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

Author

Listed:
  • 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.)

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.

Suggested Citation

  • Benjamin D. Leibowicz & Maria Roumpani & Peter H. Larsen, 2013. "Carbon Emissions Caps and the Impact of a Radical Change in Nuclear Electricity Costs," International Journal of Energy Economics and Policy, Econjournals, vol. 3(1), pages 60-74.
  • Handle: RePEc:eco:journ2:2013-01-7
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    References listed on IDEAS

    as
    1. 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.
    2. Bosetti, Valentina & Tavoni, Massimo, 2009. "Uncertain R&D, backstop technology and GHGs stabilization," Energy Economics, Elsevier, vol. 31(Supplemen), pages 18-26.
    3. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
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    Citations

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    Cited by:

    1. Lovering, Jessica R. & Yip, Arthur & Nordhaus, Ted, 2016. "Historical construction costs of global nuclear power reactors," Energy Policy, Elsevier, vol. 91(C), pages 371-382.
    2. 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.

    More about this item

    Keywords

    MARKAL; nuclear electricity; value of information; foresight;

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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