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Ecosystem Service Flow Perspective of Urban Green Land: Spatial Simulation and Driving Factors of Cooling Service Flow

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  • Yanru Zhou

    (School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China)

  • Zhe Feng

    (School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
    Technology Innovation Center of Land Engineering, Ministry of Natural Resources (MNR), Beijing 100035, China
    Key Laboratory of Land Consolidation, Ministry of Natural Resources (MNR), Beijing 100035, China)

  • Kaiji Xu

    (School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China)

  • Kening Wu

    (School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
    Technology Innovation Center of Land Engineering, Ministry of Natural Resources (MNR), Beijing 100035, China
    Key Laboratory of Land Consolidation, Ministry of Natural Resources (MNR), Beijing 100035, China)

  • Hong Gao

    (School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China)

  • Peijia Liu

    (School of Politics and Public Administration, Zhengzhou University, Zhengzhou 450001, China
    Henan Academy of Geology, Zhengzhou 450001, China
    Public Administration Research Center, Zhengzhou University, Zhengzhou 450001, China)

Abstract

The linking of ecosystem service flows (ESFs) with urban land management is still in its nascent stage. The spatial process modeling of ESFs plays a crucial role in establishing connections between urban land sustainability and human benefits. However, the spatial processes and driving mechanisms associated with urban cooling services (UCS) remain ambiguous. In this study, we selected the area within the 6th Ring Road of Beijing as the study area, where the population is highly concentrated and the urban greenery is relatively developed. We modeled the spatial processes of cooling service flow (UCSF) in this area and elucidated the contribution of landscape patterns to UCSF. Firstly, the cooling capacity, referred to as UCS, of the urban blue–green landscape, was estimated using the InVEST tool. Subsequently, the UCSF spatial process was simulated by employing a two-dimensional Gaussian function at the pixel level. In order to characterize the landscape features in the study area, eight landscape indices were selected, and Fragstats v4.2 was employed for their calculation. Finally, GeoDetector was utilized to explore the driving mechanisms of landscape patterns on UCSF. The predominant area for both UCS and UCSF lies between the 5th and 6th Ring Road in Beijing, exhibiting a declining trend from the 6th Ring Road toward the city center. The UCSF coverage area, which represents the beneficiary area, accounted for approximately 87.78% of the study area, with the largest increase occurring within the 2nd Ring Road. The Landscape Shape Index demonstrated the strongest individual contribution to UCSF, while its combined bivariate contribution was significant. Geometry exerted a greater influence on UCSF compared to landscape scale and spatial configuration. This study presents novel insights for assessing the omnidirectional flow of ESFs through the modeling of flow functions. The findings of this study can serve as a valuable reference for sustainable urban landscape management and planning.

Suggested Citation

  • Yanru Zhou & Zhe Feng & Kaiji Xu & Kening Wu & Hong Gao & Peijia Liu, 2023. "Ecosystem Service Flow Perspective of Urban Green Land: Spatial Simulation and Driving Factors of Cooling Service Flow," Land, MDPI, vol. 12(8), pages 1-16, August.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:8:p:1527-:d:1208407
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    References listed on IDEAS

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    1. Tianlin Zhai & Yuanbo Ma & Ying Fang & Mingyuan Chang & Longyang Huang & Ziyi Ma & Ling Li & Chenchen Zhao, 2024. "Research on the Optimization of Urban Ecological Infrastructure Based on Ecosystem Service Supply, Demand, and Flow," Land, MDPI, vol. 13(2), pages 1-25, February.

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