Data-Driven Carbon Emission Dynamics Under Ship In-Port Congestion - Weiyu Liu; Bowei Xu; Junjun Li (Journal of Marine Science and Engineering)
Berthing operation heterogeneity across ship types causes significant uncertainty in assessing port congestion and carbon emissions over comparable timeframes. This study quantifies in-port emission dynamics for four cargo ship types (container, liquid bulk, dry bulk, and general cargo) using an operational phase-specific emission accounting model. We propose a hybrid deep learning model that integrates Two-Dimensional Convolutional Neural Networks (2DCNN) with Squeeze-and-Excitation Attention Mechanisms (SEAM) and Bidirectional Long Short-Term Memory Networks (BiLSTM) layers, optimized via the Triangulation Topology Aggregation Optimizer (TTAO) for hyperparameter tuning. Empirical analysis at Ningbo Zhoushan Port shows that liquid bulk carriers emit 23–41% more than other ship types due to extended auxiliary engine/boiler use during cargo handling. The 2DCNN-SEAM model significantly improves BiLSTM prediction accuracy—reducing Mean Absolute Percentage Error (MAPE) by 18.7% and increasing the R2 value to 0.94—by effectively capturing spatiotemporal congestion features. Results confirm that operational congestion is a critical emission multiplier, especially for ships requiring prolonged auxiliary system use during berthing. These insights inform targeted decarbonization strategies for port authorities, prioritizing operational efficiency and energy transition for high-emission ship categories.