With the increasingly prominent phenomenon of social stratification in urban development, it is of practical significance to study the accessibility of elderly care facilities for different social groups. The study improves the mathematical model of the two-step floating catchment area method (2SFCA) as regards three aspects: the accurate demand of elderly residents, the comprehensive supply capacity of elderly care facilities, and the precision of travel costs. Taking Tianjin as an example, the study measures the accessibility of elderly care facilities from the perspective of social stratification. The results show that: (1) The improved model is more practical in evaluating the accessibility of elderly care facilities. (2) The spatial distribution of social groups in Tianjin presents a concentric structure and the social stratification in the periphery area is more obvious. (3) The accessibility scores of elderly care facilities are higher in the city center, lower in the periphery area, higher in the south, and lower in the north. (4) High- and middle-income groups have better accessibility, while the elite and low-income groups have worse accessibility.

The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and we finally identified six main ridership patterns. We then developed a newly built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis results revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type stations, and centrality was only associated with employment-oriented hybrid type station. The research results could inform the spatial planning and design around metro stations and the planning and design of metro systems. The built environment of pedestrian catchment areas surrounding metro stations can be enhanced through rational land use planning and the appropriate allocation of urban infrastructure and public service facilities.

The positive effect of healthcare facilities on residents’ health has been extensively studied. However, few studies have focused on the role of rehabilitation services as unique healthcare services for persons with disabilities. This study aimed to examine the relationship between the spatial accessibility of rehabilitation facilities and the degree of disability. To this end, an approach of measuring the spatial accessibility of rehabilitation facilities to persons with disabilities was proposed. This approach integrates multiple key elements including the characteristics of facilities (i.e., the capacity, frequency of use and service radius), characteristics of the mobility of persons with disabilities (i.e., the mode of travel, escort support, transportation fee and barrier-free environment requirements) and travel time obtained from a routing application programme interface. The accessibility of rehabilitation facilities was calculated at the neighbourhood level within the Central Urban Area of Tianjin Municipality. The ordinal logistic regression analysis revealed that higher accessibility to rehabilitation facilities generally corresponded to lower severity of disability. However, the impact varied depending on the type of disability. Increased accessibility was associated with greater severity of intellectual disability, whereas it was linked to reduced severity of visual, hearing, limb, mental and speech disabilities. It is suggested to incorporate disability diversity and the accessibility of rehabilitation facilities into spatial planning and governance.

In the pursuit of China’s carbon peak and carbon neutrality objectives, county-level areas assume a pivotal role in orchestrating diverse initiatives for low-carbon development. However, empirical evidence is limited. This paper aims to fill this gap by exploring the driving factors of carbon peak and carbon peak path at the county level, using Shandong Province as a case study. Employing data related to economic development, industrial structure, land utilization, energy consumption, and emission characteristics, a principal component analysis (PCA) was utilized to extract the following five driving factors of carbon peak: green transformation, urbanization, industrial construction, energy consumption, and environmental constraints. Subsequently, K-means clustering identified five cluster areas: (1) agricultural transformation pending area, (2) low-carbon lagging area, (3) industrial transformation area, (4) low-carbon potential areas, and (5) low-carbon demonstration area. Based on these areas, this study further elucidates spatial combination models of carbon peak within the urban system, spanning central cities, coastal cities, resource-based cities, and agricultural cities. The paper enhances comprehension of the integral role county-level areas play in achieving China’s carbon reduction objectives. By providing nuanced insights into diverse developmental trajectories and spatial interactions, the study contributes to effective low-carbon strategy formulation. The findings underscore the importance of considering specific county attributes in urban areas to devise precise optimization strategies and trajectories, ultimately facilitating the realization of carbon peak goals.

Introduction: Long-duration commuting is widely recognized for its significant influence on health. However, while research has traditionally focused on direct impacts, there remains a critical need to explore the nonlinear dynamics of this relationship. This study aims to deepen our understanding of how behavioral preferences and built environments contribute to these complex interactions.Methods: This study was conducted in Jinan, China’s most congested city, using data from the “Jinan Residents Commuting Survey” of 1,755 participants aged 19 to 59. We applied Generalized Propensity Score Matching (GPSM) to explore the nonlinear effects of commuting time on self-rated health, adjusting for participants’ sociodemographic characteristics. Variables related to active commuting, physical activity, and perceived built environment were also examined for their potential moderating effects.Results: Commuting for less than 21 minutes enhances health, but negative effects intensify and peak at 60 minutes. Heterogeneity analysis reveals that women and older adults, especially those with higher incomes, are more susceptible to long commutes, experiencing a delayed onset of adverse effects. While active commuting offers health benefits, it may exacerbate health issues if prolonged. Conversely, regular physical activity consistently improves health outcomes related to commuting. Additionally, factors like residential greenery and walkability help alleviate commuting-related stress, improving the overall commuting-health dynamic.Discussion: This study clarifies the commuting-health relationship by identifying key time thresholds and the positive effects of active commuting and physical activity on mitigating longer commute impacts. The findings inform healthier commuting behaviors and offer practical guidelines for urban planning and policy-making to enhance commuter well-being.