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Estimating greenhouse gases using ship trajectory data

Published online by Cambridge University Press:  06 April 2026

Sanglok Yoo ,
Deuk-Jae Cho  and
Young-Hoon Yang
Sanglok Yoo
Affiliation:
Department of Artificial Intelligence, Jeonju University, Jeonju, Republic of Korea
Deuk-Jae Cho
Affiliation:
Maritime Digital Transformation Research Center, Korea Research Institute of Ships and Ocean Engineering, Daejeon, Republic of Korea
Young-Hoon Yang*
Affiliation:
Maritime Digital Transformation Research Center, Korea Research Institute of Ships and Ocean Engineering, Daejeon, Republic of Korea
*
Corresponding author: Young-Hoon Yang; Email: yhyang@kriso.re.kr
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Abstract

Maritime transport plays a vital role in global logistics and trade; however, its environmental impact, particularly CO₂ emissions, has become a growing concern. Current estimation methodologies are divided into top-down and bottom-up approaches. Top-down methods rely on macro-statistical data but often lack specificity regarding individual ship characteristics, leading to high uncertainty. Bottom-up methods, increasingly prevalent due to advancements in ship equipment and big data technology, estimate CO₂ emissions based on detailed ship activity trajectories, offering greater precision. This study integrates data from multiple vessel-position transmitting devices — AIS, V-Pass, and LTE-Maritime — to estimate CO₂ emissions from maritime activities in the coastal regions of South Korea. By combining these data sources, the study develops a comprehensive and accurate emissions assessment, improving reliability and supporting more informed decision-making in maritime environmental management and policy development.

Keywords

AISalgorithmcarbon dioxideestimationshiptrajectory

Information

Type
Research Article
Information
The Journal of Navigation , Volume 78 , Issue 4-6 , July 2025 , pp. 461 - 473
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Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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Footnotes

‡

This article has been updated since its original publication. A notice detailing this can be found here: https://doi.org/10.1017/S0373463326101556

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