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<p>This study develops an equitable carbon intensity allocation scheme to support China’s “14th Five-Year Plan” target of an 18% reduction in carbon intensity. Based on carbon intensity data from the China City CO<sub>2</sub> Emissions Dataset (2020) and relevant socioeconomic data, we construct an evaluation framework with eight indicators across five dimensions: economy, population, energy, technology innovation, and policy. The entropy method is employed to determine indicator weights, and an Improved Equal-Proportion Distribution (IEPD) method is applied to decompose the national target across 332 prefecture-level cities. K-means clustering and spatial autocorrelation analysis are employed to examine regional disparities. Key findings include: (1) The average Carbon Intensity Target Reduction Rates (CITRR) across the 332 cities is 13.89%, with Shenzhen having the highest CITRR (76.80%) and the Daxing’anling region the lowest (4.00%)); (2) CITRR shows a stair-step decline from east to west, with significant positive spatial autocorrelation (Global Moran’s I = 0.26, <i>p</i> = 0); (3) K-means clustering categorized cities into four groups with CITRR ranges of 11.24%-72.96%, 4.00%-18.10%, 7.90%-19.64%, and 12.29%-76.80%, respectively. The Kruskal-Wallis test confirmed statistically significant inter-cluster differences, χ² = 146.41, <i>p</i> &lt; 0.001. Based on the results, we propose targeted policies: high-governance cities should promote green industrial upgrades, balanced cities should build specialized industrial chains, manufacturing-intensive cities should advance industry-urban integration, and technology-leading cities should explore innovation-carbon credit synergy mechanisms.</p>

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Differentiated allocation of carbon intensity target reduction rates across 332 Chinese cities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月14日

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  • Fengmei Yang,
  • Yin Ren,
  • Shudi Zuo,
  • Jiaheng Ju,
  • Meng Yang

摘要

This study develops an equitable carbon intensity allocation scheme to support China’s “14th Five-Year Plan” target of an 18% reduction in carbon intensity. Based on carbon intensity data from the China City CO2 Emissions Dataset (2020) and relevant socioeconomic data, we construct an evaluation framework with eight indicators across five dimensions: economy, population, energy, technology innovation, and policy. The entropy method is employed to determine indicator weights, and an Improved Equal-Proportion Distribution (IEPD) method is applied to decompose the national target across 332 prefecture-level cities. K-means clustering and spatial autocorrelation analysis are employed to examine regional disparities. Key findings include: (1) The average Carbon Intensity Target Reduction Rates (CITRR) across the 332 cities is 13.89%, with Shenzhen having the highest CITRR (76.80%) and the Daxing’anling region the lowest (4.00%)); (2) CITRR shows a stair-step decline from east to west, with significant positive spatial autocorrelation (Global Moran’s I = 0.26, p = 0); (3) K-means clustering categorized cities into four groups with CITRR ranges of 11.24%-72.96%, 4.00%-18.10%, 7.90%-19.64%, and 12.29%-76.80%, respectively. The Kruskal-Wallis test confirmed statistically significant inter-cluster differences, χ² = 146.41, p < 0.001. Based on the results, we propose targeted policies: high-governance cities should promote green industrial upgrades, balanced cities should build specialized industrial chains, manufacturing-intensive cities should advance industry-urban integration, and technology-leading cities should explore innovation-carbon credit synergy mechanisms.