Sustainable Cities and Society最新文献

{"title":"Spatiotemporal variations of surface and canopy urban heat islands intensities and their divergence modulated by aerosol-driven radiative forcing in eastern China's urban agglomerations","authors":"Yimei Yuan ,&nbsp;Xuhong Wang ,&nbsp;Bingqian Li ,&nbsp;Zihao Feng ,&nbsp;Chengyu Wang ,&nbsp;Jiahang Yang ,&nbsp;Jiahui Yang","doi":"10.1016/j.scs.2025.107106","DOIUrl":"10.1016/j.scs.2025.107106","url":null,"abstract":"<div><div>Urban agglomerations, as focal points of intense human and economic activities, face growing ecological and environmental challenges due to industrial clustering, high population density, and uncontrolled urban expansion. These factors collectively limit sustainable socioeconomic development and ecological balance. Despite extensive urban heat island intensity (UHII) research, the distinct patterns of surface (SUHII) and canopy (CUHII) urban heat island intensities, particularly their responses to aerosol-driven radiative forcing, remain poorly understood. Mann-Kendall trend analysis, random forest modeling, and aerosol radiative forcing assessment based on multisource remote sensing and ground meteorological station data (2003-2023) were integrated to analyze SUHII and CUHII variations across eight major eastern China's urban agglomerations. The key findings are as follows: (1) SUHII consistently surpasses CUHII, with greater daytime effects in southern urban agglomerations but reversed nighttime trends in northern urban agglomerations; (2) Daytime SUHII increased significantly at the 95% confidence level, peaking in spring and summer; nighttime CUHII increase more marked, with 50% of cities showing significant summer rises; and (3) the daytime SUHII is governed by surface characteristic factors, while the nighttime SUHII is jointly controlled by human activities and background climatic factors. Both the daytime and nighttime CUHII are primarily driven by human activities. (4) aerosol-driven spatially and seasonally heterogeneous radiative forcing increases the UHII in northern urban agglomerations (BTH, CP, and GP) but suppresses it in southern urban agglomerations (YRD, CY, and YRMR), except in winter (cooling effect). The results provide insights into UHII-aerosol interaction mechanisms and support region-specific thermal environment management strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107106"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929044","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":"Forecasting heat-related impacts with multivariate multi-step time series models using advanced deep learning","authors":"Jung-Ching Kan ,&nbsp;Marlon Vieira Passos ,&nbsp;Georgia Destouni ,&nbsp;Karina Barquet ,&nbsp;Carla S.S. Ferreira ,&nbsp;Zahra Kalantari","doi":"10.1016/j.scs.2026.107142","DOIUrl":"10.1016/j.scs.2026.107142","url":null,"abstract":"<div><div>Record-breaking temperatures and frequent heatwaves have been experienced worldwide in recent years. Heatwaves pose an escalating threat to public health and heat-related impact forecasting is critical to implementing suitable mitigation strategies. Deep learning (DL) models, notably Long Short-Term Memory (LSTM), have been widely applied for heat-related impact forecasting. However, the emergence of state-of-the-art forecasting DL architectures such as Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) provides a novel solution for long-term heat-related impact forecasting. This study develops, evaluates, and compares multiple time series forecasting models—including advanced DL architectures (N-BEATS, N-HiTS, LSTM), a classical statistical model (ARIMA), and a Naïve seasonal baseline—to predict heat-related morbidity across 21 Swedish counties using data from 2008 to 2023. Both local (individually trained) and global (cross-learning across counties) modeling strategies were explored, incorporating exogenous variables (Heatwave Index and number of people with respiratory disease), and comparing recursive and Multi-Input-Multi-Output (MIMO) forecasting output strategies. Results indicate that the local N-BEATS model achieves superior predictive performance, particularly when both exogenous variables are included. MIMO generally yields a better performance by mitigating error propagation over extended forecasting horizons. Moreover, individually trained N-BEATS models outperform cross-learning global N-BEATS, underscoring the importance of localized adaptation plans. These findings highlight the potential utility of multivariate N-BEATS for more accurate heatwave impact forecasting. This study can complement and support early warning frameworks by integrating the developed impact forecast model with existing hazard models, thereby enabling more proactive public health interventions and improving community resilience to heatwaves.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107142"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980781","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":"Rooftop PV layout generation and optimization model for large-scale building cluster","authors":"Rui Miao ,&nbsp;Haoqing Zhu ,&nbsp;Yi Zhang ,&nbsp;Kanghua Li ,&nbsp;Yuan Yuan ,&nbsp;Pengyuan Shen ,&nbsp;He Qi","doi":"10.1016/j.scs.2026.107129","DOIUrl":"10.1016/j.scs.2026.107129","url":null,"abstract":"<div><div>Rooftop photovoltaic (PV) deployment at the cluster scale is critical for advancing urban decarbonization, yet existing methods often oversimplify available area estimation, neglect engineering constraints, and struggle to balance energy, economic, and environmental objectives. To address these gaps, this study proposes an integrated model comprising three modules: (1) high-fidelity identification of available rooftop area by explicitly excluding shadow area, obstacles with buffer zones, and maintenance pathways; (2) automated generation of 3D engineering-feasible PV layouts; and (3) a hybrid single- and multi-objective optimization with TOPSIS-based decision-making. Applied to a 245-building cluster in Shenzhen, the model identified 23,982 m² of available area (46.51 % ratio), more conservative than typical utilization-factor assumptions. It generated detailed 3D, module-level PV layouts for 198 buildings, enabling high-fidelity PV output simulation accounting for site-specific orientation, tilt, and shading. Across all budget levels, the optimized solutions outperformed 30,000 random alternatives, with internal rates of return ranging from 16.1 % to 19.2 %. Under the high-budget scenario, the CEB-optimal solution, which utilizes all technically feasible rooftops, achieves a 10-year cumulative energy yield of 61.61 GWh and carbon emission reductions of 27,725.59 t CO₂. By decoupling layout generation from optimization, the framework reduces computational complexity and enables efficient generation of optimized deployment solutions. The proposed framework facilitates large-scale rooftop PV deployment and contributes to the decarbonization of urban energy systems.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107129"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929042","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":"Scenario-based capacity optimization of multi-type energy storage in integrated energy systems using a flexible interaction model","authors":"Xin-Yu Ren ,&nbsp;Zhi-Hua Wang ,&nbsp;Liu Zhang","doi":"10.1016/j.scs.2025.107076","DOIUrl":"10.1016/j.scs.2025.107076","url":null,"abstract":"<div><div>As Integrated Energy Systems (IES) evolve toward multi-energy synergy and high operational efficiency, determining the optimal allocation of multiple energy storage technologies has become a key challenge in system planning. This study investigates the capacity optimization of cooling, heating, and electrical energy storage systems across multiple operational scenarios. A unified modeling framework and scenario-based optimization approach are developed to address this problem. Eight representative configurations are analyzed, including systems without storage, single-type storage, dual-storage combinations, and fully integrated storage systems. The proposed flexible interaction optimization model simultaneously considers supply–demand coupling, storage constraints, and multi-objective criteria related to economy, energy efficiency, and environmental performance. Simulation results reveal that coordinated thermal–cold and thermal–cold–electric storage configurations significantly enhance overall system performance. Under grid-connected conditions, wind turbines (WT), solar thermal (ST), and gas boilers contribute minimally, while under off-grid conditions, WT, ST, and electrical energy storage play important roles in maintaining system stability. Compared with a fixed-order dispatch model, the proposed approach improves economic, energy efficiency, and environmental performance by at least 27.83 %, 1.09 %, and 1.09 % in off-grid electro-thermal scenarios, and by 15.95 %, 8.14 %, and 4.57 % under grid-connected conditions. These findings confirm the effectiveness of the proposed model in achieving flexible coordination and enhancing the overall performance of IES.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107076"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929045","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 ensemble machine learning and SAR-based geospatial modelling for inclusive and equitable urban flood resilience","authors":"Javed Mallick ,&nbsp;Hoang Thi Hang ,&nbsp;Alok Das ,&nbsp;Sayanti Poddar ,&nbsp;Chander Kumar Singh","doi":"10.1016/j.scs.2026.107158","DOIUrl":"10.1016/j.scs.2026.107158","url":null,"abstract":"<div><div>Urban flooding has become a major challenge in fast-growing cities worldwide, disrupting mobility, damaging infrastructure, and increasing social vulnerability. In Bengaluru, where rapid urban expansion and inadequate drainage exacerbate monsoon floods, this study develops an integrated and data-driven framework to map flood susceptibility and exposure between 2020 and 2025. The main objective is to generate a scientifically validated, high-resolution, and explainable flood-risk assessment model combining radar-based detection, machine learning, and social-infrastructure exposure analytics. Multi-temporal Sentinel-1 dual-polarisation (VV and VH) SAR data were used to detect flood events through backscatter change analysis, ensuring accurate mapping even under cloudy conditions. An ensemble of five algorithms-Random Forest, Histogram-based Gradient Boosting, XGBoost, Quadratic Discriminant Analysis, and Gaussian Naïve Bayes-was trained and optimized to capture complex spatial relationships between floods and controlling parameters. Explainable AI (SHAP, permutation, ablation) to identify key drivers for transparent planning. Probability calibration and reliability curves to check and correct forecast bias infrastructure and demographic overlays to quantify who and what is exposed. Results show that the ensemble delivered AUC = 0.985 with accuracy 78–83%, precision 0.78–0.83, recall 0.79–0.83, and F1 ≈ 0.80 across six independent flood events; calibration improved reliability with well-aligned predicted vs observed probabilities. Spatially, 32.63 km² were mapped as Very High and 61.73 km² as High susceptibility, with stable hotspots in Electronic City, Bommanahalli, Mahadevapura, and Bellandur-Varthur. Road exposure was dominated by local streets (62,402 segments) and tertiary roads (3505 segments), indicating neighbourhood-scale disruption; female and schedule caste (SC) populations showed disproportional exposure in central/eastern wards, while schedule tribe (ST) exposure was lower but non-negligible on the periphery. This analysis enable ward-wise prioritisation of drain upgrades, permeable retrofits, safe-route planning, and targeted protection of at-risk groups, aligning with the Sendai Framework and sustainable development goals (SDG)-11/13.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107158"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039039","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":"Community-based solar-powered and open-air cooling shelter for urban heat mitigation","authors":"Ji Yoon Bae ,&nbsp;Eric Teitelbaum ,&nbsp;Sara F. Jacoby ,&nbsp;Dorit Aviv","doi":"10.1016/j.scs.2026.107153","DOIUrl":"10.1016/j.scs.2026.107153","url":null,"abstract":"<div><div>The increasing frequency of heatwaves and the Urban Heat Island (UHI) effect pose growing public health risks, particularly for urban communities with limited access to cooling infrastructure. Conventional strategies—such as air-conditioned cooling centers—present challenges related to energy consumption, resilience, and equitable access. In response, we developed and tested a novel, open-air cooling shelter that can be installed as public infrastructure such as bus stops, designed to mitigate heat stress through solar-powered radiant and conductive cooling systems. Constructed in partnership with a community organization in a heat-vulnerable Philadelphia neighborhood, the shelter integrates a shading canopy, radiant cooling panels, and a conductive cooling bench, all operated by a fully off-grid renewable energy source. To examine its impact, we conducted thermal comfort surveys with community members as well as physiological and environmental measurements. Results showed that the shelter reduced occupants’ thermal stress by 35–45% compared to unshaded outdoor conditions using the Index of Thermal Stress (ITS), and subjective survey responses corroborated this improvement. Concurrently, energy monitoring validated the system’s self-sufficiency; solar energy generation surpassed the cooling demand by 40%. The combination of scalable technology and integrated local engagement, as modeled in this study, offers a replicable strategy for sustainable and inclusive urban heat mitigation.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107153"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980142","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":"The compounding effects of pollution reduction and low-carbon policy synergies on urban economic resilience and environmental performance","authors":"Xi Wang ,&nbsp;Hua Shang ,&nbsp;Xiaofei Lv ,&nbsp;Sai Yuan","doi":"10.1016/j.scs.2026.107162","DOIUrl":"10.1016/j.scs.2026.107162","url":null,"abstract":"<div><div>The synergistic governance effects of multi-objective environmental regulations are crucial for achieving coordinated economic and ecological development. Nevertheless, existing investigations have primarily focused on their singular effects on either the environment or the economy, while their synergistic governance effects require further validation. Utilizing urban data from China spanning 2007 to 2023, we construct a policy synergies variable for pollution reduction and low-carbon (<em>PPCR</em>) based on the Key Air Quality Control Zone Policy (<em>KACP</em>) and the Low-Carbon City Pilot Policy (<em>LCCP</em>). Then, we employ the Difference-in-Differences (<em>DID</em>) model to investigate the composite effects of <em>PPCR</em> on urban economic resilience (<em>EOR</em>) and environmental performance <em>(PCR</em>). The findings indicate that <em>PPCR</em> significantly enhances both <em>EOR</em> and <em>PCR</em>, with this conclusion demonstrating robustness. Meanwhile, the mechanism analysis reveals that the primary channels fostering <em>EOR</em> are the high-skilled talent siphoning and labor productivity-driven effects. Energy structure optimization and circular economy initiatives serve as significant pathways for <em>PPCR</em> to enhance <em>PCR</em>. Furthermore, the enabling role of <em>PPCR</em> is even stronger in cities with non-resource-based economies and strong public-oriented environmental regulations (<em>PER</em>).</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107162"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980946","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":"Extreme wind events at the pedestrian level of an actual urban morphology: Statistical correlation and space-time evolution","authors":"Wei Wang,&nbsp;Yezhan Li,&nbsp;Naoki Ikegaya","doi":"10.1016/j.scs.2026.107121","DOIUrl":"10.1016/j.scs.2026.107121","url":null,"abstract":"<div><div>Extreme wind events at the pedestrian level in urban areas pose significant safety risks. While most previous studies have emphasized the statistical (“static”) aspect, the parameters that best characterize extreme values remain unclear, and few have explored their spatiotemporal (“dynamic”) evolution. To address these gaps, this study investigates both aspects using large-eddy simulation (LES) within a realistic urban morphology. The analysis integrates statistical correlations with spatiotemporal evolution to provide a comprehensive understanding of extreme winds in complex urban environments. Correlation results show that skewness has the strongest relationship with gust and peak factors, underscoring its potential for improving statistical models. Higher-order moments (up to the sixth) are mainly positively correlated with gust and peak factors, but their weak or moderate association with exceedance wind speed suggests that extreme events are mainly governed by the combined effects of mean flow and standard deviation, rather than gust or peak factors alone. Turbulence measures such as integral time scale, and peak frequency exhibit only marginal links to extremes. The spatiotemporal evolution is examined through conditional space–time proper orthogonal decomposition (CST-POD). Although the first CST-POD mode aligns well with conditional averages, over 75% of the energy resides in higher modes. Reconstructing weaker events requires multiple modes, particularly in sheltered regions, whereas dominant low-order modes near tall buildings allow accurate reconstruction with fewer modes. This study advances understanding of extreme winds by integrating statistical and dynamical perspectives, offering insights for improved urban wind risk assessment.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107121"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981021","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":"Unsupervised clustering approach to residential typo-morphologies across multiple cities for urban heat vulnerability assessment","authors":"Maha Habib ,&nbsp;Doruntina Zendeli ,&nbsp;Marjolein van Esch ,&nbsp;Wim J. Timmermans ,&nbsp;Maarten van Ham","doi":"10.1016/j.scs.2025.107107","DOIUrl":"10.1016/j.scs.2025.107107","url":null,"abstract":"<div><div>Residential environments are central to addressing urban heat stress for vulnerable populations and are prime target areas for implementing climate adaptation strategies. The reliance on urban heat island (UHI) intensity mapping alone has been argued to provide limited guidance for adaptation efforts, whereas linking heat patterns to the built environment characteristics through frameworks such as Local Climate Zones (LCZ) provides actionable insights for developing neighborhood cooling strategies. However, the widely used LCZ maps have a few limitations, such as misrepresenting variation within types because they cannot account for sub-classes beyond the standardized framework. This paper presents an unsupervised clustering approach to identify residential typo-morphologies across 99 Dutch cities, enhancing their relevance for urban heat vulnerability assessments. The analysis reveals that five morphological and canopy parameters (FSI, GSI, OSR, H_avg, and FVC) selected from 17 parameters are sufficient to identify nine distinct residential typo-morphologies relatable to LCZs within 100 m × 100 m grid cells. The evaluations demonstrate that our approach detects underrepresented LCZ types and reveals new sub-classes absent from standard LCZ classifications. Key findings include detection of high-density areas (LCZ 4₂) reflecting recent urban densification with one of the highest UHI_max next to LCZ 2 (4.2–4.9 K), and vegetation-differentiated variants within sparse and low-rise categories LCZ 9_D and LCZ 6_D, distinguished by distinctive UHI_max (0.5–0.7 K) higher compared to their reference base types. Notably, tree coverage remains low across low-rise and compact typo-morphologies, revealing substantial opportunities for greening interventions. This data-driven refinement preserves LCZ's global comparability while considering local specificity, providing improved frameworks to inform targeted climate adaptation strategies in residential environments.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107107"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981022","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 supply-flow-demand network framework for unraveling the compounded impacts of future climate and functional space on urban water-soil ecosystem services","authors":"Yu Wang ,&nbsp;Junfei Chen ,&nbsp;Tonghui Ding ,&nbsp;Yufei Han ,&nbsp;Xiaodong Yan ,&nbsp;Jiayuan Guo ,&nbsp;Qingqing Ban ,&nbsp;Ting Cheng","doi":"10.1016/j.scs.2026.107144","DOIUrl":"10.1016/j.scs.2026.107144","url":null,"abstract":"<div><div>The intensification of global climate change and human activities has exacerbated the supply-demand imbalance of urban water-soil ecosystem services (WSES). However, the compounded impacts of climate change and human-induced functional space transformation remain unclear. It is necessary to examine how dual pressures restructure the production, transfer and consumption of future WSES. Therefore, this study proposed an integrated supply-flow-demand framework to simulate the spatiotemporal WSES evolution under future composite scenarios. Through cross-scenario comparative analysis, the proposed framework examined the independent contributions and interactive effects of climate change (CC) and functional space change (FSC) on WSES supply-demand, and employed network analysis to elucidate the structural properties of WSES flow networks. Taking 69 cities in the Yellow River Basin as a representative case, CC contributed over 65.28% to the internal supply-demand variations, while FSC’s influence strengthened, doubling for water yield and rising about 6.5-fold for soil retention, emerging as a key regulator. CC-FSC interactions exhibited antagonistic effects across 65.75% of the basin due to geographical heterogeneity. Moreover, extreme climate increases inter-urban WSES flows, raising water yield by 4.40–5.06 billion m³ and soil retention by 0.83–0.85 billion tons, partially alleviates internal supply-demand imbalances. The biophysical differences drive contrasting WSES network responses: water yield flow network become centralized and fragile, while soil retention flows gain redundancy. From the perspectives of internal supply-demand balance and external flow networks, this study highlights the critical role of ecosystem service flows in mitigating supply-demand imbalances across urban agglomerations, providing a scientific basis for precision spatial planning and climate-adaptive strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107144"},"PeriodicalIF":12.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980139","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}