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<p>The rapid urbanization and economic growth have significantly increased urban carbon emissions. Analyzing how urban form and socioeconomic factors are influencing per capita carbon emission (PCCE) is essential for developing effective sustainable development strategies. This study focuses on the Yangtze River Economic Belt as the research area, employing Multiscale Geographically Weighted Regression (MGWR) to analyze spatial heterogeneity, and then uses machine learning to identify nonlinear relationships, thereby explaining and validating the MGWR results. The results indicate that, regarding socio-economic factors, higher per capita GDP leads to a rise in PCCE, with this effect becoming more pronounced after 2010. Before 2010, the proportion of the secondary industry was the main factor increasing PCCE, but its influence gradually weakened over time. The rising number of permanent residents has a long-term suppressing effect on PCCE. The impact of fixed asset investment on increasing PCCE only became evident after 2010. Regarding urban form factors, the COHESION generally reduces PCCE but transiently increases it during periods of rapid urban expansion. The largest patch index consistently raises PCCE. Both the mean shape index and city area minimally affected PCCE before 2010 but significantly increased PCCE during the urban consolidation phase after 2010. Urban planning strategies should be adjusted based on citys’ development stages. Smaller, less developed cities with low density need control of urban sprawl, while larger, more developed cities should adopt polycentric development models to optimize spatial efficiency. These findings help guide low-carbon urban planning in China and other rapidly urbanizing countries.</p>

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Exploring the spatial heterogeneity and nonlinearity of socioeconomic and urban form factors on carbon emissions

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Yibo Yan,
  • Chunmei Mao,
  • Qing Yao,
  • Qi Chen,
  • Xin Zhang

摘要

The rapid urbanization and economic growth have significantly increased urban carbon emissions. Analyzing how urban form and socioeconomic factors are influencing per capita carbon emission (PCCE) is essential for developing effective sustainable development strategies. This study focuses on the Yangtze River Economic Belt as the research area, employing Multiscale Geographically Weighted Regression (MGWR) to analyze spatial heterogeneity, and then uses machine learning to identify nonlinear relationships, thereby explaining and validating the MGWR results. The results indicate that, regarding socio-economic factors, higher per capita GDP leads to a rise in PCCE, with this effect becoming more pronounced after 2010. Before 2010, the proportion of the secondary industry was the main factor increasing PCCE, but its influence gradually weakened over time. The rising number of permanent residents has a long-term suppressing effect on PCCE. The impact of fixed asset investment on increasing PCCE only became evident after 2010. Regarding urban form factors, the COHESION generally reduces PCCE but transiently increases it during periods of rapid urban expansion. The largest patch index consistently raises PCCE. Both the mean shape index and city area minimally affected PCCE before 2010 but significantly increased PCCE during the urban consolidation phase after 2010. Urban planning strategies should be adjusted based on citys’ development stages. Smaller, less developed cities with low density need control of urban sprawl, while larger, more developed cities should adopt polycentric development models to optimize spatial efficiency. These findings help guide low-carbon urban planning in China and other rapidly urbanizing countries.