Sustainable Cities and Society最新文献

{"title":"The impact of urban spatial forms on marine cooling effects in mainland and island regions: A case study of Xiamen, China","authors":"Yuanping Shen ,&nbsp;Qiaqia Zhang ,&nbsp;Qunyue Liu ,&nbsp;Meng Huang ,&nbsp;Xiong Yao ,&nbsp;Kunneng Jiang ,&nbsp;Meihong Ke ,&nbsp;Yongju Ren ,&nbsp;Zhipeng Zhu","doi":"10.1016/j.scs.2025.106210","DOIUrl":"10.1016/j.scs.2025.106210","url":null,"abstract":"<div><div>The marine cooling effect (MCE) plays a crucial role in mitigating urban heat island (UHI) in coastal cities. However, limited research has explored how urban spatial forms (USF) influence MCE, particularly between mainland and island regions, which may exhibit distinct cooling dynamics. This study proposes a marine cooling spatial impact value (SIV) index to quantify the impacts of USF on MCE. Utilizing interpretable machine learning models, we explore the nonlinear impacts of key USF on MCE. The research shows that MCE extends further on Xiamen Island than on the mainland. Specially, water bodies enhance MCE, while both impervious surfaces (Im_p) and building density (BD) weaken it. Moreover, elevation - waters interaction enhances their MCE contribution on the mainland, while waters - canopy height (CHM) interaction boosts CHM's contribution on island. This study emphasizes the significant role of the 3D structure of urban forms in shaping MCE, highlighting notable differences between island and mainland regions. Our findings offer a new framework for quantifying the USF-MCE relationship and provide valuable guidance for coastal urban planners to optimize spatial layouts and effectively mitigate UHI effects by considering regional differences in MCE.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106210"},"PeriodicalIF":10.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419473","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":"Inter- and intra-LCZ thermal heterogeneity: The dominant role of external environments in shaping local land surface temperature","authors":"Xinlu Lin ,&nbsp;Xiaodie Lin ,&nbsp;Chao Yan","doi":"10.1016/j.scs.2025.106188","DOIUrl":"10.1016/j.scs.2025.106188","url":null,"abstract":"<div><div>In light of escalating urbanization and climate change, understanding the Urban Heat Island (UHI) effect is crucial to improving urban resilience. The Local Climate Zones (LCZs) classification, introduced in 2012, has become a vital tool in urban climate research. While most studies focus on inter-LCZ heterogeneity (temperature differences between LCZ types), this study highlights the less-explored intra-LCZ heterogeneity (variations within the same LCZ type). Using ECOSTRESS LST data, we examine spatial and diurnal LST variations in Fuzhou, China—a representative “Furnace City”. Random Forest models and Shapley values analysis reveal that external factors, such as distance to the city center and proximity to hotspots (like LCZ 3 or 8) or blue–green infrastructure, play significant roles in both inter- and intra-LCZ LST variability. Water bodies typically lower surrounding daytime temperatures but increase them at night, while greenery consistently mitigates surrounding LST throughout the day. Our findings suggest that applying the LCZ framework requires not only attention to local ( 100 m) surface properties but also consideration of neighborhood and city-scale characteristics to better capture the spatio-temporal heterogeneity of urban thermal environments. These insights emphasize the need for urban planning strategies that integrate blue–green infrastructure and manage thermal hotspots to mitigate UHI effects.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106188"},"PeriodicalIF":10.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445181","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":"Investigating the relationship between spatial morphology, meteorological factors, and elderly people well-being in a traditional algerian village","authors":"Lilia Mahia ,&nbsp;Djihed Berkouk ,&nbsp;Tallal Abdel Karim Bouzir ,&nbsp;Ilaria Pigliautile ,&nbsp;Anna Laura Pisello","doi":"10.1016/j.scs.2025.106212","DOIUrl":"10.1016/j.scs.2025.106212","url":null,"abstract":"<div><div>In the face of climate change, outdoor spaces have become increasingly unfavorable due to heat conditions and the lack of adequate design strategies. The real consequences of these factors on human beings, both physiologically as well as physically and psychologically, are often overlooked in the short term that can turn into a long term with continuous exposure, particularly among elderly people. This study investigates the relationship between spatial morphology, meteorological factors, and the physiological and psychophysical responses of elderly people during a walk. The results demonstrated the elevation as a key factor influencing various physical environments and, consequently, elderly people's responses. High Sky View Factor (SVF) and low Height to Width ratio (H/W) significantly increased Skin Temperature of the Forehead (STForehead) and stress level, leading to a weakened state (P ≤ 0.001) and heightened thermal sensation (0.01 &lt; P &lt; 0.05), ultimately resulting in a decreased walking speed (P ≤ 0.001). In contrast, a higher Visible Green Index (VGI) and Building View Factor (BVF) under low mean radiant temperature (Tmrt) significantly reduced STForehead and stress level, leading to a relaxed and energetic state (P ≤ 0.001) while improving thermal perception (0.01 &lt; P &lt; 0.05). Thanks to the spatial configurations, including optimal H/W ratio, greenery, and northeast-southwest orientation, Tmrt was reduced approximately 5°C on one route compared to the other two. Additionally, the thermal sensation vote (TSV) could be predicted by the skin temperature of the forehead and the energetic-weak state. Meanwhile, the thermal pleasantness vote (TPV) could be predicted by STForehead, walking speed, and the relaxed-unrelaxed state.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106212"},"PeriodicalIF":10.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445233","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":"Review and prediction: Carbon emissions from the materialization of residential buildings in China","authors":"Xing Xiong ,&nbsp;Xiaojun Li ,&nbsp;Shaobo Chen ,&nbsp;Dian Chen ,&nbsp;Jinchen Lin","doi":"10.1016/j.scs.2025.106211","DOIUrl":"10.1016/j.scs.2025.106211","url":null,"abstract":"<div><div>Previous studies on carbon emissions from the materialization of residential buildings differ significantly in their case sources, methods, and research findings. Consequently, it is essential to investigate the general characteristics and driving factors of carbon emissions in this context. A systematic review was carried out with research papers on carbon emissions of the materialization stage of residential buildings in China. Analysis of the carbon emission results reveals an average carbon emission intensity (CEI) of 409.04 kgCO₂e/m². Through standardized coefficients and significance tests, the effects of 20 driving factors were quantified. Four explanatory models were developed using enter regression to interpret the results of existing multi-family building samples. Additionally, four predictive models were created through backward elimination to assist designers in predicting CEI during the conceptual design phase. The findings indicate that minimizing new construction areas is the most effective strategy for reducing total carbon emissions, and adopting prefabricated construction methods significantly decreases CEI. Conversely, enhancements in building performance may inadvertently increase CEI. Other key impact factors on CEI include building age, climate zone, and calculating object. It is important to recognize that the driving factors for the three sub-stages of production, transportation, and construction vary considerably, and thus should be studied separately whenever feasible. This research contributes to promoting carbon reduction in the building industry by advancing research on the calculation of carbon emissions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106211"},"PeriodicalIF":10.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474893","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":"Quantifying contributions of geographical features to urban GDP outputs via interpretable machine learning","authors":"Peiran Zhang ,&nbsp;Haonan Guo ,&nbsp;Fabiano L. Ribeiro ,&nbsp;Pavel L. Kirillov ,&nbsp;Alla G. Makhrova ,&nbsp;Ziyou Gao ,&nbsp;Liang Gao","doi":"10.1016/j.scs.2025.106185","DOIUrl":"10.1016/j.scs.2025.106185","url":null,"abstract":"<div><div>Urban scaling laws, which assume homogeneous population interactions, traditionally describe the relationship between urban population and GDP. However, this approach often overlooks the complexity of urban environments, particularly geographical features such as land use, road networks, and points of interest, which significantly shape urban economies. To address this gap, we propose an interpretable machine learning framework that quantifies the impact of urban geographical features (UGFs) on economic outputs (GDP) across five countries: the USA, Brazil, Nigeria, China, and India. Our study can be summarized in three parts: (1) Using the CatBoost algorithm for GDP estimation, which achieves an average <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.96 across countries, we demonstrate the substantial effects of UGFs (2) The Shapley Additive Explanations (SHAP) method is employed to quantify feature contributions on GDP, revealing that UGFs account for 45% to 89% variance, with influences differing across and within countries. (3) By classifying cities based on feature contribution vectors, we show that cities with similar GDP levels often exhibit analogous contributions from both population and UGFs, suggesting that shared strategies could be applied to cities with comparable economic profiles. Our findings provide valuable insights into the role of UGFs in shaping GDP, advancing the understanding of how UGFs influence economic development, and offering policymakers more informed suggestions. Furthermore, this framework opens new opportunities to integrate diverse urban features into urban studies through machine learning, enhancing our understanding of the complexity of urban systems.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106185"},"PeriodicalIF":10.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387857","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":"How to plant trees on an elevated road to improve thermal comfort in a street canyon","authors":"Geon Kang ,&nbsp;Jae-Jin Kim","doi":"10.1016/j.scs.2025.106207","DOIUrl":"10.1016/j.scs.2025.106207","url":null,"abstract":"<div><div>This study investigates the impact of trees planted on an elevated road on airflow and thermal comfort in a street canyon using a computational fluid dynamics (CFD) model, incorporating parameterization schemes for tree drag, shading, and evapotranspiration. To systematically assess the effects of trees on in-canyon airflows and temperatures, varying tree heights and locations were evaluated for three solar altitudes: morning, noon, and afternoon. The elevated road reduced surrounding wind speeds and increased air temperatures due to surface heating. When trees were planted on the elevated road, their drag and cooling effects further decreased wind speeds and temperatures. Specifically, trees taller than 6 m reduced average temperatures by -1.7 °C on the elevated road and -0.6 °C beneath it in pedestrian areas. Thermal comfort was assessed using the Universal Thermal Climate Index (UTCI). Shrubs under 1 meter showed high cooling efficiency but limited shading, whereas trees taller than 4 m improved UTCI by 2–8 °C. Central tree planting on the elevated road provided the most significant UTCI improvement, while planting on both sides resulted in higher UTCI in the center. These findings offer guidance for optimizing vegetation placement to enhance thermal comfort and promote sustainable urban environments.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106207"},"PeriodicalIF":10.5,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the relationship between urban green development and heat island effect within the Yangtze River Delta Urban Agglomeration","authors":"Zhanyu Liu ,&nbsp;Sansan Zhang","doi":"10.1016/j.scs.2025.106204","DOIUrl":"10.1016/j.scs.2025.106204","url":null,"abstract":"<div><div>Exploring the spatiotemporal dynamic relationship between urban green development and urban heat island effect (UHIE) is crucial for balancing urban society, economy, and ecosystem development. This paper measures the green development and UHIE in the Yangtze River Delta Urban Agglomeration (YRDUA) from 2012 to 2021 using the entropy weight TOPSIS method and urban-rural dichotomy to analyze their spatiotemporal evolution. Then the gray incidence model and bivariate spatial autocorrelation are used to explore the spatiotemporal dynamic relationship between the two. Results reveal that urban green development generally improved, showing a decreasing distribution pattern from east to west, and the regional disparities increased. Urban heat island intensity (UHII) in the center of the 27 cities increased and urban heat island's extent expanded. UHIE was more serious in the southeastern, such as Shanghai, and Hangzhou. Urban green development composite index, green production, green living, green policy, and UHIE were generally positively correlated. Green environment did not always mitigate the UHIE. The spatial correlation pattern between urban green development and UHIE was characterized by High-High agglomeration in Shanghai, Suzhou, and Wuxi, and Low-Low agglomeration in Yancheng. The results reveal that urban green development planning should emphasize UHIE mitigation measures to realize sustainable urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106204"},"PeriodicalIF":10.5,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436808","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":"Groundwater infiltration inverse estimation in urban sewers network: A two-stage simulation-optimization model","authors":"Zihan Liu ,&nbsp;Yexin He ,&nbsp;Wenli Liu ,&nbsp;Hanbin Luo ,&nbsp;Han Gao","doi":"10.1016/j.scs.2025.106205","DOIUrl":"10.1016/j.scs.2025.106205","url":null,"abstract":"<div><div>As an important component of urban infrastructure, sewer system has a significant influence on the attainment of all sustainable development goals. Groundwater infiltration (GWI) into sewers imposes a hydraulic burden on wastewater collection networks, which eventually decreases the overall effectiveness of wastewater treatment. To tackle this challenge, it is crucial to develop an efficient and accurate approach for identifying the sources and measuring the infiltration volume. Therefore, this paper introduces a two-stage simulation-based inverse optimization model (SIOM). At the regional scale, an initial clustering analysis is conducted on the influencing indicators related to local spatial dependence in pipe network degradation. Then, the spatially clustering effect of GWI is encapsulated into the inverse optimization procedure, which is predicated on the segmental-level modeling. GWI sources and flows can be more precisely delineated and elucidated using a cluster-based genetic algorithm (CGA). The spatial statistical approach of Geographically Weighted Regression Model (GWR) is leveraged to determine the influence of explanatory factors on increased infiltration propensity in sewers based on spatial heterogeneity. In our case study, GWI contributed approximately 36 % of the total dry-weather inflow (34,373 m³/d) to the sewer system. CGA leads to 25 % and 7.6 % improvements in the convergence speed and prediction accuracy respectively. Meanwhile, the application of the membership function characterized by Gaussian distribution with a lower mean value enables the model to achieve optimal performance, with a Nash-Sutcliffe Efficiency (NSE) value of 0.779. Explanatory factors such as pipeline diameter, slope, burial depth, road density, and building density show obvious spatial heterogeneity and have varying effects on the infiltration tendency, among which pipe diameter shows the most significant local effect. In the investigation of GWI within large-scale sewer systems, this method exhibits superior performance over traditional CCTV and other direct measurement techniques in terms of computational efficiency and modeling accuracy.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106205"},"PeriodicalIF":10.5,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378237","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":"Mapping high-resolution spatio-temporal patterns of pedestrian thermal comfort at different scales using street view imagery and deep learning","authors":"Jie Qin ,&nbsp;Meng Tian ,&nbsp;Xuesong Xu ,&nbsp;Lei Yuan","doi":"10.1016/j.scs.2025.106209","DOIUrl":"10.1016/j.scs.2025.106209","url":null,"abstract":"<div><div>Accurate evaluating pedestrian-level thermal comfort is important for improving resident well-being and mitigating urban heat island (UHI). This study proposes a framework for calculating high-resolution pedestrian-level thermal comfort at both neighborhood and urban scales. We identified the geolocations and three-dimensional (3D) structure of street trees using triangulation and deep learning models of mask region-based convolutional neural network (Mask R-CNN) and Monodepth2 from street view imagery. By incorporating meteorological forcing data, land use classification, building information and digital elevation model (DEM), the mean radiant temperature (MRT) and universal thermal climate index (UTCI) were obtained. To explore the feasibility of the framework, we calculated the spatio-temporal UTCI patterns in Shenzhen, China at 2 m and 50 m resolutions at 9:00, 12:00, 15:00 and 17:00 during heatwave and non-heatwave periods, along with local climate zone (LCZ) classification. Results indicated that the proposed framework not only accurately identified the high-resolution pedestrian-level thermal comfort in neighborhoods, but also rapidly captured the spatio-temporal distribution of pedestrian thermal comfort across the city. We also found that the thermal comfort in high-rise areas (LCZs 1 and 4) was better than in open low-rise areas (LCZ 6). These findings contribute to guiding sustainable and resilient urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106209"},"PeriodicalIF":10.5,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378238","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":"GeoBEM: A geospatial computing empowered framework for urban-scale building energy modeling","authors":"Shihong Zhang ,&nbsp;Liutao Chen ,&nbsp;Lingming Xu ,&nbsp;Zhe Wang","doi":"10.1016/j.scs.2025.106203","DOIUrl":"10.1016/j.scs.2025.106203","url":null,"abstract":"<div><div>Cities are major electricity consumers and carbon emitters. Reducing urban building energy consumption plays a crucial role in decarbonizing energy systems and mitigating climate change. Modeling is always the first step to propose energy saving measures. Urban building energy modeling (UBEM) is an emerging research field that integrates building technology with Geographic Information System (GIS). However, the integration of UBEM with geospatial computations in existing studies is often fragmented. In this study, we propose GeoBEM, a framework for urban-scale building energy modeling that incorporates geospatial computing. GeoBEM features a comprehensive and automated workflow that includes data fusion, geospatial computing-based thermal zoning, terrain-effect shading modeling, and prototype building model development. Energy simulations are conducted on over 120,000 buildings in Hong Kong, and achieved a validation root mean square error of 19.86%. Furthermore, we identified areas with high energy usage intensity and analyzed energy consumption patterns for seven typical building types across three time periods. Finally, we developed the Hong Kong UBEM Digital Twin Platform, which provides valuable insights for planning the electricity grid, comparing energy management strategies, and promoting sustainable urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106203"},"PeriodicalIF":10.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394625","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}