IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v191y2024ics1366554524003156.html
   My bibliography  Save this article

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

Author

Listed:
  • Li, Zhijun
  • Fei, Jiangang
  • Du, Yuquan
  • Ong, Kok-Leong
  • Arisian, Sobhan

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.

Suggested Citation

  • Li, Zhijun & Fei, Jiangang & Du, Yuquan & Ong, Kok-Leong & Arisian, Sobhan, 2024. "A near real-time carbon accounting framework for the decarbonization of maritime transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:transe:v:191:y:2024:i:c:s1366554524003156
    DOI: 10.1016/j.tre.2024.103724
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554524003156
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2024.103724?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Guo, Xiaoyan & He, Junliang & Yu, Hang & Liu, Mei, 2023. "Carbon peak simulation and peak pathway analysis for hub-and-spoke container intermodal network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    3. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    4. Lee, Sang-Jeong & Sun, Qinghe & Meng, Qiang, 2023. "Vessel weather routing subject to sulfur emission regulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Fitzgerald, Warren B. & Howitt, Oliver J.A. & Smith, Inga J. & Hume, Anthony, 2011. "Energy use of integral refrigerated containers in maritime transportation," Energy Policy, Elsevier, vol. 39(4), pages 1885-1896, April.
    6. Elyakim BenHakoun & Eddy Van De Voorde & Yoram Shiftan, 2024. "The daily port and shoreline emission inventory model," Maritime Policy & Management, Taylor & Francis Journals, vol. 51(4), pages 588-616, May.
    7. Guomin Li & Wei Li & Yinke Dou & Yigang Wei, 2022. "Antarctic Shipborne Tourism: Carbon Emission and Mitigation Path," Energies, MDPI, vol. 15(21), pages 1-17, October.
    8. Chen, Xinqiang & Lv, Siying & Shang, Wen-long & Wu, Huafeng & Xian, Jiangfeng & Song, Chengcheng, 2024. "Ship energy consumption analysis and carbon emission exploitation via spatial-temporal maritime data," Applied Energy, Elsevier, vol. 360(C).
    9. Du, Yuquan & Meng, Qiang & Wang, Shuaian & Kuang, Haibo, 2019. "Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 88-114.
    10. Roar Adland & Pierre Cariou & Francois-Charles Wolff, 2020. "Optimal ship speed and the cubic law revisited: Empirical evidence from an oil tanker fleet," Post-Print hal-03422276, HAL.
    11. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    12. Araks Ekmekçioğlu & Kaan Ünlügençoğlu & Uğur Buğra Çelebi, 2022. "Estimation of shipping emissions based on real-time data with different methods: A case study of an oceangoing container ship," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 4451-4470, March.
    13. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan, Ran & Yang, Dong & Wang, Tianyu & Mo, Haoyu & Wang, Shuaian, 2024. "Improving ship energy efficiency: Models, methods, and applications," Applied Energy, Elsevier, vol. 368(C).
    2. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    3. Ghosh, Indranil & De, Arijit, 2024. "Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    4. Han, Peixiu & Liu, Zhongbo & Li, Chi & Sun, Zhuo & Yan, Chunxin, 2024. "A novel federated learning-based two-stage approach for ship energy consumption optimization considering both shipping data security and statistical heterogeneity," Energy, Elsevier, vol. 309(C).
    5. Wen Yi & Robyn Phipps & Hans Wang, 2020. "Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry," Sustainability, MDPI, vol. 12(21), pages 1-12, October.
    6. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    7. Philip Cammin & Jingjing Yu & Stefan Voß, 2023. "Tiered prediction models for port vessel emissions inventories," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 142-169, March.
    8. Wu, Lingxiao & Wang, Shuaian & Laporte, Gilbert, 2021. "The Robust Bulk Ship Routing Problem with Batched Cargo Selection," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 124-159.
    9. Ruan, Zhang & Huang, Lianzhong & Wang, Kai & Ma, Ranqi & Wang, Zhongyi & Zhang, Rui & Zhao, Haoyang & Wang, Cong, 2024. "A novel prediction method of fuel consumption for wing-diesel hybrid vessels based on feature construction," Energy, Elsevier, vol. 286(C).
    10. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    11. Tan, Zhijia & Zeng, Xianyang & Shao, Shuai & Chen, Jihong & Wang, Hua, 2022. "Scrubber installation and green fuel for inland river ships with non-identical streamflow," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    12. Niu, Baozhuang & Dong, Jian & Wang, Hongzhi, 2024. "Smart port vs. port integration to mitigate congestion: ESG performance and data validation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 191(C).
    13. Hrosul, Viktoriia & Kruhlova, Olena & Kolesnyk, Alina, 2023. "Digitalization of the agricultural sector: the impact of ICT on the development of enterprises in Ukraine," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(4), December.
    14. Gaviria-Marin, Magaly & Merigó, José M. & Baier-Fuentes, Hugo, 2019. "Knowledge management: A global examination based on bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 194-220.
    15. Asghari, Mohammad & Jaber, Mohamad Y. & Mirzapour Al-e-hashem, S.M.J., 2023. "Coordinating vessel recovery actions: Analysis of disruption management in a liner shipping service," European Journal of Operational Research, Elsevier, vol. 307(2), pages 627-644.
    16. Petersen, Alexander M. & Rotolo, Daniele & Leydesdorff, Loet, 2016. "A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of Medical Subject Headings," Research Policy, Elsevier, vol. 45(3), pages 666-681.
    17. Hailiang Li & M. James C. Crabbe & Haikui Chen, 2020. "History and Trends in Ecological Stoichiometry Research from 1992 to 2019: A Scientometric Analysis," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    18. Nina Sakinah Ahmad Rofaie & Seuk Wai Phoong & Muzalwana Abdul Talib & Ainin Sulaiman, 2023. "Light-emitting diode (LED) research: A bibliometric analysis during 2003–2018," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 173-191, February.
    19. Serhat Burmaoglu & Ozcan Saritas, 2019. "An evolutionary analysis of the innovation policy domain: Is there a paradigm shift?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 823-847, March.
    20. Yanrong Qiu & Kaihuai Liao & Yanting Zou & Gengzhi Huang, 2022. "A Bibliometric Analysis on Research Regarding Residential Segregation and Health Based on CiteSpace," IJERPH, MDPI, vol. 19(16), pages 1-21, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:191:y:2024:i:c:s1366554524003156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.