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Urban Carbon Metabolism Optimization Based on a Source–Sink–Flow Framework at the Functional Zone Scale

Author

Listed:
  • Cui Wang

    (College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China)

  • Liuchang Xu

    (College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China)

  • Xingyu Xue

    (College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China)

  • Xinyu Zheng

    (College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China)

Abstract

Carbon flow tracking and spatial pattern optimization at the scale of urban functional zones are key scientific challenges in achieving carbon neutrality. However, due to the complexity of carbon metabolism processes within urban functional zones, related studies remain limited. To address these scientific challenges, this study, based on the “source–sink–flow” ecosystem services framework, develops an integrated analytical approach at the scale of urban functional zones. The carbon balance is quantified using the CASA model in combination with multi-source data. A network model is employed to trace carbon flow pathways, identify critical nodes and interruption points, and optimize the urban spatial pattern through a low-carbon land use structure model. The research results indicate that the overall carbon balance in Hangzhou exhibits a spatial pattern of “deficit in the center and surplus in the periphery.” The main urban area shows a significant carbon deficit and relatively poor connectivity in the carbon flow network. Carbon sequestration services primarily flow from peripheral areas (such as Fuyang and Yuhang) with green spaces and agricultural functional zones toward high-emission residential–commercial and commercial–public functional zones in the central area. However, due to the interruption of multiple carbon flow paths, the overall carbon flow transmission capacity is significantly constrained. Through spatial optimization, some carbon deficit nodes were successfully converted into carbon surplus nodes, and disrupted carbon flow edges were repaired, particularly in the main urban area, where 369 carbon flow edges were restored, resulting in a significant improvement in the overall transmission efficiency of the carbon flow network. The carbon flow visualization and spatial optimization methods proposed in this paper provide a new perspective for urban carbon metabolism analysis and offer theoretical support for low-carbon city planning practices.

Suggested Citation

  • Cui Wang & Liuchang Xu & Xingyu Xue & Xinyu Zheng, 2025. "Urban Carbon Metabolism Optimization Based on a Source–Sink–Flow Framework at the Functional Zone Scale," Land, MDPI, vol. 14(8), pages 1-18, August.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:8:p:1600-:d:1718537
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    References listed on IDEAS

    as
    1. Zhang, Yan & Li, Juan & Fath, Brian D. & Zheng, Hongmei & Xia, Linlin, 2015. "Analysis of urban carbon metabolic processes and a description of sectoral characteristics: A case study of Beijing," Ecological Modelling, Elsevier, vol. 316(C), pages 144-154.
    2. Xiaoping Li & Sai Hu & Lifu Jiang & Bing Han & Jie Li & Xuan Wei, 2023. "Spatiotemporal Patterns and the Development Path of Land-Use Carbon Emissions from a Low-Carbon Perspective: A Case Study of Guizhou Province," Land, MDPI, vol. 12(10), pages 1-17, October.
    3. Zhang, Yang & Yan, Da & Hu, Shan & Guo, Siyue, 2019. "Modelling of energy consumption and carbon emission from the building construction sector in China, a process-based LCA approach," Energy Policy, Elsevier, vol. 134(C).
    4. Hong, Jingke & Gu, Jianping & Liang, Xin & Liu, Guiwen & Shen, Geoffrey Qiping & Tang, Miaohan, 2019. "Spatiotemporal investigation of energy network patterns of agglomeration economies in China: Province-level evidence," Energy, Elsevier, vol. 187(C).
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