Enhancing the Benefit of the Doubt model through ‘Ensemble-DEA’: achieving the Sustainable Development Goals

Abstract

This study presents an innovative approach for constructing composite indicators by combining the Benefit of the Doubt method from Data Envelopment Analysis with ensemble techniques, i.e., ‘Ensemble-DEA’, with randomization in observations and variables selection. Our methodology mitigates the curse of dimensionality, which limits the effectiveness of traditional approaches when dealing with numerous indicators. By maintaining data integrity and improving robustness through an ensemble-based technique, our method delivers high-discriminatory power and clear rankings for Decision Making Units. Additionally, it enhances benchmarking capabilities by offering unit-specific peer comparisons. Our contributions therefore include the development of robust composite indicators and improved benchmarking insights, ensuring their reliability even in high-dimensional settings. We validate our approach using a real-world dataset containing 72 indicators aligned with Sustainable Development Goals for European Union countries. The results show that performance in meeting Sustainable Development Goals is correlated with the level of socioeconomic development and environmental consciousness. In particular, Scandinavian, Northern European and Benelux countries tend to perform best, while Eastern European countries lag in sustainability effectiveness. Furthermore, a comparative analysis against conventional methods underscores the advantages of our approach in managing complex datasets, specifically in terms of improvement in discriminatory power and benchmarking opportunities.