Carbon Emission Prediction for Sichuan Province Using a SHAP-Explained Machine Learning Framework

ChangZhi Xie

doi:10.63313/EBM.9159

Authors

ChangZhi Xie School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China Author

DOI:

https://doi.org/10.63313/EBM.9159

Keywords:

Carbon emission forecasting, Transformer model, SHAP analysis, Sichuan Province

Abstract

This study develops a provincial carbon accounting and forecasting framework for Sichuan Province covering 2005–2022 under a consumption-based boundary. Direct fossil fuel emissions and indirect electricity-related emissions are clearly distinguished to ensure accounting consistency and additive closure. On this basis, a Transformer-based machine learning model integrated with SHAP is constructed to predict carbon emissions and identify peak characteristics. The results show high historical fitting accuracy and project that Sichuan’s carbon emissions will reach a plateau-type peak around 2031, followed by a gradual decline. SHAP-based interpretation indicates that total energy consumption and the carbon emission factor are the dominant drivers, while economic scale and energy intensity exert secondary but significant effects. Robustness tests confirm the stability of the explanatory structure. The proposed framework integrates accounting consistency, predictive modeling, and interpretability analysis, providing empirical support for regional peak management and low-carbon transition planning.

References

[1] Guo, C. X. (2010). Decomposition of China’s carbon emissions: Based on the LMDI decomposition technique. *China Population, Resources and Environment*, 20(12), 4–9.

[2] Guo, Y. G., Wang, D. D., & Lin, F. C. (2010). A study on the carbon emission footprint of energy utilization in Shanghai. *China Population, Resources and Environment*, 20(02), 103–108.

[3] Zhang, L. F. (2006). *Research on China’s energy supply and demand forecasting model and development strategies* (Doctoral dissertation). Capital University of Economics and Business, Beijing.

[4] Xu, G. Y. (2010). *Research on the relationship among China’s energy consumption, carbon emissions, and economic growth* (Doctoral dissertation). Huazhong University of Science and Technology, Wuhan.

[5] Li, Y. T., & Li, F. (2009). An analysis of the Environmental Kuznets Curve between economic growth and environmental protection. *Economic Theory and Business Management*, (2), 35–39.

[6] York, R., E.A. Rosa and T. Dietz, STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 2003. 46: p. 351-365.

[7] Ang, B.W., LMDI decomposition approach: A guide for implementation. Energy Policy, 2015. 86: p. 233-238.

[8] Ang, B.W., The LMDI approach to decomposition analysis: a practical guide. Energy Policy, 2005. 33: p. 867-871.

[9] Sun, Q., H. Chen and A. Et, Can Chinese cities reach their carbon peaks on time? Scenario analysis based on machine learning and LMDI decomposition. Applied Energy, 2023. 347: p. 121427-121427.