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As peri-urbanisation becomes widespread, quantitative methods for identifying peri-urban areas (PUAs) are increasingly abundant. This study aims to quantitatively compare the differences in the results obtained from multiple PUA identification methods within the same study area and to analyse the spatial patterns of the PUAs. The Threshold Method, Breakpoint Clustering, and Multilayer Perceptron were chosen to compare the identification results of PUAs in Zhengzhou City, China. The results show that the Threshold method identified the most PUAs but with the lowest accuracy. The accuracy of the Breakpoint Clustering was slightly lower than that of the Multilayer Perceptron. Regarding spatial layout, these identification results all show that the PUAs in the northeastern plains of Zhengzhou are more numerous than in the southwestern mountainous regions. Moreover, PUAs can generally be categorised into ring-shaped, belt-shaped, and patch-shaped forms. The spatial evolution of PUAs is influenced by urban expansion, geographical factors, and urban planning, and it can also provide timely reflections on urban development dynamics. These findings can provide valuable references for future research in selecting PUA identification methods, promoting in-depth longitudinal studies and cross-regional research, and enhancing the attention given to PUAs in urban planning and policy-making., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

During urbanization in developing countries, fragmentation of green infrastructure due to increasing populations and the expansion of construction land leads to an extremely serious imbalance between the supply and demand for urban ecosystem services. In this study, the central city of Zhengzhou, a central city in central China, was selected as the study area and the excessive demand for six ecosystem services, namely, air purification, flood regulation, heat regulation, hydrological regulation, CO 2 sequestration and recreational services, was quantitatively evaluated. The entropy method was used to calculate the weights of various ecosystem services, and spatial overlay analysis was performed to obtain the comprehensive ecosystem service excessive demand. Finally, bivariate spatial autocorrelation analysis was used to explore the response of population density to comprehensive excessive demand for ESs. The results of this study indicate that: (1) The most prevalent need is for more CO 2 regulation service throughout the study area. (2) Except for hydrological regulation service, the spatial distribution of the remaining highly excessive ecosystem service demands are mostly concentrated in old neighborhoods. (3) Of the six excessively demanded economic services, rainwater regulation obtained the greatest weight, reflecting the poor urban infrastructure configuration for countering the rapidly increasing threat of flooding caused by climate change in the city. (4) The comprehensive ecosystem service excessive demand results show that there are eight priority green infrastructure implementation blocks in the central city of Zhengzhou. (5) There were three agglomeration types between population density and comprehensive excessive demand for ESs: high-high type, low-high type and low-low type. The spatial distribution characteristics of population density and comprehensive ES demand are positively correlated. The results of this study could help to provide information for decision making when delineating the priority areas and types of green infrastructure implementation in developing cities.