Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port

Abstract

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.

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

“…Finally, combining the factors such as the type of the ship and the state of the ship's voyage, the processed data were entered into the STEAM2 model, and then we utilized the specific emission factors to calculate the pollution emission of the ship in the area. The emission for a type of ship pollution (the measurement unit is g) caused by the ship's main engine, auxiliary engine, and boiler (the measurement unit is kWh) can be formulated as Equation (1) [11].…”

“…With the increase in maritime activities, ship pollution reduction has been a subject of significant focus in the maritime community [10]. Many studies have been conducted to estimate the carbon emissions of the main engine, auxiliary engines, and boilers of ships under different sailing conditions [11]. A novel SE inventory supported by bottom-up activity was proposed to identify overall pollution discharge in the SE control areas [12].…”

Ship emission projections based on time series forecasting model for sustainable shipping in the strait of Malacca and Singapore

“…In order to construct a reliable projection model, the predictive element is required to sustain precisions over a time series of period, where machine learning methods are often used to handle big data for processing the prediction part [6]. With the prevailing AIS-based emission estimation, the emission data pivoted with spatiotemporal and other navigational data can be further processed to form the elements for projection to visualize future emission trend [7]. Likewise, with consideration of scenarios like policies implementation and technological maturity, the most optimal alternatives could be reverse engineered from the projections.…”