Sustainable Cities and Society最新文献

{"title":"Unveiling city-scale urban roadside charging piles capacity: Geospatial knowledge-assisted small object detection and SDG 7-driven planning","authors":"Qin Huang ,&nbsp;Teng Zhong ,&nbsp;Liangchen Zhou ,&nbsp;Rui Zhu ,&nbsp;Xiao Fu ,&nbsp;Changchang Zhou ,&nbsp;Min Chen ,&nbsp;Guonian Lü","doi":"10.1016/j.scs.2025.106789","DOIUrl":"10.1016/j.scs.2025.106789","url":null,"abstract":"<div><div>The rapid rise of electric vehicles (EVs) requires efficient detection and planning of urban roadside charging piles (RCPs) to support sustainable urban management. This study proposes a novel framework to optimize urban RCPs, integrating geospatial knowledge-assisted small object detection and Sustainable Development Goal 7 (SDG 7)-driven planning. We developed RCPs-YOLO, a tailored model that leverages geospatial knowledge to improve small object detection, achieving 89.8 % precision and 77.4 % [email protected] in detecting RCPs from street view images, and a multi-line-of-sight method for precise geographic localization. Based on the EVs roadside charging demand across Nanjing Central Districts (NCDs) in year 2024, we suggest that the RCPs could support up to 301,537 kWh/day in NCDs. We develop four SDG 7-driven planning scenarios, including business-as-usual, equity-oriented, efficiency-oriented, and balanced development. Under these scenarios, the potential annual roadside charging capacity in NCDs by 2030 is approximately 85.8 GWh, 153.5 GWh, 103.2 GWh, and 148.3 GWh, respectively. Our findings suggest prioritizing the development of RCPs in newly developed downtown areas to promote equitable access and enhance energy efficiency. This approach offers a scalable, data-driven solution for urban planners aiming to advance progress toward SDG 7 and the development of smart cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106789"},"PeriodicalIF":12.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027494","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 environmental quality of shrinking cities in China improved less than that of non-shrinking cites during 2000-2020: A quantitative comparison based on propensity score matching method","authors":"Kaihong Yue ,&nbsp;Kang Wu ,&nbsp;Qingxu Huang ,&nbsp;Yijin Wang ,&nbsp;Tianci Gu ,&nbsp;Yiming Hou ,&nbsp;Xingyun Feng ,&nbsp;Shiyu Zhang ,&nbsp;Yizhou Xie ,&nbsp;Jiasheng Wang","doi":"10.1016/j.scs.2025.106759","DOIUrl":"10.1016/j.scs.2025.106759","url":null,"abstract":"<div><div>Shrinking cities face challenges such as underutilized resources and environmental changes in light of declining disturbance of anthropogenic activities and lack of financial support. In this context, the evolution of their environmental quality must be analysed timely and effectively. This study aimed to investigate the spatiotemporal changes in the environmental quality of shrinking cities in China from 2000 to 2020, and compared the changes between shrinking cities and non-shrinking cities. We leveraged the Google Earth Engine platform to analyse the spatiotemporal changes in the environmental quality of shrinking cities in China over the past two decades, based on the comprehensive environmental index (CEI). Then, we applied the propensity score matching method to match shrinking cities with comparable non-shrinking counterparts for comparative analysis. We evaluated the effects of policy interventions by combining the Difference-in-Differences (DID) model and the synthetic control method (SCM). The findings showed that the environmental quality of shrinking cities follows a U-shaped trajectory. From 2000–2007, the environmental quality of shrinking cities decreased, followed by a substantial improvement after 2013. Over the past 20 years, the CEI of shrinking cities has increased by 5.70 %, which was slightly lower than the 6.89 % increase observed for non-shrinking cities. Specifically, shrinking cities in northern China exhibited the highest environmental quality and the highest improvement rate (5.99 %), whereas those in the southern, northwestern, and Qinghai‒Tibet regions exhibited modest improvements (increases of 5.34 %, 4.65 %, and 5.32 %, respectively). The decrease in PM_2.5 concentration was the main contributing factor to the improvement of environmental quality. Policy analysis showed that environmental policies such as the \"Air Pollution Prevention and Control Action Plan\" had a significant promoting effect on improving the environmental quality of shrinking cities. To promote sustainable development, it is vital to formulate targeted and regional-specific environmental improvement policies for shrinking cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106759"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932355","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 site experiment on the effects of cool coating in a residential community under the tropical climate","authors":"Yong Ping Long ,&nbsp;E. V. S. Kiran Kumar Donthu ,&nbsp;Di Han ,&nbsp;Man Di Zhou ,&nbsp;Xiao Qin Zhang ,&nbsp;Bing Feng Ng ,&nbsp;Man Pun Wan","doi":"10.1016/j.scs.2025.106754","DOIUrl":"10.1016/j.scs.2025.106754","url":null,"abstract":"<div><div>With global warming, the urban thermal environment has seen temperatures rising, especially in the tropical region, where the warming effect is twice the global average. Among the strategies for cooling cities, highly reflective coating (cool coating) stands out as a passive technology that is highly scalable and affordable. However, most of the existing studies on cool coatings are still limited to numerical simulations or bench-top experiments. Here, we provide direct evidence on the performance of cool coating in a real-scale demonstration. Cool coatings were applied to the roofs, walls and roads in a residential community in Singapore comprising of two high-rise buildings. Another two high-rise buildings were used as controls. The results indicate that cool coatings can change the energy balance of the urban surfaces, reduce the energy entering the building envelopes and the ground, leading to the maximum hourly surface temperature reduction by up to 22 °C on the concrete roof, and the outdoor air temperature reduction reached 2 °C at the pedestrian level and the mid-canyon level. The thermal comfort at the pedestrian level was improved accordingly, and the maximum hourly reduction reached 3 °C in terms of universal thermal climate index.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106754"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932360","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":"Modeling high-resolution air temperature variability with multi-source data: predictive insights for urban heat mitigation strategies","authors":"Yi-Chien Chen,&nbsp;Jen-Yu Han","doi":"10.1016/j.scs.2025.106788","DOIUrl":"10.1016/j.scs.2025.106788","url":null,"abstract":"<div><div>Understanding the spatial and temporal variability of urban air temperature is essential for addressing urban heat island (UHI) effects and supporting climate-responsive planning. However, modeling fine-scale air temperature variability remains a challenge due to the complex interplay of urban morphology, land cover, and anthropogenic factors. This study presents a scalable and spatially explicit machine learning (ML) framework to model near-surface air temperature at high spatial resolution, using multi-source geospatial and meteorological data. By integrating satellite-derived land surface temperature (LST), Internet-of-Things (IoT) sensor observations, urban morphology, and anthropogenic activity indicators, the model captures diurnal dynamics of urban heat. To account for spatial autocorrelation, spatial lag features were incorporated as model inputs, improving predictive performance and spatial coherence. The best-performing random forest model with spatial lag features achieved an R² of 0.9939 and an RMSE of 0.4363 °C. Compared to conventional interpolation or physical modeling approaches, the proposed framework offers reduced dependence on dense sensor networks, and enhancement in spatial detail. This approach enables detailed mapping of thermal exposure and supports the identification of priority areas for UHI mitigation strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106788"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988155","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":"Evaluating Spatial Inequities in Multimodal Accessibility to Convenience Stores: A 3SFCA-Based Study of Jongno-gu, Seoul","authors":"Yu Xu,&nbsp;Jaekyoung Kim,&nbsp;Gunwon Lee","doi":"10.1016/j.scs.2025.106782","DOIUrl":"10.1016/j.scs.2025.106782","url":null,"abstract":"<div><div>This study investigates spatial justice in convenience store accessibility by examining how travel modes, mobility conditions, and spatial structures jointly shape service equity. Drawing on the “capability space” framework, it first constructs a behavior-based accessibility measure using the Three-Step Floating Catchment Area (3SFCA) method and credit card transaction data, capturing individuals’ actual ability to access retail services. This empirically grounded indicator is then analyzed alongside socio-spatial and demographic variables to uncover disparities rooted in capability differences and institutional structures.</div><div>Owing to Jongno-gu’s compact urban form, walking showed the highest equity. In contrast, cycling and driving revealed pronounced gaps in accessibility, which is attributable to uneven resource allocation and transportation infrastructure. While walking provides relatively equitable access overall, it does not serve older adults and residents living in peripheral neighborhoods. Infrastructural discontinuities and mode-user mismatches constrain cycling accessibility, whereas driving expands spatial coverage but reinforces structural advantages for car-owning households. These findings underscore how transport modes, land use patterns, and population characteristics interact to produce unequal service landscapes.</div><div>Accordingly, this study presents an integrated framework that operationalizes spatial justice through behavior-based accessibility assessment. By connecting actual service use with socio-spatial disparities, it enables targeted diagnosis of capability inequalities in retail provision. Beyond methodological contribution, the findings offer actionable insights for equitable and inclusive mobility planning in dense, aging urban contexts.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106782"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048350","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":"Energy-resilient performance-based generative design to adapt to future climate change using urban building energy model: A case study of residential block design","authors":"Chenxi Shi ,&nbsp;Miaokun Huang ,&nbsp;Shu Su ,&nbsp;Qinran Hu ,&nbsp;Wei Wang ,&nbsp;Xiangfeng Li","doi":"10.1016/j.scs.2025.106769","DOIUrl":"10.1016/j.scs.2025.106769","url":null,"abstract":"<div><div>As global concern about climate change intensifies, optimizing building energy consumption, reducing carbon emissions, and enhancing urban energy resilience of buildings have emerged as critical priorities. This study presents a multi-objective optimization framework for urban residential blocks in the Yangtze River Delta, evaluating the impact of building morphology on energy performance and solar energy potential under future climate scenarios. Through performing parameter validation, parametric modeling, energy simulation, and multi-objective optimization using a real-world case study, the Pareto-optimal solutions we identified exhibited significantly improved overall performance. Energy consumption was decreased by at least 0.76 kWh/m² in 2020, 0.88 kWh/m² in 2030, and 1.30 kWh/m² in 2060, demonstrating that optimized building forms enhance both energy efficiency and climate adaptability under projected climate change conditions. Furthermore, the Pareto-optimal solutions indicate that the existing strategy of energy-efficient building layouts exhibits climate adaptability and energy resilience inherently. Compact building layouts with open spaces strategically positioned around buildings yield superior energy performance. Regarding building typology, minimizing unit high-rise structures while increasing standalone high-rise buildings is advisable. To maximize adaptation to prevailing monsoons, high-rise buildings should be concentrated on the western and northern sides of residential blocks, with lower heights and open spaces allocated to the southern and eastern sectors. In addition, maximizing solar energy potential through optimized spatial configurations can effectively eliminate climate change impacts on building energy demands.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106769"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932286","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":"Evaluating urban green resilience through deep learning and multicriterion decision-making approaches: A spatiotemporal analysis of Guangdong Province","authors":"Minglong Han ,&nbsp;Yupeng Liu","doi":"10.1016/j.scs.2025.106755","DOIUrl":"10.1016/j.scs.2025.106755","url":null,"abstract":"<div><div>Extreme climate conditions place considerable environmental pressure on urban systems. Therefore, determining how to evaluate and enhance urban green resilience (UGR) is crucial to achieving sustainable development. This paper introduces a methodological framework that combines deep learning and multicriterion decision-making (MCDM) approaches to evaluate UGR and examine its spatiotemporal characteristics. We defined the concept of UGR and developed a corresponding evaluation index system. Additionally, we used a modified assessment model optimized by graph convolutional network and gated recurrent unit architectures to evaluate the green resilience of Guangdong Province’s cities. We conducted spatial heterogeneity and city network analyses to explore the spatial distribution of green resilience. Our results indicate the following. First, ecology has a major effect on green resilience, acting as a source-driven factor. Second, new energy penetration, water pollution pressure, urban greening, and low-carbon infrastructure considerably influence UGR. Third, the green resilience of cities in Guangdong Province is improving, with northern mountainous areas experiencing considerably high growth. Fourth, Pearl River Delta cities excel in terms of infrastructure and social engagement, whereas northern mountainous regions excel in terms of governance. This paper also outlines optimization paths for three representative cities, providing a theoretical basis and practical guidance for future studies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106755"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920251","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":"Spatial-temporal modeling of heat hazards for a high-density subtropical city: A case study of Hong Kong","authors":"Feiyang Zhang ,&nbsp;Chao Ren ,&nbsp;Xidong Chen ,&nbsp;Guangzhao Chen ,&nbsp;Qingyao Qiao ,&nbsp;Shuang Liu","doi":"10.1016/j.scs.2025.106763","DOIUrl":"10.1016/j.scs.2025.106763","url":null,"abstract":"<div><div>Heatwaves are becoming more frequent, intense, and prolonged over the last two decades. Air temperature-based indicators more accurately capture heat hazards than commonly used land surface temperature measures, yet few studies have explored their relationship with urban characteristics or undertaken long-term spatial-temporal modeling. To fill these gaps, this study explores the applicability of land use regression, machine learning methods, and stacking ensemble learning to map Very Hot Day Hours and Hot Night Hours from 2000 to 2023 in Hong Kong, using meteorological data from 40 ground-level stations. Feature importance analyses are used to clarify how urban characteristics influence heat hazards. With the yearly heat hazard maps produced, the long-term trend of heat hazards in different areas is also examined. It is found that the stacking ensemble learning method further reduced the mean absolute error from 38.32 to 35.19 for Very Hot Day Hours, and from 96.55 to 88.73 for Hot Night Hours. Wind and elevation are found to be critical in mitigating daytime heat hazards, while vegetation is found to be more important in mitigating nighttime heat hazards. Practical implications for increasing community heat resilience are also provided.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106763"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048443","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":"Prioritizing low-carbon materials and reducing material quantities to mitigate embodied GHG emissions in buildings","authors":"Buket Tozan,&nbsp;Endrit Hoxha,&nbsp;Emilie Brisson Stapel,&nbsp;Harpa Birgisdóttir","doi":"10.1016/j.scs.2025.106770","DOIUrl":"10.1016/j.scs.2025.106770","url":null,"abstract":"<div><div>As regulations for buildings become increasingly stringent, Life Cycle Assessment (LCA) is emerging as a key method of documenting and reducing embodied greenhouse gas emissions (GHGe). Mitigation strategies often focus on optimizing material quantities or substituting conventional materials with low-carbon alternatives. However, these approaches are typically applied in isolation and to specific building components. This study examines which mitigation strategies should be prioritized to reduce embodied GHGe at the whole-building level while ensuring compliance with regulatory limits for new construction in Denmark. Using data from 172 residential buildings and 1054 Environmental Product Declarations (EPDs) across 21 material categories, Monte Carlo Simulations were employed to generate LCAs by combining real buildings' material intensity coefficients (MICs) with EPD data. The results indicate a 66 % probability of compliance with the regulatory limit values for row houses and 64 % for multi-family buildings, but only 15 % for single-family homes. Sensitivity analyses across material categories identified key contributors to both total embodied emissions and variability, such as mineral boards and ready-mixed concrete. This highlights areas where mitigation efforts should be concentrated, either by selecting lower-impact materials or by reducing material quantities. The findings suggest that prioritizing reductions in material quantities may be the most effective approach, though low-carbon materials remain a crucial strategy. These results provide valuable insights for making informed material choices early in the design process and offer strategies for improving life cycle embodied GHGe in line with regulatory compliance for building projects.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106770"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920252","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":"Value assessment and design optimization of local energy communities: a Dutch case study","authors":"Farzaneh Mousavi Motlagh,&nbsp;Pieter-Jan Hoes,&nbsp;Jan Hensen","doi":"10.1016/j.scs.2025.106738","DOIUrl":"10.1016/j.scs.2025.106738","url":null,"abstract":"<div><div>Local energy communities (LECs) offer a promising solution to improve the integration of renewable energy and support the energy transition. This study proposes a simulation-based methodology to optimize LEC design and assess its added value over conventional design and operation of (individual) buildings, using Dutch office buildings as a case study. Various LEC design configurations are simulated, considering building retrofits, photovoltaic system sizes, battery energy storage capacities, and heating system types. Additionally, multiple battery control strategies are modeled using a mixed-integer linear programming (MILP) optimization approach and their impact on key performance indicators is evaluated. The best performing LEC design, aligning with stakeholders’ interests, includes the replacement of gas boilers with heat pumps, a 2800 kWh community battery, and PV systems twice the total roof area. This configuration reduces operational costs by 66 % and operational CO₂ by 60 % and achieves a return on investment of 41 %. Furthermore, LECs are found to outperform individual building-level optimization by lowering operational costs up to 8 % , decreasing operational CO₂ up to 7 %, and eliminating contractor peak violations. However, local network cable capacity violations are a significant challenge in the community battery configuration, while individual building-level batteries mitigate these issues more effectively. Considering these limitations, LECs’ full potential for cost and CO₂ emission reductions can be unlocked through advanced community battery control strategies and targeted infrastructure upgrades to ensure a viable and scalable solution for future energy systems.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106738"},"PeriodicalIF":12.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921711","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}