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

Yanru Zhou, Zhe Feng (), Kaiji Xu, Kening Wu, Hong Gao and Peijia Liu
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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
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
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

Land, 2023, vol. 12, issue 8, 1-16

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.

Keywords: urban cooling service; ecosystem service flow; green landscape management; GeoDetector (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:8:p:1527-:d:1208407

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