Examining the Spatial Mode, Supply–Demand Relationship, and Driving Mechanism of Urban Park Green Space: A Case Study from China

• Published in: Forests Vol. 15; no. 1; p. 131
• Main Authors: Zhao, Kaixu, Chen, Chao, Wang, Jianming, Liu, Kaixi, Wu, Fengqi, Cao, Xiaoteng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Case studies; China; Cities; City planning; driving mechanism; Equality; Equity; Evolution; Green infrastructure; Infrastructure; International organizations; Matching; Parks & recreation areas; Per capita; Population; Public services; spatial mismatch model; spatio-temporal characteristics; Supply & demand; supply–demand relationship; Urban areas; urban park green space; Urbanization; Zoning; China; Mexico; Germany
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15010131

Park green space is a big part of public infrastructure in cities, and how to evaluate and optimize the mismatch of urban park green space (UPGS) has become the focus of current research in academia and industry. Taking China’s 286 cities as an example, this paper used the spatial cluster and Boston Consulting Group Matrix to analyze the aggregation laws and changing modes of UPGS from 2010 to 2020, introduced the spatial mismatch model to analyze the matching of its supply and demand with GDP and population, and adopted the Geodetector to analyze the influencing factors. The findings: (1) The evolution of UPGS in China had long been characterized by a “pyramidal” pattern, i.e., limited green cities > developing green cities > steady green cities > booming green cities, exhibiting the spatial characteristics of gradient differences between the coasts and inland areas, and the aggregation of blocks in some areas. (2) The supply and demand mismatches of the UPGS were relatively stable, with negative matching being the main supply mismatch type, and positive matching being the main demand mismatch type. The contribution of supply and demand mismatches similarly showed a spatial pattern of a gradual decrease from the coast to inland and the aggregation of blocks in some areas. (3) Five types of factors played different driving roles on UPGS, with social development remaining a weak factor, and the strong factor switching from urban infrastructure to construction land scale. The interaction detection was dominated by a bilinear enhancement, with super-interaction factors changing from an output value of the tertiary industry and population urbanization rate to education expenditure in local general public budgets. (4) Based on the mismatch between the supply and demand for UPGS in China, 286 cities were classified into four types, namely a smart shrinking zone, smart growing zone, status quo zone, and overlay policy zone, and differentiated development proposals for the corresponding zoning were put forward. This paper constructed an application framework of “evolution pattern + supply demand match + driving factors + policy zoning” for UPGS at a large scale, which will effectively enhance the effective allocation of its resources across the country.