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Projections of US GHG reductions from nuclear power new capacity based on historic levels of investment

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  • Besmann, Theodore M.

Abstract

Historical rates of capital investment in nuclear plant construction were used as a guide to estimate the potential rate of future capacity introduction. The total linear rate of capital expenditure over the entire period of historical construction from 1964 to 1990 was determined to equal $11.5 billion/yr, and that for the period of peak construction from 1973 to 1985 was computed as $17.9 billion/yr, all in 2004$. These values were used with a variety of current capital cost estimates for nuclear construction to obtain several scenarios for possible future nuclear capacity additions. These values were used to obtain the effect of projected nuclear generating capacity on GHG emissions assuming nuclear would directly replace coal-fired generation. It was concluded that actual reductions in emissions would not be experienced until 2038, yet growth in emissions from electrical production would be slowed through that period. Due to the significant time to introduce large-scale changes in the utility sector, nuclear energy cannot have a dramatic short-term effect on emissions. Nuclear power, however, can have a major positive longer term impact, particularly under more favorable cost and investment conditions.

Suggested Citation

  • Besmann, Theodore M., 2010. "Projections of US GHG reductions from nuclear power new capacity based on historic levels of investment," Energy Policy, Elsevier, vol. 38(5), pages 2431-2437, May.
  • Handle: RePEc:eee:enepol:v:38:y:2010:i:5:p:2431-2437
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    References listed on IDEAS

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    1. Koomey, Jonathan & Hultman, Nathan E., 2007. "A reactor-level analysis of busbar costs for US nuclear plants, 1970-2005," Energy Policy, Elsevier, vol. 35(11), pages 5630-5642, November.
    2. Weisser, Daniel, 2007. "A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies," Energy, Elsevier, vol. 32(9), pages 1543-1559.
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    1. Tolón-Becerra, A. & Lastra-Bravo, X. & Bienvenido-Bárcena, F., 2010. "Methodology proposal for territorial distribution of greenhouse gas reduction percentages in the EU according to the strategic energy policy goal," Applied Energy, Elsevier, vol. 87(11), pages 3552-3564, November.
    2. Tang, Ling & Yu, Lean & He, Kaijian, 2014. "A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 128(C), pages 1-14.
    3. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    4. Kiriyama, Eriko & Kajikawa, Yuya & Fujita, Katsuhide & Iwata, Shuichi, 2013. "A lead for transvaluation of global nuclear energy research and funded projects in Japan," Applied Energy, Elsevier, vol. 109(C), pages 145-153.
    5. Ling Tang & Shuai Wang & Kaijian He & Shouyang Wang, 2015. "A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, Springer, vol. 234(1), pages 111-132, November.
    6. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).

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