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On the performance of the United States nuclear power sector: A Bayesian approach

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  • Bernstein, David H.
  • Parmeter, Christopher F.
  • Tsionas, Mike G.

Abstract

Concerns over climate change and global emissions has again placed attention on clean energy sources. Nuclear power plants are one of many sources of clean energy and yet few studies have examined the structure of technology exclusively in this area. We utilize Bayesian empirical likelihood methods to estimate a stochastic frontier model to examine scale economies, technical efficiency and technological change in the United States nuclear energy generation sector. We find decreasing scale economies, a fact consistent with the recent decline of the industry. Our results suggest that small nuclear reactors may benefit the sector as a whole.

Suggested Citation

  • Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
  • Handle: RePEc:eee:eneeco:v:125:y:2023:i:c:s0140988323003821
    DOI: 10.1016/j.eneco.2023.106884
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    More about this item

    Keywords

    Nuclear energy; Small nuclear reactor; Returns to scale; Exponential tilting; Asymmetric Laplace; Empirical likelihood;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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