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Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China

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  • Maowen Sun

    (Precision Forestry Key Laboratory of Beijing, College of Forestry, Beijing Forestry University, Beijing 100083, China)

  • Boyi Liang

    (Precision Forestry Key Laboratory of Beijing, College of Forestry, Beijing Forestry University, Beijing 100083, China)

  • Xuebin Meng

    (Precision Forestry Key Laboratory of Beijing, College of Forestry, Beijing Forestry University, Beijing 100083, China)

  • Yunfei Zhang

    (Precision Forestry Key Laboratory of Beijing, College of Forestry, Beijing Forestry University, Beijing 100083, China)

  • Zong Wang

    (Precision Forestry Key Laboratory of Beijing, College of Forestry, Beijing Forestry University, Beijing 100083, China)

  • Jia Wang

    (Precision Forestry Key Laboratory of Beijing, College of Forestry, Beijing Forestry University, Beijing 100083, China)

Abstract

Industrialization has increased global carbon emissions, necessitating effective climate change mitigation measures. China, the most populous developing nation, faces the challenge of strategizing emissions to meet national carbon neutrality objectives. However, research on specific regions’ carbon emissions drivers and causal factors is limited, particularly across prefectural-level cities. This study estimates the spatial and temporal patterns of carbon emissions across China’s prefectural cities and utilizes both OLS regression and stepwise regression models to analyze the impact of various factors influencing carbon emissions in these cities. Results reveal the following: (1) The country’s overall 20-year carbon emissions continue to grow from 3020.29 Mt in 2001 to 9169.74 Mt in 2020, with an average annual growth rate of 5.71%; the eastern region has seen a gradual deceleration in emissions, whereas the western region continues to experience an increase. Carbon emissions in cities within each subregion consistently rise. (2) Carbon emissions in Chinese prefectural-level cities exhibit strong spatial autocorrelation and clustering (Z > 1.96, p < 0.05), with hot spots primarily in the eastern coastal areas and cold spots in the northwest to southwest regions. (3) Economic and demographic factors significantly increase carbon emissions, while climate and urbanization effects are more complex and variable. Economic growth and population increase are the most significant influencing factors, but regional variances exist in carbon emissions determinants in subregional prefectural cities. These insights provide valuable insights into national emission dynamics at the prefectural level, providing a theoretical basis for enhancing carbon emission strategies across various jurisdictions.

Suggested Citation

  • Maowen Sun & Boyi Liang & Xuebin Meng & Yunfei Zhang & Zong Wang & Jia Wang, 2024. "Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China," Land, MDPI, vol. 13(6), pages 1, June.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:828-:d:1411598
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    References listed on IDEAS

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    1. Lee, Chien-Chiang & Zhao, Ya-Nan, 2023. "Heterogeneity analysis of factors influencing CO2 emissions: The role of human capital, urbanization, and FDI," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    2. Zhang, Xing-Ping & Cheng, Xiao-Mei, 2009. "Energy consumption, carbon emissions, and economic growth in China," Ecological Economics, Elsevier, vol. 68(10), pages 2706-2712, August.
    3. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    4. Ma, Jun & Cheng, Jack C.P. & Jiang, Feifeng & Chen, Weiwei & Zhang, Jingcheng, 2020. "Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques," Land Use Policy, Elsevier, vol. 94(C).
    5. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    6. Wang, Yanan & Yin, Shiwen & Fang, Xiaoli & Chen, Wei, 2022. "Interaction of economic agglomeration, energy conservation and emission reduction: Evidence from three major urban agglomerations in China," Energy, Elsevier, vol. 241(C).
    7. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
    8. Moutinho, Victor & Moreira, António Carrizo & Silva, Pedro Miguel, 2015. "The driving forces of change in energy-related CO2 emissions in Eastern, Western, Northern and Southern Europe: The LMDI approach to decomposition analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1485-1499.
    9. Luo, Haizhi & Li, Yingyue & Gao, Xinyu & Meng, Xiangzhao & Yang, Xiaohu & Yan, Jinyue, 2023. "Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China," Applied Energy, Elsevier, vol. 348(C).
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