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The Spatial Optimization and Evaluation of the Economic, Ecological, and Social Value of Urban Green Space in Shenzhen

Author

Listed:
  • Yuhan Yu

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518034, China
    Shenzhen Urban Planning and Land Resource Research Center, Shenzhen 518034, China)

  • Wenting Zhang

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Peihong Fu

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Wei Huang

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Keke Li

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Kai Cao

    (School of Information Systems, Singapore Management University, Singapore 178902, Singapore)

Abstract

Urban green space (UGS) is important in urban systems, as it benefits economic development, ecological conservation, and living conditions. Many studies have evaluated the economic, ecological, and social value of UGS worldwide, and spatial optimization for UGS has been carried out to maximize its value. However, few studies have simultaneously examined these three values of UGS in one optimization system. To fill this gap, this study evaluated the economic value of UGS in terms of promoting housing prices, its ecological value through the relief of high land surface temperature (LST), and its social value through the provision of recreation spaces for residents within a 255 m distance. Subsequently, these three values were set as objectives in a genetic algorithm (GA)-based multi-objective optimization (MOP) system. Shenzhen was taken as the case study area. The results showed that the influencing distance of UGS in Shenzhen for house prices was 345 m, and the influencing distance of UGS for LST was 135 m. Using MOP, the Pareto solutions for increasing UGS were identified and presented. The results indicate that MOP can simultaneously optimize UGS’s economic, ecological, and social value.

Suggested Citation

  • Yuhan Yu & Wenting Zhang & Peihong Fu & Wei Huang & Keke Li & Kai Cao, 2020. "The Spatial Optimization and Evaluation of the Economic, Ecological, and Social Value of Urban Green Space in Shenzhen," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1844-:d:326723
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    References listed on IDEAS

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    2. Gu Jinjin & Lyu Xiaoqian & Fang Buyun & Hui Qiang & Cao Yuan, 2023. "Study on Planning and Design of Blue-Green-Gray Transformation of Lakeside Cities to Deal with the Complex Urban Waterlogging Caused by Extreme Rainstorm," Land, MDPI, vol. 12(2), pages 1-16, January.
    3. Raziyeh Teimouri & Sadasivam Karuppannan & Alpana Sivam & Ning Gu & Komali Yenneti, 2023. "Exploring International Perspective on Factors Affecting Urban Socio-Ecological Sustainability by Green Space Planning," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
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    6. Kai Cao & Wenwen Li & Richard Church, 2020. "Big data, spatial optimization, and planning," Environment and Planning B, , vol. 47(6), pages 941-947, July.

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