Active Learning and Optimal Climate Policy

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.
LanguageEnglish
JournalEnvironmental and Resource Economics
DOIs
Publication statusAccepted/In press - 2019

Fingerprint

environmental policy
learning
pollution tax
climate change
Climate policy
Active learning
climate
monitoring
unemployment
Gross Domestic Product
Uncertainty
temperature
education
Decision maker
Climate
Climate change
Learning model
Temperature
Monitoring
decision

Keywords

  • Active learning
  • Climate policy
  • Irreversibility
  • Learning
  • Uncertainty

Cite this

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title = "Active Learning and Optimal Climate Policy",
abstract = "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.",
keywords = "Active learning, Climate policy, Irreversibility, Learning, Uncertainty",
author = "Hwang, {In Chang} and Tol, {Richard S.J.} and Hofkes, {Marjan W.}",
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doi = "10.1007/s10640-018-0297-x",
language = "English",
journal = "Environmental and Resource Economics",
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Active Learning and Optimal Climate Policy. / Hwang, In Chang; Tol, Richard S.J.; Hofkes, Marjan W.

In: Environmental and Resource Economics, 2019.

Research output: Contribution to JournalArticleAcademicpeer-review

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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.

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