Research on carbon drivers of agricultural exports combined with XGBoost and SHAP
Krishnan Vijayaletchumy
Accurate identification of agricultural export carbon emission drivers is essential for developing effective mitigation strategies under global climate change. This study proposes an integrated analytical framework combining the XGBoost machine learning model with SHAP interpretability analysis to capture nonlinear relationships and quantify factor contributions. Using panel data from Central China (1993–2019), model parameters were optimized through grid search and cross-validation. Results sho