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Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling

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  • Kahouli, Sondès

Abstract

This paper studies the effects of returns to scale, technological learning, i.e. learning-by-doing and learning-by-searching, and uranium price on the prospects of nuclear cost decrease. We use an extended learning curve specification, named multiple factors learning curve (MFLC). In a first stage, we estimate a single MFLC. In a second stage, we estimate the MFLC under the framework of simultaneous system of equations which takes into account the uranium supply and demand. This permits not only to enhance the reliability of the estimation by incorporating the uranium price formation mechanisms in the MFLC via the price variable, but also to give preliminary insights about uranium supply and demand behaviors and the associated effects on the nuclear expansion. Results point out that the nuclear cost has important prospects for decrease via capacity expansion, i.e. learning-by-doing effects. In contrast, they show that the learning-by-searching as well as the scale effects have a limited effect on the cost decrease prospects. Conversely, results also show that uranium price exerts a positive and significant effect on nuclear cost, implying that when the uranium price increases, the nuclear power generation cost decreases. Since uranium is characterized by important physical availability, and since it represents only a minor part in the total nuclear cost, we consider that in a context of increasing demand for nuclear energy the latter result can be explained by the fact that the positive learning effects on the cost of nuclear act in a way to dissipate the negative ones that an increase in uranium price may exert. Further, results give evidence of important inertia in the supply and demand sides as well as evidence of slow correlation between the uranium market and oil market which may limit the inter-fuels substituability effects, that is, nuclear capacity expansion and associated learning-by-doing benefits.

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  • Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:5:p:840-852
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    References listed on IDEAS

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    Cited by:

    1. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    2. Rooney, Matthew & Nuttall, William J. & Kazantzis, Nikolaos, 2015. "A dynamic model of the global uranium market and the nuclear fuel cycle," Resources Policy, Elsevier, vol. 43(C), pages 50-60.
    3. 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.
    4. Linares, Pedro & Conchado, Adela, 2013. "The economics of new nuclear power plants in liberalized electricity markets," Energy Economics, Elsevier, vol. 40(S1), pages 119-125.

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