Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port
IntroductionIn recent years, the adverse effects of escalating maritime trade and international shipping– particularly in regard to increased greenhouse gas emissions and their impact on human health– have come to the fore. These issues have thus instigated a surge in pressure to enhance the regulat...
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| Format: | Article |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2023.1174411/full |
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| author | Yong Li Wenxin Xie Yang Yang Qiang Mei Qiang Mei Zhishan Wang Zhaoxuan Li Peng Wang Peng Wang |
| author_facet | Yong Li Wenxin Xie Yang Yang Qiang Mei Qiang Mei Zhishan Wang Zhaoxuan Li Peng Wang Peng Wang |
| author_sort | Yong Li |
| collection | DOAJ |
| description | IntroductionIn recent years, the adverse effects of escalating maritime trade and international shipping– particularly in regard to increased greenhouse gas emissions and their impact on human health– have come to the fore. These issues have thus instigated a surge in pressure to enhance the regulation of shipborne carbon emissions.MethodsThe study utilized the automatic identification system (AIS) data, Lloyd’s register data, and pollutant emission parameters to calculate the carbon emissions from the main engine, auxiliary engine, and boiler of vessels under varying sailing conditions, utilizing the dynamic method of ships. In relation to geographic information and ship trajectory, a comprehensive inventory of ship carbon emissions was developed, revealing pronounced spatiotemporal characteristics. To assure the accuracy of the substantial AIS dataset, procedures including data cleaning, trajectory integration, data fusion, and completion were executed. Such processes are indispensable, given the potential for transmission and storage errors associated with AIS data. To forecast CO2 emissions over diverse time intervals, a temporal fusion transformer model equipped with attention mechanisms was employed.ResultThe paper furnishes a case study on Tianjin Port, wherein a high-resolution carbon emissions inventory was devised based on AIS data acquired from vessels. This inventory was subsequently employed to generate multi-feature predictions of future carbon emissions. Given the optimal parameter configuration, the proposed method attained P50 and P90 values of 0.244 and 0.118 respectively, thereby demonstrating its efficacy.DiscussionRecognizing the sources of ship carbon emissions in this region and forecasting such emissions in the future substantiates that this method accurately portrays the laws of ship carbon emissions. Our study provides a scientific basis for decision-making in port and pollution management, enabling the creation of targeted emission reduction policies for ships. |
| first_indexed | 2024-03-13T04:03:48Z |
| format | Article |
| id | doaj.art-0574864e810f4ef9b83a71345e54618a |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-03-13T04:03:48Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-0574864e810f4ef9b83a71345e54618a2023-06-21T09:55:50ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452023-06-011010.3389/fmars.2023.11744111174411Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin PortYong Li0Wenxin Xie1Yang Yang2Qiang Mei3Qiang Mei4Zhishan Wang5Zhaoxuan Li6Peng Wang7Peng Wang8Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaNavigation Institute, Jimei University, Xiamen, ChinaNavigation Institute, Jimei University, Xiamen, ChinaMerchant Marine Academy, Shanghai Maritime University, Shanghai, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaMerchant Marine Academy, Shanghai Maritime University, Shanghai, ChinaInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaIntroductionIn recent years, the adverse effects of escalating maritime trade and international shipping– particularly in regard to increased greenhouse gas emissions and their impact on human health– have come to the fore. These issues have thus instigated a surge in pressure to enhance the regulation of shipborne carbon emissions.MethodsThe study utilized the automatic identification system (AIS) data, Lloyd’s register data, and pollutant emission parameters to calculate the carbon emissions from the main engine, auxiliary engine, and boiler of vessels under varying sailing conditions, utilizing the dynamic method of ships. In relation to geographic information and ship trajectory, a comprehensive inventory of ship carbon emissions was developed, revealing pronounced spatiotemporal characteristics. To assure the accuracy of the substantial AIS dataset, procedures including data cleaning, trajectory integration, data fusion, and completion were executed. Such processes are indispensable, given the potential for transmission and storage errors associated with AIS data. To forecast CO2 emissions over diverse time intervals, a temporal fusion transformer model equipped with attention mechanisms was employed.ResultThe paper furnishes a case study on Tianjin Port, wherein a high-resolution carbon emissions inventory was devised based on AIS data acquired from vessels. This inventory was subsequently employed to generate multi-feature predictions of future carbon emissions. Given the optimal parameter configuration, the proposed method attained P50 and P90 values of 0.244 and 0.118 respectively, thereby demonstrating its efficacy.DiscussionRecognizing the sources of ship carbon emissions in this region and forecasting such emissions in the future substantiates that this method accurately portrays the laws of ship carbon emissions. Our study provides a scientific basis for decision-making in port and pollution management, enabling the creation of targeted emission reduction policies for ships.https://www.frontiersin.org/articles/10.3389/fmars.2023.1174411/fullship pollutantcarbon emissions inventoryTianjin PortAIStransformerforecast CO2 emissions |
| spellingShingle | Yong Li Wenxin Xie Yang Yang Qiang Mei Qiang Mei Zhishan Wang Zhaoxuan Li Peng Wang Peng Wang Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port Frontiers in Marine Science ship pollutant carbon emissions inventory Tianjin Port AIS transformer forecast CO2 emissions |
| title | Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port |
| title_full | Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port |
| title_fullStr | Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port |
| title_full_unstemmed | Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port |
| title_short | Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port |
| title_sort | research on the carbon emissions traceability inventory and multi horizon prediction of ship carbon emissions a case study of tianjin port |
| topic | ship pollutant carbon emissions inventory Tianjin Port AIS transformer forecast CO2 emissions |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2023.1174411/full |
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