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Carbon capture and storage at scale: Lessons from the growth of analogous energy technologies

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  • Rai, Varun
  • Victor, David G.
  • Thurber, Mark C.

Abstract

At present carbon capture and storage (CCS) is very expensive and its performance is highly uncertain at the scale of commercial power plants. Such challenges to deployment, though, are not new to students of technological change. Several successful technologies, including energy technologies, have faced similar challenges as CCS faces now. To draw lessons for the CCS industry from the history of other energy technologies that, as with CCS today, were risky and expensive early in their commercial development, we have analyzed the development of the US nuclear-power industry, the US SO2-scrubber industry, and the global liquefied natural gas (LNG) industry. Through analyzing the development of the analogous industries we arrive at three principal observations. First, government played a decisive role in the development of all of these analogous technologies. Second, diffusion of these technologies beyond the early demonstration and niche projects hinged on the credibility of incentives for industry to invest in commercial-scale projects. Third, the conventional wisdom that experience with technologies inevitably reduces costs does not necessarily hold. Risky and capital-intensive technologies may be particularly vulnerable to diffusion without accompanying reductions in cost.

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  • Rai, Varun & Victor, David G. & Thurber, Mark C., 2010. "Carbon capture and storage at scale: Lessons from the growth of analogous energy technologies," Energy Policy, Elsevier, vol. 38(8), pages 4089-4098, August.
    • Handle: RePEc:eee:enepol:v:38:y:2010:i:8:p:4089-4098
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    1. Greaker, Mads & Lund Sagen, Eirik, 2008. "Explaining experience curves for new energy technologies: A case study of liquefied natural gas," Energy Economics, Elsevier, vol. 30(6), pages 2899-2911, November.
    2. MacKerron, Gordon, 1992. "Nuclear costs : Why do they keep rising?," Energy Policy, Elsevier, vol. 20(7), pages 641-652, July.
    3. 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.
    4. Rubin, Edward S. & Chen, Chao & Rao, Anand B., 2007. "Cost and performance of fossil fuel power plants with CO2 capture and storage," Energy Policy, Elsevier, vol. 35(9), pages 4444-4454, September.
    5. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    6. Karsten Neuhoff, 2008. "Learning by Doing with Constrained Growth Rates:An Application to Energy Technology Policy," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 165-182.
    7. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
    8. Marvin B. Lieberman, 1984. "The Learning Curve and Pricing in the Chemical Processing Industries," RAND Journal of Economics, The RAND Corporation, vol. 15(2), pages 213-228, Summer.
    Full references (including those not matched with items on IDEAS)

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