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Learning curves for environmental technology and their importance for climate policy analysis

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  • Rubin, Edward S.
  • Taylor, Margaret R
  • Yeh, Sonia
  • Hounshell, David A.

Abstract

We seek to improve the ability of integrated assessment (IA) models to incorporate changes in CO2 capture and sequestration (CCS) technology cost and performance over time. This paper presents results of research that examines past evidence in controlling other major power plant emissions that might serve as a reasonable guide to future rates of technological progress in CCS systems. In particular, we focus on US and worldwide experience with sulfur dioxide (SO2) and nitrogen oxide (NOx) control technologies over the past 30 years, and derive empirical learning rates for these technologies. Applying these rates to CCS costs in a large-scale IA model shows that the cost of achieving a climate stabilization target are significantly lower relative to scenarios with no learning for CCS technologies.

Suggested Citation

  • Rubin, Edward S. & Taylor, Margaret R & Yeh, Sonia & Hounshell, David A., 2007. "Learning curves for environmental technology and their importance for climate policy analysis," Institute of Transportation Studies, Working Paper Series qt2b35s2b3, Institute of Transportation Studies, UC Davis.
  • Handle: RePEc:cdl:itsdav:qt2b35s2b3
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    1. Colpier, Ulrika Claeson & Cornland, Deborah, 2002. "The economics of the combined cycle gas turbine--an experience curve analysis," Energy Policy, Elsevier, vol. 30(4), pages 309-316, March.
    2. Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
    3. Peter Thompson, 2001. "How Much Did the Liberty Shipbuilders Learn? New Evidence for an Old Case Study," Journal of Political Economy, University of Chicago Press, vol. 109(1), pages 103-137, February.
    4. Steven Klepper & Elizabeth Graddy, 1990. "The Evolution of New Industries and the Determinants of Market Structure," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 27-44, Spring.
    5. 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.
    6. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    7. Dutton, John M. & Thomas, Annie & Butler, John E., 1984. "The History of Progress Functions as a Managerial Technology," Business History Review, Cambridge University Press, vol. 58(2), pages 204-233, July.
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    Keywords

    UCD-ITS-RP-07-22; Engineering;

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