Deep Learning-Enabled Policy Optimization for Sustainable Ship Registry Selection

The global maritime industry faces a conflict between economic competition and sustainability standards. Economic pressure often incentivizes ship registries toward regulatory leniency, degrading environmental and social standards. Traditional static models often overlook how current flag choices impact future inspection risks and financing costs. To address this, we propose a Deep Reinforcement Learning framework that models flag selection as a sequential decision problem. Using a Markov Decision Process, we integrate economic, environmental, and social rewards. We analyze Port State Control records, AIS data, and 27 policy factors to quantify policy effectiveness within the simulation environment. The results show significant heterogeneity in policy performance. Reducing corporate income tax yielded the highest reward improvement (+131.37, p < 0.001). This suggests that, within the model, economic viability serves as a foundation for environmental investments. Enhanced safety standards also generate significant value (+58.35, p < 0.001) by reducing accident penalties and improving reputation metrics. Conversely, increasing tonnage taxes incentivizes the agent toward registries with lax oversight (−87.61, p < 0.001). These findings demonstrate that economic competitiveness and sustainability are mutually reinforcing. This framework provides maritime administrations with a “policy sandbox” for evidence-based decision-making, enabling a transition to sustainability without sacrificing competitiveness.