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Carbon Emissions Caps and the Impact of a Radical Change in Nuclear Electricity Costs

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Author Info

  • Benjamin D. Leibowicz

    (Management Science and Engineering Department, Stanford University, United States.)

  • Maria Roumpani

    (Management Science and Engineering Department, Stanford University, United States.)

  • Peter H. Larsen

    (Management Science and Engineering Department, Stanford University, United States.)

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    Abstract

    In this study we analyze the impact of a radical change in nuclear electricity costs on the optimal electricity generation technology mix (EGTM) and constrain the value of information (VOI) on future nuclear costs. We consider three nuclear cost events and four carbon emissions caps. We develop a two-stage framework for energy-economic model MARKAL to eliminate foresight of future nuclear cost movements. We examine how the EGTM responds to these movements under alternative caps and analyze how these movements affect the cost of each cap. We define the expected savings from perfect foresight (ESPF), an upper bound on the VOI. We found that with current technologies, carbon mitigation that does not rely heavily on nuclear electricity is economically insensible. The Strong Cap is extremely costly because it restricts flexibility to respond to cost signals in choosing among technologies. The ESPF is highest under the Medium Cap by a substantial margin.

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    Bibliographic Info

    Article provided by Econjournals in its journal International Journal of Energy Economics and Policy.

    Volume (Year): 3 (2013)
    Issue (Month): 1 ()
    Pages: 60-74

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    Handle: RePEc:eco:journ2:2013-01-7

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    Web page: http://www.econjournals.com

    Related research

    Keywords: MARKAL; nuclear electricity; value of information; foresight;

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    References

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    1. Mark K. Jaccard & John Nyboer & Crhis Bataille & Bryn Sadownik, 2003. "Modeling the Cost of Climate Policy: Distinguishing Between Alternative Cost Definitions and Long-Run Cost Dynamics," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 49-73.
    2. Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.
    3. Babiker, Mustafa & Gurgel, Angelo & Paltsev, Sergey & Reilly, John, 2009. "Forward-looking versus recursive-dynamic modeling in climate policy analysis: A comparison," Economic Modelling, Elsevier, vol. 26(6), pages 1341-1354, November.
    4. Goulder, Lawrence H. & Schneider, Stephen H., 1999. "Induced technological change and the attractiveness of CO2 abatement policies," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 211-253, August.
    5. Bosetti, Valentina & Tavoni, Massimo, 2009. "Uncertain R&D, backstop technology and GHGs stabilization," Energy Economics, Elsevier, vol. 31(Supplemen), pages S18-S26.
    6. Goulder, Lawrence H. & Mathai, Koshy, 2000. "Optimal CO2 Abatement in the Presence of Induced Technological Change," Journal of Environmental Economics and Management, Elsevier, vol. 39(1), pages 1-38, January.
    7. Gillingham, Kenneth & Newell, Richard G. & Pizer, William A., 2008. "Modeling endogenous technological change for climate policy analysis," Energy Economics, Elsevier, vol. 30(6), pages 2734-2753, November.
    8. Hu, Ming-Che & Hobbs, Benjamin F., 2010. "Analysis of multi-pollutant policies for the U.S. power sector under technology and policy uncertainty using MARKAL," Energy, Elsevier, vol. 35(12), pages 5430-5442.
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    Cited by:
    1. Carlos Ferran & Ricardo Salim, 2013. "Infoenergy: Technology for Replacing Massive Degradation with Speedier (Cleaner) Energy," International Journal of Energy Economics and Policy, Econjournals, vol. 3(4), pages 447 - 458.

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