Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions

• Published in: Journal of environmental management 2021-01, Vol.278 (Pt 1), p.111520, Article 111520
• Main Authors: Parviainen, Tuuli, Goerlandt, Floris, Helle, Inari, Haapasaari, Päivi, Kuikka, Sakari
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Bayesian networks; ISO 31000:2018; Oil spills; Petroleum Pollution; Pollution preparedness and response; Risk Assessment; Risk management; Uncertainty
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2020.111520

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. •The ISO 31000:2018 standard provides a comprehensive framework for oil spill risk management.•Bayesian Networks support the implementation of ISO 31000:2018-based risk management.•Bayesian Networks aid robust decision-making when uncertainties matter.•Bayesian Networks support participatory risk management based on continuous learning.