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

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  • Petrissa Eckle
  • Peter Burgherr

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.

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  • Petrissa Eckle & Peter Burgherr, 2013. "Bayesian Data Analysis of Severe Fatal Accident Risk in the Oil Chain," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 146-160, January.
    • Handle: RePEc:wly:riskan:v:33:y:2013:i:1:p:146-160
    DOI: 10.1111/j.1539-6924.2012.01848.x
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    References listed on IDEAS

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    1. Lee Fawcett & David Walshaw, 2006. "A hierarchical model for extreme wind speeds," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(5), pages 631-646, November.
    2. Felder, Frank A., 2009. "A critical assessment of energy accident studies," Energy Policy, Elsevier, vol. 37(12), pages 5744-5751, December.
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    1. Vaezi, Ali & Verma, Manish, 2018. "Railroad transportation of crude oil in Canada: Developing long-term forecasts, and evaluating the impact of proposed pipeline projects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 98-111.
    2. Anna Kalinina & Matteo Spada & Peter Burgherr, 2020. "Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non‐OECD Countries with Focus on Time Trend Analysis," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1723-1743, September.
    3. Marco Cinelli & Matteo Spada & Miłosz Kadziński & Grzegorz Miebs & Peter Burgherr, 2019. "Advancing Hazard Assessment of Energy Accidents in the Natural Gas Sector with Rough Set Theory and Decision Rules," Energies, MDPI, vol. 12(21), pages 1-17, November.
    4. Iwona Gorzeń-Mitka & Monika Wieczorek-Kosmala, 2023. "Mapping the Energy Sector from a Risk Management Research Perspective: A Bibliometric and Scientific Approach," Energies, MDPI, vol. 16(4), pages 1-32, February.
    5. Sovacool, Benjamin K. & Kryman, Matthew & Laine, Emily, 2015. "Profiling technological failure and disaster in the energy sector: A comparative analysis of historical energy accidents," Energy, Elsevier, vol. 90(P2), pages 2016-2027.
    6. Nima Khakzad & Faisal Khan & Paul Amyotte, 2015. "Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1336-1347, July.
    7. Spada, Matteo & Paraschiv, Florentina & Burgherr, Peter, 2018. "A comparison of risk measures for accidents in the energy sector and their implications on decision-making strategies," Energy, Elsevier, vol. 154(C), pages 277-288.
    8. Matteo Spada & Peter Burgherr, 2020. "Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging," Energies, MDPI, vol. 13(2), pages 1-21, January.
    9. Jamalnia, Aboozar & Gong, Yu & Govindan, Kannan & Bourlakis, Michael & Mangla, Sachin Kumar, 2023. "A decision support system for selection and risk management of sustainability governance approaches in multi-tier supply chain," International Journal of Production Economics, Elsevier, vol. 264(C).

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