Spatial-temporal analysis of carbon emissions from ships in ports based on AIS data.

The current analysis method for temporal-spatial characteristics of carbon emissions from port vessels overlooks the multidimensionality of the carbon emission process. To address this issue, this paper proposes a multidimensional approach for extracting characteristics of carbon emissions from vessels, aiming to enable multidimensional analysis of carbon emission information. Firstly, an inventory for calculating carbon emissions from port vessels was established using AIS data. Secondly, a method for constructing a three-dimensional tensor of carbon emissions from port vessels was proposed. This approach entails discretizing and encoding time, space, vessel type, and operating mode to generate a dataset with carbon emission labels. Following this, a three-dimensional tensor of carbon emissions was created using mapping rules. Thirdly, non-negative tensor factorization (NTF) was employed to decompose the tensor into multiple sub-vectors. The impact of tensor rank on the decomposition results was discussed, and an appropriate tensor rank value was determined, achieving multidimensional feature extraction of vessel carbon emissions. To validate the feasibility of this method, Tianjin Port was taken as the study area, and six major characteristics of vessel carbon emissions are obtained. This study reveals the temporal and spatial distribution of carbon emissions from different vessels under various operating modes. Compared to unidimensional analysis, this method provides more detailed multidimensional information on vessel carbon emissions, thus offering data support and decision-making basis for formulating energy-saving and emission reduction measures for ships and promoting green and sustainable development in the shipping industry. • A spatial-temporal analysis method of carbon emissions from ships in ports is proposed. • A three-dimensional ship carbon emission tensor construction method is developed. • The proposed model is verified through real case analysis and model comparison. [ABSTRACT FROM AUTHOR]

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