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Differentiated allocation of carbon intensity target reduction rates across 332 Chinese cities
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  • Published: 14 January 2026

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

  • Fengmei Yang1,
  • Yin Ren2,
  • Shudi Zuo2,
  • Jiaheng Ju2,3 &
  • …
  • Meng Yang4 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Climate-change impacts
  • Climate-change policy
  • Environmental social sciences

Abstract

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.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Contact email: jhju@iue.ac.cn.

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Funding

This work was supported by the National Natural Science Foundation of China 42001210.

Author information

Authors and Affiliations

  1. Academy of Art Design, Fujian Business University, Fuzhou, 350000, China

    Fengmei Yang

  2. State Key Laboratory for Ecological Security of Regions and Cities, State Key Laboratory of Advanced Environmental Technology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China

    Yin Ren, Shudi Zuo & Jiaheng Ju

  3. Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, 315100, China

    Jiaheng Ju

  4. Urban Planning and Development Institute, Yangzhou University, Yangzhou, 225127, China

    Meng Yang

Authors
  1. Fengmei Yang
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  2. Yin Ren
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Contributions

Fengmei Yang: Writing – original draft, Resources, Methodology, Investigation.Yin Ren: Project administration, Conceptualization. Shudi Zuo: Funding acquisition. Jiaheng Ju: Formal analysis, Software, Data curation. Meng Yang: Writing – review & editing.

Corresponding author

Correspondence to Jiaheng Ju.

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The authors declare no competing interests.

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Cite this article

Yang, F., Ren, Y., Zuo, S. et al. Differentiated allocation of carbon intensity target reduction rates across 332 Chinese cities. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35781-2

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  • Received: 06 March 2025

  • Accepted: 08 January 2026

  • Published: 14 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-35781-2

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Keywords

  • Carbon intensity reduction target allocation
  • Fairness
  • Efficiency
  • Entropy method
  • The improved equal-proportion distribution method
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