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• Enhance the CA-Markov model by incorporating additional parameters to predict land use change around railway stations. • Validate the enhanced model by simulating land use changes around Dujiangyan station. • Put forward the optimization strategy for the coordinated development of transport and land use. Predicting land use change around railway stations is crucial for facilitating the coordinated development of transport and land use. Previous studies have seldom focused on simulating and predicting small-scale land use changes at a railway station. Therefore, this study employs an enhanced Cellular Automata-Markov (CA-Markov) model, aiming to achieve simulations and predictions with heightened precision. Firstly, two additional driving factors, namely accessibility to railway stations and the kernel density of points of interest (POIs), are incorporated into the CA-Markov model. Secondly, the validation of the enhanced model is achieved through simulating land use changes around Dujiangyan Station. Finally, this model is applied to predict land use around Mianzhu South Station in 2026, and optimization strategies for railway station areas are proposed. The results indicate an 83.43-hectare reduction in farmland area, accompanied by a moderate increase in 35.8 hectares of forest land and 41.52 hectares of residential land. This enhanced model provides valuable technical support for strategic planning in railway station areas. [ABSTRACT FROM AUTHOR]

Highlights • Propose a transit accessibility measure that considers transfer. • Enrich existing land use plan evaluation methods by incorporating a transit accessibility measure. • Evaluate the performance of urban land use plan in a transit-dominated and car-lite context using the presented approach. Abstract This study proposes a measure to evaluate the urban land use plan with transit accessibility, more specifically, the spatial accessibility of transit stations. The spatial accessibility of transit stations is measured with the number of effective reachable grids, and the influence of transfer on reduction in spatial accessibility is considered. A geographically weighted regression model is used to determine the correlation between transit accessibility and urban land use characteristics. Moreover, the methodology is applied to Xiamen, China and the corresponding results demonstrate the usefulness and effectiveness of the proposed methodology. Researchers can adopt the proposed approach to evaluate urban land use plan, particularly in transit-dominated and car-lite contexts. [ABSTRACT FROM AUTHOR]

• Effects of future climatic uncertainties on building retrofit decisions are estimated. • Life-cycle economics and carbon emissions are selected as the multiple objectives. • Future climatic uncertainties alter optimal values of some building retrofit measures. • Optimal infiltration standard and insulation options are susceptible to future climate. • Optimal glazing materials and shading options are not susceptible to future climate. Energy consumption of and carbon emissions from buildings, which substantially depend on outdoor climate conditions, may be susceptible to future climate change. However, how future climatic uncertainties affect decision-making regarding building energy retrofits is unclear from the life-cycle analysis perspective. This study used typical high-rise public rental housing buildings in subtropical Hong Kong as a case study to consider future climatic uncertainties under representative concentration pathway scenarios and in different general circulation models to estimate their effect on multi-objective building retrofit decisions considering trade-offs among different objectives, namely, life-cycle economics, life-cycle carbon emissions, and operational energy use impacts. The results indicate the significant influence of future climatic uncertainties on the optimal values of building energy retrofit measures. The passive house infiltration standard and thick (0.06–0.1 m) polyurethane foam insulation can be recommended for long-term future climatic conditions (RCP8.5–2065s). By contrast, optimal values of glazing materials, horizontal shading projection factors, and window-to-wall ratio are not susceptible to future climatic uncertainties. Uncertainties from different general circulation models are another notable factor affecting optimal values and retrofit options. This study reveals the importance of considering future climatic uncertainties when deciding optimal values and selecting building energy retrofit options. [ABSTRACT FROM AUTHOR]

• Use mobile phone signaling data to assess urban vitality. • Call for temporally and spatially varying policies for urban vitality enhancement. • The spatial distribution of urban vitality is clustered and polycentric. • Urban vitality varies considerably throughout the day. • The effects of metro accessibility are more evenly distributed on weekends. Urban vitality has long been a focus of urban planners and designers. This study portrays the spatiotemporal patterns of urban vitality in Chengdu, China, on weekdays and weekends and examines their relationship with a set of factors using spatial regression and geographically weighted regression models. The results reveal that the spatial distribution of urban vitality displays a clustered and polycentric pattern, which can be attributed to urban spatial structure. Urban vitality varies considerably throughout the day on weekdays and weekends, and it consistently decreases as the distance to the city center increases. Furthermore, weekday vitality is much more predictable (less random) than weekend vitality, indicating that human activity patterns present higher regularity on weekdays than on weekends. The normalized difference vegetation index (NDVI), road density, the number of residential facilities, and land-use mix have positive influences on urban vitality on weekdays and weekends, while the distance to the metro station has negative influences. The influences of NDVI on urban vitality in the city center are negative on weekdays but become positive on weekends. The influences of the distance to the metro station are more homogeneously distributed on weekends than on weekdays. Finally, this study calls for temporally and spatially varying policies for urban vitality enhancement. [ABSTRACT FROM AUTHOR]

• Short EBSS trips mainly substitute walking and the BSS. • Long EBSS trips mostly substitute cars, buses, and the subway. • The integration of the EBSS and transit has reduced carbon emissions. • The CO 2 emissions per km are 19.47 g. • The EBSS has saved 75.52% of CO 2 emissions. This paper presents a quantitative analysis of the mode substitution and usage-phase carbon emission impacts of the electric bike-sharing system (EBSS) on urban transportation using self-administrated survey data and EBSS operating data. The substitution probabilities of a given EBSS trip for different transport modes is determined using the Bayesian inference method. Emission impact is measured by comparing the current emissions to those generated by other transport modes without the EBSS. Remarkably, the partial-trip substitution impact of the EBSS on car travel is investigated, where the EBSS serves as a connection to public transit. The results show that the EBSS barely generates new trips; most short-distance EBSS trips were transferred from walking and conventional bike-sharing, and most long-distance trips were shifted from cars, buses, and the subway. Around 5% of EBSS trips less than 2 km were transferred from the car by integrating with public transit while accounting for over half of the emission reduction. Based on our method, the CO 2 emissions per km of EBSS are 19.47 g, with 6.91 g generated by e-bikes due to electricity consumption and 12.56 g by trucks for battery swapping and bike relocation. The studied EBSS has saved 75.52% of CO 2 emissions that other transport modes could have generated. [ABSTRACT FROM AUTHOR]

• Outdoor thermal comfort is evaluated in an urban park in a hot-summer and cold-winter region of China (Chengdu). • Machine learning techniques are applied to determine the (nonlinear) relationship between thermal sensation vote and meteorological factors. • The effect of landscape spaces on human thermal comfort varies across the season. • NTR of PET is 6.79 °C to 19.90 °C, and NTR of UTCI is 13.27 °C to 27.25 °C. TAR of PET is 2.8 °C to 21.9 °C, and TAR of UTCI is 8.1 °C to 28.3 °C. Global warming and rapid urbanization have exacerbated the urban heat island effect. Urban parks contribute to alleviating such an effect and achieving the "carbon emission peak before 2030" and "carbon neutrality before 2060" goals of China. Their popularity is considerably influenced by human thermal comfort. However, limited thermal comfort studies have been conducted in the hot-summer and cold-winter region of China. This study examines human thermal comfort in different landscapes of an urban park in Chengdu and determines the thermal benchmarks. A machine learning (random forest) analysis shows that human thermal sensation is affected by different meteorological factors in different seasons. In addition, the influences of landscape space on human thermal comfort have considerable differences in different seasons. Residents prefer strong solar radiation in winter but fast wind speed in summer. UTCI (universal thermal climate index) is better than PET (physiological equivalent temperature) for outdoor thermal comfort assessment in the study area. This study serves as a valuable baseline and technical reference, contributing to sustainable urban park design. [ABSTRACT FROM AUTHOR]

• We explored the moderation effect of urban density on changes in physical activity during the COVID-2019 pandemic. • The results were based on a longitudinal study. • Urban density mitigated the decrease in physical activity caused by the pandemic. • The duration of physical activity performed in country parks increased during the pandemic. • A flexible and porous urban development strategy could enhance the resilience of a city during the pandemic and beyond. Various social distancing measures were carried out in many cities worldwide during the coronavirus disease 2019 pandemic (COVID-19). These measures have led to decreased physical activity levels and higher health risks among urban populations. Strong evidence has been established that built environment characteristics can stimulate physical activity and thus improve public health during non-pandemic periods. Urban density was arguably one of the most important built environment characteristics. However, little is known about whether high urban density amplifies or attenuates the decline in physical activity during the pandemic. Based on two-wave physical activity data collected before and during the pandemic (in January and May 2020, respectively), we used moderation analysis to compare the changes in physical activity levels between people living in low- and high-density neighborhoods. Our results showed that people living in low-density areas have a smaller decrease in physical activity conducted in neighborhood, compared to those living in high-density areas. Our findings suggest that a flexible and porous urban development strategy could enhance the resilience of a city during the coronavirus pandemic and beyond. [ABSTRACT FROM AUTHOR]

• Propose a people – metro – bike – route – urban space framework to describe the built environment. • Introduce the multilevel modeling framework to the studied problem. • Compare the integrated usage of dockless bike-sharing and the metro in different scenarios. • Investigate built environment effects on using dockless bike-sharing to connect the metro. • Discuss practical implications drawn from the empirical findings. The newly prevailing dockless bike-sharing system offers a decent solution to the first- and last-mile problem and connects trip origins/destinations and transit (mostly metro) stations. Few studies, however, have explored the effects of built environment characteristics on the integrated usage of dockless bike-sharing and the metro, especially in different conditions (e.g., access versus egress and morning peak versus evening peak) and using panel data. To fill the gap, this study proposes a people – metro – bike – route – urban space framework to describe the feeder-related built environment from the perspective of the feeder process. Using 3-day data of ofo bikes in Shenzhen, China, this study then develops multilevel negative binomial models that incorporate random effects and address the intracluster correlation attributed to repeated measures to scrutinize the feeder-related built environment effects on the integrated usage. The findings are listed as follows: (1) The majority of access and egress integrated trips have a distance range of 500–2000 m and a duration range of 2.5–10 min; (2) Popular metro stations (with a large ridership) are positively related to the access integrated usage; (3) The number of available shared bikes and the length of bikeway in the catchment areas of the metro are positively related to the integrated usage; and (4) Mixed land use increases the integrated usage, whereas urban villages are places with few demands for the integrated usage. These findings are beneficial in developing a bike-friendly built environment that facilitates the seamless connection between dockless bike-sharing and the metro. [ABSTRACT FROM AUTHOR]

A significant increase in the older population has been observed in numerous cities. Understanding the correlates of older adults' mobility is, therefore, of critical importance to meet their needs and preferences. Based on multisource data, including Google Street View street images and Hong Kong Travel Characteristics Survey 2011 data, we developed a series of multilevel and geographically weighted logistic regression models to scrutinize the global and local associations between urban greenery (eye-level street greenery, the number of parks, and the normalized difference vegetation index (NDVI)) and the travel propensity of older adults. Notably, eye-level street greenery was assessed by using readily available street images and machine learning techniques. The modeling results reveal that eye-level street greenery has a positive effect on older adults' mobility, but the number of parks and the NDVI do not significantly affect the mobility. Robustness checks verified the plausibility of these findings. Furthermore, the effect of street greenery varies over space, larger in suburban areas than in urban areas. This study advances the understanding of relationships between urban greenery and travel behavior. [ABSTRACT FROM AUTHOR]

• URT has a positive effect on nearby housing prices. • Transfer stations have a larger effect on nearby property prices than regular stations. • The price effects of URT accessibility are stronger in the suburban area than in the urban. • Practical implications, such as differential, location-specific value capture schemes, are discussed. Urban rail transit (URT) is shown to mitigate numerous contemporary urban problems (e.g., traffic congestion and environmental degradation). To reap such gains, many Chinese cities have actively constructed URT and/or have been on their way of doing so. However, these URT projects often encumber local governments with debt. Value capture schemes can be used to finance URT development. To implement such schemes, we need to understand the relationship between URT and property prices. Despite numerous studies on this topic in other countries, limited empirical studies in urban China have been conducted. Moreover, most previous studies are silent on two issues that are important to value capture, namely: (1) whether transfer stations provide a larger housing price premiums than regular stations, and (2) whether the price effect of URT accessibility is more perceptible in the suburban area than that in the urban. This study contributes to our understanding of these issues by constructing a set of hedonic pricing models using price data from 722 residential complexes in the vicinity of a URT line in Shenzhen, China. Our empirical results show that (1) URT has a positive impact on nearby property prices; (2) the positive impact diminishes quickly as the distance to URT station increases; (3) transfer stations have a larger positive effect on nearby property prices than regular stations; and (4) the price effects of URT accessibility are stronger in the suburban area than in the urban. The first two findings are in line with most previous studies, but the last two findings are seldom reported in existing literature. Practical implications of our findings, such as differential, location-specific value capture schemes, are further discussed. [ABSTRACT FROM AUTHOR]

• Combine the use of Geodetector analysis, PCA, and the GWR model. • Present a novel approach to analyze complex spatially non-stationary relationships. • Assess the spatial variation of PM 2.5 concentrations in the Pearl River Delta region by using the proposed methodology. This study develops an augmented geographically weighted regression (GWR) model to analyze the spatial distribution of PM 2.5 concentrations through the incorporation of Geodetector analysis and principal component analysis (PCA). The modeling approach we propose allows an effective identification of important PM 2.5 drivers and their spatial variation. Geodetector analysis is used to select predictor variables that truly affect the dependent variable, and PCA is adopted to eliminate multicollinearity among the variables. The spatial distribution of PM 2.5 concentrations within the Pearl River Delta region, China, is analyzed using the augmented GWR model. The augmented GWR model has an obvious advantage of parsimony. Moreover, it significantly outperforms the traditional regression model. [ABSTRACT FROM AUTHOR]

• Effects of accessibility to BRT stations and proximity to the BRT corridor on property prices in a Chinese city are simultaneously evaluated. • Accessibility to BRT stations is positively related to property prices, while proximity to the BRT corridor is negatively associated with the prices. • BRT provides higher accessibility benefits in peripheral areas, compared with central areas. • Many policy implications, such as value capture and reducing the negative externality of BRT corridor, are discussed. Bus rapid transit (BRT) is popular worldwide because it provides high-quality transit services at low costs. China is not an exception in this regard, and it tops the world rankings in BRT network expansion rate. Many studies conducted in the West have shown that BRT accessibility has a positive impact on nearby property prices (accessibility effect). Comparatively, few studies have been conducted in urban China. Moreover, there are even fewer empirical studies on the undesirable factors associated with proximity to the BRT corridor, such as noise, air pollution, and unpleasant visual scenes (proximity effect). Based on 16,165 housing samples in Xiamen (China), we estimated a set of, non-spatial and spatial, hedonic pricing models to simultaneously investigate the accessibility and proximity effects of BRT and tested whether or not accessibility-based premiums and proximity-induced discounts coexist in the housing market. Empirical results of this study suggest that: (1) accessibility to BRT positively affects nearby property prices; (2) proximity to the BRT corridor negatively affects property prices; and (3) accessibility to BRT has a stronger positive impact on property prices in peripheral areas than in central areas. Though the first outcome has been well recognized, the other two have rarely been studied in existing literature, thereby representing the potential contributions of this study. Finally, we proposed policy implications from these results. [ABSTRACT FROM AUTHOR]