Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

Matteo Spada and Peter Burgherr
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Matteo Spada: Laboratory for Energy System Analysis, Paul Scherrer Institut, CH-5232 Villigen PSI, Switzerland
Peter Burgherr: Laboratory for Energy System Analysis, Paul Scherrer Institut, CH-5232 Villigen PSI, Switzerland

Energies, 2020, vol. 13, issue 2, 1-21

Abstract: The accident risk of severe (≥5 fatalities) accidents in fossil energy chains (Coal, Oil and Natural Gas) is analyzed. The full chain risk is assessed for Organization for Economic Co-operation and Development (OECD), 28 Member States of the European Union (EU28) and non-OECD countries. Furthermore, for Coal, Chinese data are analysed separately for three different periods, i.e., 1994–1999, 2000–2008 and 2009–2016, due to different data sources, and highly incomplete data prior to 1994. A Bayesian Model Averaging (BMA) is applied to investigate the risk and associated uncertainties of a comprehensive accident data set from the Paul Scherrer Institute’s ENergy-related Severe Accident Database (ENSAD). By means of BMA, frequency and severity distributions were established, and a final posterior distribution including model uncertainty is constructed by a weighted combination of the different models. The proposed approach, by dealing with lack of data and lack of knowledge, allows for a general reduction of the uncertainty in the calculated risk indicators, which is beneficial for informed decision-making strategies under uncertainty.

Keywords: ENSAD; risk indicators; energy sector; bayesian model averaging; uncertainty; decision support (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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