Article http://dx.doi.org/10.26855/oajrces.2025.06.003

Analysis of Factors Influencing Carbon Emissions in Chinese Cities Based on LMDI and Threshold STIRPAT Model

TOTAL VIEWS: 1183

Qingjuan Xu, Kehong Li*, Chunran Jiang, Ting Su

School of Mathematics and Statistics, Nanning Normal University, Nanning 531000, Guangxi, China.

*Corresponding author:Kehong Li

Published: July 25,2025

Abstract

Rapid development of the global economy has led to increasingly prominent environmental issues, particularly carbon emissions. This study analyzed panel data related to carbon emissions and their influencing factors from 284 prefecture-level cities in China from 2004 to 2020. Utilizing the improved Kaya identity and the LMDI model, this study decomposes and identifies the main factors influencing CO2 emissions: population size, per capita affluence, industrial structure, energy consumption intensity, and energy structure. Subsequently, a threshold-STIRPAT model was established, employing population size and energy consumption intensity as threshold variables to analyze the heterogeneity of population size and the three major economic regions. The findings indicate that from 2004 to 2020, population size, per capita affluence, and energy consumption intensity exhibited a positive driving effect on carbon emissions, whereas industrial structure and energy structure demonstrated a negative inhibiting effect. The threshold-STIRPAT model results reveal that industrial structure has the most significant impact on carbon emissions, whereas population size has the least impact. Furthermore, the impact of population size and energy consumption intensity on carbon emissions is stage-specific. The heterogeneity analysis results demonstrated that the same factor does not uniformly affect carbon emissions under different conditions.

Keywords

Carbon emission; Kaya identity; LMDI model; Threshold-STIRPAT model; Heterogeneity analysis

References

[1] IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC; 2014.

[2] Yuan J, Xu Y, Hu Z, Zhao C, Xiong M, Guo J. Peak energy consumption and CO2 emissions in China. Energy Policy. 2014;68:508-23.

[3] Huiling G, Bowen S, Guangming R. Regional differences, dynamic distribution and evolution trends of carbon emission intensity in urban agglomerations in China. China Price J. 2024;(1):113-8.

[4] Chen J, Weng S, Tao W, Song M, Zhang L. Measuring carbon neutrality and exploring the threshold effects of its driving factors: Evidence from China. Appl Energy. 2024;373:123824.

[5] Sufeng W, Jiantao H, Huafu L. Spatial and temporal heterogeneity of factors influencing carbon emissions from energy consumption in Chinese cities. World Reg Stud. 2024;33(8):102-16.

[6] Kaya Y. Impact of carbon dioxide emission control on GNP growth: interpretation of proposed scenarios. Geneva: IPCC Response Strategies Working Group; 1989.

[7] Yang J, Cai W, Ma M, Li L, Liu C, Ma X, et al. Driving forces of China's CO2 emissions from energy consumption based on Kaya-LMDI methods. Sci Total Environ. 2020;711:133-8.

[8] Junhua C, Qiaochu L. Research on the influencing factors of energy consumption carbon emission in Sichuan Province under the background of the construction of Chengdu-Chongqing Double City Economic Circle: from the perspective of LMDI method. Ecol Econ. 2021;37(12):30-6.

[9] Yu Q, Yan L. Research on influencing factors and development path of carbon emission of China's logistics industry under the carbon peaking and carbon neutrality goals: based on the extended Kaya identity and LMDI model. Times Econ Trade. 2024;21(2):41-5.

[10] Chun-Ran J, Qing-Juan X, Ting S. Influential factors and scenario prediction of carbon emissions in China's coal producing areas: based on LMDI model and threshold-STIRPAT model. J Nanning Norm Univ (Nat Sci Ed). 2023;40(1):32-42.

[11] Ning W, Chengyu H, Yang Z, Zhaolin G. Regional carbon emission peaking based on threshold-stirpat extension model: a case study on east China. Environ Eng. 2024;42(5):154-62.

[12] Lin B, Li Z. Is more use of electricity leading to less carbon emission growth? An analysis with a panel threshold model. Energy Policy. 2020;137:111121-8.

[13] Yong W, Ziyi X, Yaxin Z. Influencing factors and combined scenario prediction of carbon emission peaks in megacities in China: based on threshold-STIRPAT model. Acta Sci Circumstantiae. 2019;39(12):4284-92.

[14] Liang Y, Mazlan NS, Mohamed AB, Mhd Bani NYB, Liang B. Regional impact of aging population on economic development in China: evidence from panel threshold regression (PTR). PLoS One. 2023;18(3):e0282913.

[15] Liang Y, Xinping H, Mazlan NS, Liang B, Ting L. Regional impact of aging population on carbon dioxide emissions in China: evidence from panel threshold regression (PTR). PLoS One. 2023;18(9):158-82.

[16] Chen P, Shantong L, Qiang L. Driving forces of Chinese provincial CO2 emissions from the perspective of consumption. Econ Manag. 2022;36(3):58-66.

[17] Lei W, Zhemin L. Analysis on the scale evolution characteristics and driving factors of rural homestead: based on extended Kaya identity and LMDI. Agric Outlook. 2021;17(9):10-6.

[18] Xuankai D. Research on influencing factors of land use carbon emission in Wuhan City: based on extended Kaya equation and LMDI decomposition method. Agric Technol. 2021;41(20):104-9.

[19] Jiangyuan L, Tao D. Driving factors for the growth of China's carbon emissions based on LMDI model. Coal Econ Res. 2020;40(6):47-56.

[20] Yuheng F. Research on influencing factors of carbon emissions in China's coal production areas: based on the improved LMDI model. Coal Econ Res. 2020;40(12):40-5.

[21] YuXin G, Liang X. Calculation and analysis of transportation carbon emission in Yangtze River Delta. Logist Eng Manag. 2022;44(10):85-8.

[22] Yan Z, YuShu B, KangDe W, XiaoHong J. Carbon emission efficiency evaluation and driving factors analysis of logistics enterprises. J Transp Syst Eng Inf Technol. 2023;23(2):11-21.

[23] Ehrlich PR, Holdren JP. Impact of population growth: complacency concerning this component of man's predicament is unjustified and counterproductive. Science. 1971;171(3977):1212-7.

[24] Dietz T, Rosa EA. Rethinking the environmental impacts of population, affluence and technology. Hum Ecol Rev. 1994;1(2):277-300.

[25] Gungyu T. The impact of population aging on green innovation: analysis based on STIRPAT model and threshold effect. Rev Financ Technol Econ. 2023;(1):102-19.

[26] Li J, Chen Y, Li Z, Liu Z. Quantitative analysis of the impact factors of conventional energy carbon emissions in Kazakhstan based on LMDI decomposition and STIRPAT model. J Geogr Sci. 2018;28(7):1001-19.

[27] Xinwei G, Yuan Z. Do research inputs constrain carbon emission from carbon emission factors based on LMDI model and STIRPAT model? Resour Ind. 2020;22(6):37-45.

[28] Qinglong W, Jianming W, Pibin G. Peak regional carbon emissions based on open STIRPAT modeling in an energy-producing region of Shanxi. Resour Sci. 2018;40(5):1051-62.

[29] Shen-Ning Q, Chao-Xian G. Forecast of China's carbon emissions based on STIRPAT model. China Popul Resour Environ. 2010;20(12):10-5.

[30] Hansen BE. Threshold effects in non-dynamic panels: estimation, testing, and inference. J Econom. 1999;93(2):345-68.

[31] Di Z, Fengnian Z, Xueqin W. Impact of low-carbon pilot policy on the performance of urban carbon emissions and its mechanism. Resour Sci. 2019;41(3):546-56.

[32] Feng H, Rui X. Does the agglomeration of producer services reduce carbon emissions? J Quant Tech Econ. 2017;34(3):40-58.


How to cite this paper

Analysis of Factors Influencing Carbon Emissions in Chinese Cities Based on LMDI and Threshold STIRPAT Model

How to cite this paper: Qingjuan Xu, Kehong Li, Chunran Jiang, Ting Su(2025) Analysis of Factors Influencing Carbon Emissions in Chinese Cities Based on LMDI and Threshold STIRPAT ModelOAJRC Environmental Science6(1), 19-33.

DOI: http://dx.doi.org/10.26855/oajrces.2025.06.003