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<p>The proportion of renewable energy has increased in the context of zero-carbon targets, highlighting the need to explore its role in carbon emission reduction. This study first calculated Moran's I to assess the existence of spatial autocorrelation in carbon emissions. Next, the geographical detector method was employed to evaluate the contributions of six factors to the temporal-spatial dynamics of carbon emissions. Finally, the role of these factors in driving carbon emissions was assessed using the Spatial Durbin Model (SDM). The results indicate that carbon emissions exhibit significant spatial autocorrelation characteristics. The analysis revealed that private car ownership (q = 0.2993) emerged as the dominant driving force influencing the evolution of carbon emission patterns. Additionally, the interaction detector identified interaction links between pairs of factors as either enhanced and bivariate (EB) or enhanced and nonlinear (EN). The findings from the Spatial Durbin Model revealed an inverse U-shaped relationship between the expansion of renewable energy and carbon emission outcomes.</p>

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How Renewable Energy Expansion Affects Carbon Emissions from the Perspective of Spatial Correlation—Evidence in China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月22日

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  • Wenqi Wu,
  • Ming Li,
  • Yujia Wang,
  • Han Huang,
  • George Q. Huang

摘要

The proportion of renewable energy has increased in the context of zero-carbon targets, highlighting the need to explore its role in carbon emission reduction. This study first calculated Moran's I to assess the existence of spatial autocorrelation in carbon emissions. Next, the geographical detector method was employed to evaluate the contributions of six factors to the temporal-spatial dynamics of carbon emissions. Finally, the role of these factors in driving carbon emissions was assessed using the Spatial Durbin Model (SDM). The results indicate that carbon emissions exhibit significant spatial autocorrelation characteristics. The analysis revealed that private car ownership (q = 0.2993) emerged as the dominant driving force influencing the evolution of carbon emission patterns. Additionally, the interaction detector identified interaction links between pairs of factors as either enhanced and bivariate (EB) or enhanced and nonlinear (EN). The findings from the Spatial Durbin Model revealed an inverse U-shaped relationship between the expansion of renewable energy and carbon emission outcomes.