Most Chinese cities have spent decades achieving urbanisation. So far, rural urbanisation has shifted to urban renewal. However, the distinction between a rapidly changing social environment and the establishment of an institution has led to the failure of urban renewal policies to sustainably achieve complete transformation through urban modernisation involving many stakeholders. Owing to the top-down political system in China, the formulation and implementation of urban renewal policies are carried out in a closed-loop process in which “decisions are issued by the central government to the local government which gives feedback to the centre”. This seems to affect urban renewal through a transfer of renewal policies in a local area. Therefore, it is essential to explore the differences between the urban renewal policies at different government levels and to analyse these policies in diverse urban contexts with multiple stakeholders. Based on the policy transfer theory, this paper selects 216 core policy texts at the state level and at the level of four first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen), and uses the methods of text mining and semantic analysis to form open coding, axial coding, and selective coding. Furthermore, it discusses the policy transfer and impact mechanism of urban renewal policy at different levels with diverse characteristics of policy subsystems. We found that the transfer of urban renewal policy occurred in China through top-down coercive vertical transfer, bottom-up combination reverse transfer, and voluntary horizontal transfer among cities. Finally, we suggest that “inspiration”-type policy transfer is an effective method to promote urban renewal in China.

With the rise of urban agglomerations, regional divergence of China’s real estate market has gradually intensified. City-specialized policies have become the main emphasis for promoting the healthy development of the regional real estate market. By adopting a gravity model, social net-work analysis, and impulse response analysis, this paper examines the spatial-temporal heterogeneity of housing prices’ association in the Beijing-Tianjin-Hebei Urban Agglomeration (BTH-UA), the Yangtze River Delta Urban Agglomeration (YRDUA), and the Pearl River Delta Urban Agglomeration (PRDUA), which are the most developed urban agglomerations in China. Meanwhile, the formation mechanism of the housing prices’ association network and spillover effect in urban agglomeration were theoretically analyzed. This paper found that (1) significant aggregation phenomena of housing prices were observed in the urban agglomerations; (2) characteristics of overall and individual networks were dynamically heterogeneous. In the BTHUA and the PRDUA, the associations of housing prices were polarized and sparse, while they were more linked and complex in the YRDUA; (3) polycentric network structure has been demonstrated in the urban agglomerations and the spillover effects of central cities varied in intensity and breadth on responding cities and persisted during the lag period. Accordingly, several policy recommendations have been made.

The exponential growth of urban development has brought about several environmental problems, land subsidence being one of them. Land subsidence within building-containing urban areas is an important concern for urban safety management. Nevertheless, the nonlinear associations between multiple impact factors and building-oriented land subsidence susceptibility (BOLSS) across different spatial scales, together with the corresponding planning and monitoring implications, remain insufficiently understood. In this study, the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique, a random forest (RF) model, and geographic information system (GIS) tools were used to map the susceptibility of building-containing spatial units to land subsidence, hereafter referred to as BOLSS. SHapley Additive exPlanations (SHAP), partial dependence plots, and variable interaction plots were then used to analyze the associations of 15 selected impact factors with BOLSS at the built-up area (BUA) and southwest-BUA scales in Chongqing. The findings show that the factors associated with BOLSS differ between the BUA and southwest-BUA scales. Specifically, built-environment factors make the largest contributions at the BUA scale, whereas natural-environment factors and changes in built-environment conditions make larger contributions at the southwest-BUA scale. In addition, most factors show nonlinear relationships with BOLSS, with scale-dependent differences. This BOLSS mapping provides a preliminary visualization tool for identifying building-containing areas with relatively high subsidence susceptibility. The scale-dependent analysis offers useful information for prioritizing urban monitoring and planning measures.