Bayesian Data Analysis of Severe Fatal Accident Risk in the Oil Chain

Petrissa Eckle and Peter Burgherr

Risk Analysis, 2013, vol. 33, issue 1, 146-160

Abstract: We analyze the risk of severe fatal accidents causing five or more fatalities and for nine different activities covering the entire oil chain. Included are exploration and extraction, transport by different modes, refining and final end use in power plants, heating or gas stations. The risks are quantified separately for OECD and non‐OECD countries and trends are calculated. Risk is analyzed by employing a Bayesian hierarchical model yielding analytical functions for both frequency (Poisson) and severity distributions (Generalized Pareto) as well as frequency trends. This approach addresses a key problem in risk estimation—namely the scarcity of data resulting in high uncertainties in particular for the risk of extreme events, where the risk is extrapolated beyond the historically most severe accidents. Bayesian data analysis allows the pooling of information from different data sets covering, for example, the different stages of the energy chains or different modes of transportation. In addition, it also inherently delivers a measure of uncertainty. This approach provides a framework, which comprehensively covers risk throughout the oil chain, allowing the allocation of risk in sustainability assessments. It also permits the progressive addition of new data to refine the risk estimates. Frequency, severity, and trends show substantial differences between the activities, emphasizing the need for detailed risk analysis.

Date: 2013
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Citations: View citations in EconPapers (9)

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https://doi.org/10.1111/j.1539-6924.2012.01848.x

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Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:33:y:2013:i:1:p:146-160

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