E-bike, characterized as a low-carbon and health-beneficial active travel mode, is gradually becoming popular in China. Although built environment factors are considered to be key parameters that can facilitate or hinder active transportation, such as cycling or walking, few studies have explored the impact of built environment on e-bikes. To fill this gap, this study was the first to explore the relationship between e-bike usage and built environment factors based on population level travel survey in central Jinan, China. Both macro and micro levels of built environment were measured using multi-source data. We employed ordinary least squares (OLS) and geographically weighted regression (GWR) models to explore the aggregation patterns of e-bike trips. Besides, the local Moran's I was employed to classify the aggregation patterns of e-bike trips into four types. The results from OLS model showed that eye-level greenery, building floor area, road density and public service POI were positive significantly related to e-bike trips, while open sky index and NDVI had negative association with e-bike trips. The usage of GWR model provided more subtle results, which revealed significant spatial heterogeneity on the impacts of different built environment parameters. Road density and public service POI posed positive effects on e-bike travel while NDVI and open sky index were found mainly pose negative impacts on e-bike travel. Moreover, we found similar coefficient distribution patterns of eye-level greenery, building floor area and distance to bus stop. Therefore, tailored planning interventions and policies can be developed to facilitate e-bike travel and promote individual's health level.

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.

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.

In the current context of aging and urbanization, the rapid increase in the prevalences of disabilities (PoDs) has become an important consideration in healthy urban planning. Previous studies have focused on the spatial prevalence of total disabilities based on large-scale survey data. However, few studies have examined different types of PoDs and the factors contributing to spatial disparities in micro-urban units at the municipal level. This study aims to fill this gap by exploring the spatial PoDs, related built environments, and socio-economic factors across the Tianjin municipality in 2020. The study employed Getis–Ord GI* analysis to identify urban-rural disparities and ordinary least square (OLS) and quantile regression (QR) analyses to model the heterogeneous effects of the spatial PoDs-associated factors across quantiles. The results reveal that the PoDs, especially of visual, hearing, and limb disabilities, in the urban center, are significantly higher than those in rural areas, which is inconsistent with previous studies conducted in China. The urbanization rate, medical facilities, and education facilities significantly reduced the total PoDs, while the elderly population, migrant population, bus route density, and road density significantly increased it. The built environments and socio-economic factors had heterogeneous impacts on different types of PoDs, which were summarized into three categories based on these dominant factors: (1) visual and hearing disabilities were medical facility-dominated; (2) intellectual and limb disabilities were urbanization- and aging-dominated; and (3) mental and speech disabilities were migrant-dominated. This study provides scientific advice to adapt to the expected increase in demand for disability-related medical and public health services and to expand the range of effective strategies and interventions aimed at preventing the deterioration of disability and improving disability management in the population.

In the current context of aging and urbanization, the rapid increase in the prevalence of disabilities (PoDs) has become an important consideration in healthy urban planning. Previous studies have focused on the spatial prevalence of total disabilities based on large-scale survey data. However, few studies have examined different types of PoDs and the factors contributing to spatial disparities in micro-urban units at the municipal level. This study aims to fill this gap by exploring the spatial PoDs, related built environments, and socio-economic factors across Tianjin municipality in 2020. The study employed Getis-Ord GI* analysis to identify urban-rural disparities and OLS and quantile regression analyses to model the heterogeneous effects of the spatial PoDs determinants across quantiles. The results reveal that the PoDs, especially visual, hearing, and limb disabilities, in the urban centre, are significantly higher than those in rural areas, which is inconsistent with previous studies conducted in China. Urbanization rate, medical facilities, and education facilities significantly reduced total PoDs, while the elderly population, migrant population, bus route density, and road density significantly increased it. The built environments and socio-economic factors had heterogeneous impacts on different types of PoDs, which were summarized into three categories based on the dominant determinants: (1) visual and hearing disabilities were medical facility dominated; (2) intellectual and limb disabilities were urbanization, and aging dominated; and (3) mental and speech disabilities were migrant dominated. This study provides scientific advice to adapt to the expected increase in demand for disability-related medical and public health services and to expand the range of effective strategies and interventions aimed at preventing the deterioration of disability and improving disability management in the population.