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

Bao, Yanrong; He, Jianjia; Li, Junxiang; He, Shengxue

doi:10.3390/en19030851

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

¹

Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

²

School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai 200093, China

³

Center for Supernetworks Research, University of Shanghai for Science and Technology, Shanghai 200093, China

^*

Author to whom correspondence should be addressed.

Energies 2026, 19(3), 851; https://doi.org/10.3390/en19030851

Submission received: 2 December 2025 / Revised: 11 January 2026 / Accepted: 12 January 2026 / Published: 5 February 2026

(This article belongs to the Special Issue Energy Security, Transition, and Sustainable Development)

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Abstract

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.

Keywords: energy transition; carbon emission reduction and pollution; key contributors; explainable machine learning

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MDPI and ACS Style

Bao, Y.; He, J.; Li, J.; He, S. Energy as a Lingering Barrier: Identifying Persistent Challenges in China’s Carbon Reduction and Pollution Abatement via Explainable Machine Learning. Energies 2026, 19, 851. https://doi.org/10.3390/en19030851

AMA Style

Bao Y, He J, Li J, He S. Energy as a Lingering Barrier: Identifying Persistent Challenges in China’s Carbon Reduction and Pollution Abatement via Explainable Machine Learning. Energies. 2026; 19(3):851. https://doi.org/10.3390/en19030851

Chicago/Turabian Style

Bao, Yanrong, Jianjia He, Junxiang Li, and Shengxue He. 2026. "Energy as a Lingering Barrier: Identifying Persistent Challenges in China’s Carbon Reduction and Pollution Abatement via Explainable Machine Learning" Energies 19, no. 3: 851. https://doi.org/10.3390/en19030851

APA Style

Bao, Y., He, J., Li, J., & He, S. (2026). Energy as a Lingering Barrier: Identifying Persistent Challenges in China’s Carbon Reduction and Pollution Abatement via Explainable Machine Learning. Energies, 19(3), 851. https://doi.org/10.3390/en19030851

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

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