Estimation of shipping emissions from maritime big data: A comprehensive review and prospective

Xun Yang, Nikolaos Tsoulakos, Zhe Xiao, Xiaoyang Wei, Xiuju Fu and Ran Yan

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 202, issue C

Abstract: Maritime industry plays a critical role in global trade and is currently faced with the urgent need for decarbonization to address climate change and environmental degradation. This paper reviews the state-of-the-art in ship emission estimation studies, particularly focusing on academic literature with automatic identification system (AIS) data as the major data source. Our comprehensive review covers 78 academic publications from 2009 to 2024. The research data status, ship emission estimation and validation methods, analysis of emission outcomes, potential impacts, and feasible countermeasures of these studies are extensively summarized and discussed. We find significant gaps in research data availability, especially the detailed engine and emissions records. Methodological issues arise from the oversimplification of maritime operations in current studies driven by artificial intelligence (AI) and inadequate models for in-port emissions estimation and fuel switching dynamics. We also discuss evaluation challenges, including the lack of real ship emission data and the difficulties in differentiating between maritime and urban emissions in coastal areas. To improve current data condition, we recommend improving data collection procedure with enhanced monitoring technologies and adapting new multi-source data fusion techniques like transfer learning. AI-driven methodologies should be enhanced with domain knowledge to adapt varying maritime contexts. We also propose a generic structure of open-source maritime database and encourage building collaborative data-sharing system to promote partnerships across various sectors for emission tracking, analysis, and mitigation.

Keywords: Maritime big data; Ship emission estimation; Maritime decarbonization; Green shipping; Automatic identification system (AIS); Artificial intelligence (AI) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104313

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