Data-Driven Carbon Emission Dynamics Under Ship In-Port Congestion

Abstract

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.