Risk acceptance criterion for tanker oil spill risk reduction measures

This paper is aimed at investigating whether there is ample support for the view that the acceptance criterion for evaluating measures for prevention of oil spills from tankers should be based on cost-effectiveness considerations. One such criterion can be reflected by the Cost of Averting a Tonne of oil Spilt (CATS) whereas its target value is updated by elaborating the inherent uncertainties of oil spill costs and establishing a value for the criterion’s assurance factor. To this end, a value of $80,000/t is proposed as a sensible CATS criterion and the proposed value for the assurance factor F = 1.5 is supported by the retrieved Protection and Indemnity (P&I) Clubs’ Annual Reports. It is envisaged that this criterion would allow the conversion of direct and indirect costs into a non-market value for the optimal allocation of resources between the various parties investing in shipping. A review of previous cost estimation models on oil spills is presented and a probability distribution (log-normal) is fitted on the available oil spill cost data, where it should be made abundantly clear that the mean value of the distribution is used for deriving the updated CATS criterion value. However, the difference between the initial and the updated CATS criterion in the percentiles of the distribution is small. It is found through the current analysis that results are partly lower than the predicted values from the published estimation models. The costs are also found to depend on the type of accident, which is in agreement with the results of previous studies. Other proposals on acceptance criteria are reviewed and it is asserted that the CATS criterion can be considered as the best candidate. Evidence is provided that the CATS approach is practical and meaningful by including examples of successful applications in actual risk assessments. Finally, it is suggested that the criterion may be refined subject to more readily available cost data and experience gained from future decisions.     

Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective

In risk assessment studies it is important to determine how uncertain and imprecise knowledge should be included into the simulation and assessment models. Thus, proper evaluation of uncertainties has become a major concern in environmental and health risk assessment studies. Previously, researchers have used probability theory, more commonly Monte Carlo analysis, to incorporate uncertainty analysis in health risk assessment studies. However, in conducting probabilistic health risk assessment, risk analyst often suffers from lack of data or the presence of imperfect or incomplete knowledge about the process modeled and also the process parameters. Fuzzy set theory is a tool that has been used in propagating imperfect and incomplete information in health risk assessment studies. Such analysis result in fuzzy risks which are associated with membership functions. Since possibilistic health risk assessment studies are relatively new, standard procedures for decision-making about the acceptability of the resulting fuzzy risk with respect to a crisp standard set by the regulatory agency are not fully established. In this paper, we are providing a review of several available approaches which may be used in decision-making. These approaches involve defuzzification techniques, the possibility and the necessity measures. In this study, we also propose a new measure, the risk tolerance measure, which can be used in decision making. The risk tolerance measure provides an effective metric for evaluating the acceptability of a fuzzy risk with respect to a crisp compliance criterion. Fuzzy risks with different membership functions are evaluated with respect to a crisp compliance criterion by using the possibility, the necessity, and the risk tolerance measures and the results are discussed comparatively.