Accounting for wave-induced environmental uncertainty in CO₂ emission predictions for maritime operations

Reliable estimation of greenhouse gas (GHG) emissions from maritime operations is vital for understanding their environmental impact and supporting international decarbonization policies such as those mandated by the International Maritime Organization (IMO). However, current CO₂ prediction methods often overlook environmental uncertainties-particularly wave-induced resistance-which significantly influence engine performance and emissions in real-world conditions. This study presents an enhanced CO₂ emission prediction framework that explicitly accounts for wave-induced environmental variability using empirical wave data and Monte Carlo simulations. By integrating wave resistance with telegraph-mode-based engine operations, our model improves prediction accuracy under variable sea states. Comparative analysis with traditional design-based and activity-based methods shows that those approaches may underestimate emissions by up to 86 % under full navigation scenarios. The proposed method more closely aligns with actual emission measurements, demonstrating that wave-induced uncertainty is a non-negligible factor in CO₂ modeling. These findings emphasize the necessity of incorporating dynamic oceanographic conditions into emissions assessments to improve environmental accountability and regulatory compliance in maritime transport.