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Optimal Technological Portfolios for Climate-Change Policy under Uncertainty: A Computable General Equilibrium Approach

Author

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  • David F. Bradford
  • Seung-Rae Kim
  • Klaus Keller

Abstract

When exploring solutions to long-term environmental problems such as climate change, it is crucial to understand how the rates and directions of technological change may interact with environmental policies in the presence of uncertainty. This paper analyzes optimal technological portfolios for global carbon emissions reductions in an integrated assessment model of the coupled social-natural system. The model used here is a probabilistic, two-technology extension of Nordhaus" earlier model (Nordhaus and Boyer, 2000) by incorporating endogenous technological choice between conventional and carbon-free technologies. Taking into account the possible competitions among the technological options, we address the issues of optimal timing, costs and burden-sharing of optimal carbon mitigation strategies in the inherently uncertain world. We perform various analyses related to the major uncertainties about natural, socioeconomic and technological parameters, and investigate the effects of uncertainties resolution, risks and alternative political preferences. The results show that analyses ignoring uncertainty could lead to inefficient and biased technology-policy recommendations for the future.

Suggested Citation

  • 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.
    • Handle: RePEc:sce:scecf4:140
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    References listed on IDEAS

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    1. Stephen H. Schneider, 2001. "What is 'dangerous' climate change?," Nature, Nature, vol. 411(6833), pages 17-19, May.
    2. 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.
    3. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    4. 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.
    5. William D. Nordhaus, 2002. "Productivity Growth and the New Economy," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 211-265.
    6. 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.
    7. Michael Oppenheimer, 1998. "Global warming and the stability of the West Antarctic Ice Sheet," Nature, Nature, vol. 393(6683), pages 325-332, May.
    8. 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.
    9. repec:aen:journl:1997v18-01-a01 is not listed on IDEAS
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    Cited by:

    1. 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.

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    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • 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

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