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Article

Transitions of Carbon Dioxide Emissions in China: K-Means Clustering and Discrete Endogenous Markov Chain Approach

Department of Economics, Chung-Ang University, Seoul 06974, Republic of Korea
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Author to whom correspondence should be addressed.
Climate 2025, 13(8), 165; https://doi.org/10.3390/cli13080165 (registering DOI)
Submission received: 20 June 2025 / Revised: 29 July 2025 / Accepted: 1 August 2025 / Published: 3 August 2025

Abstract

This study employs k-means clustering to group 30 Chinese provinces into four CO2 emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach.
Keywords: China’s carbon dioxide emissions; k-means clustering analysis; carbon dioxide emissions patterns; endogenous Markov chain; probability transition matrix China’s carbon dioxide emissions; k-means clustering analysis; carbon dioxide emissions patterns; endogenous Markov chain; probability transition matrix

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MDPI and ACS Style

Chen, S.; Kang, X.; Park, S.Y. Transitions of Carbon Dioxide Emissions in China: K-Means Clustering and Discrete Endogenous Markov Chain Approach. Climate 2025, 13, 165. https://doi.org/10.3390/cli13080165

AMA Style

Chen S, Kang X, Park SY. Transitions of Carbon Dioxide Emissions in China: K-Means Clustering and Discrete Endogenous Markov Chain Approach. Climate. 2025; 13(8):165. https://doi.org/10.3390/cli13080165

Chicago/Turabian Style

Chen, Shangyu, Xiaoyu Kang, and Sung Y. Park. 2025. "Transitions of Carbon Dioxide Emissions in China: K-Means Clustering and Discrete Endogenous Markov Chain Approach" Climate 13, no. 8: 165. https://doi.org/10.3390/cli13080165

APA Style

Chen, S., Kang, X., & Park, S. Y. (2025). Transitions of Carbon Dioxide Emissions in China: K-Means Clustering and Discrete Endogenous Markov Chain Approach. Climate, 13(8), 165. https://doi.org/10.3390/cli13080165

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