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Data-driven decarbonization in the maritime industry: An integrative analytical review

Jing Wang et al.

Communications in Transportation Research2026https://doi.org/10.26599/commtr.2026.9640002article
ABDC C
Weight
0.50

What the paper says

The maritime sector, responsible for around 2.9% of global greenhouse gas (GHG) emissions, is under increasing pressure to decarbonize towards the International Maritime Organization’s (IMO) 2050 targets. A number of exisiting reviews have summarized different aspects such as policy frameworks, alternative fuels, and ship technologies, yet few have examined the integrative role of data in advancing maritime decarbonization across these domains. This review establishes a four-pillar framework to capture how data-driven approaches are reshaping maritime decarbonization. It begins by analyzing emerging digital governance policies on the basis of conventional maritime policies. Second, it synthesizes the development of emission datasets, highlighting the indirect databases with direct real-time measurements such as unmanned aerial vehicle (UAV) and portable emission measurement systems (PEMS) monitoring. Third, it reviews methodological advancements in emission modeling, from empirical, economic, and hybrid models to machine learning-based approaches. Finally, it evaluates how these data and modeling advancements support coordinated decarbonization strategies across vessels, fuels, and ports, illustrating the growing role of data-informed decision-making in guiding systemic emission reductions throughout the maritime supply chain. This review links regulatory, data, modeling, and implementation perspectives through a data-centric perspective. By mapping research progress and identifying knowledge gaps across these four pillars, the review offers a structured foundation to support the acceleration of maritime decarbonization efforts.

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https://doi.org/https://doi.org/10.26599/commtr.2026.9640002

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@article{jing2026,
  title        = {{Data-driven decarbonization in the maritime industry: An integrative analytical review}},
  author       = {Jing Wang et al.},
  journal      = {Communications in Transportation Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.26599/commtr.2026.9640002},
}

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Data-driven decarbonization in the maritime industry: An integrative analytical review

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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