A SHAP-Based Analysis for Explaining Income Group Misclassification Through Environmental Health Performance

From the 1987 Brundtland Report to the present day, sustainable development has been a fundamental principle influencing the global environmental and economic agenda. The Environmental Performance Index (EPI) serves as a comprehensive and multidimensional framework for objectively assessing countries’ progress in sustainable development through measurable indicators of national environmental outcomes. The 2024 EPI Framework classifies 58 indicators into 11 issue categories and three policy objectives. This study utilized 13 indicators pertaining to environmental health policy as input features, with income level serving as the output feature. The income level was predicted utilizing the stacking classifier algorithm. The stacking classifier algorithm is an ensemble learning method that integrates various base estimators to enhance predictive accuracy. This study employed extreme gradient boosting, light gradient boosting machine, and Extra Trees classifiers as the base estimators for the stacking classifier, while the Random Forest classifier served as the final estimator. It was observed that the income level could be predicted with an overall precision of 88.4% and recall of 89.5%, with class-level F1 scores ranging from 0.796 to 0.991.