TY - JOUR
T1 - Active Learning and Optimal Climate Policy
AU - Hwang, In Chang
AU - Tol, Richard S.J.
AU - Hofkes, Marjan W.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved 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 make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, 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, for instance, the precision at which we observe GDP, unemployment, or the quality of education.
AB - This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved 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 make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, 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, for instance, the precision at which we observe GDP, unemployment, or the quality of education.
KW - Active learning
KW - Climate policy
KW - Irreversibility
KW - Learning
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85055998164&partnerID=8YFLogxK
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U2 - 10.1007/s10640-018-0297-x
DO - 10.1007/s10640-018-0297-x
M3 - Article
AN - SCOPUS:85055998164
SN - 0924-6460
VL - 73
SP - 1237
EP - 1264
JO - Environmental and Resource Economics
JF - Environmental and Resource Economics
IS - 4
ER -