Advanced Search
MyIDEAS: Login

Active Learning about Climate Change

Contents:

Author Info

  • 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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.sussex.ac.uk/economics/documents/wps-65-2013.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Department of Economics, University of Sussex in its series Working Paper Series with number 6513.

as in new window
Length:
Date of creation: Nov 2013
Date of revision:
Handle: RePEc:sus:susewp:6513

Contact details of provider:
Postal: Jubilee Building G08, Falmer, Brighton, BN1 9SL
Phone: +44 (0) 1273 678889
Fax: +44 (0)1273 873715
Email:
Web page: http://www.sussex.ac.uk/economics
More information through EDIRC

Related research

Keywords: Climate policy; deep uncertainty; active learning; Bayesian statistical decision; integrated assessment; dynamic programming;

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models," Quantitative Economics, Econometric Society, vol. 2(2), pages 173-210, 07.
  2. G. Berttocchi, 1995. "Growth Under Uncertainty with Experimentation," Working Papers 95-12, Brown University, Department of Economics.
  3. Kaushik Mitra & James Bullard, . "Learning About Monetary Policy Rules," Discussion Papers 00/41, Department of Economics, University of York.
  4. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
  5. Robert S. Pindyck, 2010. "Fat Tails, Thin Tails, and Climate Change Policy," NBER Working Papers 16353, National Bureau of Economic Research, Inc.
  6. Antony Millner, 2013. "On Welfare Frameworks and Catastrophic Climate Risks," CESifo Working Paper Series 4442, CESifo Group Munich.
  7. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August.
  8. Martin L. Weitzman, 2012. "A Precautionary Tale of Uncertain Tail Fattening," NBER Working Papers 18144, National Bureau of Economic Research, Inc.
  9. Yetman, James, 2000. "Probing Potential Output: Monetary Policy, Credibility, and Optimal Learning under Uncertainty," Working Papers 00-10, Bank of Canada.
  10. Pindyck, Robert S., 2012. "Uncertain outcomes and climate change policy," Journal of Environmental Economics and Management, Elsevier, vol. 63(3), pages 289-303.
  11. Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-50, May.
  12. Lilia Maliar & Serguei Maliar, 2004. "Solving Nonlinear Dynamic Stochastic Models: An Algorithm Computing Value Functions By Simulations," Working Papers. Serie AD 2004-37, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  13. Sweder van Wijnbergen & Tim Willems, 2012. "Optimal Learning on Climate Change: Why Climate Skeptics should reduce Emissions," Tinbergen Institute Discussion Papers 12-085/2, Tinbergen Institute.
  14. Millner, Antony, 2013. "On welfare frameworks and catastrophic climate risks," Journal of Environmental Economics and Management, Elsevier, vol. 65(2), pages 310-325.
  15. Kolstad, Charles D., 1996. "Fundamental irreversibilities in stock externalities," Journal of Public Economics, Elsevier, vol. 60(2), pages 221-233, May.
  16. Ferrero, Giuseppe, 2007. "Monetary policy, learning and the speed of convergence," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 3006-3041, September.
  17. Johnson, Timothy C., 2007. "Optimal learning and new technology bubbles," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2486-2511, November.
  18. McKitrick, Ross, 2011. "A simple state-contingent pricing rule for complex intertemporal externalities," Energy Economics, Elsevier, vol. 33(1), pages 111-120, January.
  19. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
  20. Thomas Sterner & U. Martin Persson, 2008. "An Even Sterner Review: Introducing Relative Prices into the Discounting Debate," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 2(1), pages 61-76, Winter.
  21. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
  22. Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," Working Papers in Economics 354, University of Gothenburg, Department of Economics.
  23. Andrew J. Leach, 2004. "The Climate Change Learning Curve," Cahiers de recherche 04-03, HEC Montréal, Institut d'économie appliquée.
  24. Martin L. Weitzman, 2011. "Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 5(2), pages 275-292, Summer.
  25. Yongyang Cai & Kenneth L. Judd & Thomas S. Lontzek, 2012. "Continuous-Time Methods for Integrated Assessment Models," NBER Working Papers 18365, National Bureau of Economic Research, Inc.
  26. William D. Nordhaus & David Popp, 1997. "What is the Value of Scientific Knowledge? An Application to Global Warming Using the PRICE Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-45.
  27. Martin L. Weitzman, 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 1-19, February.
  28. William D. Nordhaus, 2011. "The Economics of Tail Events with an Application to Climate Change," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 5(2), pages 240-257, Summer.
  29. Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," Discussion Papers dp-09-19, Resources For the Future.
  30. Traeger, Christian, 2012. "A 4-stated DICE: quantitatively addressing uncertainty effects in climate change," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6jx2p7fv, Department of Agricultural & Resource Economics, UC Berkeley.
  31. Mario J. Miranda & Paul L. Fackler, 2004. "Applied Computational Economics and Finance," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633094, December.
  32. Kolstad, Charles D., 1996. "Learning and Stock Effects in Environmental Regulation: The Case of Greenhouse Gas Emissions," Journal of Environmental Economics and Management, Elsevier, vol. 31(1), pages 1-18, July.
  33. 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.
  34. Tol, Richard S. J. & Anthoff, David, 2010. "Climate Policy under Fat-Tailed Risk: An Application of FUND," Papers WP348, Economic and Social Research Institute (ESRI).
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. 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.
  2. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, 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.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:sus:susewp:6513. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Russell Eke).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.