Advancing the modernization of the national governance system and governance capacity in Chinese style has increasingly become a focus of attention in the political and academic spheres. Although many scholars have explored the framework system and enhancement pathways for urban governance modernization, the research measuring the modernization level of the governance capacity of specific cities is still limited. Therefore, in this study, a three-capacity framework and an evaluation system for urban governance capacity modernization (UGCM) are constructed, integrating development capacity, autonomy capacity, and inclusiveness capacity. The entropy method and analytic hierarchy process are employed to build a comprehensive measurement model. The empirical study of Wuhan shows that: (1) The UGCM level of Wuhan's 14 districts in 2021 is overall low and varies obviously, with greater potential for improvement in distant districts. Among the three capacities, development capacity is a major driver, namely development capacity > inclusiveness capacity > autonomy capacity, and there are regional differences among different districts; (2) The UGCM level of Wuhan is primarily influenced by seven obstacle factors, namely disaster prevention and emergency management capability, GDP growth rate, GDP per capita, the value addition of cultural and relevant industries, the percentage of days with good air quality, the rate of urban employees participating in basic endowment insurance, and the amount of hospital and clinic beds per 10,000 individuals; (3) There is a certain constraining relationship in each district between the level of UGCM and environmental governance; that is, the UGCM level is high, while the level of environmental governance is low, and vice versa.

Urban expansion and shrinkage are two sides of the same coin in the process of urbanization, jointly influencing the evolution of land use. However, the specific impacts of urban expansion and shrinkage on land-use evolution have not yet been thoroughly explored. This paper selects the urban agglomeration in the middle reaches of the Yangtze River as the research area and constructs an evaluation index system to understand the multidimensional expansion and shrinkage of cities. On this basis, the system dynamics model and cellular automata model are combined to predict the multiscenario evolution of land use. The results indicate that from 2010 to 2020, the proportion of shrinking cities in terms of population and economy was 51.61%, while the proportion in terms of social environment was 45.16%. Compared with the natural development scenario, the rapid expansion of construction land in the economic growth scenario from 2020 to 2050 will lead to a decrease in land-use efficiency. The social development scenario focuses more on improving social public services, and the expansion of construction land is second only to the economic growth scenario. In environmental protection scenario, the transition between land-use types is the smoothest, which is beneficial for environmental protection. The evolution of future land use should gradually shift toward an environmental protection scenario, thereby promoting the sustainable use of land resources. This study provides a deep understanding of the impact of different levels of urban expansion and shrinkage on land-use evolution, which can help provide a reference for the formulation of land space planning and coordinated development.

In the context of global climate change and a new wave of urbanization, the spatiotemporal evolution of urban resilience has special importance in urban agglomerations. While the literature has covered the static quantitative assessment of urban resilience, long-term dynamic quantitative studies are lacking, and little attention has been paid to the mesolevel characteristics of urban agglomerations. This study assessed statistical data from 2002 to 2021 in an index with weighted entropy and geographic information spatial analysis to quantitatively measure the urban resilience of the Beijing–Tianjin–Hebei urban agglomeration. The results indicate that the level of resilience across cities within the agglomeration is unbalanced and insufficient, and the number of cities with moderate resilience or above is low. In terms of temporal evolution, the measures of individual dimensions and resilience overall are on the rise. Spatially, the agglomeration exhibits a core-edge phenomenon. Beijing–Tianjin–Shijiazhuang form a triangular core area, which has a continuous influence on the surrounding cities. This study provides an important measurable basis for the sustainable development of the Beijing–Tianjin–Hebei urban agglomeration and a corresponding reference for similar regions worldwide.

This study examines the dynamic interplay between tourism, urbanization, and the ecological environment in the ecologically rich yet vulnerable karst regions of Yunnan, Guangxi, and Guizhou provinces in Southwest China. These areas, known for their significant biodiversity, face challenges from rapid tourism growth and urbanization, which threaten their fragile ecological balance. Our research constructs a relational model to explore this tripartite interaction using time-series data from 2000 to 2021. The aim is to identify the scientific patterns of human–nature interaction and the integrative mechanism linking developmental activities with regional ecology. The findings reveal a synergistic relationship characterized by high coordination among these sectors, termed eco-priority. This development model, which departs from traditional approaches prioritizing either development at the expense of ecology or vice versa, offers a novel perspective on sustainable development in ecologically sensitive areas. It demonstrates how regions like Yunnan, Guangxi, and Guizhou have achieved sustainable progress by harmonizing tourism and urbanization with ecological preservation. Our study provides valuable insights for similar regions globally, proposing a sustainable development framework that respects and leverages the ecological characteristics of such areas, contributing to a broader environmental management discourse.

The big cities of Pakistan have become dysfunctional due to severe congestion and parking disorders in the commercial areas. One solution to the problem is pedestrianization, which is always based on a pros and cons strategy. Thus, it is critical to identify and rank the potential sites before any decision. The research attempted to assess the potential and constraints of pedestrianization in historically built-up significant commercial areas of Lahore, Pakistan. The methodology encompasses a summative assessment of physical, social, and environmental situations; footfall analysis; pedestrian latent demand modalities; and stakeholder perceptions. The research findings show that smooth pedestrianization can be achieved by adopting statutory measures such as regulating public spaces and pedestrian infrastructure, including sidewalks, crosswalks, curb ramps, street furniture, lighting, and signage. In this connection, some policy measures, like improvements to pedestrian infrastructure, pedestrianization policies, etc., have also been recommended.

Due to the relatively poor road traffic conditions and insufficient logistics facilities in China’s rural areas, logistics distribution is less efficient and more costly. For this reason, the construction of rural logistics multilevel distribution nodes becomes an important means to improve logistics distribution efficiency and reduce logistics costs. This study first analyzes the advantages of the axial-spoke layout of logistics distribution nodes and combines the characteristics of rural areas to propose a multilevel distribution node layout scheme suitable for rural logistics. Second, an improved sparrow search algorithm is proposed to solve the problem of too many nodes for rural logistics node location, and finally, the location analysis of multilevel distribution nodes based on the spoke-and-axle structure is carried out. The research results show that the improved sparrow search algorithm can be used to solve the problem in this paper, and the degree of road development in China's rural areas and the fixed cost of secondary distribution centers will affect the layout of rural logistics nodes to a certain extent.

During the COVID-19 pandemic, sufficient access to urban green spaces enhanced residents’ health, particularly in densely populated areas subjected to lockdown and stay-at-home mandates. However, limited research has examined the effects of transitioning from strict containment measures to “reopening.” In this study, we used a unique dataset from two major Chinese cities, Beijing and Shanghai, to identify how changes in COVID-19 lockdown policy affected residents’ duration of green space use. A self-administered online survey (N = 2,545) was conducted between October 11 and December 29, 2022, covering the first stage of the COVID-19 containment policy change on November 21. The timing of the survey provides an opportunity to employ a regression discontinuity design (RDD) to evaluate how the removal of the containment policy affected the duration of green space use within and outside residential communities. Subgroup analyses were conducted to compare how the effects of COVID-19 policy vary according to demographic and socioeconomic factors. The findings indicate that the change to China’s COVID-19 lockdown policy led to an increase in the duration of green space use within residential communities among elderly people, while a decrease was observed among younger residents and females. High-income residents preferred outdoor recreational activities in urban and forest parks, and individuals living in commercial housing communities increased their duration of visits to green spaces within the community.

Based on challenges pertaining to energy security and environmental concerns, China has set carbon peak and carbon neutrality goals. As the region with the highest concentration of industrial carbon emissions, it is important to identify key carbon emission parks, and clarifying the related carbon emission characteristics is crucial for standardizing regional carbon emissions. Therefore, this study analyzed the energy-related carbon emission characteristics across 40 industrial sectors in China. Subsequently, enterprise-level data from industrial parks and nighttime light data were integrated to quantitatively account for the energy-related carbon emissions in 14 national-level industrial parks within the urban agglomeration in western China. The results showed that the key carbon-emitting industrial parks in the region was located in the more economically developed and industrially concentrated areas of Chengdu, the main city of Chongqing, Wanzhou, and Changshou. The primary industries contributing to the carbon emissions in the industrial parks were electricity, heat production, and supply, followed by the nonmetallic mineral products industry and chemical raw materials and the chemical product manufacturing industry. Some of the parks exhibited higher carbon emissions from the industries associated with the dominant sectors within these parks. Moreover, this study identified significant differences in the carbon emission intensities between the Economic Development Zone and the High-Tech Industrial Development Zone, as well as the regional characteristics of park emissions. Finally, based on the findings of this study, a set of recommendations were provided. This study presented a foundational data set and decision reference for facilitating the parks’ progress toward low-carbon initiatives and contributing to the timely achievement of national climate change mitigation objectives.

Whether industrial agglomeration (IA) can effectively improve green development efficiency (GDE) is crucial for both the smooth realization of China’s dual carbon goals and the country’s future green development. This paper evaluated the GDE and the IA of 283 cities in China by constructing an improved data envelopment analysis model and a location entropy model. The nonlinear effect of IA on GDE was investigated empirically using the recently created spatial statistical model and mediating effect model. The results showed that (1) China’s overall GDE shows a good upward trend, while IA shows a slight downward trend. The differences between cities are very obvious. High-GDE cities are concentrated in coastal areas, and their average annual growth rate (AAGR) is low or even negative. Low-GDE cities are concentrated mainly in inland areas, with high AAGR. (2) There is a strong nonlinear U-shaped correlation between IA and GDE. (3) Influence factors such as economic development level (GDP) will promote GDE improvement, while influence factors such as energy structure will inhibit GDE improvement. (4) Industrial structure upgrading plays a major intermediary role between IA and GDE. Therefore, this paper proposes corresponding policy implications for how IA can promote green development.