AccScience Publishing / JES / Online First / DOI: 10.36922/JES025490032
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ORIGINAL RESEARCH ARTICLE

Integrating factor decomposition and scenario decoupling into a modified STIRPAT model for regional carbon emission forecasting

Lei Zhu1 ,  Yuan Li1 ,  Wei Qian1 ,  Liwei Liu1 ,  Kuo Meng1 ,  Shufeng Yuan2* ,  Jia Yang2* ,  Jun Zhang1,3* ,  Lu Guo1,3,4*
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1 School of Materials and Energy, Yunnan University, Kunming, Yunnan, China
2 Department of Technology, Malong Branch, Qujing Tobacco Company, Qujing, Yunnan, China
3 Yunnan Key Laboratory of Carbon Neutrality and Green Low-Carbon Technologies, Yunnan University, Kunming, Yunnan, China
4 Yunnan Malaya Institute, School of Engineering, Yunnan University, Kunming, Yunnan, China
JES, 025490032 https://doi.org/10.36922/JES025490032
Received: 5 December 2025 | Revised: 28 January 2026 | Accepted: 30 January 2026 | Published online: 18 March 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Calculating carbon emissions is a crucial step in achieving carbon reduction and neutralization. However, current carbon emission prediction models face challenges, including inaccuracies in accounting results and a lack of regional modelling frameworks. This study developed a modified stochastic impact by regression on population, affluence, and technology (STIRPAT) carbon emission model via key factors decomposition and scenario decoupling. Initially, the framework employed ridge regression to address multicollinearity and implement stepwise variable selection based on t-tests, F-tests, and R2 criteria. Subsequently, parameter calibration was refined using weighted least squares, thereby ensuring enhanced robustness and predictive accuracy. Based on the modified STIRPAT model, nine scenario pathways for Yunnan’s future carbon emissions were constructed through multidimensional parameter optimization. Multi-pathway analysis revealed distinct carbon peaking timelines (2025/2027/2030) under varying strategic decarbonization levers, with urbanization rate and energy intensity emerging as the most influential drivers through standardized sensitivity analysis. The optimized STIRPAT model developed in this study provides methodological insights for designing regional carbon-emission forecasting frameworks.

Keywords
Carbon peaking
Carbon emission
Modified STIRPAT model
Scenario analysis
Sensitivity analysis
Funding
This study is supported by the National Natural Science Foundation of China (grant number 22308304), and also by the “Yunnan Revitalization Talent Support Program”. Additional financial support is obtained from the Natural Science Foundation of Yunnan Province (grant number 202401AT070431).
Conflict of interest
The authors declare that they have no competing interests.
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