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Collaborative Optimal Allocation of Urban Land Guide by Land Ecological Suitability: A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area

Author

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  • Tingting Pan

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yu Zhang

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Fengqin Yan

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Fenzhen Su

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Urban land optimization in urban agglomerations plays an important role in promoting territorial spatial planning to achieve high-quality development, land ecological suitability (LES) is one of the important variables influencing its urbanization and needs to be considered in urban growth simulation and modeling. This research proposed a multi-objective urban land optimization (MULO) model based on the non-dominated sorting genetic algorithm II (NSGA-II) which integrates the LES assessment. MULO starts with LES analysis based on a fuzzy analytical hierarchy process (AHP) and a minimum cumulative resistance (MCR) model. Then, two-step linear regression is used to optimize the quantity structure of built-up land. Finally, suitability and compactness are assigned to NSGA-II as objectives to obtain optimal spatial patterns. Taking the example of the Guangdong–Hong Kong–Macao Greater Bay Area, we found that all the newly added built-up land in 2030 is distributed in peri-urban areas around the original settlements, with approximate clustering in the northern part of Guangzhou and the southern part of Foshan under a balanced development scenario. This study highlights the importance of LES in urban growth modeling, and MULO can provide effective support for the spatial planning of urban agglomerations.

Suggested Citation

  • Tingting Pan & Yu Zhang & Fengqin Yan & Fenzhen Su, 2023. "Collaborative Optimal Allocation of Urban Land Guide by Land Ecological Suitability: A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area," Land, MDPI, vol. 12(4), pages 1-17, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:754-:d:1108630
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    References listed on IDEAS

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    Cited by:

    1. Yulin Liu & Yi Lu & Dawei Xu & Herui Zhou & Shengnan Zhang, 2024. "Enhancing the MSPA Method to Incorporate Ecological Sensitivity: Construction of Ecological Security Patterns in Harbin City," Sustainability, MDPI, vol. 16(7), pages 1-23, March.
    2. Tingting Pan & Fengqin Yan & Fenzhen Su & Liang Xu, 2024. "The Assessment of Land Suitability for Urban Expansion and Renewal for Coastal Urban Agglomerations: A Pilot Study of the Guangdong-Hong Kong-Macao Greater Bay Area," Land, MDPI, vol. 13(11), pages 1-17, October.

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