With the socio-economic transformation, the recombination of regional development factors and the followed reconstruction of the rural development elements system have profoundly changed the rural landscape of the Kashgar region in Northwest China. The factors affecting the rural production and lifestyle interact with each other, shaping different types of rural development. Accordingly, basing on the main factors influencing the rural development ability and long-term development potential, the assessment indicator system of rural comprehensive development (RCD) was established to reveal the differentiation of rural development and identify the dominant factors affecting rural development. The principal component analysis method and the cluster analysis method was used to distinguish the different types. The results show that the high-level rural development areas are mainly concentrated in the center of the region, while the low-level areas are mainly distributed in the periphery, with significant spatial differentiation characteristics. We divided the rural development into three categories and 11 zones for which the basic natural conditions and external challenges are different. The categories reflect three possible results of rural development: grow, decline, and vanish, which is in the industrialization development stage. With the transformation of human society and the change of urban–rural relationship in its mode and content, the external economy, society, and changing environment has put pressures on rural areas. Therefore, according to different rural development types, it is necessary to take measures to strengthen the rural areas to cope with external environmental challenges.

China’s urbanization has attracted considerable academic attention. However, more work is required to understand and examine the driving mechanisms of urbanization, especially within the context of the transition from socialism. We used a regression model and a geographical detector technique to determine the direction and strength of factors influencing urbanization in prefecture-level cities in China. We found that China’s urbanization development model has two characteristics: administrative hierarchy and spatial agglomeration. Based on our conceptual framework, we identified the positive effect exerted by the real estate investment and per capita fiscal expenditure on urbanization, as well as the negative impact exerted by the urban–rural income ratio. The results revealed that China’s urbanization is a hybrid process: the forces from power and capital contribute to high levels of urbanization. When comparing the strength of the factors influencing urbanization between coastal and inland cities, we found that real estate investment and per capita fiscal expenditure in coastal cities have a greater impact on urbanization than in inland cities. In contrast, the impact of the urban–rural income ratio on urbanization is larger in inland cities than in coastal cities. A close examination on the factors influencing China’s rapid urbanization is the key to further advancing our understanding of the dynamic mechanisms of China’s urbanization. Our findings can help Chinese policy makers formulate more detailed urbanization strategies with consideration of regional characteristics.

Land urbanization is a comprehensive mapping of the relationship between urban production, life and ecology in urban space and a spatial carrier for promoting the modernization of cities. Based on the remote sensing monitoring data of the land use status of the Yangtze River Delta urban agglomeration collected in 2010 and 2020, the spatial differentiation characteristics and influencing factors of land urbanization in the area were analyzed comprehensively using hot spot analysis, kernel density estimation, the multi-scale geographically weighted regression (MGWR) model and other methods. The results indicated the following: (1) From 2010 to 2020, the average annual growth rate of land urbanization in the Yangtze River Delta urban agglomeration was 0.50%, and nearly 64.28% of the counties had an average annual growth rate that lagged behind the overall growth rate. It exhibited dynamic convergence characteristics. (2) The differentiation pattern of land urbanization in the Yangtze River Delta urban agglomeration was obvious from the southeast to the northwest. The hot spots of land urbanization were consistently concentrated in the southeastern coastal areas and showed a trend of spreading, while the cold spots were concentrated in the northwest of Anhui Province, showing a shrinking trend. (3) Compared with the GWR model and the OLS model, the MGWR model has a better fitting effect and is more suitable for studying the influencing factors of land urbanization. In addition, there were significant spatial differences in the scale and degree of influence of different influencing factors. Analyzing and revealing the spatiotemporal characteristics and driving mechanism of land urbanization in the Yangtze River Delta urban agglomeration has important theoretical value and practical significance for the scientific understanding of new-type urbanization and the implementation of regional integration and rural revitalization strategies.

Exploring population dynamics and its driving factors has important practical significance for guiding reasonable population distribution. In view of this, this paper systematically analyzes the population dynamics and driving factors in China based on the latest three decennial censuses, using research methods such as the population concentration index, the center of gravity model, relative change in population density and multiple linear regression. The conclusions are as follows: (1) China’s population distribution is uneven, and the trend of polarization in population distribution is increasingly evident. The spatial differences in population growth are shifting from east–west to north–south. Under the influence of the “core–periphery effect”, more people are gathering in a few large cities. (2) The factors affecting population changes have obvious temporal variability: terrain and temperature have an increasing impact on China’s population changes. Temperature in particular has become an important factor in China’s population changes. Population changes are gradually shifting from being driven by a single economic factor to being driven jointly by social and economic factors. (3) The factors affecting population changes also have obvious spatial heterogeneity: temperature affects population changes in both the eastern and central–western regions, while terrain only affects population changes in the central–western regions. Currently, population changes in the economically developed eastern region are more driven by economic factors, while the central–western regions are driven by both economic and social factors. Central cities in the central–western regions are experiencing accelerated population agglomeration, while central cities in the eastern region are losing their ability to attract population agglomeration. The above conclusion basically clarifies the patterns and influencing factors of China’s population changes since the 21st century, which can provide a useful reference for future population development and regional planning.

PM2.5 pollution seriously affects the environment, human health and socio-economic development, which has garnered wide-spread attention. Technological innovation plays a great role in PM2.5 concentration. Thus, better understanding of the influence of technological innovation on PM2.5 concentration is necessary. The Yangtze River Delta (YRD) was selected as the study area. This paper explored the spatial characteristic of PM2.5 concentration and technological innovation. Then, bivariate Moran’s I was applied to analyzed the spatial correlation between technological innovation and PM2.5 concentration. The geographically and temporally weighted regression (GTWR) model was applied to explore the spatial heterogeneity of the influence of technological innovation on PM2.5 concentration, and geographical detector model was applied to reveal the interactive influence between technological innovation and other factors. The main conclusions were as follows. (1) No city’s PM2.5 concentration fell below the standard which is considered harmless to human health. Cities in the northwest of the YRD had higher PM2.5 concentration. (2) Cities in the core region of the YRD had relatively higher technological innovation. (3) There existed negative spatial correlation between technological innovation and PM2.5 concentration, and three local spatial correlation types were found. (4) Technological innovation contributed to the decrease in PM2.5 concentration. The interactive influence between technological innovation and natural factors was generally greater than that between technological factors and socio-economic factors. Comparing to other socio-economic factors, technological innovation and foreign investment had greater interactive influence on PM2.5 concentration. Finally, some policy recommendations were drawn.