Active Learning and Optimal Climate Policy

  • In Chang HwangEmail author
  • Richard S. J. Tol
  • Marjan W. Hofkes


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.


Climate policy Irreversibility Uncertainty Learning Active learning 

JEL Classification

Q54 O3 C63 



An earlier version of this paper was presented at the 22nd annual conference of the European Association of Environmental and Resource Economists (EAERE 2016, Zurich, Switzerland), and at the ECOMOD 2016 conference (Lisbon, Portugal). The authors are grateful to Reyer Gerlagh, Andreas Lange, Ulrike Lehr, Thomas Lontzek, Antony Millner, David Popp, Hans-Peter Weikard, Cees Withagen, and two anonymous reviewers for their valuable comments and suggestions. All remaining errors are the authors’.


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.The Seoul InstituteSeoulSouth Korea
  2. 2.Department of EconomicsUniversity of SussexBrightonUK
  3. 3.Institute for Environmental StudiesVU University AmsterdamAmsterdamThe Netherlands
  4. 4.Department of Spatial EconomicsVU University AmsterdamAmsterdamThe Netherlands
  5. 5.Tinbergen InstituteAmsterdamThe Netherlands
  6. 6.CESifoMunichGermany

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