Sustainable Cities and Society最新文献

{"title":"Microclimatic impacts of building projects on the local neighborhood: criteria for well-founded urban planning","authors":"Martin Schneider,&nbsp;Tanja Tötzer,&nbsp;Marianne Bügelmayer-Blaschek","doi":"10.1016/j.scs.2026.107215","DOIUrl":"10.1016/j.scs.2026.107215","url":null,"abstract":"<div><div>City administration is struggling with steering urban development into a climate resilient direction and needs supportive guidelines for informed decision-making. The results of this research present a planning criteria catalogue to identify microclimate-sensitive development projects to the surrounding area. It provides selected parameters and thresholds characterizing construction projects to request spatially extended microclimatic evaluations based on changes of expected spatial extension and intensity of 2 m air temperature in the surrounding area. To quantify the impact of project characteristics on this evaluation metric in the neighborhood, 50 experiments were conducted for inner-city and periphery domains using the urban climate model PALM with varying static input parameters including area size, building height, and Local Climate Zone (LCZ) classifications representing different building densities and soil sealing patterns. The resulting impacts are evaluated in a distance of 50-100 meters from construction sites. Development projects in an LCZ compact style, showed air temperature increases of up to 1.5°C during evening hours in an inner-city domain. LCZ open configurations caused slightly higher temperatures during the night and morning hours of up to 0.7°C. For a periphery domain, LCZ open did not show any notable impact on the surroundings, while LCZ large low-rise caused persistent temperature increases peaking at 1.5°C in evening hours. Based on these findings, a practical catalogue of criteria was developed to guide authorities in determining when spatially extended microclimate analyses (including the potentially affected neighborhood) should be required or recommended. The study suggests extended assessments when air temperature changes exceed 1°C in surrounding areas during any time during the day, which is particularly the case for compact and large low-rise built environments. This quantitative framework guides authorities to decide in which cases a climate simulation is recommended or required for the assessment of projects with potential significant microclimatic impacts on neighborhoods.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107215"},"PeriodicalIF":12.0,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192885","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 heat, rising sirens: Spatiotemporal disparities and socio-spatial drivers of heat-related illness exposure risk in Japan","authors":"Jie Chen ,&nbsp;Zhenglun Yang ,&nbsp;Yuxiao Jiang ,&nbsp;Haosen Yang ,&nbsp;Linchuan Yang","doi":"10.1016/j.scs.2026.107210","DOIUrl":"10.1016/j.scs.2026.107210","url":null,"abstract":"<div><div>With the intensification of climate change, heat-related health risks have emerged as a critical challenge to sustainable urban development. This study investigates the spatiotemporal dynamics and correlates of heat-related illness exposure risk across Japan over the period 2003–2020. Municipality-level heat-related illness risk was estimated using heat-related ambulance transport records alongside multi-source environmental and socio-demographic datasets, with spatial mapping subsequently applied to identify temporal trends and the spatial shift of high-risk zones. Machine learning models were then employed to evaluate the nonlinear and interactive effects of urbanization and social vulnerability on long-term risk trajectories. The key findings are as follows: (1) High-risk areas and exposed populations have expanded outward from major metropolitan regions; (2) Approximately one-quarter of municipalities experienced a significant increase in heat-related illness exposure risk; (3) Marked regional and urban inequalities are evident in both risk levels and temporal trends; and (4) Pronounced nonlinear relationships and interactive effects exist between urbanization, social vulnerability, and temporal trends in exposure risk. This study advances our understanding of the dynamic evolutionary characteristics underpinning heat-related health risks and elucidates their coupling mechanisms with urbanization and demographic attributes, thereby providing empirical support for the formulation of targeted, precise, and sustainable urban planning and heat adaptation strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107210"},"PeriodicalIF":12.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192921","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":"Integrating machine learning and interval fuzzy AHP for assessing metro station resilience to urban flooding","authors":"Wen He ,&nbsp;Yue Pan ,&nbsp;Shuo Zhang ,&nbsp;Guanlin Ye ,&nbsp;Jin-Jian Chen","doi":"10.1016/j.scs.2026.107213","DOIUrl":"10.1016/j.scs.2026.107213","url":null,"abstract":"<div><div>Under the dual pressures of climate change and urbanization, increasing extreme rainfall events pose significant challenges to the operation and management of urban metro systems. This study develops a multi-source, phased resilience indicator system to describe key resilient characteristics of the metro network. A novel quantitative framework called PU-IFAHP-KMEANS is developed by integrating Positive-Unlabeled (PU) learning, Interval Fuzzy Analytical Hierarchy Process (IFAHP), and K-means clustering, aiming to comprehensively assess metro station resilience under extreme rainfall conditions. The proposed PU-IFAHP-KMEANS is applied to the Shanghai metro network, evaluating resilience across three dimensions: pre-disaster flood risk prediction, vulnerability during disasters, and post-disaster recovery. By combining these three dimensions, the resilience levels of metro stations to flood disasters can be precisely quantified and visualized through Geographic Information System (GIS). Several key findings are revealed: (i) High-resilience metro stations are typically located in the suburbs of Shanghai, while low-resilience stations are mostly concentrated in the downtown areas; and (ii) Key factors such as Point of Interest (POI) density, maximum hourly rainfall, and betweenness centrality significantly impact resilience levels. Moreover, comparative experiments demonstrate that PU-IFAHP-KMEANS reduces subjectivity and uncertainty from expert input while demonstrating strong adaptability to varying rainfall scenarios. Practically, PU-IFAHP-KMEANS offers practical utility in identifying at-risk stations and enhancing targeted flood mitigation and emergency response strategies, thereby advancing the resilience of metro systems in the face of intensifying climatic extremes.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107213"},"PeriodicalIF":12.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192883","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":"Context-sensitive analysis of disaster resilience and equity through geospatial explainable machine learning","authors":"Yirong Ding ,&nbsp;Lu Zhang ,&nbsp;Yang Zhang","doi":"10.1016/j.scs.2026.107203","DOIUrl":"10.1016/j.scs.2026.107203","url":null,"abstract":"<div><div>Recurrent hurricanes pose significant challenges for disaster resilience and equitable recovery, yet there is limited research focusing on examining equity’s role in resilience across multiple, temporally distinct disasters. To address this gap, our study analyzes Florida communities impacted sequentially by Hurricanes Irma (2017) and Ian (2022), using FEMA Individual Assistance declarations to delineate the overlapping disaster zones. Leveraging geo-information embedded explainable machine learning, which integrates spatially explicit relationships into machine learning frameworks, we explored the interplay between disaster equity and disaster resilience. Specifically, we examined how (1) place-based equity, measured by building code standards, hazard exposure, and building conditions, and (2) capacity-based equity, measured by socioeconomic and demographic factors, influence three critical dimensions of resilience: disaster impact containment, resource mobilization, and recovery capability. Additionally, we assessed how these relationships and the relative influence of equity determinants change between the two hurricane events. This research contributes to the body of knowledge on community resilience by providing new insights into the dynamics of resilience-equity interactions across communities experiencing recurrent disasters. The findings offer actionable guidance for designing context-sensitive disaster management strategies for disaster-prone communities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107203"},"PeriodicalIF":12.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147116","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":"Spatiotemporal dynamics and multidimensional drivers of urban diurnal temperature range: Evidence from integrated learning at the national scale in China","authors":"Tao Wu ,&nbsp;Shujie Yang ,&nbsp;Jingkai Zhao ,&nbsp;Ruhang Wei ,&nbsp;Siying Li ,&nbsp;Zeyin Chen ,&nbsp;Renlu Qiao ,&nbsp;Zhiqiang Wu ,&nbsp;Shiqi Zhou","doi":"10.1016/j.scs.2026.107202","DOIUrl":"10.1016/j.scs.2026.107202","url":null,"abstract":"<div><div>Urbanization and global warming are profoundly altering the diurnal temperature range (DTR), a key indicator of climate change with direct implications for public health and urban resilience. Yet, systematic evidence disentangling DTR dynamics across climatic zones at the national scale remains scarce. Using 1 km resolution MODIS LST data for China (2010–2020), this study integrates spatiotemporal trend analysis with explainable machine learning to characterize national DTR patterns and identify their heterogeneous drivers. The results show that: (1) daytime surface warming (+0.013 °C yr⁻¹) has outpaced nighttime warming (+0.008 °C yr⁻¹), leading to an overall slight increase in DTR, with the trend most pronounced in the warm temperate zone; (2) natural systems exert the strongest influence (42.1 %), with proximity to water bodies acting as the most critical regulator—reducing DTR by 2–3 °C within 5 km—while vegetation effects are strongly climate-dependent; (3) urban physical morphology exerts dual impacts, as high built-up density generally amplifies DTR, whereas taller buildings mitigate it by enhancing ventilation; and (4) socioeconomic factors overall moderate DTR, with population density showing the most consistent effect, while nighttime light intensity anomalously amplifies DTR in humid regions. By systematically revealing the climatic heterogeneity of DTR drivers, this study underscores the pivotal role of water bodies and urban form in regulating urban heat. The findings provide a scientific basis for context-specific nature-based solutions and resilience-oriented planning strategies to mitigate thermal risks under accelerating climate change and urbanization.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107202"},"PeriodicalIF":12.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147119","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":"Sensor configuration optimization for source term estimation of time-varying emissions","authors":"Yiping Lin ,&nbsp;Hong Huang ,&nbsp;Jing Wang ,&nbsp;Xiaole Zhang","doi":"10.1016/j.scs.2026.107186","DOIUrl":"10.1016/j.scs.2026.107186","url":null,"abstract":"<div><div>When a hazardous gas leakage accident occurs, accurate source term estimation is essential for timely emergency response. However, the release rate of source is usually time-varying, making the estimation extremely challenging. This paper employs a sensor configuration optimization method to improve the performance of source term estimation for time-varying sources. The method integrates an objective function based on the gradient of the adjoint concentration field with a Genetic Algorithm to identify the most sensitive sensor location combinations. The result shows that the proposed optimum configuration significantly improves the accuracy of source location estimation, compared with uniform configurations. Three release scenarios (constant/periodic/decaying) are analyzed, and the proposed optimum configuration significantly enhances the accuracy and stability of estimations on both the location and the strength of the source. Besides, the analysis reveals that accurate source strength estimation requires more sensors than source location estimation.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107186"},"PeriodicalIF":12.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147117","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 scalable and efficient framework for city-scale building energy modeling with microclimate considerations","authors":"Ke Liu ,&nbsp;Xiaodong Xu ,&nbsp;Deqing Lin ,&nbsp;Ran Zhang ,&nbsp;Linzhi Zhao ,&nbsp;Abudureheman Abuduwayiti ,&nbsp;Francesco Causone","doi":"10.1016/j.scs.2026.107187","DOIUrl":"10.1016/j.scs.2026.107187","url":null,"abstract":"<div><div>Urban building energy modeling (UBEM) is essential for supporting urban energy efficiency assessment and low-carbon policy making. This study proposes a scalable and efficient UBEM framework and applies it to Nanjing, China. Multi-source urban data were integrated to construct a unified urban building geodatabase. Building archetypes were developed based on building type and construction year, and modified weather files for each block were generated using the Urban Weather Generator (UWG). Through an automated workflow combining Python scripting and parallel computing, annual building energy simulations were conducted for 49,793 buildings across 1693 urban blocks. The comparative analysis reveals that neglecting microclimate effects results in an 11.4% underestimation of cooling demand and a 10.5% overestimation of heating demand, with the largest deviations occurring in high-density districts. The results also indicate notable variations in average energy use intensity (EUI) across building types, with healthcare and commercial buildings exhibiting the highest demand. Spatially, high-rise clusters in newly developed areas and large public facilities form major energy hotspots, whereas older low-rise residential areas show lower overall demand. A comparison with measured data showed that the simulated EUIs of public buildings were within ±20%, confirming the reliability of the framework. The proposed approach completed city-scale simulations within approximately 36 hours on standard hardware, highlighting its scalability and computational efficiency. Overall, this study provides a practical UBEM framework for identifying spatial patterns and energy hotspots of building energy use at the city scale, supporting energy-efficient urban planning and targeted mitigation strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107187"},"PeriodicalIF":12.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080992","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":"Microclimatic dynamics and hydrological patterns in urban heat islands - A comprehensive perspective","authors":"Nasim Sadra ,&nbsp;Mohammad Reza Nikoo ,&nbsp;Abolfazl Nazari Giglou ,&nbsp;Amir H. Gandomi","doi":"10.1016/j.scs.2026.107184","DOIUrl":"10.1016/j.scs.2026.107184","url":null,"abstract":"<div><div>Urban Heat Islands (UHIs) and their associated microclimatic variability significantly impact hydrological patterns, necessitating the accurate quantification of these effects for effective urban water resource management. This study synthesises research from the early 2000s to 2025 on the complex interactions between urban microclimates and hydrology, focusing on precipitation patterns, runoff, evapotranspiration, and water quality in UHI. The research examines various methodologies employed to study these interactions, including observational research, modelling approaches, and advanced technologies such as remote sensing and machine learning. While certain methods prove effective for specific aspects of UHI hydrology, their performance varies across urban contexts and climates. Machine learning techniques have shown promise in capturing microclimatic nuances, but challenges persist in data integration and model generalisation. This review makes a distinct contribution to literature by bringing together recent research with an introduction to the novel Hydrological Urban Heat Island (HUHI) framework. It extends beyond conventional UHI research by explicitly accounting for the interconnection between thermal-hydrological processes, which leads to a novel and integrated understanding of urban water systems. We also propose a novel methodology for related studies with a strategic application of remote sensing proxies, a unified classification technique for enhanced transferability between models, and a critical transition from correlation to causal inference. It is a comprehensive strategy in which the goal is to overcome present difficulties associated with reducing urban water hazards and support more efficient and cost-effective climate-resilient planning.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107184"},"PeriodicalIF":12.0,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081086","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":"Thermal resilience in the built environment: A critical review","authors":"Yingyue Li ,&nbsp;Eirini Tsouknida ,&nbsp;Tom Collins ,&nbsp;Ashley Bateson ,&nbsp;Rui Tang ,&nbsp;Esfandiar Burman","doi":"10.1016/j.scs.2026.107180","DOIUrl":"10.1016/j.scs.2026.107180","url":null,"abstract":"<div><div>With the increasing frequency and severity of extreme weather events including heatwaves and cold snaps, enhancing thermal resilience has become a critical priority for the built environment. Existing studies offer advanced knowledge on building overheating risk, resilient cooling, and related adaptation strategies, but often remain fragmented and focused on isolated topics. Despite this growing body of research, no comprehensive review has yet synthesized these developments. This paper presents a comprehensive review of more than 100 peer‑reviewed journal articles on thermal resilience in the built environment, covering definitions, application domains, disturbance categories, scenario construction, and performance evaluation methods. The review critically examines current research trends from a broader perspective and reveals the diversity in current approaches. This paper further proposes a cross-scale framework linking urban and building thermal resilience and offers practical recommendations for different stakeholders. It also advocates integrating climate resilience with net-zero targets for the transition to a robust and future-ready built environment.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107180"},"PeriodicalIF":12.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081081","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":"Recent advancements in blockchain-enabled Digital Twins for sustainable energy systems in urban development: A review","authors":"Farzaneh Mohammadi ,&nbsp;Seyed Hamid Montazeri ,&nbsp;Fatemeh Sadat Ayatollahi ,&nbsp;Masoud Emamian Verdi ,&nbsp;Samaneh Danaeifar ,&nbsp;Zahra Dehghani Arani ,&nbsp;Hamid Reza Pilehvar Javid ,&nbsp;Fatemeh MotieShirazi ,&nbsp;Rosa Tayebli ,&nbsp;Samaneh Mohammadisharmeh ,&nbsp;Danial Buruni ,&nbsp;Mohammad Hossein Alizadeh Roknabadi ,&nbsp;G.B. Gharehpetian","doi":"10.1016/j.scs.2026.107179","DOIUrl":"10.1016/j.scs.2026.107179","url":null,"abstract":"<div><div>Urban environments are sophisticated ecosystems with detailed energy needs, and identifying solutions that improve both efficiency and security is essential for sustainable development. Digital twin (DT) technology has emerged as a promising tool for optimizing energy efficiency in smart cities. However, its widespread adoption is hindered by concerns over data integrity, interoperability, and cybersecurity. Blockchain (BC) technology offers a compelling solution by providing decentralization, transparency, and a tamper-proof data architecture. While existing studies recognize the potential of DTs, their scope remains limited; they neither address how BC-enabled DTs can solve critical shortcomings nor sufficiently explore specialized applications within sustainable urban energy systems. Therefore, this review paper aims to explore how BC enables DTs for sustainable energy systems in urban development by addressing the following research goals: identifying the main categories, architectures, and design models of BC-enabled DTs; evaluating their SWOT analysis; pinpointing unresolved research gaps in their integration; and outlining future opportunities to enhance their effectiveness. A structured literature review using the Web of Science was conducted, resulting in the selection of 481 documents. A SWOT analysis then examines the combined potential of these technologies across key sustainable energy domains. The paper further highlights the role of urban DTs in supporting smart city development across urban planning, mobility, water systems, climate resilience, and governance. Our analysis reveals that BC-enhanced DTs can significantly improve data security, operational transparency, and system resilience, while also identifying persistent challenges such as scalability, regulatory alignment, and technical integration. Major outcomes indicate a growing trend toward AI-augmented, decentralized DT architectures, though interdisciplinary collaboration and standardized frameworks remain underdeveloped. The paper concludes by outlining critical research gaps and future directions, emphasizing the need for holistic architectures, policy-supportive ecosystems, and real-world pilot implementations to advance sustainable urban energy systems.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107179"},"PeriodicalIF":12.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081083","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}