Sustainable and Smart Logistics Transition in European Maritime–Port Systems: A Decision Tree Classification Approach

Background: Sustainable and smart logistics transition requires tools that connect environmental, energy, social and digital performance with transport structure. This study proposes an exploratory classification framework for European maritime–port logistics systems using Eurostat-based country-year observations. Methods: A composite transition profile was constructed from environmental, energy, social and digital indicators using min–max normalization, equal weighting and tercile classification into low, medium and high profiles. A shallow decision tree classifier was applied to identify transport, modal structure and maritime–port activity variables that discriminate between profiles. Results: Road freight transport intensity was the main discriminator, followed by inland passenger modal structure variables. Maritime–port activity variables were included in the initial predictor set but were not retained by the final tree, indicating that transition profiles are more strongly differentiated by inland logistics and modal configuration at the country-year level. The model showed moderate performance, with a five-fold cross-validated accuracy of 0.561, above the majority-class baseline. Conclusions: The framework provides an interpretable diagnostic tool for identifying logistics-related transition patterns and supporting sustainable logistics planning. Its exploratory scope and data limitations are explicitly acknowledged.