Leveraging artificial intelligence for sustainable development and policy integration in the shipping industry

Shipping is a critical industry for global trade but is associated with environmental externalities like air, underwater noise, and light pollution. Transitioning to a sustainable future requires optimizing propulsion systems, which currently suffer from fossil fuel reliance and energy losses. This research develops an AI-assisted evidence-synthesis framework to examine sustainable shipping and propulsion technologies and support a policy framework for a sustainable maritime agenda. The study maps how engineering and policy research align across post-2005 literature. The findings demonstrate that sustainability is explicitly multi-dimensional and cannot be reduced to emissions alone. By normalizing and comparing concept intensities across technical and policy-oriented corpora, this study uncovers significant gaps in literature attention. Specifically, propulsion-specific engineering heavily emphasizes electrification and noise reduction, whereas policy discourse focuses primarily on emissions and renewable energy targets. This structured mapping provides academia and practice with a modular decision-support framework to identify topic misalignments, track temporal theme variations, and enhance policy monitoring. Furthermore, these contributions directly support multiple United Nations Sustainable Development Goals (SDGs), including SDGs 7, 9, 12, 13, 14, and 17. The proposed AI-assisted workflow serves as a transparent sense-making mechanism adaptable to other industries undergoing sustainability-driven technological transitions.