Enhanced predictive modeling vs. LCA simulation: A comparative study on CO2 emissions from ship operations

Abstract

The shipping industry's reliance on fossil fuels significantly contributes to increased greenhouse gas emissions, which is a global issue. Thus, to reduce these emissions, the International Maritime Organization has developed numerous regulations and technical measures. However, these rely on simple estimates derived from ship types and fuel consumption, without considering detailed external conditions. This case study develops a predictive model for carbon dioxide emissions from the exhaust gases of a ship during actual operation using statistical methods based on the telegraph modes of its speed. The results are compared and analyzed with Life Cycle Assessment (LCA) simulation outcomes. The developed predictive model and LCA simulation results confirmed that carbon dioxide emission amounts increase as revolutions per minute rise across various telegraph modes. This result indicates a corresponding increase in fuel consumption. The model predicted levels approximately 83% higher than those obtained from LCA simulations. These differences occurred due to a failure to consider factors such as engine characteristics and external forces acting on the ship in LCA simulations. Furthermore, this study applies ANCOVA analysis to examine the predictive model and LCA simulation trends, confirming that the groups' trends were statistically equal. Despite a quantitative difference between the actual outcomes based on the predictive model and LCA simulation, the latter still provides a suitable and quick way to analyze different case types to make sustainable choices.