IDEAS home Printed from https://ideas.repec.org/p/sus/susewp/6513.html
   My bibliography  Save this paper

Active Learning about Climate Change

Author

Listed:
  • 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.

Suggested Citation

  • In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Active Learning about Climate Change," Working Paper Series 6513, Department of Economics, University of Sussex Business School.
    • Handle: RePEc:sus:susewp:6513
    as

    Download full text from publisher

    File URL: http://www.sussex.ac.uk/economics/documents/wps-65-2013.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," RFF Working Paper Series dp-09-19, Resources for the Future.
    3. 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.
    4. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August.
    5. Sanford J. Grossman & Richard E. Kihlstrom & Leonard J. Mirman, 1977. "A Bayesian Approach to the Production of Information and Learning By Doing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 44(3), pages 533-547.
    6. Maliar, Lilia & Maliar, Serguei, 2005. "Solving nonlinear dynamic stochastic models: an algorithm computing value function by simulations," Economics Letters, Elsevier, vol. 87(1), pages 135-140, April.
    7. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
    8. Bullard, James & Mitra, Kaushik, 2002. "Learning about monetary policy rules," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1105-1129, September.
    9. Yetman, James, 2003. "Probing potential output: Monetary policy, credibility, and optimal learning under uncertainty," Journal of Macroeconomics, Elsevier, vol. 25(3), pages 311-330, September.
    10. 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, July.
    11. van Wijnbergen, Sweder & Willems, Tim, 2015. "Optimal learning on climate change: Why climate skeptics should reduce emissions," Journal of Environmental Economics and Management, Elsevier, vol. 70(C), pages 17-33.
    12. Antony Millner, 2013. "On Welfare Frameworks and Catastrophic Climate Risks," CESifo Working Paper Series 4442, CESifo.
    13. Millner, Antony, 2013. "On welfare frameworks and catastrophic climate risks," Journal of Environmental Economics and Management, Elsevier, vol. 65(2), pages 310-325.
    14. Martin Weitzman, 2013. "A Precautionary Tale of Uncertain Tail Fattening," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 55(2), pages 159-173, June.
    15. 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.
    16. Robert S. Pindyck, 2011. "Fat Tails, Thin Tails, and Climate Change Policy," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 5(2), pages 258-274, Summer.
    17. 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.
    18. 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.
    19. Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-650, May.
    20. 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.
    21. David Anthoff & Richard Tol, 2014. "Climate policy under fat-tailed risk: an application of FUND," Annals of Operations Research, Springer, vol. 220(1), pages 223-237, September.
    22. 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 Business School.
    23. Gabriele C. Hegerl & Thomas J. Crowley & William T. Hyde & David J. Frame, 2006. "Climate sensitivity constrained by temperature reconstructions over the past seven centuries," Nature, Nature, vol. 440(7087), pages 1029-1032, April.
    24. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729, Elsevier.
    25. 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.
    26. Bertocchi, Graziella & Spagat, Michael, 1998. "Growth under uncertainty with experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 23(2), pages 209-231, September.
    27. Kolstad, Charles D., 1996. "Fundamental irreversibilities in stock externalities," Journal of Public Economics, Elsevier, vol. 60(2), pages 221-233, May.
    28. Pindyck, Robert S., 2012. "Uncertain outcomes and climate change policy," Journal of Environmental Economics and Management, Elsevier, vol. 63(3), pages 289-303.
    29. McKitrick, Ross, 2011. "A simple state-contingent pricing rule for complex intertemporal externalities," Energy Economics, Elsevier, vol. 33(1), pages 111-120, January.
    30. Christian Traeger, 2014. "A 4-Stated DICE: Quantitatively Addressing Uncertainty Effects in Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(1), pages 1-37, September.
    31. 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.
    32. 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.
    33. Johnson, Timothy C., 2007. "Optimal learning and new technology bubbles," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2486-2511, November.
    34. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    35. Hwang, In Chang & Reynès, Frédéric & Tol, Richard S.J., 2017. "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Elsevier, vol. 48(C), pages 1-18.
    36. 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.
    37. 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.
    38. Alan Manne & Richard Richels, 1992. "Buying Greenhouse Insurance: The Economic Costs of CO2 Emission Limits," MIT Press Books, The MIT Press, edition 1, volume 1, number 026213280x, December.
    39. 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.
    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


    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 Business School.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. In Chang Hwang & Richard S. J. Tol & Marjan W. Hofkes, 2019. "Active Learning and Optimal Climate Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1237-1264, August.
    2. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex Business School.
    3. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
    4. In Hwang & Frédéric Reynès & Richard Tol, 2013. "Climate Policy Under Fat-Tailed Risk: An Application of Dice," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(3), pages 415-436, November.
    5. Hwang, In Chang & Tol, Richard S.J. & Hofkes, Marjan W., 2016. "Fat-tailed risk about climate change and climate policy," Energy Policy, Elsevier, vol. 89(C), pages 25-35.
    6. Hwang, In Chang & Reynès, Frédéric & Tol, Richard S.J., 2017. "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Elsevier, vol. 48(C), pages 1-18.
    7. Tol, Richard S.J., 2013. "Targets for global climate policy: An overview," Journal of Economic Dynamics and Control, Elsevier, vol. 37(5), pages 911-928.
    8. Maria Antonieta Cunha-e-Sa & Vasco Santos, 2007. "Experimentation with accumulation," Nova SBE Working Paper Series wp503, Universidade Nova de Lisboa, Nova School of Business and Economics.
    9. 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 Business School.
    10. In Chang Hwang, 2017. "A Recursive Method for Solving a Climate–Economy Model: Value Function Iterations with Logarithmic Approximations," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 95-110, June.
    11. Kelly, David L. & Tan, Zhuo, 2015. "Learning and climate feedbacks: Optimal climate insurance and fat tails," Journal of Environmental Economics and Management, Elsevier, vol. 72(C), pages 98-122.
    12. Cunha-e-Sa, Maria A. & Santos, Vasco, 2008. "Experimentation with accumulation," Journal of Economic Dynamics and Control, Elsevier, vol. 32(2), pages 470-496, February.
    13. David Anthoff & Richard S. J. Tol, 2022. "Testing the Dismal Theorem," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 9(5), pages 885-920.
    14. Mark Kagan, 2012. "Climate Change Skepticism in the Face of Catastrophe," Tinbergen Institute Discussion Papers 12-112/VIII, Tinbergen Institute, revised 29 Sep 2014.
    15. W. Botzen & Jeroen Bergh, 2014. "Specifications of Social Welfare in Economic Studies of Climate Policy: Overview of Criteria and Related Policy Insights," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(1), pages 1-33, May.
    16. Chambers, Robert G. & Melkonyan, Tigran, 2017. "Ambiguity, reasoned determination, and climate-change policy," Journal of Environmental Economics and Management, Elsevier, vol. 81(C), pages 74-92.
    17. 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.
    18. Andrea Rampa, 2020. "Climate change, catastrophes and Dismal Theorem: a critical review [Klimawandel, Katastrophen und das „Dismal Theorem“: eine kritische Überprüfung]," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 40(2), pages 113-136, October.
    19. De Bruin, Kelly & Kiran Krishnamurthy, Chandra, 2021. "Optimal Climate Policy with Fat-tailed Uncertainty: What the Models Can Tell Us," Papers WP697, Economic and Social Research Institute (ESRI).
    20. Karp, Larry & Zhang, Jiangfeng, 2001. "Bayesian Learning and the Regulation of Greenhouse Gas Emissions," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2fr0783c, Department of Agricultural & Resource Economics, UC Berkeley.

    More about this item

    Keywords

    Climate policy; deep uncertainty; active learning; Bayesian statistical decision; integrated assessment; dynamic programming;
    All these keywords.

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sus:susewp:6513. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: University of Sussex Business School Communications Team (email available below). General contact details of provider: https://edirc.repec.org/data/ecsusuk.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.