Revealing the spatial and temporal changing characteristics and key factors driving the relationship between the supply of and demand for ecosystem services (SDES) is paramount for the effective management of regional ecosystems and the rational allocation of natural resources. This study calculated the supply of ecosystem services (ESS) in Jiangsu Province, China, in 2000, 2010, and 2020, and quantitatively evaluated the ecosystem service demand (ESD) in those years by considering land development intensity, population density, and economic conditions. The ‘coupling coordination degree and relative development degree’ (CDRD) model is introduced to explore the degree of coordination and change in the relationship of supply versus demand of ecosystem services, and then to reveal the impact of various factors on the spatial differentiation characteristics of the SDES. The results show that: 1) the ESS is high in the Taihu Lake Plain, the southwest of Huaihai area and the eastern coastal zone of Jiangsu Province, yet low in the northwest and middle of Huaihai area, and along the Yangtze River Plain. The overall distribution pattern of ESD and the coordinating degree of the SDES is high in the south and north of Jiangsu Province but low in the province’s middle. 2) The degree of coordination for the SDES increased from 0.371 to 0.415, and the relationship between supply and demand changed from one of moderate imbalance to one of basic coordination. The imbalance of supply and demand is mainly concentrated in the Lixiahe area, coastal plain area, and middle of the Huaihe River Basin, for which the predominant type is a moderate imbalance–demand lag. Meanwhile, the analysis of influencing factors shows that there is an obvious synergistic effect between different factors. The results of this research could contribute to sustainable ecosystem management and decision-making for the construction of ecological civilization in Jiangsu Province.

The development model of rural settlements in economically developed regions has reference and guiding significance for other developing rural settlements. The study was conducted to discuss the spatial distribution and scale structure evolution characteristics, in order to understand the development process and the problems of rural settlements in Changshu City, Jiangsu Province. Then, based on the multi-stage goals of Rural Revitalization of “pole–field–zone–network”, a multi-stage rural settlement spatial structure was revealed to promote the optimization of settlement layout and promote urban–rural integration. The data of rural settlements were extracted from the land use data of nearly 20 years from 2000 to 2020. Different research methods were utilized for the study. The results revealed that the spatial pattern and scale structure of rural settlements in Changshu had experienced two periods of drastic changes and stable adjustment in the past 20 years. The rural settlement density generally presented a spatial pattern of dense in the north, sparse in the South and sparse in the East. The scale system of rural settlements tended to disperse from centralization, but the spatial agglomeration was enhanced. Finally, based on the above research results and problems, a “pole–field–zone–network” multi-stage settlement structure was revealed, which helps to form a reasonable urban and rural network.

Research on the development performance of small towns is critical for promoting their revitalization, advancing urbanization, and high-quality development and transformation for realizing urban–rural integration. We used the DPSIR-DEA model to study the spatiotemporal evolution process and characteristics of the development performance of 14 small towns within the administrative division of Jiangyin city from 2001 to 2019. We subsequently applied a geographical detector model to analyze the spatiotemporal heterogeneity of the factors influencing the development performance of small towns. The results showed that 2012 was a turning point in the overall development performance index of small towns in Jiangyin, revealing initially decreasing and then increasing trends. The development performance index values of different types of small towns evidenced three trends: a steady increase, a continuous decrease, and an initial decrease followed by an increase. During 2001–2019, the development performance of Jiangyin’s small towns reflected a spatial evolution pattern of complete dispersion → small agglomeration → large agglomeration. An optimal spatial pattern comprised an increase in the number of towns demonstrating a high development performance and a decrease in the number of towns with a low development performance. GDP per capita, industrial investments, and construction land density were key influencing factors of development performance, which was mainly driven by economic and social factors, with ecological factors having a relatively weak influence.