A near real-time carbon accounting framework for the decarbonization of maritime transport

Zhijun Li, Jiangang Fei, Yuquan Du, Kok-Leong Ong and Sobhan Arisian

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 191, issue C

Abstract: The recently proposed marine greenhouse gas (GHG) emission pricing mechanism has pressured shipping companies and regulators to adopt effective methods for the real-time monitoring of carbon emissions. This study proposes a Near Real-Time (NRT) carbon accounting framework that leverages machine learning models to enable carbon emission tracking at a 15-minute time interval. The framework incorporates critical factors, such as ship navigation characteristics, weather, and sea conditions to achieve accurate carbon accounting. We validate the framework’s efficacy through a case study of four mega-container ships of varying sizes and navigation scenarios. Our results show a maximum cumulative error of 5.83% for all ship navigation scenarios, even without critical data, and during the most extended voyages of their respective services. The proposed framework provides a new perspective on the decarbonization application of ship energy efficiency prediction research. By integrating it with a cloud-computing platform, shipping companies can enhance their voyage planning and route adjustment to optimize operational efficiency and reduce carbon footprints. Using this framework, international maritime transport regulators can develop an early warning system for carbon emissions to coordinate and improve environmental sustainability practices in the shipping industry.

Keywords: Near real-time carbon accounting; Fuel efficiency; Decarbonization; Maritime transport; Artificial intelligence; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tre.2024.103724

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