AI-Driven Pollution Emission Control in Marine Transportation: Intelligent Monitoring, Governance, and Compliance

The emission reduction targets set by the International Maritime Organization pose a severe challenge to the shipping industry — and to the marine and coastal ecosystems most exposed to its air and water pollution. Shipping emissions degrade coastal air quality, contribute to ocean acidification, and threaten the health of near-shore and port-adjacent environments. Traditional pollution control methods face limitations such as difficulties in monitoring, optimization, and enforcement in the dynamic and complex marine environment. Artificial intelligence (AI), with its strengths in data mining, predictive analysis, and intelligent decision-making, offers an innovative path for ship emission monitoring, energy efficiency optimization, and compliance management.

While the decarbonization of maritime operations has been widely discussed, this Topic focuses specifically on AI for intelligent pollution monitoring, governance, and regulatory compliance — and, critically, on the resulting benefits for marine and coastal environments. Systematically reviewing AI-driven research on shipping pollution control is of great significance for protecting coastal ecosystems and promoting green transformation and regulatory innovation across the industry.
This Topic aims to establish an AI-driven, system-level framework for shipping pollution control spanning “technologies, methods, and policies”, and integrating ships, ports, waterways, and regulatory frameworks. It focuses on three key issues: first, how to integrate multi-source heterogeneous data to achieve precise monitoring and tracing of ship emissions; second, how to establish a dynamically collaborative intelligent control and decision-making mechanism across the ship–port–waterway system; and third, how to bridge the gap between AI technology performance and current regulatory policies. To achieve these goals, systematic measures are proposed: at the technical level, to build an intelligent perception and emission inversion system based on the integration of space, air, land, and sea; at the methodological level, to develop an intelligent decision-making and collaborative regulation mechanism for full-chain optimization; and at the policy level, to design an institutional framework and regulatory innovation path suitable for intelligent governance — thereby delivering measurable environmental benefits for coastal and marine systems and providing theoretical support and practical paradigms for the green, low-carbon transformation of the shipping industry.

Focusing on the three dimensions of technology, method, and policy in AI for shipping pollution emission control, this Topic emphasizes the synergy and system-level integration of ships, ports, waterways, and regulatory frameworks, and the associated benefits for marine and coastal environments. Its distinctive focus is intelligent pollution monitoring, governance, and compliance — complementing, rather than duplicating, existing collections on AI-enabled maritime decarbonization and sustainable maritime operations.
Specific topics (including but not limited to)
o Intelligent perception, multi-source data fusion, and high-precision inversion of ship emissions
o Optimization of ship speed, route planning, and coordinated fleet scheduling based on deep reinforcement learning
o Integrated port–waterway–ship intelligent emission control mechanisms
o AI-driven design of shipping pollution regulatory policies, regulation adaptation, and governance innovation
o Digital twins and edge intelligence for green shipping
o Assessment of the marine and coastal environmental benefits of AI-based emission control (e.g., coastal air quality, near-shore water quality, ecosystem exposure)
o Efficacy evaluation and decision support of AI systems for the low-carbon transition

Manuscript type

We welcome original research papers, reviews, case studies, and policy analysis submissions, and particularly encourage submissions from interdisciplinary perspectives and those that demonstrate technical implementation.