A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: A Bayesian network model for the Gulf of Finland

J Environ Manage. 2015 Aug 1:158:122-32. doi: 10.1016/j.jenvman.2015.04.042. Epub 2015 May 14.

Abstract

Large-scale oil accidents can inflict substantial costs to the society, as they typically result in expensive oil combating and waste treatment operations and have negative impacts on recreational and environmental values. Cost-benefit analysis (CBA) offers a way to assess the economic efficiency of management measures capable of mitigating the adverse effects. However, the irregular occurrence of spills combined with uncertainties related to the possible effects makes the analysis a challenging task. We develop a probabilistic modeling approach for a CBA of oil spill management and apply it in the Gulf of Finland, the Baltic Sea. The model has a causal structure, and it covers a large number of factors relevant to the realistic description of oil spills, as well as the costs of oil combating operations at open sea, shoreline clean-up, and waste treatment activities. Further, to describe the effects on environmental benefits, we use data from a contingent valuation survey. The results encourage seeking for cost-effective preventive measures, and emphasize the importance of the inclusion of the costs related to waste treatment and environmental values in the analysis. Although the model is developed for a specific area, the methodology is applicable also to other areas facing the risk of oil spills as well as to other fields that need to cope with the challenging combination of low probabilities, high losses and major uncertainties.

Keywords: Bayesian network; Cost-benefit analysis; Environmental valuation; Gulf of Finland; Maritime safety; Oil spill.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Cost-Benefit Analysis
  • Environmental Restoration and Remediation / economics*
  • Finland
  • Humans
  • Models, Statistical
  • Oceans and Seas
  • Petroleum Pollution / statistics & numerical data*
  • Uncertainty
  • Water Pollution, Chemical / statistics & numerical data*