Sustainable Cities and Society最新文献

{"title":"A multi-agent system for neighborhood-scale carbon simulation: Integrating intelligent multi-source sensing with interaction-driven modeling","authors":"Ao Sun ,&nbsp;Xiucheng Zhai ,&nbsp;Lefan Pian ,&nbsp;Lu Li ,&nbsp;Shuyuan Jia ,&nbsp;Jingfeng Yuan ,&nbsp;Shu Su","doi":"10.1016/j.scs.2026.107166","DOIUrl":"10.1016/j.scs.2026.107166","url":null,"abstract":"<div><div>Excessive carbon emissions pose a major barrier to sustainable urban development; however, neighborhood-scale assessments are often hindered by data scarcity and low resolution, limiting the ability to explain how human activities and environmental factors jointly shape emissions. This study develops a neighborhood-scale carbon emissions simulation framework that integrates unmanned aerial vehicle-based remote sensing, computer vision, geographic information system techniques, and field surveys to acquire multi-source data. A multi-agent system was constructed to represent human behavior, built environments, and climate conditions as interacting agents with heterogeneous attributes and behavioral rules, enabling interaction-driven emissions estimation. A case study in Nanjing, China, estimated annual carbon emissions of 2.61 × 10<span><math><msup><mrow></mrow><mrow><mn>8</mn></mrow></msup></math></span> kg CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>e, corresponding to a land-based emission intensity of 106.53 kgCO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>e/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. The results reveal detailed spatiotemporal emission patterns and pronounced seasonal variability: buildings dominate emissions, mobility contributes 1.6%, and open spaces act as a net sink, offsetting over 2% of neighborhood emissions. Seasonally, summer emissions were approximately 40% higher than those in the transitional seasons, primarily driven by extended cooling operations and climate-sensitive behavioral shifts. The proposed approach advances explanatory power and computational efficiency in neighborhood-scale carbon accounting, providing a practical basis for low-carbon neighborhood management and sustainable urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107166"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399001","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":"Assessing the impact of industrial areas on land surface temperature: A comparison with urban and green spaces in the context of global warming","authors":"Asfra Rizwan Toor,&nbsp;Zubair Khalid","doi":"10.1016/j.scs.2026.107185","DOIUrl":"10.1016/j.scs.2026.107185","url":null,"abstract":"<div><div>This study provides a comprehensive analysis of the urban heat island (UHI) effect and land surface temperature (LST) variations across Lahore, Pakistan, focusing on industrial, residential, and green spaces. Within the broader UHI framework, the study foregrounds the intra-urban heat island (IHI) phenomenon, which captures fine-scale thermal contrasts among industrial, residential, and green spaces. This perspective helps show how industrial growth creates localized hotspots in the urban environment. Using Landsat imagery, the split-window algorithm, and vegetation and built-up indices, the study tracks LST trends over 24 years (2000–2024). A comparative analysis of supervised classification algorithms and Otsu thresholding with K-means clustering was conducted to assess vegetation distribution, demonstrating the value of combining automated and supervised methods for improved land-cover assessment. Results reveal a clear industrial thermal footprint following the establishment of the industrial estate in 2007, with peak summer LST reaching 43.2 <span><math><mrow><mo>°</mo><mi>C</mi></mrow></math></span> at the core and declining to 41.6 <span><math><mrow><mo>°</mo><mi>C</mi></mrow></math></span> within a 5 km radius. A maximum temperature contrast (<span><math><mrow><mi>Δ</mi><msub><mrow><mi>T</mi></mrow><mrow><mo>max</mo></mrow></msub></mrow></math></span>) of 3 <span><math><mrow><mo>°</mo><mi>C</mi></mrow></math></span> confirms localized heat accumulation. Residential areas show persistent warming, whereas green spaces consistently maintain the lowest LST. Vegetation loss is associated with an increase in LST of up to 4.6 <span><math><mrow><mo>°</mo><mi>C</mi></mrow></math></span>, underscoring the cooling role of green infrastructure. Despite peak rainfall of 905 mm in 2021, IA temperatures remained high, indicating that impervious surfaces limit precipitation-driven cooling. The study offers long-term empirical insight but is constrained by Landsat’s spatial resolution and the absence of socioeconomic or atmospheric pollution variables, which future work should integrate for a more holistic assessment. The study advocates for targeted nature-based interventions to reduce heat stress and enhance urban resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107185"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398996","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 circular disaster management framework for resilient cities","authors":"Mojtaba Parsaee ,&nbsp;Tarlan Abazari ,&nbsp;Navid Nickdoost ,&nbsp;Mohsen Goodarzi ,&nbsp;Mehran Shahhosseini ,&nbsp;Fariborz Haghighat","doi":"10.1016/j.scs.2026.107189","DOIUrl":"10.1016/j.scs.2026.107189","url":null,"abstract":"<div><div>This study explores the integration of circular economy (CE) and life cycle strategies into disaster management to enhance urban resilience and address environmental sustainability. Climate-related disasters generate vast debris volumes that overwhelm waste systems, disrupt essential services, and incur significant social, economic, and environmental challenges. The prevailing linear practices prioritize rapid disposal over resource recovery, resulting in extensive landfilling and significant environmental footprints. In contrast, a CE approach offers sustainable alternatives through material recovery strategies such as reuse and recycling. However, CE integration in disaster management remains underdeveloped. Through a systematic literature review of studies from 1995 to 2025, this research studies disaster management practices and identifies barriers, opportunities, and enablers across technical, institutional, economic, and social domains for effective CE integration. The findings are synthesized to characterize a scientifically grounded, adaptive framework for circular disaster management. The framework unfolds in three stages: (1) developing an integrative decision-support system, (2) creating a comprehensive education and training platform, and (3) catalyzing institutional and business transformation and strengthening public-private partnerships. This framework systematically addresses technical, institutional, economic, and social barriers while leveraging identified opportunities to facilitate widespread adoption of CE principles across disaster management phases. This framework establishes a foundation for future research, policy development, and CE implementation in disaster contexts. Through this framework, the paper contributes to advancing sustainable disaster management and urban resilience, providing stakeholders with actionable guidance to drive systemic changes across multiple domains.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107189"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399431","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":"Urban village recognition based on multimodal data fusion with an interpretable graph neural network","authors":"Zhenkang Wang ,&nbsp;Nan Xia ,&nbsp;Jiechen Wang","doi":"10.1016/j.scs.2026.107199","DOIUrl":"10.1016/j.scs.2026.107199","url":null,"abstract":"<div><div>Recognition of urban villages (UVs) is of strategic importance for urban sustainable development. Existing studies mostly rely on visual information or statistics data, ignore the spatial relationship between regions and the complementarity of multimodal data, and lack the analysis of the importance of drivers. Therefore, this study integrates multimodal data, including remote sensing imageries, street view images, geographic big data, and building footprint data, and constructs both two- and three-dimensional features system that fuses visual perception, socio-economic factors, and urban morphology. A UV recognition model is developed using a graph neural network-based GraphSAGE with city blocks as graph nodes and spatial adjacencies as edges, and the GNNExplainer is introduced to analyze node importance and explain graph structure. Results showed 225 identified UVs totaling 61.4 km² in Nanjing, a representative high-density megacity in China, with an obvious ring-structured spatial distribution pattern containing 29.8 % in the core area. Recognition accuracy reached 97.2 %, which was 12.3 % and 5.1 % higher than the unimodal data and node ablation models on average, verifying the synergistic effect of visual, socio-economic, and morphological features, and the complementarity between self and neighbor node information. Cross-domain experiments in Changzhou and Suzhou cities identified 101 and 171 UVs with accuracies of 94.7 % and 92.4 %, demonstrating the model transferability. Moreover, explainability analysis revealed that high floor area ratio, high space congestion, high proportion of old buildings, and specific point-of-interest distribution patterns were typical UV characteristics. The proposed framework can provide efficient scientific support for urban planning and renewal decision-making.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107199"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399432","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":"Can nighttime light proxy comprehensive economic well-being? Evidence from China","authors":"Wei Jiang ,&nbsp;Jie Liu ,&nbsp;Tengfei Long ,&nbsp;Ming Liu ,&nbsp;Akiyuki Kawasaki ,&nbsp;Zhiguo Pang ,&nbsp;Denghua Yan ,&nbsp;Yizi Shang ,&nbsp;Elhadi Adam ,&nbsp;Xiaohui Ding ,&nbsp;Shiai Cui","doi":"10.1016/j.scs.2026.107191","DOIUrl":"10.1016/j.scs.2026.107191","url":null,"abstract":"<div><div>Although nighttime light data offer the potential for spatially measuring economic well-being (EWB) to achieve sustainable development goals, empirical validation remains insufficient. Fusion EWB of six representative Chinese counties (Yanshou, Bazhou, Yugan, Yongsheng, Wafangdian, Raoping) was analyzed using household surveys, statistical yearbooks, and entropy weight methodology—approaches that have proven more reliable than equal weight approaches for EWB calculation. The results revealed that (1) significant spatial heterogeneity exists between subjective and objective EWB across townships, demonstrating the necessity of fusion approaches; and (2) strong correlations exist between nighttime light and EWB—objective EWB (R²=0.801, <em>p&lt;0.001</em>) and fusion EWB (R²=0.808, <em>p&lt;0.001</em>)—validating nighttime light as an effective proxy indicator. This study first proposes and validates the fusion EWB concept, addressing the limitations of single-dimensional assessments while establishing the utility of nighttime light for EWB measurement. The methodology provides a scalable framework for spatial EWB simulation and sustainable poverty reduction assessment, offering crucial implications for sustainable urban–rural development planning and SDG monitoring in developing regions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107191"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399435","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 diurnal spatiotemporal heterogeneity in urban heat exposure: A novel perspective from urban form-function coupling","authors":"Aihemaiti Namaiti ,&nbsp;Suiping Zeng ,&nbsp;Weijie He ,&nbsp;Xiang Liu ,&nbsp;Jian Zeng","doi":"10.1016/j.scs.2026.107161","DOIUrl":"10.1016/j.scs.2026.107161","url":null,"abstract":"<div><div>Understanding the spatiotemporal heterogeneity of diurnal urban heat exposure is crucial for addressing urban heat governance challenges. However, most studies focus on either urban form or function, lacking an integrated perspective to fully capture heat exposure heterogeneity. This study, using Nanchang, a typical Chinese “furnace city,” as a case study, proposes a novel form-function coupling framework. Through K-means clustering’s flexibility and local adaptability, it divided the study area into 12 homogeneous form-function clusters. By integrating high-resolution ECOSTRESS LST and mobile signaling data, it assessed diurnal heat exposure levels and their heterogeneity with precision. The High-Risk Contribution Index (HCI) was introduced to quantify each cluster’s contribution to high heat exposure risk. Results showed that the 12 clusters, based on form-function coupling, exhibited distinct heat exposure patterns, effectively capturing urban heat exposure heterogeneity. Kruskal-Wallis H tests and post hoc multiple comparisons confirmed highly significant differences in heat exposure among clusters across all time points (H values 3713.242–4367.439, p&lt;0.001), with 71.21 %–80.30 % of pairwise comparisons showing significant differences (p&lt;0.05). Three contribution patterns emerged: (1) consistently high contribution (clusters 8, 9, 10, 11, 12; average HCI &gt;2; high-density commercial and residential zones), requiring priority intervention; (2) consistently low contribution (clusters 1, 2, 3, 4; average HCI &lt;0.6; ecological zones), needing protection to leverage their “cool source” role; and (3) diurnal variation (clusters 6, 7; daytime HCI &gt;1, nighttime &lt;1; influenced by industrial activity timing), requiring flexible interventions based on production schedules. These findings provide a replicable paradigm for precise, dynamic, and localized urban heat exposure governance and offer a theoretical-methodological framework for similar cities, enhancing the scientific rigor and practicality of heat governance strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107161"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025731","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 typology of energy scaling in Chinese cities and its implication for sustainable urban transitions","authors":"Zhaoping Wu ,&nbsp;Xuandi Wang ,&nbsp;Yandong Tan ,&nbsp;Xu Wu ,&nbsp;Kai Fang","doi":"10.1016/j.scs.2026.107168","DOIUrl":"10.1016/j.scs.2026.107168","url":null,"abstract":"<div><div>Urbanization has driven global urban transition, making urban energy scaling critical for effective energy management. Yet, most studies focused on static scaling between energy use and city size, overlooking their dynamics and typological differences. This study analyzed energy scaling across 324 Chinese cities from 2005 to 2021, exploring both its temporal evolution and variations across seven city types. Using the XGBoost-SHAP model, we further identified key drivers of per capita energy use. Results show that: (1) Urban energy use follows a stable linear scaling (95CI%: 0.83–1.04) across all cities; (2) Service-based and high-tech cities display near-linear scaling (<em>β</em> ∼ 1), while light-industry, pre-industry, and agriculture cities present sublinear scaling (β ∼ 0.85), and energy and heavy-industry cities show the strongest scale effects (β ∼ 2/3); (3) Scaling exponent fell by 12% and 27% in service-based and energy cities, showing inverted U-shapes in high-tech and agriculture cities, and U-shapes in pre- and light-industry cities over time; (4) Per capita GDP and road length emerged as the crucial determinants of energy intensity across all city types; (5) Scaling models predict service-based cities will experience the most substantial increases in both population (24%) and energy use (26%). These findings suggest differentiated policy pathways: service-based and high-tech cities should enhance energy infrastructure and adopt clean energy to offset linear scaling pressures; energy and heavy-industry cities should use scale effects to reduce energy intensity through innovation and low-carbon transition; and agriculture and pre-industry cities need to optimize infrastructure to ensure balanced regional development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107168"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081085","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":"2026-03-01","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":"Toward greener, smarter, and more resilient cities: Assessing the impact of dual pilot policies of forest city and smart city on urban climate resilience in China","authors":"Yaobin Liu ,&nbsp;Sheng Hu ,&nbsp;Shuoshuo Li ,&nbsp;Weifeng Deng","doi":"10.1016/j.scs.2026.107155","DOIUrl":"10.1016/j.scs.2026.107155","url":null,"abstract":"<div><div>Climate risks threaten the sustainable development of urban economies, societies, and ecosystems. Urban climate resilience (UCR) is difficult to comprehensively improve through single policies. Forest cities and smart cities are typical examples of nature-based and technology-driven solutions, which have been extensively studied for their environmental impacts. However, it remains uncertain whether dual pilot policies (DPP) for forest cities and smart cities can synergistically enhance UCR. Based on the social-ecological-technological systems (SETS) perspective, this paper employs the staggered DID models to examine the policy synergetic effects of DPP on UCR. Results show that DPP increases UCR by an average of 5.43%. DPP establishes comprehensive adaptation mechanisms, including expanding green spaces, developing digital infrastructure, and promoting green technological innovation. The policy effect is more pronounced in inland cities, small cities, and cities with lower climate risks. While boosting the local UCR, DPP also improves the average UCR by 27.00% in neighboring areas. Our research highlights the synergistic governance between ecological and technological systems. It provides empirical evidence for climate adaptation through green and smart transformations in rapidly urbanizing regions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107155"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039531","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":"Urban blue-green spaces for wintertime microclimate regulation: Interactive and marginal effects","authors":"Wei Ding ,&nbsp;Mengyang Liu ,&nbsp;Yunni Wu ,&nbsp;Wei Feng ,&nbsp;Hong Chen","doi":"10.1016/j.scs.2025.107095","DOIUrl":"10.1016/j.scs.2025.107095","url":null,"abstract":"<div><div>Urban blue-green space planning is a critical strategy for mitigating urban heat islands and air pollution. However, the mechanisms of their climatic regulation and interactive effects remain insufficiently understood, particularly under wintertime conditions when atmospheric stability and pollution accumulation are most pronounced. Using the six green wedges of Wuhan as a case, this study employed the WRF-chem model coupled with an urban canopy scheme, validated against national monitoring data, to simulate multiple winter scenarios and examine the regulatory role of blue-green spaces at the urban-rural fringe. Results show that: (1) both blue and green spaces within the wedges exert significant effects on 2 m air temperature, though their impacts are largely independent; (2) while the direct microclimatic effects of green spaces are limited, they synergise with blue spaces to enhance urban ventilation and facilitate PM_2.5 dispersion; and (3) blue spaces define the spatial extent of temperature regulation, whereas green spaces moderate diurnal temperature variations. Together, blue-green spaces constrained thermal differences within a 2.05 km buffer to &lt;0.1 °C. In the green-only scenario, the maximum ventilation length (Lmax) decreased by 0.68 km; in the absence of both, Lmax decreased by 0.75 km; in the blue-only scenario, the maximum daytime temperature difference (Dmax) increased by 0.23 °C. These findings highlight that rational configuration of blue and green spaces, considering their diurnal variability and seasonal performance in winter, can generate synergistic benefits for urban ventilation and particulate dispersion. This study provides practical insights for blue-green infrastructure design and spatial planning strategies aimed at improving wintertime climate resilience in rapidly urbanising regions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107095"},"PeriodicalIF":12.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399064","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}