EU MRV-Based Fleet-Level Benchmarking of Container Ship Distance-Normalised Fuel Consumption Using Explainable Machine Learning

Maritime decarbonisation requires transparent use of verified emissions data without overstating what regulatory datasets can explain. This study examines how EU Monitoring, Reporting, and Verification (MRV) data can support descriptive benchmarking of container ship distance-normalised fuel consumption. Of 14,147 2024 MRV records, 2097 container ships were retained. Fuel consumption per nautical mile (FC/nm) is treated as an absolute distance-normalised fuel-use indicator, not a cargo-adjusted efficiency metric. Four algorithms were assessed under target-related diagnostic and reduced specifications. Diagnostic results represent target-related reconstruction, not genuine prediction. Training-only hyperparameter tuning yielded reduced-specification test R2 values of 0.8958 for Gradient Boosting and 0.8888 for XGBoost. Random-Forest attribution in the reduced specification was dominated by emissions-derived predictors. A strict non-emissions Random Forest using only sea time and certification performed worse than the mean-value baseline (R2 = −0.2890). Comparison with MRV-reported fuel consumption per transport work (mass), available for 95.1% of vessels, showed a strong inverse association (ρ = −0.822), confirming that FC/nm quartiles are not cargo-adjusted efficiency rankings. The study shows that explainable machine learning can transparently diagnose information structure in MRV data, while demonstrating that these variables alone cannot support causal, voyage-level, cross-vessel efficiency, or policy interpretations.