Abstract
The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.
Highlights
Global trade largely relies on international maritime transport: an estimated 80 per cent of the volume of world trade is seaborne and in ternational maritime transport has been projected to continue growing in the coming decades (UNCTAD 2019)
We provide a systemic analysis of the use of Bayesian networks (BNs)-based oil spill risk assessment (OSRA-BN) models for pollution preparedness and response (PPR) planning and decision-making, i.e. models addressing the acci dent occurrence, the response effectiveness, and the ecological, eco nomic, health, and socio-cultural impacts of oil spills
Other countries where BNs have been used for oil spill risk related modeling for pollution preparedness and response include Australia, the United States of America, Norway, Germany, Poland, and Sweden
Summary
Global trade largely relies on international maritime transport: an estimated 80 per cent of the volume of world trade is seaborne and in ternational maritime transport has been projected to continue growing in the coming decades (UNCTAD 2019). While significant improvements have been made in terms of maritime safety (UNCTAD 2019; Hassler, 2011; Haapasaari and Dahlbo 2014; Hanninen and Rytkonen 2006; Knudsen and Hassler, 2011; Lagring et al, 2012; Mitchell 1994; Ring bom 2018), the risk of a large-scale oil spill remains significant. Aven and Renn (2009) define the term as “uncertainty about and severity of the consequences (or outcomes) of an activity with respect to something that humans value”. We focus on studies that have mainly analyzed oil spill risk in terms of the uncertainty about the occurrence of an oil spill in the marine environment, and the related environmental, economic, humanhealth, and socio-cultural consequences. The environmental as well as the socio-economic impacts could be substantial in the case of a largescale oil spill from maritime transport, as evidenced by historic accident cases such as Prestige (Spain), Erika (France), and Exxon Valdez (United States) (e.g. Garza-Gil et al, 2006; Miraglia 2002; Kontovas et al, 2010; Peterson et al, 2003)
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