Sustainable Cities and Society可持续城市与社会
Sustainable Cities and Society(英文缩写 SUSTAIN CITIES SOC),ISSN 2210-6707,eISSN 2210-6715,中文译名:可持续城市与社会 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 10.696 | Q1 |
| 2022 | 11.700 | Q1 |
| 2023 | 10.500 | Q1 |
| 2024 | 12.000 | Q1 |
| 2025 | 13.300 | Q1 |
Sustainable Cities and Society 最新收录文献
-
1. {"_":"Potential causal links of long-term PM components exposure with diabetes incidence and mortality in the United States.","sub":["2.5"]}
1. {"_":"美国长期PMPMID:日期:2024-12-20There is limited evidence on the relationship of diabetes burden with fine particulate matter (PM) and its components, which is not conducive to sustainable development in the context of rapid urbanization. To obtain relevant clues in the United States (US), we collected annual county-level diabetes incidence and mortality, concentrations of PM and five major components (including elemental carbon, organic carbon, sulfate, nitrate, and ammonium), temperature, and socioeconomic factors during 2008-2017. Through an integrating method of difference-in-differences approach and quantile G-computation, we observed that (i) long-term PM components mixture exposure was associated with diabetes mortality, but not incidence, with percent risk increase (IR%) of 3.58% (95%CI: 1.84%, 5.36%); (ii) among the five components of PM, sulfate was estimated to have the largest weight (0.519); (iii) the effect of PM and its components mixture was higher when the summer mean temperature was 2 or 3 degrees below the 10-year average temperature; (iv) in counties with higher health insurance coverage, nitrate was the most important component (with the greatest weight of 0.829). Our findings suggest that long-term PM exposure is associated with increased diabetes mortality, and reducing sulfate and nitrate emission could effectively alleviate the burden of PM-related diabetes mortality in the US.
-
2. Reducing the social inequity of neighborhood visual environment in Los Angeles through computer vision and multi-model machine learning.
2. 通过计算机视觉和多模型机器学习减少洛杉矶社区视觉环境的社会不平等PMID:日期:2024-12-15Aligning with the United Nations' Sustainable Development Goals, the focus on creating safe, sustainable cities and enhancing the wellbeing of individuals across all age groups has become a central aspect of urban planning and environmental management. The environments we live in significantly influence our thoughts, emotions, and interactions with the world around us. Our study aims to unveil the social inequity of citizens' wellbeing, reflected by their perception on neighborhood environment (e.g., feeling of depression), across different social/vulnerable groups (i.e., White, Black, Asian, Hispanic, low-income, low-educated, and unemployed) via crowdsourced street view imageries and computer vision. Specifically, we quantified the actual built environment in the 5D dimensions (i.e., density, diversity, design, distance, and destination) based on multiple sources; measured six types of neighborhood visual environment (i.e., perception of beautiful, safe, wealthy, liveable, boring and depressing) and the overall neighborhood soundness index by using computer vision technique and street view imageries collected from Mapillary; and unveiled the actual built environmental features that are associated with people's visual perception towards the surrounding environment via multi-model machine learning methods. Our pilot study in Los Angeles County finds that neighborhoods with higher concentrations of Black, Hispanic, low-income, low-educated, and unemployed populations are perceived as less beautiful, liveable, safe, and wealthy. The most important actual built environment features positively influencing human perception include the density of canopy, followed by the density of multiple units, the distance to CBD, and car commuting to destinations, regardless of social groups. Our key findings provide place-based evidence for the design and upgrading of the community environment that further affects people's daily activity and living style. Our framework and methods can be applied to cross-disciplinary studies, aiding urban planning and healthy city initiatives with place-based evidence.
-
3. Potential causal links and mediation pathway between urban greenness and lung cancer mortality: Result from a large cohort (2009 to 2020).
3. 城市绿度与肺癌死亡率之间的潜在因果关联及中介路径：基于一项大型队列研究（2009年至2020年）的结果PMID:日期:2023-11-22Urban greenness, as a vital component of the urban environment, plays a critical role in mitigating the adverse effects of rapid urbanization and supporting urban sustainability. However, the causal links between urban greenness and lung cancer mortality and its potential causal pathway remain poorly understood. Based on a prospective community-based cohort with 581,785 adult participants in southern China, we applied a doubly robust Cox proportional hazard model to estimate the causal associations between urban greenness exposure and lung cancer mortality. A general multiple mediation analysis method was utilized to further assess the potential mediating roles of various factors including particulate matter (PM, PM, and PM), temperature, physical activity, and body mass index (BMI). We observed that each interquartile range (IQR: 0.06) increment in greenness exposure was inversely associated with lung cancer mortality, with a hazard ratio (HR) of 0.89 (95 % CI: 0.83, 0.96). The relationship between greenness and lung cancer mortality might be partially mediated by particulate matter, temperature, and physical activity, yielding a total indirect effect of 0.826 (95 % CI: 0.769, 0.887) for each IQR increase in greenness exposure. Notably, the protective effect of greenness against lung cancer mortality could be achieved primarily by reducing the particulate matter concentration.
-
4. Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city.
PMID:日期:2023-06-28This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.
-
5. Towards the new generation of courtyard buildings as a healthy living concept for post-pandemic era.
5. 面向后疫情时代作为健康生活理念的新一代庭院式建筑PMID:日期:2023-06-21COVID-19 has laid a context for holistic research and practical approaches towards health issues in buildings. This study focuses on one particular residential building type, which is a combination of a modern apartment building with private double-oriented terraces, and a traditional courtyard building. This principle improves several aspects of healthy buildings and contributes to address indoor-outdoor interactions, daylighting, and the use of natural ventilation. The purpose of this study is to determine the factors underlying a particular type of semi-outdoor space within building forms and to explain their microclimatic behavior in buildings. One solid model and twelve porous apartment buildings with different numbers of porous sides, and terrace widths are evaluated using computational fluid dynamics. The k-ε turbulence model is adapted to simulate airflow in and around a four-story building. CFD simulations were validated against the wind-tunnel measurements. Investigations indicated that increasing the number of porous sides reduces the internal mean and maximum ages of air by -15.75 and -36.84%, which means improved ventilation performance. However, it leaves a negative trace on ventilation of the semi-outdoor spaces. Meanwhile, increasing the width of the terraces enhances the ventilation performance by reducing the mean age of air in units, courtyards, and terraces by -20%, -20%, and -9%, respectively.
-
6. Sustainable urban mobility: Flexible bus service network design in the post-pandemic era.
PMID:日期:2023-06-12The excessive traffic congestion in vehicles lowers the service quality of urban bus system, reduces the social distance of bus passengers, and thus, increases the spread speed of epidemics, such as coronavirus disease. In the post-pandemic era, it is one of the main concerns for the transportation agency to provide a sustainable urban bus service to balance the travel convenience in accessibility and the travel safety in social distance for bus passengers, which essentially reduces the in-vehicle passenger congestion or smooths the boarding-alighting unbalance of passengers. Incorporating the route choice behavior of passengers, this paper proposes a sustainable service network design strategy by selecting one subset of the stops to maximize the total passenger-distance (person kilometers) with exogenously given loading factor and stop-spacing level, which can be captured by constrained non-linear programming model. The loading factor directly determines the in-vehicle social distance, and the stop-spacing level can efficiently reduce the ridership with short journey distance. Therefore, the sustainable service network design can be used to help the government minimize the spread of the virus while guaranteeing the service quality of transport patterns in the post-pandemic era. A real-world case study is adopted to illustrate the validity of the proposed scheme and model.
-
7. Changes in public bike usage after the COVID-19 outbreak: A survey of Seoul public bike sharing users.
PMID:日期:2023-06-10When the COVID-19 pandemic swept across the world, people tended to seek more individualized and viable transportation modes, such as a bicycle. In this study, we examined the factors influencing changes in public bike sharing (PBS) in Seoul, to assess this trend post-pandemic. We conducted an online survey of 1,590 Seoul PBS users between July 30 and August 7, 2020. Using a difference-in-differences analysis, we found that participants who were affected by the pandemic used PBS 44.6 h more than unaffected individuals throughout the year. In addition, we used a multinomial logistic regression analysis to identify the factors affecting changes in PBS usage. In this analysis, the discrete dependent variables of , and were considered, representing the changes in PBS usage after the COVID-19 outbreak. Results revealed that PBS usage increased among female participants during weekday trips such as commuting to work and when there were perceived health benefits of using PBS. Conversely, PBS usage tended to decrease when the weekday trip purpose was for leisure or working out. Our findings offer insight into PBS user behaviors within the context of the COVID-19 pandemic and present policy implications to revitalize PBS usage.
-
8. Crowding on public transport using smart card data during the COVID-19 pandemic: New methodology and case study in Chile.
PMID:日期:2023-06-08Most crowding measures in public transportation are usually aggregated at a service level. This type of aggregation does not help to analyze microscopic behavior such as exposure risk to viruses. To bridge such a gap, our paper proposes four novel crowding measures that might be well suited to proxy virus exposure risk at public transport. In addition, we conduct a case study in Santiago, Chile, using smart card data of the buses system to compute the proposed measures for three different and relevant periods of the COVID-19 pandemic: before, during, and after Santiago's lockdown. We find that the governmental policies diminished public transport crowding considerably for the lockdown phase. The average exposure time when social distancing is not possible passes from 6.39 min before lockdown to 0.03 min during the lockdown, while the average number of encountered persons passes from 43.33 to 5.89. We shed light on how the pandemic impacts differ across various population groups in society. Our findings suggest that poorer municipalities returned faster to crowding levels similar to those before the pandemic.
-
9. Generative design for COVID-19 and future pathogens using stochastic multi-agent simulation.
9. 利用随机多智能体模拟进行COVID-19和未来病原体的生成式设计PMID:日期:2023-06-01We propose a generative design workflow that integrates a stochastic multi-agent simulation with the intent of helping building designers reduce the risk posed by COVID-19 and future pathogens. Our custom simulation randomly generates activities and movements of individual occupants, tracking the amount of virus transmitted through air and surfaces from contagious to susceptible agents. The stochastic nature of the simulation requires that many repetitions be performed to achieve statistically reliable results. Accordingly, a series of initial experiments identified parameter values that balanced the trade-off between computational cost and accuracy. Applying generative design to a case study based on an existing office space reduced the predicted transmission by around 10% to 20% compared with a baseline set of layouts. Additionally, a qualitative examination of the generated layouts revealed design patterns that may reduce transmission. Stochastic multi-agent simulation is a computationally expensive yet plausible way to generate safer building designs.
-
10. A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19.
10. 新冠疫情早期阶段住宅垃圾收集率的跨辖区比较PMID:日期:2023-05-28There is currently a lack of studies on residential waste collection during COVID-19 in North America. SARIMA models were developed to predict residential waste collection rates (RWCR) across four North American jurisdictions before and during the pandemic. Unlike waste disposal rates, RWCR is relatively less sensitive to the changes in COVID-19 regulatory policies and administrative measures, making RWCR more appropriate for cross-jurisdictional comparisons. It is hypothesized that the use of RWCR in forecasting models will help us to better understand the residential waste generation behaviors in North America. Both SARIMA models performed satisfactorily in predicting Regina's RWCR. The SARIMA D model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA B). The skewness of overprediction ratios was noticeably different between jurisdictions, and modeling errors were generally lower in less populated cities. Conflicting behavioral changes might have altered the residential waste generation characteristics and recycling behaviors differently across the jurisdictions. Overall, SARIMA D performed better in the Canadian jurisdiction than in U.S. jurisdictions, likely due to the model's bias on a less variable input dataset. The use of RWCR in forecasting models helps us to better understand the residential waste generation behaviors in North America and better prepare us for a future global pandemic.