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Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model

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  • Congguang Xu

    (Science Island Branch, University of Science and Technology of China, Hefei 230026, China
    Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Anhui Provincial Key Laboratory of Building Earthquake Disaster Mitigation and Green Operations, Anhui Institute of Building Research & Design, Hefei 230088, China)

  • Wei Xiong

    (Science Island Branch, University of Science and Technology of China, Hefei 230026, China
    Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Simin Zhang

    (Anhui Provincial Key Laboratory of Building Earthquake Disaster Mitigation and Green Operations, Anhui Institute of Building Research & Design, Hefei 230088, China)

  • Hailiang Shi

    (Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Shichao Wu

    (Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Shanju Bao

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241000, China)

  • Tieqiao Xiao

    (School of Architecture and Planning, Anhui Jianzhu University, Hefei 230009, China)

Abstract

Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonlinear relationship between the urban built environment and CEs from residential land. By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. The research results show that, first, the goodness of fit of the XGBoost model reached 91.9%, and its prediction accuracy was better than that of gradient boosting decision tree (GBDT), random forest (RF), the Adaboost model, and the traditional logistic model. Second, compared with other ML models, the XGBoost-SHAP model explained the influencing factors of CEs from residential land more clearly. The SHAP attribution analysis results indicate that BD, FAR, and Phh were the most important factors affecting CEs. Third, there was a significant nonlinear relationship and threshold effect between built environment characteristic variables and CEs from residential land. Fourth, there was an interaction between different dimensions of environmental factors, and BD, FAR, and Phh played a dominant role in the interaction. Reducing FAR is considered to be an effective CE reduction strategy. This research provides practical suggestions for urban planners on reducing CEs from residential land, which has important policy implications and practical significance.

Suggested Citation

  • Congguang Xu & Wei Xiong & Simin Zhang & Hailiang Shi & Shichao Wu & Shanju Bao & Tieqiao Xiao, 2025. "Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model," Land, MDPI, vol. 14(3), pages 1-21, February.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:440-:d:1595378
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    References listed on IDEAS

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    1. Reza Piraei & Seied Hosein Afzali & Majid Niazkar, 2023. "Assessment of XGBoost to Estimate Total Sediment Loads in Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5289-5306, October.
    2. Dingjun Chang & Shuling Tang, 2024. "Research on Low-Carbon Building Development and Carbon Emission Control Based on Mathematical Models: A Case Study of Jiangsu Province," Energies, MDPI, vol. 17(18), pages 1-22, September.
    3. Reid Ewing & Guang Tian & JP Goates & Ming Zhang & Michael J Greenwald & Alex Joyce & John Kircher & William Greene, 2015. "Varying influences of the built environment on household travel in 15 diverse regions of the United States," Urban Studies, Urban Studies Journal Limited, vol. 52(13), pages 2330-2348, October.
    4. Haibing Wang & Bowen Li & Muhammad Qasim Khan, 2022. "Prediction of Shanghai Electric Power Carbon Emissions Based on Improved STIRPAT Model," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    5. Yixiong Feng & Zhaoxi Hong & Jin Cheng & Likai Jia & Jianrong Tan, 2017. "Low Carbon-Oriented Optimal Reliability Design with Interval Product Failure Analysis and Grey Correlation Analysis," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
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