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Analyzing Integrated Carbon Emissions from Regional Transport and Land Use in the Context of National Spatial Planning

Author

Listed:
  • Weiwei Liu

    (Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China)

  • Xiuhong Zhang

    (Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China)

  • Yangyang Zhu

    (Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China)

  • Xiaomei Li

    (Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China)

  • Liang Jin

    (Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China)

  • Sijie Hu

    (Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China)

Abstract

Against the backdrop of intensified governance of territorial spatial planning, investigating carbon emissions from the perspective of territorial spatial planning for transport-land use integration holds significant academic and practical value. Taking Cangnan County as the case study, this research first dissects the reciprocal feedback mechanism between regional transport and land use at the territorial spatial planning level, while exploring transport-influencing factors. Subsequently, it constructs an integrated reciprocal feedback system for regional transport and land use by integrating accessibility drivers, cost matrices, and neighborhood weights through land use simulation–prediction models and the four-stage transport model. Finally, based on critical land use factors, diverse development scenarios under this integrated system are formulated; carbon emissions from transport and land use under each scenario are quantified; and their interrelationships are analyzed across multiple dimensions to explore the nexus of carbon emissions in transport–land use integration. Results indicate the following: (1) Integrated feedback enhances model accuracy (Kappa: 0.795→0.893; overall accuracy: 0.893→0.915), facilitating more precise land use simulation. (2) The county’s core construction area demonstrates the highest carbon emissions across all scenarios, meriting prioritized attention. (3) As deduced from the analysis of territorial spatial land use patterns, the significantly higher transport carbon emissions under the ecological protection priority scenario, compared to other scenarios, originate from over-concentrated construction land and imbalanced planning of carbon source land. These findings offer insights for regional planning; policy recommendations for Cangnan County include expanding carbon sink land, scientifically planning carbon source land, optimizing transport structures, and promoting new energy vehicles to advance carbon emission reduction and sustainable development.

Suggested Citation

  • Weiwei Liu & Xiuhong Zhang & Yangyang Zhu & Xiaomei Li & Liang Jin & Sijie Hu, 2025. "Analyzing Integrated Carbon Emissions from Regional Transport and Land Use in the Context of National Spatial Planning," Sustainability, MDPI, vol. 17(17), pages 1-21, September.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:17:p:7873-:d:1739684
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    References listed on IDEAS

    as
    1. Su, Hailong & Wu, Jia Hao & Tan, Yinghui & Bao, Yuanqiu & Song, Bing & He, Xinghua, 2014. "A land use and transportation integration method for land use allocation and transportation strategies in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 329-353.
    2. Qiu, Ai-Ya & Yue, Heng & You, Ze & An, Hao, 2025. "Forecasting of factors influencing carbon emission from land-use in Liaoning Province, China, under the “double carbon” target," Ecological Modelling, Elsevier, vol. 509(C).
    3. Sarri, Paraskevi & Kaparias, Ioannis & Preston, John & Simmonds, David, 2023. "Using Land Use and Transportation Interaction (LUTI) models to determine land use effects from new vehicle transportation technologies; a regional scale of analysis," Transport Policy, Elsevier, vol. 135(C), pages 91-111.
    4. Zhe Gao & Jianming Ye & Xianwei Zhu & Miaomiao Li & Haijiang Wang & Mengmeng Zhu, 2024. "Characteristics of Spatial Correlation Network Structure and Carbon Balance Zoning of Land Use Carbon Emission in the Tarim River Basin," Land, MDPI, vol. 13(11), pages 1-19, November.
    5. Lu Han & Yanbo Qu & Shufeng Liang & Luyan Shi & Min Zhang & Haiyan Jia, 2024. "Spatiotemporal Differentiation of Land Ecological Security and Optimization Based on GeoSOS-FLUS Model: A Case Study of the Yellow River Delta in China Toward Sustainability," Land, MDPI, vol. 13(11), pages 1-21, November.
    6. Ivan Muñiz & Andrés Dominguez, 2020. "The Impact of Urban Form and Spatial Structure on per Capita Carbon Footprint in U.S. Larger Metropolitan Areas," Sustainability, MDPI, vol. 12(1), pages 1-19, January.
    7. Feng, Kuishuang & Hubacek, Klaus & Guan, Dabo, 2009. "Lifestyles, technology and CO2 emissions in China: A regional comparative analysis," Ecological Economics, Elsevier, vol. 69(1), pages 145-154, November.
    8. Ying, Jiang Qian, 2024. "Optimization of regulation and fiscal policies for urban residential land use and traffic network management," Regional Science and Urban Economics, Elsevier, vol. 105(C).
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