Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development

doi:10.3389/fmars.2023.1308981

	Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development
	Xie, Wenxin 1; Li, Yong 1; Yang, Yang 2; Wang, Peng 3; Wang, Zhishan 1; Li, Zhaoxuan 1; Mei, Qiang 4; Sun, Yaqi 1
	2023-12-01
发表期刊	FRONTIERS IN MARINE SCIENCE
卷号	10 页码:17
摘要	The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union's incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port-a region characterized by high shipping activity-this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under various operational conditions. Coupled with the prediction results, the study offers valuable theoretical and data-driven support for formulating emission reduction strategies and optimizing resource allocation, thereby contributing to sustainable maritime transformation.
关键词	greenhouse gas emission inventory AIS transformer emission prediction
DOI	10.3389/fmars.2023.1308981
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[71804059] ; Natural Science Foundation of Fujian Province[2021J01821] ; Shanghai Science and Technology Committee[18DZ1206300] ; National Key Research and Development Program of China[2018YFC1407400]
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
WOS类目	Environmental Sciences ; Marine & Freshwater Biology
WOS记录号	WOS:001124539000001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38436
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Yong; Mei, Qiang
作者单位	1.Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China 2.East China Normal Univ, Sch Geog Sci, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Jimei Univ, Nav Inst, Xiamen, Peoples R China
推荐引用方式 GB/T 7714	Xie, Wenxin,Li, Yong,Yang, Yang,et al. Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development[J]. FRONTIERS IN MARINE SCIENCE,2023,10:17.
APA	Xie, Wenxin.,Li, Yong.,Yang, Yang.,Wang, Peng.,Wang, Zhishan.,...&Sun, Yaqi.(2023).Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development.FRONTIERS IN MARINE SCIENCE,10,17.
MLA	Xie, Wenxin,et al."Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development".FRONTIERS IN MARINE SCIENCE 10(2023):17.