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Active learning and optimal climate policy

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posted on 2023-06-09, 15:39 authored by In Chang Hwang, Marjan W Hofkes, Richard TolRichard Tol
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.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Environmental and Resource Economics

ISSN

0924-6460

Publisher

Springer

Department affiliated with

  • Economics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2018-10-30

First Open Access (FOA) Date

2019-11-01

First Compliant Deposit (FCD) Date

2018-10-29

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