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“Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China

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  • Quanchuan Fu

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China)

  • Jingyuan Zhu

    (School of Architecture, Tsinghua University, Beijing 100084, China)

  • Xiaodi Zheng

    (School of Architecture, Tsinghua University, Beijing 100084, China
    Key Laboratory of Eco-Planning and Green Building (Tsinghua University), Ministry of Education, Beijing 100084, China)

  • Zhengxiang Li

    (School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China)

  • Maini Chen

    (School of Architecture, Tsinghua University, Beijing 100084, China)

  • Yuyuwei He

    (School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice.

Suggested Citation

  • Quanchuan Fu & Jingyuan Zhu & Xiaodi Zheng & Zhengxiang Li & Maini Chen & Yuyuwei He, 2025. "“Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China," Land, MDPI, vol. 14(6), pages 1-19, June.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:6:p:1213-:d:1672720
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    References listed on IDEAS

    as
    1. Song, Yang & Lyu, Yang & Qian, Sitong & Zhang, Xinjia & Lin, Huiying & Wang, Shijun, 2022. "Identifying urban candidate brownfield sites using multi-source data: The case of Changchun City, China," Land Use Policy, Elsevier, vol. 117(C).
    2. Li, Wenbo & Wang, Dongyan & Li, Hong & Wang, Jianguo & Zhu, Yuanli & Yang, Yuewen, 2019. "Quantifying the spatial arrangement of underutilized land in a rapidly urbanized rust belt city: The case of Changchun City," Land Use Policy, Elsevier, vol. 83(C), pages 113-123.
    3. Kamila Turečková & Jan Nevima & Jaroslav Škrabal & Stanislav Martinát, 2018. "Uncovering Patterns of Location of Brownfields to Facilitate Their Regeneration: Some Remarks from the Czech Republic," Sustainability, MDPI, vol. 10(6), pages 1-14, June.
    4. Holl, Adelheid, 2004. "Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain," Regional Science and Urban Economics, Elsevier, vol. 34(3), pages 341-363, May.
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