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A dynamic model of the global uranium market and the nuclear fuel cycle

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  • Rooney, Matthew
  • Nuttall, William J.
  • Kazantzis, Nikolaos

Abstract

In order to study the global uranium market, a dynamic model for the period 1990–2050 has been developed. It incorporates globally aggregated stocks and flows of uranium moving through the nuclear fuel cycle, as well as a price formation mechanism. Analysis illustrates some of the key features of the market for this commodity, including the role that time lags play in the formation of price volatility. Specific demand reduction and substitution strategies and technologies are explored, and potential external shocks are simulated to investigate the effect on price and how the uranium mining industry responds. Sensitivity analysis of key model parameters indicates that the time constant related to the formation of traders׳ expectations of future market prices embedded in the proposed price discovery mechanism has a strong influence on both the amplitude and frequency of price peaks. Finally, our analysis leads us to believe that the existing uranium resource base will be sufficient to satisfy demand well into the second half of the 21st century.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jrpoli:v:43:y:2015:i:c:p:50-60
    DOI: 10.1016/j.resourpol.2014.11.003
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    References listed on IDEAS

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    1. Paul Cashin & C. John McCDermott, 2002. "The Long-Run Behavior of Commodity Prices: Small Trends and Big Variability," IMF Staff Papers, Palgrave Macmillan, vol. 49(2), pages 1-2.
    2. Chi, K.C. & Reiner, D.M. & Nuttall, W.J., 2009. "Dynamics of the UK Natural Gas Industry: System Dynamics Modelling and Long-Term Energy Policy Analysis," Cambridge Working Papers in Economics 0922, Faculty of Economics, University of Cambridge.
    3. 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.
    4. Cai, Zhiming & Clarke, Richard H. & Glowacki, Bartek A. & Nuttall, William J. & Ward, Nick, 2010. "Ongoing ascent to the helium production plateau--Insights from system dynamics," Resources Policy, Elsevier, vol. 35(2), pages 77-89, June.
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    Cited by:

    1. Zirui Wang & Wanli Xing, 2022. "Study on the Characteristics and Evolution Trends of Global Uranium Resource Trade from the Perspective of a Complex Network," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    2. Kong, Rui & Xue, Fangfang & Wang, Jing & Zhai, Haiyan & Zhao, Lina, 2017. "Research on Mineral Resources and Environment of Salt Lakes in Qinghai Province based on System Dynamics Theory," Resources Policy, Elsevier, vol. 52(C), pages 19-28.
    3. Arnaut, Javier L., 2022. "The importance of uranium prices and structural shocks: Some implications for Greenland," Energy Policy, Elsevier, vol. 161(C).

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