Study based on machine learning on the impact of fluoride-contaminated farmland remediation on farmers’ income structure and rural economic revitalization

This study addresses the specific question of how the remediation of fluoride-contaminated farmland can quantify its impact on farmers’ income structure and its transmission to rural economic revitalization. To solve this problem, firstly, multi-source data were integrated to construct governance characteristic variables; then, the Light Gradient Boosting Machine (LightGBM) model was used to predict farmers’ multidimensional income, and SHapley Additive explanations (SHAP) values were employed to analyze the contribution and interaction effects of key factors. Finally, path analysis and Granger causality tests were used to verify the micro-to-macro transmission mechanisms. Experimental results shows that the governance intensity and non-agricultural skills are key driving factors, and the synergy between governance intensity and non-agricultural skills yields a substantial positive contribution to income structure optimization. Mediation analysis shows that the indirect path through income restructuring accounts for the majority of the total policy effect on rural economic revitalization. This study confirms that the hybrid framework integrating machine learning interpretability and econometric causal inference can effectively reveal the complex transmission mechanism through which environmental governance influences household-level responses and subsequently contributes to macro-level rural development, providing quantitative evidence and methodological support for the coordinated promotion of ecological restoration and rural revitalization.