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Active Learning about Climate Change

Author Info

Listed author(s):
  • In Chang Hwang

    (Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands)

  • Richard S.J. Tol

    ()

    (Department of Economics, University of Sussex, Falmer, United Kingdom
    Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands
    Department of Spatial Economics, Vrije Universiteit, Amsterdam, The Netherlands
    Tinbergen Institute, Amsterdam, The Netherlands)

  • Marjan W. Hofkes

    (Department of Economics, Vrije Universiteit, Amsterdam, The Netherlands
    Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands
    Department of Spatial Economics, Vrije Universiteit, Amsterdam, The Netherlands)

Abstract

We develop a climate-economy model with active learning. We consider three ways of active learning: improved observations, adding observations from the past and improved theory from climate research. From the model, we find that the decision maker invests a significant amount of money in climate research. Expenditures to increase the rate of learning are far greater than the current level of expenditure on climate research, as it helps in taking improved decisions. The optimal carbon tax for the active learning model is nontrivially lower than that for the uncertainty model and the passive learning model.

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File URL: http://www.sussex.ac.uk/economics/documents/wps-65-2013.pdf
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Paper provided by Department of Economics, University of Sussex in its series Working Paper Series with number 6513.

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Date of creation: Nov 2013
Handle: RePEc:sus:susewp:6513
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Keywords: Climate policy; deep uncertainty; active learning; Bayesian statistical decision; integrated assessment; dynamic programming;

Find related papers by JEL classification:

  • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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Cited by:

  1. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
  2. Hwang, In Chang, 2014. "A recursive method for solving a climate-economy model: value function iterations with logarithmic approximations," MPRA Paper 54782, University Library of Munich, Germany.
  3. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Tail-effect and the Role of Greenhouse Gas Emissions Control," Working Paper Series 6613, Department of Economics, University of Sussex.

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