Citation

Lu, Qian and Khaw, Khai Wah and Chew, Xin Ying and Yeong, Wai Chung and Ng, Wei Chien (2025) Towards Carbon Neutrality: A Novel STIRPAT- XGBoost-SHAP Framework for Provincial Energy Transition Prediction in China. ASM Science Journal, 20 (2). pp. 1-12. ISSN 1823-6782

Text 54_towards_carbon_neutrality.pdf - Published Version Restricted to Repository staff only Download (806kB)

Abstract

China's commitment to carbon neutrality by 2060 demands accurate and interpretable forecasting tools at the provincial level. This study develops and validates a hybrid framework integrating the STIRPAT model, XGBoost machine learning, and SHAP interpretability analysis to forecast coal and total energy consumption across six representative Chinese provinces (2005-2021). The STIRPAT model reveals industrial structure as the dominant driver of coal dependence, while SHAP confirms structural consistency and highlights nonlinear effects of urbanisation and income. The XGBoost model achieves competitive forecasting performance (Mean Absolute Percentage Error (MAPE): 5.26% for coal and 3.02% for total energy) and effectively captures regional disparities in coal transition trajectories. These results support differentiated and structurally grounded policy interventions, offering practical guidance for subnational energy planning under China's dual-carbon strategy. The framework offers broad applicability to other structurally diverse and data-constrained contexts.

Item Type:	Article
Uncontrolled Keywords:	China, coal consumption, energy transition, green finance, SHAP, STIRPAT, subnational forecasting, XGBoost
Subjects:	H Social Sciences > HD Industries. Land use. Labor
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	07 Nov 2025 06:34
Last Modified:	10 Nov 2025 01:35
URII:	http://shdl.mmu.edu.my/id/eprint/14772

Downloads

Edit (login required)