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

机译：油链中严重致命事故风险的贝叶斯数据分析

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摘要

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.
机译：我们分析了导致五人或更多人死亡的严重致命事故的风险，并分析了覆盖整个石油链的九种不同活动。包括勘探和开采，以不同方式运输，在电厂，供热或加油站进行精炼和最终用途。经合组织国家和非经合组织国家分别对风险进行了量化，并计算了趋势。通过使用贝叶斯层次模型分析风险，该模型对频率（泊松）和严重性分布（广义帕累托）以及频率趋势产生分析函数。这种方法解决了风险估计中的一个关键问题，即数据的稀缺性导致了高度不确定性，尤其是对于极端事件的风险，在极端事件中，这种风险的推断超出了历史上最严重的事故。贝叶斯数据分析允许汇集来自不同数据集的信息，这些数据集涵盖例如能源链的不同阶段或不同的运输方式。此外，它还固有地提供了不确定性。这种方法提供了一个框架，该框架全面涵盖了整个石油链中的风险，从而可以在可持续性评估中分配风险。它还允许逐步添加新数据以完善风险估计。频率，严重性和趋势显示活动之间存在实质性差异，强调需要进行详细的风险分析。

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