prevents the tail-effect. Climate policy based on hyperbolic absolute risk aversion (HARA) utility is less sensitive to fat tails than climate policy based on constant relative risk aversion (CRRA) utility.