AIS-Based Forecasting of STEAM-Estimated Ship CO2 Emissions for Sustainable Maritime Transportation Using a Spatio-Temporal Graph Transformer

Abstract

Accurate short-horizon forecasting of ship CO2 emissions is important for maritime carbon monitoring, operational assessment, and the transition toward sustainable maritime transportation. However, reliable forecasting remains challenging because ship emissions are affected by temporally varying operating states, vessel-specific characteristics, and nonlinear emission relationships. This study proposes a Carbon-oriented Spatio-Temporal Graph Transformer (C-STGT) for forecasting STEAM-estimated ship CO2 emissions from Automatic Identification System (AIS) trajectories. The proposed model integrates temporal representation learning, single-vessel graph modeling, vessel-specific attributes, carbon-related attention bias, and emission-oriented decoding to capture vessel operating and emission dynamics. C-STGT is evaluated on five containerships operating along the Shanghai–Los Angeles corridor and compared with ten baseline models. Experimental results show that C-STGT achieves a root mean squared error (RMSE) of 0.89, a mean absolute error (MAE) of 0.51, a weighted absolute percentage error (WAPE) of 3.11%, and an R2 of 0.96, outperforming the compared models. Ablation, emission-formulation sensitivity, and statistical analyses further support the effectiveness and robustness of the proposed framework. By improving short-horizon vessel-level carbon-emission forecasting, the proposed approach can support more timely emission monitoring and operational assessment, providing quantitative information for maritime operations and maritime decarbonization. In summary, C-STGT provides an effective forecasting framework that contributes to the development of more sustainable maritime transportation.