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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 1-24, 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. Wen Wang & Xin Wang & Li Wang & Zhihua Zhang & Daren Lyu, 2024. "Spatio-Temporal Variation and Drivers of Land-Use Net Carbon Emissions in Chengyu Urban Agglomeration, China," Land, MDPI, vol. 13(12), pages 1-18, December.
    2. Yi-Xin Zhang & Yi-Shan Zhang, 2025. "Heterogeneous and Interactive Effects of Multi-Governmental Green Investment on Carbon Emission Reduction: Application of Hierarchical Linear Modeling," Sustainability, MDPI, vol. 17(3), pages 1-23, January.

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