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Comprehensive empirical assessment of nuclear power risks

Author

Listed:
  • Ali Ayoub

    (Massachusetts Institute of Technology (MIT))

  • Didier Sornette

    (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology)

Abstract

The opposition in a number of countries to the inclusion of nuclear energy in a sustainable energy portfolio, in part due to the dread of what the “nuclear” word inspires, has limited quantitative scientific foundation of the real benefits and risks. This has been amplified by the lack of a sound operational risk estimate due to the scarcity of the relevant empirical data. Using what is by far the largest - recently constructed - open database on accident precursors, and using our in-house generic probabilistic safety assessment (PSA) models, we provide the first comprehensive statistical study of the operational risks in the civil nuclear sector. We quantify a Pareto distribution of precursor severities as well as a special runaway Dragon Kings regime for the largest events. With respect to risk assessment, our main finding is that risk is dominated by exogenous factors (95%). We calculate that, by focusing on these factors in new design concepts, the frequency of accidents of the Fukushima scale can be brought down to about one per 300 years of operation of the worldwide fleet. Our results also demonstrate the need for an international cooperation focused on the construction of full blockchains of the cascades of accident precursors.

Suggested Citation

  • Ali Ayoub & Didier Sornette, 2022. "Comprehensive empirical assessment of nuclear power risks," Swiss Finance Institute Research Paper Series 22-69, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp2269
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    More about this item

    Keywords

    nuclear energy; probabilistic safety assessment; accident precursors; dragon kings; operational risks; open-source database;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • K32 - Law and Economics - - Other Substantive Areas of Law - - - Energy, Environmental, Health, and Safety Law
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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