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

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.