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In recent years, an increasing number of Chinese cities have experienced population decline, impacting the urban economy, public services, and paid use of state-owned land (PUL). This paper employs a coupled coordination degree model and the gray correlation coefficient method to examine the relationship between the rate of population change from 1990 to 2020, the rate of PUL change from 2000 to 2020, and the coupled coordination of economy and public services. The study arrives at several conclusions: (1) Cities with declining populations exhibit a lower PUL rate than those without population shrinkage, indicating reduced activity in the land market. (2) Population growth plays a significant role in the coordinated development of the urban economy and public services, fostering urbanization. (3) The economy and public services in shrinking cities rely heavily on revenue generated from the transfer of PUL rights to support their coordinated development. This research provides an insightful comparison between shrinking and non-shrinking cities by integrating the unique compensated land transfer system in China with the critical variable of population change. [ABSTRACT FROM AUTHOR]

A decrease in population is an emerging challenge in China's urbanization trajectory. This study utilizes data from the Sixth and Seventh Population Censuses to identify Chinese cities experiencing population decline, focusing on prefecture-level cities. It employs 30 indicators relevant to nine Sustainable Development Goals (SDGs) concerning urban areas and populations to evaluate the progress of these shrinking cities in 2010 and 2020. A novel metric, "SDG population elasticity", is introduced to quantify the association between population reduction and SDG indexes. The study employs K-means++ clustering for 130 shrinking cities. The findings reveal that out of the 130 cities, 63 exhibit a downward trend in SDG index scores, predominantly in China's northeast and western regions. The Northeast region has negative SDG population elasticity, indicating a lag in sustainable development compared to that in other areas. Additionally, the research categorizes China's shrinking cities into four groups: cities with strong population elasticity feedback-significantly declining SDGs, cities with weak population elasticity feedback-slightly regressing SDGs, cities with high population loss-negative SDGs in decline, and cities with smart population decline-steady SDGs growth. These findings offer a foundation for crafting bespoke policies in shrinking cities to ensure a balanced approach to sustainable development and monitor advancement toward achieving the SDGs. [ABSTRACT FROM AUTHOR]

The study of intercity network structure from the perspective of flow space can effectively reveal the connection patterns, hierarchical laws, and spatial organization of cities. However, current studies mostly focus on a single real or virtual space, neglecting the comprehensive perspective. Furthermore, the research subject of network relationships is usually the complex original aligned network rather than the spatially cohesive network structure. In this study, spatial network structures are constructed and extracted based on the migration flow and attention flow, which represent people's objective behavior and subjective will, respectively. This study then reveals the association between virtual and real networks from three aspects: node location, node attributes, and local network structure characteristics. Results indicate that compared with the real network structure, the virtual network structure has a strong same-level node agglomeration and a significant node size difference. Spatial dislocations are observed in the high association of the various measures of China's real and virtual network structures. The degree of differences in network structures between virtual and real spaces varies spatially. This paper reveals the dynamics of urban development in different spaces, helping build a human-centered, high-quality space governance system for virtual and real spaces. • Spatial network structure considering the geographical proximity and interaction strength among cities is extracted. • Location, attribution, and structure-based association analysis method for non-aligned intercity networks is proposed. • The degree of differences in network structures between virtual and real spaces varies spatially. • The association pattern between real and virtual networks is spatially dislocated in different indicators. [ABSTRACT FROM AUTHOR]