IDEAS home Printed from https://ideas.repec.org/p/sce/scecf5/54.html
   My bibliography  Save this paper

Uncertainty, Learning, and Optimal Technological Portfolios: A Dynamic General Equilibrium Approach to Climate Change

Author

Listed:
  • Seung-Rae Kim

    (Woodrow Wilson School Princeton University)

Abstract

How is the design of efficient climate policies affected by the potentials for induced technological change and for future learning about key parameter uncertainties? We address this question using a new integrated climate-economy model incorporating endogenous technological change to explore optimal technological portfolios against global warming in the presence of uncertainty and learning. We explicitly consider the interplays between induced innovation, the stringency of environmental policies, and possible environmental risks within the general equilibrium framework of probabilistic integrated assessment. We find that the value of resolving key scientific uncertainties would be non-trivial in the face of binding climate limits, but at the same time it can significantly decrease with induced innovation and knowledge spillovers that might otherwise be absent. The results also show that scientific uncertainties in climate change could justify immediate mitigation actions and accelerated investments in new energy technologies, reflecting risk-reducing considerations.

Suggested Citation

  • Seung-Rae Kim, 2005. "Uncertainty, Learning, and Optimal Technological Portfolios: A Dynamic General Equilibrium Approach to Climate Change," Computing in Economics and Finance 2005 54, Society for Computational Economics.
  • Handle: RePEc:sce:scecf5:54
    as

    Download full text from publisher

    File URL: http://repec.org/sce2005/up.29800.1105666407.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. 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.
    3. Obstfeld, Maurice, 1994. "Risk-Taking, Global Diversification, and Growth," American Economic Review, American Economic Association, vol. 84(5), pages 1310-1329, December.
    4. Popp, David, 2004. "ENTICE: endogenous technological change in the DICE model of global warming," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 742-768, July.
    5. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    6. David F. Bradford & Seung-Rae Kim & Klaus Keller, 2004. "Optimal Technological Portfolios for Climate-Change Policy under Uncertainty: A Computable General Equilibrium Approach," Computing in Economics and Finance 2004 140, Society for Computational Economics.
    7. Barro, Robert J, 1990. "Government Spending in a Simple Model of Endogenous Growth," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 103-126, October.
    8. Campbell, John Y, 1996. "Understanding Risk and Return," Journal of Political Economy, University of Chicago Press, vol. 104(2), pages 298-345, April.
    9. Gollier, Christian & Jullien, Bruno & Treich, Nicolas, 2000. "Scientific progress and irreversibility: an economic interpretation of the 'Precautionary Principle'," Journal of Public Economics, Elsevier, vol. 75(2), pages 229-253, February.
    10. Corsetti, Giancarlo, 1997. "A portfolio approach to endogenous growth: equilibrium and optimal policy," Journal of Economic Dynamics and Control, Elsevier, vol. 21(10), pages 1627-1644, August.
    11. 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.
    12. Ulph, Alistair & Ulph, David, 1997. "Global Warming, Irreversibility and Learning," Economic Journal, Royal Economic Society, vol. 107(442), pages 636-650, May.
    13. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    14. Michael Oppenheimer, 1998. "Global warming and the stability of the West Antarctic Ice Sheet," Nature, Nature, vol. 393(6683), pages 325-332, May.
    15. Alistair Ulph & David Ulph, "undated". "Global Warming, Irreversibility And Learning," ELSE working papers 056, ESRC Centre on Economics Learning and Social Evolution.
    16. Pizer, William A., 1999. "The optimal choice of climate change policy in the presence of uncertainty," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 255-287, August.
    17. 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.
    18. Sims, Ralph E. H. & Rogner, Hans-Holger & Gregory, Ken, 2003. "Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation," Energy Policy, Elsevier, vol. 31(13), pages 1315-1326, October.
    19. Peter A. Stott & J. A. Kettleborough, 2002. "Origins and estimates of uncertainty in predictions of twenty-first century temperature rise," Nature, Nature, vol. 416(6882), pages 723-726, April.
    Full references (including those not matched with items on IDEAS)

    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. Baker, Erin & Shittu, Ekundayo, 2008. "Uncertainty and endogenous technical change in climate policy models," Energy Economics, Elsevier, vol. 30(6), pages 2817-2828, November.
    2. David F. Bradford & Seung-Rae Kim & Klaus Keller, 2004. "Optimal Technological Portfolios for Climate-Change Policy under Uncertainty: A Computable General Equilibrium Approach," Computing in Economics and Finance 2004 140, Society for Computational Economics.
    3. 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.
    4. 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.
    5. 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.
    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. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    8. Peterson, Sonja, 2006. "Uncertainty and economic analysis of climate change: a survey of approaches and findings," Open Access Publications from Kiel Institute for the World Economy 3778, Kiel Institute for the World Economy (IfW Kiel).
    9. Peterson, Sonja, 2004. "The contribution of economics to the analysis of climate change and uncertainty: a survey of approaches and findings," Kiel Working Papers 1212, Kiel Institute for the World Economy (IfW Kiel).
    10. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    11. Baker, Erin & Shittu, Ekundayo, 2006. "Profit-maximizing R&D in response to a random carbon tax," Resource and Energy Economics, Elsevier, vol. 28(2), pages 160-180, May.
    12. Baker, Erin, 2005. "Uncertainty and learning in a strategic environment: global climate change," Resource and Energy Economics, Elsevier, vol. 27(1), pages 19-40, January.
    13. Clemens, Christiane & Soretz, Susanne, 1999. "Konsequenzen des Zins- und Einkommensrisikos auf das wirtschaftliche Wachstum," Hannover Economic Papers (HEP) dp-221, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    14. Turnovsky, Stephen J., 1999. "Productive Government Expenditure In A Stochastically Growing Economy," Macroeconomic Dynamics, Cambridge University Press, vol. 3(4), pages 544-570, December.
    15. Jin, Wei & Zhang, ZhongXiang, 2016. "On the mechanism of international technology diffusion for energy technological progress," Resource and Energy Economics, Elsevier, vol. 46(C), pages 39-61.
    16. Ingham, Alan & Ma, Jie & Ulph, Alistair, 2007. "Climate change, mitigation and adaptation with uncertainty and learning," Energy Policy, Elsevier, vol. 35(11), pages 5354-5369, November.
    17. 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.
    18. Mort Webster, 2008. "Incorporating Path Dependency into Decision-Analytic Methods: An Application to Global Climate-Change Policy," Decision Analysis, INFORMS, vol. 5(2), pages 60-75, June.
    19. 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.
    20. Turnovsky, Stephen J., 1999. "On the role of government in a stochastically growing open economy," Journal of Economic Dynamics and Control, Elsevier, vol. 23(5-6), pages 873-908, April.

    More about this item

    Keywords

    Uncertainty; Learning; Optimal technological portfolios; Endogenous technological change; Stochastic growth model; Probabilistic integrated assessment; Carbon-free technology; Expected value of information;
    All these keywords.

    JEL classification:

    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    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:sce:scecf5:54. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.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.