Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port - Li, Yong; Xie, Wenxin; Yang, Yang; Mei, Qiang; Wang, Zhishan; Li, Zhaoxuan; Wang, Peng (Frontiers in Marine Science, 2023)
The negative impacts of increasing maritime trade and international shipping on growing greenhouse gas emissions and on human health have become increasingly apparent in recent years. These concerns have resulted in increased pressure to better control ship carbon emissions. In this paper, the automatic identification system (AIS) data, Lloyd’s register data, and pollutant emission parameters of ships were used to calculate the carbon emissions of the ship’s main engine, auxiliary engine, and boiler under different sailing conditions utilizing the dynamic method of ships. Based on the geographic information and ship trajectory, the carbon emissions inventory of ships was created, showing high spatiotemporal characteristics. To ensure the accuracy of the massive AIS dataset, data cleaning, track integration, data fusion, and completion are performed. These processes are necessary due to the potential for transmission and storage errors in AIS data. A temporal fusion transformer model with attention mechanisms was employed to predict CO2 emissions across different time intervals. The method’s effectiveness was demonstrated by conducting a carbon emissions inventory and multi-time prediction of carbon emissions for 2018 based on the collected massive AIS data using Tianjin Port as an example. Identifying the sources of ship carbon emissions in this region and predicting future such emissions show that the method accurately describes ship carbon emission laws. As a result, it provides a scientific basis for decision-making in port and pollution management, enabling the creation of targeted emission reduction policies for ships.