4.7 Article

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

Journal

JOURNAL OF ENVIRONMENTAL MANAGEMENT
Volume 158, Issue -, Pages 122-132

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2015.04.042

Keywords

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

Funding

  1. Finnish ministry: Ministry of Agriculture and Forestry
  2. Finnish ministry: Ministry of the Environment
  3. Finnish ministry: Ministry of Transport and Communications
  4. Finnish ministry: Ministry of Finance

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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. (C) 2015 Elsevier Ltd. All rights reserved.

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