The Role of Governance and Sustainability Indicators in Explaining Port Economic Efficiency: A Random Forest Approach

Despite the growing relevance of governance and sustainability in port management, there is still limited empirical evidence on how economic, social, and environmental dimensions interact in a non-linear and configurational manner to explain port economic efficiency. This study applies an explainable machine learning approach based on Random Forest to classify the economic efficiency of Spanish Port Authorities using an integrated set of governance-related indicators. Economic efficiency is approximated through the E_02 indicator (EBITDA per tonne), which is discretized into three ordinal levels: low, medium, and high efficiency. A classification approach is preferred over regression because the objective is not only to predict a continuous value, but to identify interpretable efficiency profiles and extract decision rules associated with different governance configurations. Model performance was evaluated using a confusion matrix, global accuracy, precision, recall, and F1-score for each efficiency class. The results reveal three differentiated patterns: low-efficiency ports, associated with environmental weaknesses, fragile labor structures, and low profitability; medium-efficiency ports, characterized by partial strategies and transitional configurations; and high-efficiency ports, linked to coherent combinations of environmental management, balanced labor organization, and strong economic performance. Overall, the findings show that port efficiency does not depend solely on size or isolated factors, but on specific governance-related configurations. The study highlights the value of explainable artificial intelligence as a complementary tool to support evidence-based decision-making in sustainable port management.