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Spatial Association Network of Land-Use Carbon Emissions in Hubei Province: Network Characteristics, Carbon Balance Zoning, and Influencing Factors

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  • Yong Huang

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Zhong Wang

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Heng Zhao

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Di You

    (School of Business Administration and Tourism Management, Yunnan University, Kunming 650500, China)

  • Wei Wang

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Yanran Peng

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

Abstract

Understanding the spatial association network structure and carbon balance zoning of land-use carbon emissions (LUCEs) is essential for guiding regional environmental management. This study constructs a LUCE spatial association network for Hubei Province using a modified gravity model to uncover the spatial linkages in carbon emissions. Carbon balance zones are delineated by integrating LUCE network characteristics with economic and ecological indicators. To further examine the network dynamics, link prediction algorithms are employed to anticipate potential emission connections, while quadratic assignment procedure (QAP) regression analyzes how intercity differences in socioeconomic, ecological, and land-use attributes influence LUCE connectivity. The results reveal a pronounced core–periphery structure, with potential carbon spillover pathways extending toward both eastern and western cities. Based on the carbon balance analysis, six functional zones are identified, each aligned with targeted collaborative mitigation strategies. The QAP results indicate that intercity differences in innovation capacity, industrial structure, and economic development are positively associated with the formation of LUCE spatial networks, whereas disparities in urbanization level, government expenditure, and construction land use are negatively associated with LUCE connectivity. This study provides a differentiated governance framework to address the dual challenges of carbon emissions and land-use transformation in agro-urban regions.

Suggested Citation

  • Yong Huang & Zhong Wang & Heng Zhao & Di You & Wei Wang & Yanran Peng, 2025. "Spatial Association Network of Land-Use Carbon Emissions in Hubei Province: Network Characteristics, Carbon Balance Zoning, and Influencing Factors," Land, MDPI, vol. 14(7), pages 1-31, June.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:7:p:1329-:d:1684966
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    2. Johannes Glückler & Patrick Doreian, 2016. "Editorial: social network analysis and economic geography—positional, evolutionary and multi-level approaches," Journal of Economic Geography, Oxford University Press, vol. 16(6), pages 1123-1134.
    3. Han, Wenjing & Zhang, Xiaoling & Zheng, Xian, 2020. "Land use regulation and urban land value: Evidence from China," Land Use Policy, Elsevier, vol. 92(C).
    4. Juhyun Oh, 2023. "The Effects of Local Government Expenditures on Carbon Dioxide Emissions: Evidence from Republic of Korea," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    5. Gao, Yu & Yang, Zhuoer & Huang, Kuo-Feng & Gao, Shanxing & Yang, Wei, 2018. "Addressing the cross-boundary missing link between corporate political activities and firm competencies: The mediating role of institutional capital," International Business Review, Elsevier, vol. 27(1), pages 259-268.
    6. Xia, Chuyu & Chen, Bin, 2020. "Urban land-carbon nexus based on ecological network analysis," Applied Energy, Elsevier, vol. 276(C).
    7. Li, Wenjing & Bai, Min & Wang, Jing, 2024. "Ecological burden shifting associated with land transfer embodied in global trade: An ecological network analysis," Land Use Policy, Elsevier, vol. 139(C).
    Full references (including those not matched with items on IDEAS)

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