Pub Date : 2026-02-06DOI: 10.1016/j.scs.2026.107192
Abdul Ghafoor Nizamani , Dung Thi Vu , Rafeeque Ahmed Nizamani , Geir Torgersen
Urban areas face escalating climate extremes that disproportionately affect regions with systemic inequities. Traditional infrastructure is inadequate. Nature-based solutions (NbS) offer transformative pathways but risk maladaptive outcomes without justice-centred frameworks. This systematic review synthesises 144 recent studies on urban NbS, examining governance architectures and distributive justice using PRISMA guidelines. The analysis reveals three persistent imbalances: (1) Geographic bias: 69% of research concentrates in Global North contexts, overlooking high-risk Global South regions. (2) Governance: Polycentric models prove unstable; technocratic approaches entrench inequities; and grassroots initiatives face marginalisation. (3) Equity: 93% of studies fail to disaggregate outcomes, masking disproportionate benefits to affluent groups. Digital tools risk algorithmic exclusion without community data sovereignty. To realise NbS as climate justice instruments, three shifts are required: institutionalising mandatory equity metrics, reallocating funding to high-vulnerability regions, and democratising digital transitions through community data sovereignty. Ultimately, Governance reform, not technical innovation, proves paramount for equitable resilience.
{"title":"Urban resilience through nature-based solutions in an era of environmental extremes: A global systematic analysis","authors":"Abdul Ghafoor Nizamani , Dung Thi Vu , Rafeeque Ahmed Nizamani , Geir Torgersen","doi":"10.1016/j.scs.2026.107192","DOIUrl":"10.1016/j.scs.2026.107192","url":null,"abstract":"<div><div>Urban areas face escalating climate extremes that disproportionately affect regions with systemic inequities. Traditional infrastructure is inadequate. Nature-based solutions (NbS) offer transformative pathways but risk maladaptive outcomes without justice-centred frameworks. This systematic review synthesises 144 recent studies on urban NbS, examining governance architectures and distributive justice using PRISMA guidelines. The analysis reveals three persistent imbalances: (1) Geographic bias: 69% of research concentrates in Global North contexts, overlooking high-risk Global South regions. (2) Governance: Polycentric models prove unstable; technocratic approaches entrench inequities; and grassroots initiatives face marginalisation. (3) Equity: 93% of studies fail to disaggregate outcomes, masking disproportionate benefits to affluent groups. Digital tools risk algorithmic exclusion without community data sovereignty. To realise NbS as climate justice instruments, three shifts are required: institutionalising mandatory equity metrics, reallocating funding to high-vulnerability regions, and democratising digital transitions through community data sovereignty. Ultimately, Governance reform, not technical innovation, proves paramount for equitable resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107192"},"PeriodicalIF":12.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147118","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}
Pub Date : 2026-02-06DOI: 10.1016/j.scs.2026.107218
Huifang Sun , Rujie Chen , Wenxin Mao , Dang Luo
Rapid urbanization and climate change have exacerbated urban flooding, thereby positioning urban flood resilience as a critical component of sustainable social development. This study develops an evaluation indicator system for urban flood resilience grounded in the dimensions of resistance, emergency response, recovery, and adaptation. Using the Pearl River Delta urban agglomeration as a case study, this paper examines the spatiotemporal evolution, influencing factors, and dynamic transition characteristics of flood resilience from 2003 to 2023. The analysis employs a combined methodological framework incorporating the standard deviation ellipse, geographic detector, geographically and temporally weighted regression, and spatial Markov chain models. The results indicate that: (1) The flood resilience in Pearl River Delta urban agglomeration exhibits a distinct core-periphery spatial pattern. While the overall level of resilience increased steadily between 2003 and 2023, inter-city disparities widened significantly. (2) The interactions among economic, infrastructural, and ecological factors have progressively intensified, with influencing factors demonstrating nonlinear synergistic effects. (3) The urban flood resilience centroid has gradually shifted toward cities with advanced infrastructure, and the spatial evolution exhibits phased characteristics defined by “core-driven growth, spatial expansion, and directional adjustment.” (4) The per capita investment in urban public facilities generates positive spillover effects that enhance the resilience of neighboring cities; conversely, the proportion of the high-risk population acts as a negative constraint, exacerbating regional vulnerability. (5) The evolution of urban flood resilience exhibits path dependence and a bottleneck effect of high-level transitions. Optimizing the resilience structure is most effectively driven by an integrated engineering-ecological pathway. This study provides a theoretical basis and practical guidance for flood risk governance and the enhancement of spatial resilience in urban agglomerations.
{"title":"Spatiotemporal evolution and influencing factors of urban flood resilience in the Pearl River Delta urban agglomeration","authors":"Huifang Sun , Rujie Chen , Wenxin Mao , Dang Luo","doi":"10.1016/j.scs.2026.107218","DOIUrl":"10.1016/j.scs.2026.107218","url":null,"abstract":"<div><div>Rapid urbanization and climate change have exacerbated urban flooding, thereby positioning urban flood resilience as a critical component of sustainable social development. This study develops an evaluation indicator system for urban flood resilience grounded in the dimensions of resistance, emergency response, recovery, and adaptation. Using the Pearl River Delta urban agglomeration as a case study, this paper examines the spatiotemporal evolution, influencing factors, and dynamic transition characteristics of flood resilience from 2003 to 2023. The analysis employs a combined methodological framework incorporating the standard deviation ellipse, geographic detector, geographically and temporally weighted regression, and spatial Markov chain models. The results indicate that: (1) The flood resilience in Pearl River Delta urban agglomeration exhibits a distinct core-periphery spatial pattern. While the overall level of resilience increased steadily between 2003 and 2023, inter-city disparities widened significantly. (2) The interactions among economic, infrastructural, and ecological factors have progressively intensified, with influencing factors demonstrating nonlinear synergistic effects. (3) The urban flood resilience centroid has gradually shifted toward cities with advanced infrastructure, and the spatial evolution exhibits phased characteristics defined by “core-driven growth, spatial expansion, and directional adjustment.” (4) The per capita investment in urban public facilities generates positive spillover effects that enhance the resilience of neighboring cities; conversely, the proportion of the high-risk population acts as a negative constraint, exacerbating regional vulnerability. (5) The evolution of urban flood resilience exhibits path dependence and a bottleneck effect of high-level transitions. Optimizing the resilience structure is most effectively driven by an integrated engineering-ecological pathway. This study provides a theoretical basis and practical guidance for flood risk governance and the enhancement of spatial resilience in urban agglomerations.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"139 ","pages":"Article 107218"},"PeriodicalIF":12.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147120","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}
Pub Date : 2026-02-02DOI: 10.1016/j.scs.2026.107203
Yirong Ding , Lu Zhang , Yang Zhang
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.
{"title":"Context-sensitive analysis of disaster resilience and equity through geospatial explainable machine learning","authors":"Yirong Ding , Lu Zhang , 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}
Pub Date : 2026-02-02DOI: 10.1016/j.scs.2026.107202
Tao Wu , Shujie Yang , Jingkai Zhao , Ruhang Wei , Siying Li , Zeyin Chen , Renlu Qiao , Zhiqiang Wu , Shiqi Zhou
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.
{"title":"Spatiotemporal dynamics and multidimensional drivers of urban diurnal temperature range: Evidence from integrated learning at the national scale in China","authors":"Tao Wu , Shujie Yang , Jingkai Zhao , Ruhang Wei , Siying Li , Zeyin Chen , Renlu Qiao , Zhiqiang Wu , 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}
Pub Date : 2026-01-28DOI: 10.1016/j.scs.2026.107186
Yiping Lin , Hong Huang , Jing Wang , Xiaole Zhang
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.
{"title":"Sensor configuration optimization for source term estimation of time-varying emissions","authors":"Yiping Lin , Hong Huang , Jing Wang , 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}
Pub Date : 2026-01-26DOI: 10.1016/j.scs.2026.107187
Ke Liu , Xiaodong Xu , Deqing Lin , Ran Zhang , Linzhi Zhao , Abudureheman Abuduwayiti , Francesco Causone
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.
{"title":"A scalable and efficient framework for city-scale building energy modeling with microclimate considerations","authors":"Ke Liu , Xiaodong Xu , Deqing Lin , Ran Zhang , Linzhi Zhao , Abudureheman Abuduwayiti , 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}
Pub Date : 2026-01-25DOI: 10.1016/j.scs.2026.107184
Nasim Sadra , Mohammad Reza Nikoo , Abolfazl Nazari Giglou , Amir H. Gandomi
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.
{"title":"Microclimatic dynamics and hydrological patterns in urban heat islands - A comprehensive perspective","authors":"Nasim Sadra , Mohammad Reza Nikoo , Abolfazl Nazari Giglou , 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}
Pub Date : 2026-01-23DOI: 10.1016/j.scs.2026.107180
Yingyue Li , Eirini Tsouknida , Tom Collins , Ashley Bateson , Rui Tang , Esfandiar Burman
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.
{"title":"Thermal resilience in the built environment: A critical review","authors":"Yingyue Li , Eirini Tsouknida , Tom Collins , Ashley Bateson , Rui Tang , 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}
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.
城市环境是复杂的生态系统，具有详细的能源需求，确定既能提高效率又能提高安全性的解决方案对可持续发展至关重要。数字孪生（DT）技术已成为优化智慧城市能源效率的有前途的工具。然而，对数据完整性、互操作性和网络安全的担忧阻碍了它的广泛采用。b区块链（BC）技术通过提供去中心化、透明性和防篡改数据架构，提供了一个引人注目的解决方案。虽然现有的研究认识到直接治疗的潜力，但其范围仍然有限；它们既没有解决bc驱动的DTs如何解决关键缺陷，也没有充分探索可持续城市能源系统中的专门应用。因此，本文旨在通过解决以下研究目标，探讨BC如何使DTs实现城市发展中的可持续能源系统：确定BC支持的DTs的主要类别、架构和设计模型；评估他们的SWOT分析；在它们的整合中找出尚未解决的研究差距；并概述了未来提高其有效性的机会。使用Web of Science进行结构化文献综述，最终选择了481篇文献。然后进行SWOT分析，考察这些技术在关键可持续能源领域的综合潜力。本文进一步强调了城市DTs在支持智慧城市发展方面的作用，包括城市规划、交通、水系统、气候适应能力和治理。我们的分析表明，bc增强的dt可以显著提高数据安全性、操作透明度和系统弹性，同时也可以识别可扩展性、监管一致性和技术集成等持续挑战。主要结果表明，尽管跨学科合作和标准化框架仍然不发达，但人工智能增强、分散的数字数据挖掘架构的趋势正在增长。最后，本文概述了关键的研究差距和未来方向，强调需要整体架构、政策支持的生态系统和现实世界的试点实施来推进可持续城市能源系统。
{"title":"Recent advancements in blockchain-enabled Digital Twins for sustainable energy systems in urban development: A review","authors":"Farzaneh Mohammadi , Seyed Hamid Montazeri , Fatemeh Sadat Ayatollahi , Masoud Emamian Verdi , Samaneh Danaeifar , Zahra Dehghani Arani , Hamid Reza Pilehvar Javid , Fatemeh MotieShirazi , Rosa Tayebli , Samaneh Mohammadisharmeh , Danial Buruni , Mohammad Hossein Alizadeh Roknabadi , 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}
Pub Date : 2026-01-23DOI: 10.1016/j.scs.2026.107177
Bingyin Chen , Zhiquan Zhu , Wanxue Zhu , Xuemei Wang , Weiwen Wang
Amid accelerating global warming and intensifying urban heat islands, green roofs (GRs) are promoted as nature-based solutions for urban heat mitigation. However, their thermal performance in hot-humid subtropical climates remains underexplored, and observed warming phenomena are often overlooked. Here we use rooftop field experiments in Guangzhou, China, to compare GRs planted with Sedum spp. and Schefflera spp. against a bare roof during summer 2022 heatwaves. Combining high-frequency flux measurements, Bowen ratio energy-balance calculations and structural equation modelling, we analyse the mechanisms of roof–canopy thermal regulation. GRs reduced roof-surface temperature by 4–5 °C, associated with higher albedo (0.16–0.19 vs 0.10) and reduced net radiation (by 10–15 W m-2). Yet at 30 cm height, daytime canopy air temperature over GRs was 0.5–0.7 °C warmer than over the bare roof, with this warming intensifying by 0.06–0.15 °C per 1 °C increase in ambient temperature above 30 °C. We term this vertically non-uniform response the canopy heat-trapping effect, and show that it arises from reduced within-canopy wind speeds and diminished latent heat fluxes under heat stress, which limit the upward export of cool air. These findings challenge the common assumption that GRs provide uniformly cooling effects, and highlight the need to explicitly consider canopy ventilation and plant physiological responses when designing GRs to enhance thermal resilience in heat-vulnerable subtropical cities.
随着全球变暖的加速和城市热岛的加剧，绿色屋顶（GRs）被推广为基于自然的城市热缓解解决方案。然而，它们在亚热带湿热气候下的热性能仍未得到充分研究，观测到的变暖现象往往被忽视。在这里，我们在中国广州进行了屋顶田间试验，比较了在2022年夏季热浪期间种植景天属植物和舍弗勒属植物的GRs与光秃秃的屋顶。结合高频通量测量、波文比能量平衡计算和结构方程建模，分析了顶棚热调节的机理。GRs使屋顶表面温度降低4-5°C，反照率提高（0.16-0.19 vs 0.10），净辐射降低（10-15 W - m-2）。然而，在30厘米高度，GRs上方的日间冠层空气温度比光秃秃的屋顶高0.5-0.7°C，并且在30°C以上，环境温度每增加1°C，这种变暖就会加剧0.06-0.15°C。我们将这种垂直不均匀响应称为冠层吸热效应，并表明它是由于热应力下冠层内风速的降低和潜热通量的减少，这限制了冷空气的向上出口。这些研究结果挑战了GRs提供均匀冷却效果的普遍假设，并强调了在设计GRs以增强热脆弱的亚热带城市的热恢复能力时，需要明确考虑冠层通风和植物生理反应。
{"title":"Unveiling the canopy heat trapping effect in green roofs: Thermo-dynamic mechanisms during subtropical urban heatwaves","authors":"Bingyin Chen , Zhiquan Zhu , Wanxue Zhu , Xuemei Wang , Weiwen Wang","doi":"10.1016/j.scs.2026.107177","DOIUrl":"10.1016/j.scs.2026.107177","url":null,"abstract":"<div><div>Amid accelerating global warming and intensifying urban heat islands, green roofs (GRs) are promoted as nature-based solutions for urban heat mitigation. However, their thermal performance in hot-humid subtropical climates remains underexplored, and observed warming phenomena are often overlooked. Here we use rooftop field experiments in Guangzhou, China, to compare GRs planted with Sedum spp. and Schefflera spp. against a bare roof during summer 2022 heatwaves. Combining high-frequency flux measurements, Bowen ratio energy-balance calculations and structural equation modelling, we analyse the mechanisms of roof–canopy thermal regulation. GRs reduced roof-surface temperature by 4–5 °C, associated with higher albedo (0.16–0.19 vs 0.10) and reduced net radiation (by 10–15 W m<sup>-2</sup>). Yet at 30 cm height, daytime canopy air temperature over GRs was 0.5–0.7 °C warmer than over the bare roof, with this warming intensifying by 0.06–0.15 °C per 1 °C increase in ambient temperature above 30 °C. We term this vertically non-uniform response the canopy heat-trapping effect, and show that it arises from reduced within-canopy wind speeds and diminished latent heat fluxes under heat stress, which limit the upward export of cool air. These findings challenge the common assumption that GRs provide uniformly cooling effects, and highlight the need to explicitly consider canopy ventilation and plant physiological responses when designing GRs to enhance thermal resilience in heat-vulnerable subtropical cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107177"},"PeriodicalIF":12.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080598","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}
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