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

Maowen Sun, Boyi Liang (), Xuebin Meng, Yunfei Zhang, Zong Wang and Jia Wang
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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

Land, 2024, vol. 13, issue 6, 1-24

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.

Keywords: carbon neutrality; carbon emissions; spatiotemporal pattern; attribution analysis; urbanization (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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