Benchmarking Efficiency, Sustainability, and Corporate Responsibility in Maritime Logistics: An Entropy-GRA Model with Sensitivity Analysis

As global awareness of sustainability and corporate social responsibility (CSR) intensifies, container shipping lines (CSLs) face growing pressure to align their operations with stakeholder expectations. However, existing studies in maritime logistics often examine CSR and environmental performance separately, rely on qualitative methods, or focus on broader shipping contexts without targeting CSLs specifically. Moreover, few studies provide data-driven benchmarking tools to evaluate performance across multiple sustainability dimensions. This study addresses these gaps by developing a quantitative benchmarking model that integrates entropy weighting and the grey relational analysis (GRA) to assess the performance of ten major CSLs using real-world data from 2022. The model incorporates operational, environmental, and social indicators, with entropy weighting objectively capturing the relative importance of each criterion. The GRA method is applied to rank CSLs based on their closeness to an ideal performer. A sensitivity analysis is then conducted by varying the distinguishing coefficient to test the robustness of the results. The findings reveal that cost-related criteria, such as the number of employees, energy consumption, and greenhouse gas emissions, carry the most weight. CSLs that perform consistently across multiple indicators tend to outperform peers that show inconsistency or rely heavily on a narrow set of strengths. This study contributes to the literature by offering an integrated, replicable approach for efficiency, sustainability, and CSR performance benchmarking in maritime logistics and by providing practical insights for policymakers, industry managers, and researchers.