Machine Learning Based Feature Importance Regarding Recycling Rates for Municipal Waste in European Countries

Gradient Boost Regression reveals that real GDP is the variable that has the highest feature importance, followed by education level, total employment and life expectancy in feature importance ranking. Simultaneously, in the context of feature importance, Slovakia and Germany play a special role in predicting municipal waste recycling rates due to their contrasting positions. Maintaining negative correlations, these two countries account for a significant portion of the first principal component that governs the variation of variables including recycling rates. Moreover, the groupings of European countries in the context of PCA biplot or feature importance ranking can be compared with those by recycling rates only or by Ward’s hierarchical cluster analysis used in other studies. Partial Dependence Plots (PDP) more clarify the association of each variable with the recycling rates. Country specific dummy variables enhance model performance measured by RMSE or R2, which can be useful in facilitating integrated and well-coordinated policy design, not only at EU level but also at each national level.