Urban green spaces (UGSs) contribute significantly to public health and well-being. However, research on urban park resilience during the pandemic remains scarce. Studies on the impact of the pandemic on green space usage have mostly focused on spatial distribution using cross-sectional data, thus neglecting changes from a “resilience” perspective. Additionally, current methods based on mobile signal data often overestimate exposure by including non-use behaviors, which can lead to the misjudgment of usage behavior. This study constructs an effective green space exposure (EGSE) index based on mobile signal data, integrating precise UGS spatial boundaries and reasonably setting stay duration. An innovative resilience index is also proposed by combining annual variation rates. This approach allows for a systematic analysis of resilience changes in Guangzhou’s 269 UGSs across the pre-pandemic (2019), early pandemic (2020), and post-pandemic (2022) periods. The key findings are as follows. Regarding spatiotemporal heterogeneity, central urban areas demonstrated pronounced vulnerability, with EGSE surging significantly during weekends in the post-pandemic period. Concerning park-type heterogeneity, visitation patterns shifted from the predominant use of small-scale “community parks” and “recreational parks” in the early pandemic stage to a markedly increased utilization of “comprehensive parks” during post-pandemic recovery. In terms of demographic group heterogeneity, vulnerable groups’ EGSE sharply declined during the early pandemic and showed sluggish recovery. Regarding EGSE inequality, significant disparities emerged between weekdays and weekends during post-pandemic phases. This study presents a resilience analysis framework and exposure index that offer actionable recommendations for governments to plan and allocate UGSs in densely populated areas.

Shared micromobility has been widely recognized as a promising solution for promoting sustainable urban transportation, experiencing rapid growth and diversifying into various services, such as bike-sharing (BS) and electric bike-sharing (EBS). However, existing studies have primarily examined BS and EBS separately, leaving comparative analyses of their travel patterns and determinants notably limited. Moreover, although machine learning approaches have become prevalent for modeling nonlinear relationships, these methods typically overlook spatiotemporal heterogeneity, potentially resulting in biased estimations and inaccurate interpretations. To address these gaps, this study develops a novel modeling framework integrating XGBoost with geographically and temporally weighted regression (GTWR), enabling simultaneous consideration of spatiotemporal heterogeneity and nonlinearity. Using trip data from Hefei, China, we comparatively analyze the travel characteristics of BS and EBS and apply the integrated modeling framework to investigate the built environment’s influence on both modes. The results indicate that both BS and EBS exhibit distinct peak-hour usage patterns, while spatially, BS usage is concentrated in downtown areas and EBS usage is more evenly distributed citywide. Among examined factors, distance to metro stations and employment density emerge as the most significant predictors for both modes. Additionally, nonlinear relationships reveal that higher branch road density and lower major road density are associated with increased BS but reduced EBS usage, while land use mix demonstrates clear threshold effects, beyond which usage of both modes significantly increases. These findings provide valuable insights for operators to optimize fleet deployment and for policymakers to design targeted interventions supporting coordinated and sustainable shared micromobility development.

High-precision and wide coverage data on rural household wealth are essential for bridging national-level rural revitalization policies with micro-level rural entities, enabling the precise allocation of public resources. However, the vast number and dispersed distribution of rural communities in China make wealth data difficult to collect and scarce in availability. To address this challenge, this study proposes an integrated technical framework that combines “sky” remote sensing imagery with “ground” village street view imagery to construct a fine-grained, computable representation of rural household wealth. Centered on the intelligent interpretation of rural housing features, we extract wealth-related visual elements from imagery and regress them against benchmark survey-based household wealth indicators to develop a high-accuracy township-level wealth prediction model (R² = 71%). This model is used to generate a nationwide, township-scale rural household wealth map. Our findings reveal a distinct “bimodal” spatial distribution of rural wealth in China, characterized by a polarization pattern: higher in the south and east, and lower in the north and west. This approach offers a scalable and cost-effective alternative to traditional household surveys, supporting the identification of rural development gaps and facilitating more targeted and effective rural policy implementation.

Along with the rise of the sharing economy, recent years have seen a novel type of shared micro-mobility, shared electric bikes (e-bikes), emerge in various cities and regions worldwide. Although shared e-bikes play a more and more critical role in daily city life, it is unclear what contributes to the usage of shared e-bike. This study aims to reveal what characteristics of cities are linked to the prevalence of e-bike sharing, and how the city-level factors affect the utilisation of e-bike sharing. We find that high tertiary industry ratios and high employment density of cities are correlated to the prevalence and utilisation efficiency of e-bike sharing. Cities with complex terrain and poor public transport systems have high usage frequencies. In addition, e-bike sharing can overcome the obstacles of complicated terrain and promote travel in less developed cities. This could provide a feasible clue to arranging the shared e-bike and promote the sustainable development of transportation.

In recent years, shared electric bikes (e-bikes), as a novel shared micro-mobility mode, have experienced a significant increase in popularity. Yet, there is still limited evidence on whether the two forms of micro-mobility act as complements or substitutes. By exploring the effects of the entry of shared e-bikes on bike-sharing and overall shared cycling usage, this paper aims to explore the role of e-bikes in sustainable transportation solutions. The paper combines nearest-neighbour matching and a difference-in-differences event study with bi-monthly panel data of conventional bike-sharing and e-bike-sharing trips from 2020 to 2021. Results suggest that the effect of e-bike-share on bike-sharing at the grid level shifted from an initial complementary effect to a later substitution effect, ultimately resulting in a 22.6% reduction in bike-sharing usage and a 65.9% increase in overall shared cycling usage. The introduction of shared e-bikes simultaneously attracted new user groups and reduced existing barriers to using conventional bikes. With growing market saturation, any further expansion is found to have come to the detriment of the incumbent technology, i.e. the conventional pedal bike. Although shared e-bikes ultimately replaced part of shared bikes, they have increased the overall use of micro-mobility tools, contributing to the popularization of sustainable transportation.

As global urbanization accelerates, traffic congestion and environmental pollution have become critical issues. Cycling, as a green transportation mode, is essential for promoting sustainable urban development. However, the impact of environmental factors on cycling behavior varies among cities, and systematic comparative studies are lacking. This study focuses on five Nordic cities—Reykjavik, Oslo, Stockholm, Copenhagen, and Helsinki—utilizing multi-source data and an improved NRBO-XGBoost model to enhance predictive performance in analyzing the effects of environmental factors on cycling mobility. Through K-means clustering, differences in cycling mobility patterns among these cities are revealed. The results indicate that topographical variation, green space area, air quality, and road density significantly influence cycling flow, with varying impacts across cities. In Copenhagen and Oslo, high densities of commercial facilities and green space coverage promote cycling activity, whereas complex terrain in Stockholm and Helsinki restricts it. Cluster analysis shows that high traffic network density in the city centers of Oslo and Copenhagen enhances cycling flow, and suburban green spaces and water bodies contribute to increased cycling. Conversely, slopes in Stockholm and Helsinki inhibit cycling activity. These findings equip urban planners with both generalizable and targeted insights to optimize cycling infrastructure and promote sustainable urban mobility tailored to each city's unique environmental conditions.