Incident Risk Prediction for Global Maritime Networks Under a Changing Climate

Abstract

Maritime risk is a fundamental component of operational cost and decision‐making in global container shipping, yet quantitative, route‐comparable risk estimates, especially in monetary terms and under future climate conditions, remain limited. This study develops a climate‐driven Bayesian network (BN)‐based Global Maritime Incident Risk Assessment and Prediction tool to quantify how projected future climate conditions may alter maritime incident risks at the grid‐cell, link, and route levels. Global open waters are partitioned into 2° × 2° grid cells, and route risk is computed by aggregating cell‐level risk along a path. The framework combines: (1) an estimate of incident occurrence probability derived from historical traffic and incident patterns, (2) two tree augmented BN models that predict incident type and then severity using discretized climate and bathymetry variables, and (3) monetary consequence values to express risk in real units ($ per voyage). Using monthly climate projections for 2025–2069, the approach is demonstrated on container routes in the 2M alliance network. In addition to grid cell, link and route‐based incident probabilities and their projections, a key contribution is interpretable, global monetary risk values and predictions that enable direct comparison of competing routes.