Abstract
Global maritime transport carries nearly four-fifths of world merchandise trade and is a significant source of greenhouse gas (GHG) emissions. With the GHG reduction strategies from the International Maritime Organization (IMO), the EU’s inclusion of shipping in the Emissions Trading System and the introduction of fuel GHG-intensity standards, there is an urgent need for prediction frameworks that are more robust, transparent and adaptable to evolving policy landscapes. Drawing on a structured search of the Web of Science Core Collection for the period 2020–2024, this review synthesises 1,012 peer-reviewed studies on global shipping emissions, decarbonisation measures and AI-enabled modelling. It first compares conventional approaches—fuel-based top-down inventories, AIS-driven bottom-up models and statistical or machine learning techniques—highlighting their respective strengths and limitations in terms of spatial and temporal resolution, data requirements and policy relevance. It then examines the emerging capabilities of large language models (LLMs) in knowledge integration, code generation and tool orchestration, and proposes five LLM-enabled paradigms for shipping emissions prediction, including multi-source information extraction, model orchestration, scenario construction and intelligent compliance auditing. Key technical and governance challenges are discussed, such as data quality and confidentiality, physical consistency, explainability and the environmental footprint of AI. The study argues that coupling LLMs with physics-based and data-driven models can enhance the flexibility and policy relevance of shipping emissions prediction, while a clearly defined research agenda is needed to ensure their responsible and effective use in supporting the decarbonisation of maritime transport.