• Seaport natural disasters’ damages on oil transportation are significant and diverse. • Oil cargo throughput is calculated based on the AIS data. • A rule-based Bayesian network is developed to assess the risks. Maritime ports are encountering escalating vulnerabilities attributable to intensified risks posed by climate change and natural hazards. This study leverages Automatic Identification System-based maritime big data to formulate a risk assessment framework that evaluates the impact of natural disasters on seaports. To assess climate risks affecting global petroleum flows, a Rule-based Bayesian network is employed. Quantitative approaches are introduced for model construction, reducing the potential subjective bias and enhancing the model’s ability to distinguish the intricate relationships among various influential factors. The results indicate that Mina Al Ahmadi port, Madang port, and Floro port are identified as the three ports exhibiting the highest natural disaster risk. Conversely, the largest petroleum ports in terms of cargo throughput, such as Ras Tanura and Singapore, present a lower risk level compared to Mina Al Ahmadi port. The findings reveal that significant risks are not primarily associated with high-throughput ports; rather, certain regional ports, with comparatively lower throughput located in high hazard risk areas, encounter more substantial threats. These findings enhance the understanding of petroleum trade patterns and vulnerabilities inherent in petroleum seaport facilities, thereby providing essential data-driven insights for policy formulation, climate risk adaption strategies, and optimized port management practices.
Fan et al. (Wed,) studied this question.