Energy as a Lingering Barrier: Identifying Persistent Challenges in China’s Carbon Reduction and Pollution Abatement via Explainable Machine Learning

Persistent energy system inertia continues to hinder China’s carbon reduction progress despite global decarbonization trends. This study develops an explainable machine learning framework to dissect energy-related emission drivers through 14 secondary indicators spanning energy structure, industrial dynamics, social factors, and economic factors. Leveraging panel data from 260 Chinese cities (2000–2023), we conduct comparative analysis of six ML models and identify XGBoost as optimal for capturing nonlinear emission patterns. SHAP value decomposition and feature importance reveals that total energy consumption and energy consumption intensity remain the dominant contributors to carbon and pollution emissions, while the secondary industry still emerges as a critical driver. Our research establishes an actionable framework to identify drivers of carbon mitigation and pollution reduction, analyze their mechanisms, and support policymakers in optimizing policy implementation amid energy transition.