Estimating the frequency of nuclear accidents

TL;DR

This paper uses Bayesian analysis to compare nuclear industry risk assessments with empirical accident data, finding significant discrepancies and questioning the reliability of industry predictions, especially in India.

Contribution

It provides a rigorous statistical comparison showing the industry's risk estimates are inconsistent with empirical data, challenging current safety assessments.

Findings

Industry risk assessments are statistically invalidated by empirical data.

The Indian nuclear safety record is insufficient for reliable future risk predictions.

The industry's own conclusions align with the empirical evidence against their risk estimates.

Abstract

We used Bayesian methods to compare the predictions of probabilistic risk assessment -- the theoretical tool used by the nuclear industry to predict the frequency of nuclear accidents -- with empirical data. The existing record of accidents with some simplifying assumptions regarding their probability distribution is sufficient to rule out the validity of the industry's analyses at a very high confidence level. We show that this conclusion is robust against any reasonable assumed variation of safety standards over time, and across regions. The debate on nuclear liability indicates that the industry has independently arrived at this conclusion. We pay special attention to the Indian situation, where we show that the existing operating experience provides insufficient data to make any reliable claims about the safety of future reactors. We briefly discuss some policy implications.