Hybrid Neural Network-Based Maritime Carbon Dioxide Emission Prediction: Incorporating Dynamics for Enhanced Accuracy

The rapid expansion of international maritime transportation has led to rising greenhouse gas emissions, exacerbating climate change and environmental sustainability concerns. According to the International Maritime Organization, carbon dioxide (CO2) emissions from vessels are projected to increase by over 17% by 2050. Traditional emission estimation methods are prone to inaccuracies due to uncertainties in emission factors, and inconsistencies in fuel consumption data. This study proposes deep learning-based CO2 emission prediction models leveraging engine operation data. Unlike previous approaches that primarily relied on fuel consumption, this model incorporates multiple parameters capturing the relationship between combustion characteristics and emissions to enhance predictive accuracy. We developed and evaluated individual models—convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN)—as well as hybrid model (TCN–LSTM). The hybrid model achieved the highest predictive performance, with a coefficient of determination of 0.9726, outperforming other models across multiple quantitative metrics. These findings demonstrate the potential of deep learning for vessel emission assessment, providing a scientific basis for carbon management strategies and policy development in the international shipping industry. This study thus holds major academic and industrial value, advancing the field of deep learning-based emission prediction and extending its applicability to diverse operational scenarios.