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Vessel emissions are one of the major emissions sources affecting carbon neutralisation achievement in the Greater Bay Area (GBA). The vessel pollution prevention ability is effective in preventing vessels from discharging mass emissions. Thus, it is of great value to figure out the vessel's performance in pollution prevention in this area. This study develops a data-driven Bayesian network model focusing on vessel performance in pollution prevention in port state control (PSC) inspections in the GBA. The Tree Augmented Naïve and Expectation Maximisation approaches are applied to learn structures and parameters based on the inspection records obtained from January 2015 to September 2022. An analysis can identify key variables with significant effects on vessel performance in pollution prevention and clarify the positive influence and contributions of PSC inspections toward carbon neutralisation achievement. Practical implications are provided to foster port authorities and local governments in the GBA to minimize the vessels with poor pollution prevention performance sailing in this area. The study also identifies the characteristics of vessels with poor performance and proposes valuable suggestions to restrict the inattentive actions of ship owners and stimulate ship owners to put more effort into the improvement of pollution prevention ability. Further, the study reveals the significance of executing efficient and reliable PSC inspections in promoting carbon neutralisation progress. • A data-driven BN model to assess pollution prevention of vessels in GBA is built. • The influence of variables on vessel performance in pollution prevention is revealed. • Characteristics of vessels of poor pollution prevention performance are clarified. • Suggestions are provided for local governments to improve vessel quality in GBA. • Contribution of PSC inspections towards carbon neutralisation in GBA is expounded. [ABSTRACT FROM AUTHOR]

• A tool is developed to aid the selection of PSC detention risk control strategies. • BNs and TOPSIS are incorporated as a new methodology for decision-making. • Detention risk control strategies are quantitatively measured in a dynamic manner. • Useful insights are provided to reduce possible ship detention risk in PSC. Port State Control (PSC) inspections have been implemented as an administrative measure to detect and detain substandard ships and thus to ensure maritime safety. Advanced risk models were developed to investigate the impact of factors influencing ship detention. Although showing much attractiveness, current studies still reveal a key challenge on how such analysis can improve the ship performance in PSC inspections and aid PSC detention risk control decision. By incorporating a data-driven Bayesian network (BN) into the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, this paper proposes a new ship detention risk control methodology, in which the decision criteria are generated from the root risk variables, and the alternatives refer to the established strategies adopted by ship-owners in their practical ship detention risk control. Along with the new methodology, the main technical novelty of this paper lies in the quantitative measurement of the effectiveness of each strategy in terms of the reduction of detention rate in a dynamic manner. Its practical contributions are seen, from both ship owner and port authority perspectives, through the provisions of useful insights on dynamic evaluation of rational control strategies to reduce ship detention risk under various PSC inspection scenarios. [ABSTRACT FROM AUTHOR]