Sustainable Cities and Society最新文献

{"title":"Beyond canopy expansion: Shrubs offer greater potential for enhancing green view equity in urban streetscapes","authors":"Xiaoli Jia ,&nbsp;Tania Ploumi ,&nbsp;Ellen White","doi":"10.1016/j.scs.2025.106812","DOIUrl":"10.1016/j.scs.2025.106812","url":null,"abstract":"<div><div>Urban street greenery is a core component of the urban living environment. Despite growing attention to green-space exposure, detailed studies of urban vegetation communities remain underexplored. In the context of optimizing existing assets, it is essential to identify potential opportunities that have thus far been overlooked. Traditional regression analyses often emphasize the most prevalent significant effects within a sample, which can obscure latent relationships or optimization strategies that have not yet been widely implemented. To address this, we combined active LiDAR scanning with deep learning to analyze 746.04 km of roads and 27,926 street-view images in Zhumadian City. We found that the Green View Index (GVI) contributed by both trees and shrubs declines with increasing urbanization intensity, although the magnitude and spatial distribution of these declines differ. Random forest predictions indicate that, compared to trees, shrubs exhibit markedly greater optimization potential in the urban core. By integrating our results with Baidu heatmap data, we quantified green-space exposure demand across different urbanization gradients. While both trees and shrubs can substantially enhance greenness in various urban areas, high pedestrian flows significantly dampen the actual effectiveness of green space exposure. Notably, highly urbanized zones show a far greater demand for shrubs than for trees, and this requirement remains substantially unmet. We argue that employing multifaceted techniques for urban green-space optimization and demand analysis will be pivotal for sustainable urban management. This requires more than merely expanding tree canopy.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106812"},"PeriodicalIF":12.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A research framework for constructing an urban heat island network to mitigate the urban heat island effect: A case study of Nanchang, China","authors":"Yuxiang Lan ,&nbsp;Jiahui Zheng ,&nbsp;Xupan Yue ,&nbsp;Yingxue Wen","doi":"10.1016/j.scs.2025.106839","DOIUrl":"10.1016/j.scs.2025.106839","url":null,"abstract":"<div><div>The urban heat island (UHI) effect has become an increasingly severe issue, prompting numerous studies on its mitigation. However, most existing research has been conducted at the patch level, overlooking the overall connectivity of UHI patches, which makes it difficult to reveal the thermal landscape patterns of UHI areas. This study made the central urban area of Nanchang as the research area. First, we employed methods such as morphological spatial pattern analysis and connectivity analysis to identify UHI sources and assess their importance. Next, geographical detectors were applied to analyze the driving forces of land surface temperature (LST) from 6 factors: population density, land cover (LC), elevation, gross domestic product (GDP), night light intensity (NI), and normalized vegetation index (NDVI). Based on this, we constructed a heat diffusion resistance surface. Finally, we used circuit theory to identify thermal corridors and key nodes, constructing a UHI network. The results showed that LC, NDVI, NI, and GDP are the four strongest driving factors for LST. The spatial distribution of heat diffusion resistance exhibited a distinct pattern, with low resistance at the center and high resistance at the periphery. A total of 71 UHI sources, 190 thermal corridors, and 35 key nodes were identified, mainly distributed in the southern and central areas of the study region, showing significant spatial heterogeneity. Finally, based on the structural characteristics of the UHI network, recommendations for efficient mitigation of UHI effects were proposed. The results provide valuable theoretical insights and practical strategies for alleviating UHI effects and improving urban climate resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106839"},"PeriodicalIF":12.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rising temperatures, rising vulnerabilities: Individual and community adaptation to heat stress in urban slums of Bangladesh","authors":"Zakia Sultana ,&nbsp;Heleen L.P. Mees ,&nbsp;Bishawjit Mallick ,&nbsp;Peter P.J. Driessen ,&nbsp;Ajay Bailey","doi":"10.1016/j.scs.2025.106803","DOIUrl":"10.1016/j.scs.2025.106803","url":null,"abstract":"<div><div>Rising temperatures and intensifying heat stress disproportionately affect low-income urban communities, exacerbating existing inequalities and vulnerabilities amid accelerating climate risks. Slum residents, facing unplanned housing structures and limited access to essential services like water, electricity, and gas, adopt individual and community-driven adaptation strategies to cope with extreme heat. However, the conditions that shape their ability to adapt remain largely underexplored. Employing the Everyone’s Adaptation framework, this study examines the key conditions that shape individual and community-level responses to heat stress. Data were collected through a qualitative mixed-methods approach, employing focus group discussions, in-depth interviews, expert interviews, and field observations conducted between June and August 2024 in Korail slum, Dhaka, Bangladesh. Findings indicate that individual resources, such as income, are pivotal to adaptation, while social capital supports community resilience through resource sharing and access to key facilities and services. However, persistent inequalities in resource distribution exacerbate individual vulnerabilities, intensifying the local impacts of global climate change. Participants emphasize the need for long-term adaptation strategies, such as improved housing conditions, over temporary interventions like cooling centers. The findings highlight the necessity of gender-specific and age-sensitive measures, as women face heightened challenges due to the dual responsibilities of household management and paid work and older adults face mobility challenges to access shaded spaces. By analyzing eight conditions across individual, community, and collective levels, this study advances understanding of everyday adaptation practice to heat stress and contributes to more equitable, context-specific adaptation policies that enhance urban resilience and address climate-related vulnerabilities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106803"},"PeriodicalIF":12.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the spatial heterogeneity impact of urban expansion on pollution and carbon emissions in China","authors":"Yong-Xiang Chen ,&nbsp;Yi Xiao ,&nbsp;Huan Huang ,&nbsp;Xin Xiang ,&nbsp;Zhen Li","doi":"10.1016/j.scs.2025.106827","DOIUrl":"10.1016/j.scs.2025.106827","url":null,"abstract":"<div><div>Urban expansion has long posed persistent environmental challenges, making it essential to examine its dynamic impacts on environmental performance across different types of Chinese cities to inform sustainable development strategies. This study proposes a novel evaluation framework, ESLP, which integrates urban expansion intensity, the urban sprawl index, and the land consumption rate to population growth rate (LCRPGR) to assess the multidimensional impacts of urban expansion on pollution and carbon emissions. It analyzes the spatial-temporal evolution of pollution and carbon emissions across 283 prefecture-level cities in China from 2006 to 2022, and applies a combined approach of econometric methods and the geographically and temporally weighted regression model to uncover the multifaceted effects. The results show that urban expansion in most cities is shifting toward intensification, with the coupling coordination degree of pollution and carbon emission reduction exhibiting rapid growth. Moreover, the coupling coordination degree demonstrates strong positive spatial spillover effects, significantly driving improvements in surrounding areas. Furthermore, the role of ESLP on pollution and carbon emissions was deeply addressed, revealing a divergent and spatial heterogeneity impact. These findings offer valuable insights for urban managers, enabling the development of more scientifically grounded and targeted policies to mitigate the adverse environmental impacts of urban expansion.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106827"},"PeriodicalIF":12.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heat vulnerability assessment in an Eastern Mediterranean city - a mixed-methods approach","authors":"Alix Pahaut ,&nbsp;Alaa Obeid ,&nbsp;Maya Negev","doi":"10.1016/j.scs.2025.106847","DOIUrl":"10.1016/j.scs.2025.106847","url":null,"abstract":"<div><div>Cities with already hot climates face distinct, intensifying challenges as global temperatures rise, particularly where existing urban and social conditions heighten vulnerability. Yet urban climate vulnerability assessments remain limited in such contexts, especially in smaller cities. This study presents a mixed-methods participatory heat risk assessment in Shefa-‘Amr, an Arab city in northern Israel, to explore how exposure, lived experiences, and adaptive capacity shape vulnerability to extreme heat. Findings indicate that while exposure to heat is relatively uniform across neighborhoods, there are areas with higher socioeconomic vulnerability and occupational exposure. Residents’ experiences portray heat as a familiar environmental stressor, embedded in daily life and partially managed through adaptation – yet still a persistent burden on health, wellbeing and finances, due to rising temperatures and limited adaptive capacity. This case study highlights that even where air conditioning is widespread, it cannot serve as a standalone adaptation strategy. The integration of spatial, quantitative, and qualitative data reveals both converging and diverging insights, including how uneven forms of social capital can affect collective adaptive capacity. The multi-dimensional approach also helps avoid some biases, for example demonstrating that despite some intra-urban variation, the city as a whole is more vulnerable to heat than national averages, reflecting the broader marginalization of Arab-Palestinian citizens of Israel. For such cities or communities, equitable heat adaptation requires pairing incremental measures, locally actionable even with limited adaptive capacity (e.g., increasing urban shade), with measures that address national-level structural disparities, and thus strengthen the capacity to cope with heat, as well as with other stressors.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106847"},"PeriodicalIF":12.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting urban poverty and food insecurity: A community-informed spatial analysis and machine learning approach","authors":"Woojin Jung ,&nbsp;Quentin Stoeffler ,&nbsp;Andrew H. Kim ,&nbsp;Sajedeh Goudarzi ,&nbsp;Rofaida Benotsmane ,&nbsp;Vatsal Shah","doi":"10.1016/j.scs.2025.106799","DOIUrl":"10.1016/j.scs.2025.106799","url":null,"abstract":"<div><div>Recent advances in poverty prediction at a national scale employ new data sources and machine learning (ML) techniques. However, evidence related to the performance of these models for households experiencing poverty, food insecurity, and nutritional deficiency in urban areas is scarce. This research explores how geospatial indicators, particularly those informed by community insights, improve poverty prediction and household targeting for social transfers in urban settings. To study this issue, we combine data from a household survey collected in Lusaka, Zambia, with geospatial features such as neighborhood structure, access to infrastructure and services, satellite images, and connectivity metrics. By leveraging artificial intelligence (AI)/ML techniques, we use these features to predict welfare outcomes, focusing on asset-based wealth and food security. We find that algorithms that combine community-informed spatial features with household-level information are effective in predicting asset-based wealth scores, explaining 67.1% of the variance. While predicting food insecurity and iron deficiency is more challenging, incorporating spatial features still enhances prediction accuracy. As a result, the integration of community-informed spatial features leads to substantial gains in simulated poverty reduction, while also enhancing the transparency of targeting algorithms and addressing potential legitimacy concerns. Our process of aligning community insights with spatial data is adaptable to other urban settings. Policymakers can apply this methodology to build scalable and shock-responsive social safety net systems to address poverty and geographic inequality in urban areas.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"134 ","pages":"Article 106799"},"PeriodicalIF":12.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145278187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the impact of urban geometric detail for urban air mobility risk forecasting","authors":"Akshay Patil,&nbsp;Clara García-Sánchez","doi":"10.1016/j.scs.2025.106750","DOIUrl":"10.1016/j.scs.2025.106750","url":null,"abstract":"<div><div>Wind flow predictions in realistic urban areas are sensitive to a wide range of governing parameters such as building resolution, wind incidence, urban morphology, and underlying topography, to name a few. In this study, we systematically study the impact of the geometric level of detail (LoD) of the urban built environment using a Reynolds Averaged Navier–Stokes (RANS) computational framework specifically tailored for urban air mobility. Using a wind-incidence angular resolution of <span><math><mrow><mn>1</mn><mo>°</mo></mrow></math></span>, we simulated a total of 1440 simulations for two distinct urban areas and developed a probabilistic risk metric (<span><math><msup><mrow><mi>P</mi></mrow><mrow><mi>r</mi></mrow></msup></math></span>) based on velocity and turbulence fields that allow us to compare the impact of LoD 1.2 (lower geometric detail) and LoD 2.2 (higher geometric detail). Comparing the wind-rose weighted average velocity and the risk map, we found that LoD 2.2 provides a more conservative prediction for high-risk areas than LoD 1.2, suggesting the need to adopt higher geometric details when applicable. Our results present a cautionary view on the impact of LoD and how automatic reconstruction can further the efficiency of current wind engineering practices.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106750"},"PeriodicalIF":12.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NLP-driven crowdsourcing for urban flood monitoring: insights from mumbai","authors":"Aniket Navalkar ,&nbsp;Shrabani S. Tripathy ,&nbsp;Mayank Gupta ,&nbsp;Sanghita Basu ,&nbsp;Puja Tripathy ,&nbsp;Archismita Banerjee ,&nbsp;Sheeba Sekharan ,&nbsp;Raghu Murtugudde ,&nbsp;Subimal Ghosh","doi":"10.1016/j.scs.2025.106795","DOIUrl":"10.1016/j.scs.2025.106795","url":null,"abstract":"<div><div>With increasing extreme rainfall events, densely populated megacities face recurrent flooding, necessitating real-time flood monitoring systems for citizens. Social media platforms like Twitter (now X) can complement conventional sensor-based flood monitoring. However, their utility is limited by low geotagging and a data stream cluttered with extraneous content. We take the case study of Mumbai to demonstrate how Natural Language Processing (NLP) can be leveraged to identify flood-relevant tweets. Applying NLP to a historical Twitter dataset (2017 – 2022), we match the text patterns in tweets against lexical keyword datasets to geocode them and classify their sentiment as ‘positive’ or ‘negative’. We observe a decline in the daily positivity ratio of tweets with increasing daily average rainfall for all years. We find that negative tweets are concentrated in areas of orographic rainfall, higher population density, and lower elevations. Further, we analysed extreme rainfall impacts on urban transportation using a network theory-based global efficiency loss (GEL) metric. For this, we configured Q-NEAT, a GIS-based network model, to perform time-optimized routing by integrating geolocated negative tweets alongside hotspots identified by the municipal corporation. We found that Twitter explained an additional 1.13 %, 20.96 %, and 67 % of the total GEL detected on the extreme rainfall days, 16th July 2021 (199.87 mm), 2nd July 2019 (200.12 mm), and 25th June 2018 (153.14 mm). Such impacts are not fully detected by the municipal corporation’s thumb rule for identifying waterlogging based on precipitation amounts and field surveys. Our findings underscore the advantages of crowdsourced data for enhancing urban flood monitoring and impact assessment.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106795"},"PeriodicalIF":12.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering SMEs with SustainWater Bot to advance urban water sustainability","authors":"Muhammad Arslan ,&nbsp;Saba Munawar ,&nbsp;Zainab Riaz","doi":"10.1016/j.scs.2025.106793","DOIUrl":"10.1016/j.scs.2025.106793","url":null,"abstract":"<div><div>Climate change, population growth, and resource constraints are intensifying pressure on urban water systems (UWSs), prompting a shift toward integrated information management. Due to their agility and reach, small- and medium-sized enterprises (SMEs) are central to this transition. However, many SMEs lack access to robust information systems (ISs) that consolidate government initiatives, industry trends, and broader water-related data, impeding sustainable adoption. This study introduces <em>SustainWater Bot</em>, a chatbot driven by generative artificial Intelligence (GenAI), including large language models (LLMs) and retrieval-augmented generation (RAG). Designed to fill this information gap, <em>SustainWater Bot</em> addresses the shortcomings of conventional LLMs, such as information misalignment, over-complexity, and information deficiencies. RAG enables semantic consolidation of various sources, such as news, government reports, industry insights, academic research, and social media, into an integrated IS. The evaluation results showed that RAG with LLM-based methods outperformed traditional information retrieval (IR) techniques, with Llama3.2:3b achieving top scores in precision (95 %), completeness (95 %), and exact match (90 %). Traditional IR techniques such as term frequency-inverse document frequency (TF-IDF) and best matching 25 (BM25) performed lower but offered quicker responses. <em>SustainWater Bot</em> supports informed decision-making through a question-answering (QA) framework that delivers relevant insights on sustainable urban water initiatives (SUWIs). It is built on open-source technologies and offers SMEs a cost-effective, scalable, and sustainable solution to enhance eco-friendly water practices and operational efficiency.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106793"},"PeriodicalIF":12.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable impact of urban road class on smart transportation systems: A field data-informed exploration","authors":"Bin Sun ,&nbsp;Qiang Bai ,&nbsp;Qijun Zhang ,&nbsp;Hongjun Mao","doi":"10.1016/j.scs.2025.106792","DOIUrl":"10.1016/j.scs.2025.106792","url":null,"abstract":"<div><div>Urban transportation systems face critical sustainability challenges, including congestion and carbon emissions, where the Speed Guidance System (SGS) serves as a key smart technology. However, systematic analysis of its context-dependent impacts—particularly across urban road classes—remains limited, hindering optimized deployment. This study addresses this gap by leveraging field data from Zibo City, collecting and processing millions of vehicle trajectory records using the SGS, categorized by road class (branch, minor arterial, arterial, expressway). Through sustainability metrics (travel comfort, delay, carbon emissions), the analysis reveals significant road-class variations in SGS optimization effects. Results show that SGS performance differs markedly by road class: it degrades sustainability on branch roads (increasing delays by 11 % and emissions by 1-16 %) but optimizes minor arterials most effectively (reducing delays by 20 % and emissions by 2 %). Crucially, driver inexperience reduces comfort by 11-29 % across classes, while emission peaks controlled near 60 km/h enable superior decarbonization on minor arterials. Based on partial correlation analysis, the study develops road-class-specific carbon emission regression models and proposes three targeted strategies (e.g., Prioritizing minor arterial deployment). This work advances intelligent transportation by providing a data-informed understanding of SGS's sustainable impacts under varying urban contexts.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106792"},"PeriodicalIF":12.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}