Abstract

Systematically assessing the supply–demand disparities of urban–rural ecosystem services (ES) is a key pathway to optimizing resource allocation, promoting urban–rural integration and advancing regional sustainable development. Taking Zhengzhou City as a case study, this research evaluates and compares urban–rural differences across four dimensions: potential supply, actual supply, real human needs (RHN), and effective supply. Furthermore, focusing on actual supply, the study integrates a geographical detector and Bayesian belief network to identify key driving factors, delineate optimal optimization zones, and propose differentiated management strategies. The results show that: (1) Urban RHN accounts for 69.70% of the total in Zhengzhou, with a spatial pattern of “higher in the east and core, lower in the west and periphery”, and the internal heterogeneity is significantly greater than that of rural areas. (2) Potential supply is “higher in rural areas and in the west”, whereas actual supply is concentrated in central urban districts, reflecting a net service flow from rural to urban areas. (3) High-level effective supply areas cover 37.28% of urban regions, about 18 percentage points higher than rural regions. Rural deficits are primarily caused by low conversion efficiency of supply rather than insufficient potential. (4) Optimal urban optimization zones are mainly distributed in peripheral urban streets, while rural zones are concentrated in eastern townships. Through multidimensional supply–demand comparison and spatial optimization, this study provides a scientific basis for the coordinated enhancement of urban–rural ES, differentiated governance and regional sustainable development.

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Publication Info

Year
2025
Type
article
Volume
17
Issue
24
Pages
11049-11049
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Cite This

Yuxia Zhang, Qindong Fan, Baoguo Liu et al. (2025). Multi-Dimensional Comparison and Sustainable Spatial Optimization of Ecosystem Services Supply–Demand Matching Between Urban and Rural Areas: A Case Study of Zhengzhou City. Sustainability , 17 (24) , 11049-11049. https://doi.org/10.3390/su172411049

Identifiers

DOI
10.3390/su172411049

Data Quality

Data completeness: 77%