Pub Date : 2025-12-21DOI: 10.1016/j.scs.2025.107087
Mohammad Jawed Nabizada , Ümran Köylü
This study investigates the spatiotemporal dynamics of Land Surface Temperature (LST) and Surface Urban Heat Island (SUHI) intensity in Kabul Province, Afghanistan (2000–2024), by integrating multi-source satellite data with climatic, topographic, and surface biophysical variables to identify environmental drivers and predict spatial patterns. Monthly LST time series were standardized and analyzed using the Mann–Kendall test and Sen’s slope estimator, while the Random Forest (RF) model was applied to classify and predict LST across Land Use and Land Cover (LULC) classes. LST peaked at 47 °C in July 2023 and dropped to –4 °C in January 2006. Daytime mean LSTs were highest in bare land (41 °C) and urban areas (38 °C), followed by water (34 °C) and vegetation (32 °C). At night, urban surfaces remained the warmest (23 °C). The Mann–Kendall test revealed a nonsignificant long-term trend (p = 0.145, Z = –1.459), indicating short-term seasonal fluctuations. Hotspot analysis identified significant summer SUHI clustering in highly urbanized and sparsely vegetated areas (Kabul City, Deh Sabz, Bagrami, Surobi), while winter SUHI was minimal due to snow cover and higher surface albedo. The RF model achieved strong performance (RMSE = 2.33–2.46; r = 0.61–0.88) across MODIS, Landsat, and ERA5 datasets. This integrated remote sensing and machine learning framework provides a scalable approach for monitoring urban thermal environments and supports climate-adaptive urban planning and sustainable land management in semi-arid regions.
本研究利用多源卫星数据与气候、地形和地表生物物理变量相结合，研究了阿富汗喀布尔省2000-2024年地表温度（LST）和地表城市热岛（SUHI）强度的时空动态变化，以识别环境驱动因素并预测空间格局。采用Mann-Kendall检验和Sen’s slope estimator对月地表温度时间序列进行标准化和分析，同时采用随机森林（Random Forest， RF）模型对土地利用和土地覆盖（LULC）类别的地表温度进行分类和预测。地表温度在2023年7月达到47°C的峰值，2006年1月降至-4°C。白天平均地表温度在裸地（41°C）和城市地区（38°C）最高，其次是水域（34°C）和植被（32°C）。在夜间，城市地表仍然是最热的（23°C）。Mann-Kendall检验显示长期趋势不显著（p = 0.145, Z = -1.459），表明短期季节性波动。热点分析发现，夏季SUHI主要集中在高度城市化和植被稀疏的地区（喀布尔市、德萨布兹、巴格拉米、苏鲁比），而冬季由于积雪覆盖和地表反照率较高，SUHI最小。RF模型在MODIS、Landsat和ERA5数据集上取得了较好的表现（RMSE = 2.33-2.46; r = 0.61-0.88）。这一综合遥感和机器学习框架为监测城市热环境提供了一种可扩展的方法，并支持半干旱地区的气候适应性城市规划和可持续土地管理。
{"title":"Integrated remote sensing and machine learning approach for LST trend analysis and SUHI detection in the semi-arid climate of Kabul province using Google Earth Engine","authors":"Mohammad Jawed Nabizada , Ümran Köylü","doi":"10.1016/j.scs.2025.107087","DOIUrl":"10.1016/j.scs.2025.107087","url":null,"abstract":"<div><div>This study investigates the spatiotemporal dynamics of Land Surface Temperature (LST) and Surface Urban Heat Island (SUHI) intensity in Kabul Province, Afghanistan (2000–2024), by integrating multi-source satellite data with climatic, topographic, and surface biophysical variables to identify environmental drivers and predict spatial patterns. Monthly LST time series were standardized and analyzed using the Mann–Kendall test and Sen’s slope estimator, while the Random Forest (RF) model was applied to classify and predict LST across Land Use and Land Cover (LULC) classes. LST peaked at 47 °C in July 2023 and dropped to –4 °C in January 2006. Daytime mean LSTs were highest in bare land (41 °C) and urban areas (38 °C), followed by water (34 °C) and vegetation (32 °C). At night, urban surfaces remained the warmest (23 °C). The Mann–Kendall test revealed a nonsignificant long-term trend (<em>p</em> = 0.145, <em>Z</em> = –1.459), indicating short-term seasonal fluctuations. Hotspot analysis identified significant summer SUHI clustering in highly urbanized and sparsely vegetated areas (Kabul City, Deh Sabz, Bagrami, Surobi), while winter SUHI was minimal due to snow cover and higher surface albedo. The RF model achieved strong performance (RMSE = 2.33–2.46; <em>r</em> = 0.61–0.88) across MODIS, Landsat, and ERA5 datasets. This integrated remote sensing and machine learning framework provides a scalable approach for monitoring urban thermal environments and supports climate-adaptive urban planning and sustainable land management in semi-arid regions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107087"},"PeriodicalIF":12.0,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.scs.2025.107077
Zekun Li , Sri Ramana Saketh Vasanthawada , Keyi Chai , Linyue Luo , Xiao Xu , Ying Zhang , Kristen Kurland , Vivian Loftness
Rapid urban development has intensified soil sealing by impervious surfaces, contributing to extreme urban heat, flooding, and health risks. Although research on the impact of impervious surfaces on urban heat has grown in recent years, most studies overlook variations in surface characteristics, such as surface color, which influence urban heat. Consequently, city-wide data on sub-categorized impervious surfaces remain limited. This research fills this gap by using high-resolution remote-sensing imagery classification in ArcGIS Pro to map surface characteristics in Pittsburgh, Pennsylvania, and examine their relationship with land surface temperatures (LST) and social vulnerability. Results show that impervious surfaces cover 55% of the city, including 22% roofs, 30.7% roads and 2.3% parking lots, with 52% of these surfaces classified as dark. On average, historically redlined neighborhoods are 2.6 °C (4.7 °F) hotter and contain a higher proportion of dark surfaces. These results underscore the role of surface color and composition in shaping urban thermal inequities and emphasize the need for evidence-based decision-making in surface material selection to build more equitable and sustainable cities.
{"title":"Assessing social equity and urban heat risks with machine learning of remote sensing imagery: A Pittsburgh case study","authors":"Zekun Li , Sri Ramana Saketh Vasanthawada , Keyi Chai , Linyue Luo , Xiao Xu , Ying Zhang , Kristen Kurland , Vivian Loftness","doi":"10.1016/j.scs.2025.107077","DOIUrl":"10.1016/j.scs.2025.107077","url":null,"abstract":"<div><div>Rapid urban development has intensified soil sealing by impervious surfaces, contributing to extreme urban heat, flooding, and health risks. Although research on the impact of impervious surfaces on urban heat has grown in recent years, most studies overlook variations in surface characteristics, such as surface color, which influence urban heat. Consequently, city-wide data on sub-categorized impervious surfaces remain limited. This research fills this gap by using high-resolution remote-sensing imagery classification in ArcGIS Pro to map surface characteristics in Pittsburgh, Pennsylvania, and examine their relationship with land surface temperatures (LST) and social vulnerability. Results show that impervious surfaces cover 55% of the city, including 22% roofs, 30.7% roads and 2.3% parking lots, with 52% of these surfaces classified as dark. On average, historically redlined neighborhoods are 2.6 °C (4.7 °F) hotter and contain a higher proportion of dark surfaces. These results underscore the role of surface color and composition in shaping urban thermal inequities and emphasize the need for evidence-based decision-making in surface material selection to build more equitable and sustainable cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107077"},"PeriodicalIF":12.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.scs.2025.107083
Giovan Battista Cavadini , Gabriele Manoli , Lauren M. Cook
Cities are increasingly adopting blue-green infrastructure (BGI) to address the dual challenges of extreme rainfall and rising temperatures driven by climate change. While the potential of BGI for urban stormwater management is well-studied, the cooling effect of stormwater-focused BGI remains underexplored. This study investigates the heat mitigation potential of three stormwater BGI elements, bioretention cells, porous pavements, and detention ponds, within three urban street canyons in a Swiss town near Zurich. The Urban Tethys-Chloris (UT&C) microclimate model was modified to explicitly represent stormwater BGI and assess their influence on the Universal Thermal Climate Index (UTCI) at 2 meters above the ground. Simulations were conducted under both historical climate and a future climate projection, including a sensitivity analysis of soil types. BGI can cool up to 2.7 °C, but their effectiveness depends on the type of BGI, the surface it replaces, the time of the day, and the availability of water. Soil properties were found to significantly influence the cooling effect of bioretention cells, with finer-textured soils achieving higher soil moisture levels and greater reductions in UTCI. A trade-off between cooling and stormwater infiltration also emerged. Sandy soils favor infiltration but dry out quickly, limiting cooling, while clay-rich soils limit infiltration but retain moisture and sustain evaporative cooling, even under future climate conditions with longer dry spells. These findings highlight the importance of integrating hydrological and thermal considerations into BGI design. Integrated approaches that balance both objectives are needed.
{"title":"Cooling potential of stormwater blue-green infrastructure depends on soil type and water availability","authors":"Giovan Battista Cavadini , Gabriele Manoli , Lauren M. Cook","doi":"10.1016/j.scs.2025.107083","DOIUrl":"10.1016/j.scs.2025.107083","url":null,"abstract":"<div><div>Cities are increasingly adopting blue-green infrastructure (BGI) to address the dual challenges of extreme rainfall and rising temperatures driven by climate change. While the potential of BGI for urban stormwater management is well-studied, the cooling effect of stormwater-focused BGI remains underexplored. This study investigates the heat mitigation potential of three stormwater BGI elements, bioretention cells, porous pavements, and detention ponds, within three urban street canyons in a Swiss town near Zurich. The Urban Tethys-Chloris (UT&C) microclimate model was modified to explicitly represent stormwater BGI and assess their influence on the Universal Thermal Climate Index (UTCI) at 2 meters above the ground. Simulations were conducted under both historical climate and a future climate projection, including a sensitivity analysis of soil types. BGI can cool up to 2.7 °C, but their effectiveness depends on the type of BGI, the surface it replaces, the time of the day, and the availability of water. Soil properties were found to significantly influence the cooling effect of bioretention cells, with finer-textured soils achieving higher soil moisture levels and greater reductions in UTCI. A trade-off between cooling and stormwater infiltration also emerged. Sandy soils favor infiltration but dry out quickly, limiting cooling, while clay-rich soils limit infiltration but retain moisture and sustain evaporative cooling, even under future climate conditions with longer dry spells. These findings highlight the importance of integrating hydrological and thermal considerations into BGI design. Integrated approaches that balance both objectives are needed.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107083"},"PeriodicalIF":12.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841014","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}
Global climate warming and rapid urbanization have intensified the frequency and severity of extreme heat events, making Heat-related Health Risk (HHR) a pressing issue in public health and climate adaptation. Based on the Hazard–Exposure–Vulnerability (HEV) framework, this study integrates high-resolution remote sensing, meteorological, and socio-economic data to establish a Heat-related Health Risk Index (HRI) system for coastal mountainous cities. Using an interpretable machine learning approach (XGBoost–SHapley Additive exPlanations (SHAP)), we identified the spatial distribution, dominant factors, and nonlinear interactions of HHR in Fuzhou, China. Results show that HHR exhibits a “high-center—low-periphery” pattern, with high-risk zones in Gulou, Taijiang, and Cangshan, and lower risk in hilly and waterfront areas due to sea-breeze cooling and topographic ventilation. Urban morphology factors contributed most (46.2 %), followed by natural cover (25.7 %). The Digital Elevation Model (DEM), Impervious Surface Ratio (ISA), Road Network Density (RND), and Normalized Difference Vegetation Index (NDVI) were key drivers. HHR rises sharply when ISA exceeds 0.55–0.60, while NDVI mitigates heat risk most effectively within 0.35–0.40. Interaction analysis revealed three main effects: ISA × NDVI (diminishing marginal), NDVI × DEM (synergistic mitigation), and ISA × RND (amplifying). These findings highlight the coupled roles of urban form, terrain, and vegetation in shaping HHR and provide scientific guidance for climate adaptation and spatial optimization in coastal mountainous cities.
{"title":"Unraveling nonlinear and interactive drivers of urban heat-related health risks in a mountainous coastal city","authors":"Qunyue Liu , Huiting Zhang , Yumeng Wang , Kunneng Jiang , Xiabing Shen , Zhi Chen , Yaling Gao , Yuanping Shen , Yourui Guo","doi":"10.1016/j.scs.2025.107081","DOIUrl":"10.1016/j.scs.2025.107081","url":null,"abstract":"<div><div>Global climate warming and rapid urbanization have intensified the frequency and severity of extreme heat events, making Heat-related Health Risk (HHR) a pressing issue in public health and climate adaptation. Based on the Hazard–Exposure–Vulnerability (HEV) framework, this study integrates high-resolution remote sensing, meteorological, and socio-economic data to establish a Heat-related Health Risk Index (HRI) system for coastal mountainous cities. Using an interpretable machine learning approach (XGBoost–SHapley Additive exPlanations (SHAP)), we identified the spatial distribution, dominant factors, and nonlinear interactions of HHR in Fuzhou, China. Results show that HHR exhibits a “high-center—low-periphery” pattern, with high-risk zones in Gulou, Taijiang, and Cangshan, and lower risk in hilly and waterfront areas due to sea-breeze cooling and topographic ventilation. Urban morphology factors contributed most (46.2 %), followed by natural cover (25.7 %). The Digital Elevation Model (DEM), Impervious Surface Ratio (ISA), Road Network Density (RND), and Normalized Difference Vegetation Index (NDVI) were key drivers. HHR rises sharply when ISA exceeds 0.55–0.60, while NDVI mitigates heat risk most effectively within 0.35–0.40. Interaction analysis revealed three main effects: ISA × NDVI (diminishing marginal), NDVI × DEM (synergistic mitigation), and ISA × RND (amplifying). These findings highlight the coupled roles of urban form, terrain, and vegetation in shaping HHR and provide scientific guidance for climate adaptation and spatial optimization in coastal mountainous cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107081"},"PeriodicalIF":12.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.scs.2025.107085
Aminreza Karamoozian , Abouzar Gholamalizadeh , Saman Nadizadeh Shorabeh , Amirhossein Karamoozian , Mohammad Karimi Firozjaei
Urban outdoor thermal discomfort (UOTD) significantly affects human health, energy demand, and overall quality of life in cities. This study presents a novel comparative approach to investigate UOTD across 12 major Iranian cities, representing diverse climatic and geographical conditions. The findings of this approach are methodologically transferable to urban areas with similar climatic and environmental characteristics. Two analytical scenarios were conducted: intra-city evaluation to assess the spatial distribution of UOTD within each city, and inter-city comparison to examine disparities among the cities. Data sources included satellite imagery, digital surface models, land cover maps, and ground-based meteorological observations during the summer period. A spatial multi-criteria decision analysis approach was employed by integrating five influential factors, with weights assigned based on the correlation (R²) between each factor and the UOTD index from meteorological observations, giving higher influence to factors more strongly associated with UOTD, with the resulting weights as follows: albedo (0.12), normalized difference vegetation index (NDVI, 0.09), upward long-wave radiation (ULR, 0.23), downward long-wave radiation (DLR, 0.30), and downward short-wave radiation (DSR, 0.25). The results revealed strong spatial heterogeneity: Ardabil (cold and humid), Gorgan, and Rasht (temperate and humid) exhibited the lowest levels of UOTD, with over 70 % of their urban areas classified as low-risk. In contrast, Bandar Abbas and Ahvaz (hot and humid climates), along with Zahedan and Kerman (hot and arid climates), experienced the highest levels of UOTD, with >80 % of their urban surfaces falling into high or very high-risk categories. Tehran and Mashhad showed moderate and mixed UOTD patterns. Barren lands (0.85) and built-up areas (0.84) recorded the highest UOTD index values, whereas water bodies (as low as 0.10) and tree-covered areas (as low as 0.22) registered the lowest. High building density combined with limited vegetation significantly intensifies thermal stress, while proximity to water bodies and green spaces substantially mitigates it. These findings underscore the urgent need for adaptive strategies, including the expansion of green infrastructure and climate-sensitive urban design with a focus on water resources.
{"title":"Assessing urban outdoor thermal discomfort across scales and climates: Implications for sustainable urban management in Iran","authors":"Aminreza Karamoozian , Abouzar Gholamalizadeh , Saman Nadizadeh Shorabeh , Amirhossein Karamoozian , Mohammad Karimi Firozjaei","doi":"10.1016/j.scs.2025.107085","DOIUrl":"10.1016/j.scs.2025.107085","url":null,"abstract":"<div><div>Urban outdoor thermal discomfort (UOTD) significantly affects human health, energy demand, and overall quality of life in cities. This study presents a novel comparative approach to investigate UOTD across 12 major Iranian cities, representing diverse climatic and geographical conditions. The findings of this approach are methodologically transferable to urban areas with similar climatic and environmental characteristics. Two analytical scenarios were conducted: intra-city evaluation to assess the spatial distribution of UOTD within each city, and inter-city comparison to examine disparities among the cities. Data sources included satellite imagery, digital surface models, land cover maps, and ground-based meteorological observations during the summer period. A spatial multi-criteria decision analysis approach was employed by integrating five influential factors, with weights assigned based on the correlation (R²) between each factor and the UOTD index from meteorological observations, giving higher influence to factors more strongly associated with UOTD, with the resulting weights as follows: albedo (0.12), normalized difference vegetation index (NDVI, 0.09), upward long-wave radiation (ULR, 0.23), downward long-wave radiation (DLR, 0.30), and downward short-wave radiation (DSR, 0.25). The results revealed strong spatial heterogeneity: Ardabil (cold and humid), Gorgan, and Rasht (temperate and humid) exhibited the lowest levels of UOTD, with over 70 % of their urban areas classified as low-risk. In contrast, Bandar Abbas and Ahvaz (hot and humid climates), along with Zahedan and Kerman (hot and arid climates), experienced the highest levels of UOTD, with >80 % of their urban surfaces falling into high or very high-risk categories. Tehran and Mashhad showed moderate and mixed UOTD patterns. Barren lands (0.85) and built-up areas (0.84) recorded the highest UOTD index values, whereas water bodies (as low as 0.10) and tree-covered areas (as low as 0.22) registered the lowest. High building density combined with limited vegetation significantly intensifies thermal stress, while proximity to water bodies and green spaces substantially mitigates it. These findings underscore the urgent need for adaptive strategies, including the expansion of green infrastructure and climate-sensitive urban design with a focus on water resources.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107085"},"PeriodicalIF":12.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.scs.2025.107082
Yuzheng Zhang , Yan Lu , Yiping Wang , Xiaojian Chen
Strong sunlight significantly influences urban residents’ willingness to engage in outdoor activities. Clarifying the complex mechanisms and spatial effects of perceived thermal comfort on street commercial vitality is essential for effective thermal environment governance and enhancing commercial economic development. Using street view image data, this study constructs a spatial econometric model to evaluate the impact of sun shading on street commercial vitality and its spatial effects. Key findings include:
(1) Street commercial vitality is sensitive to the perceived thermal comfort brought by sun shading, and sun shading has a positive impact on street commercial vitality. The local and neighborhood relations together construct the basic framework of the spatial effect of sun shading on street commercial vitality.
(2) The spatial relationship between sun shading and street commercial vitality is deeply influenced by consumers’ behavior choices and spatial activities, with street-level popularity agglomeration mediating the effect of sun shading on commercial vitality.
(3) In the channel of enhancing street commercial vitality, the tree shading has boosting effect and building shading has blocking effect. The relationship between sun shading and street commercial vitality is spatially heterogeneous in the core urban area and the peripheral area, which together with the mediating effect and influencing channel relationship of street popularity build a complex spatial field.
(4) Urban planning should improve fluid space and circulation organization, enhance spatial scenario appeal and compatibility, optimize building and greenery layout—to create urban streets with perceived thermal comfort and boost commercial vitality.
{"title":"How does sun shading affect street commercial vitality? Evidence from street view images","authors":"Yuzheng Zhang , Yan Lu , Yiping Wang , Xiaojian Chen","doi":"10.1016/j.scs.2025.107082","DOIUrl":"10.1016/j.scs.2025.107082","url":null,"abstract":"<div><div>Strong sunlight significantly influences urban residents’ willingness to engage in outdoor activities. Clarifying the complex mechanisms and spatial effects of perceived thermal comfort on street commercial vitality is essential for effective thermal environment governance and enhancing commercial economic development. Using street view image data, this study constructs a spatial econometric model to evaluate the impact of sun shading on street commercial vitality and its spatial effects. Key findings include:</div><div>(1) Street commercial vitality is sensitive to the perceived thermal comfort brought by sun shading, and sun shading has a positive impact on street commercial vitality. The local and neighborhood relations together construct the basic framework of the spatial effect of sun shading on street commercial vitality.</div><div>(2) The spatial relationship between sun shading and street commercial vitality is deeply influenced by consumers’ behavior choices and spatial activities, with street-level popularity agglomeration mediating the effect of sun shading on commercial vitality.</div><div>(3) In the channel of enhancing street commercial vitality, the tree shading has boosting effect and building shading has blocking effect. The relationship between sun shading and street commercial vitality is spatially heterogeneous in the core urban area and the peripheral area, which together with the mediating effect and influencing channel relationship of street popularity build a complex spatial field.</div><div>(4) Urban planning should improve fluid space and circulation organization, enhance spatial scenario appeal and compatibility, optimize building and greenery layout—to create urban streets with perceived thermal comfort and boost commercial vitality.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107082"},"PeriodicalIF":12.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.scs.2025.107084
Gessica Sparvoli , Elena Bosi , Gabriele Bernardini , Enrico Quagliarini , Tiago Miguel Ferreira
The definition of sustainable strategies for risk mitigation in Urban Built Environments (UBEs) prone to flooding should be based on holistic yet quick approaches that comprise hazard, physical vulnerability, and user factors. In particular, the ways users occupy, live and behave in UBEs introduce significant spatiotemporal dynamics in the final risk due to their user exposure (how many?) and vulnerability (of which type?). These effects can be relevant in Historic UBEs (HUBEs), due to building heritage features and the related need to balance mitigation strategies with conservation and preservation. Therefore, quickly applicable analyses, exploiting available databases, should be developed, and the reliability of methods that incorporate user-related, dynamic parameters should be demonstrated in comparison to established “static” analyses of hazards and physical elements. This work proposes a building-scale approach for vulnerability and exposure assessment to floods, aimed at identifying “hot-spots” in HUBEs, by combining “static” and “dynamic” assessment methods. “Static” (e.g. material degradation, construction typology, urban morphological features) and “dynamic” (e.g. daily occupancy schedules, occupant densities/typologies) are combined within a GIS database, using single and multi-factor metrics. The method is demonstrated using a relevant Italian case study. Results remark that considering user exposure and vulnerability over time introduces significant differences in flood risk metrics and HUBEs hotspots, for both public buildings, due to daytime occupancy schedules, and residential buildings, where risk levels increase up to 80%, considering the possible low physical vulnerability of these buildings. This work therefore provides robust approaches to support informed decision-making in the prioritisation of targeted mitigation strategies within HUBEs.
{"title":"How much do users matter? An integrated method for building flood vulnerability and exposure assessment in Historic Urban Areas","authors":"Gessica Sparvoli , Elena Bosi , Gabriele Bernardini , Enrico Quagliarini , Tiago Miguel Ferreira","doi":"10.1016/j.scs.2025.107084","DOIUrl":"10.1016/j.scs.2025.107084","url":null,"abstract":"<div><div>The definition of sustainable strategies for risk mitigation in Urban Built Environments (UBEs) prone to flooding should be based on holistic yet quick approaches that comprise hazard, physical vulnerability, and user factors. In particular, the ways users occupy, live and behave in UBEs introduce significant spatiotemporal dynamics in the final risk due to their user exposure (how many?) and vulnerability (of which type?). These effects can be relevant in Historic UBEs (HUBEs), due to building heritage features and the related need to balance mitigation strategies with conservation and preservation. Therefore, quickly applicable analyses, exploiting available databases, should be developed, and the reliability of methods that incorporate user-related, dynamic parameters should be demonstrated in comparison to established “static” analyses of hazards and physical elements. This work proposes a building-scale approach for vulnerability and exposure assessment to floods, aimed at identifying “hot-spots” in HUBEs, by combining “static” and “dynamic” assessment methods. “Static” (e.g. material degradation, construction typology, urban morphological features) and “dynamic” (e.g. daily occupancy schedules, occupant densities/typologies) are combined within a GIS database, using single and multi-factor metrics. The method is demonstrated using a relevant Italian case study. Results remark that considering user exposure and vulnerability over time introduces significant differences in flood risk metrics and HUBEs hotspots, for both public buildings, due to daytime occupancy schedules, and residential buildings, where risk levels increase up to 80%, considering the possible low physical vulnerability of these buildings. This work therefore provides robust approaches to support informed decision-making in the prioritisation of targeted mitigation strategies within HUBEs.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107084"},"PeriodicalIF":12.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.scs.2025.107078
Mingzhi Zhang , Dian Zhou , Duo Xu , Keju Liu , Zhaolin Gu , Yujun Yang , Qian Zhang , Liuwei Chen
Urban heat islands (UHIs), shaped by both two-dimensional land-cover patterns and three-dimensional urban morphology, pose growing challenges to public health and energy systems. This study investigates microclimate variations in 16 residential areas of Xi’an using synchronous, high-temporal-resolution measurements from 105 sampling points. We develop an LCZ-based spatial machine-learning framework integrating XGBoost and SHAP to quantify the nonlinear impacts of built-environment indicators on near-surface temperature, humidity, and heat index. Results reveal a clear hierarchy of influence, with three-dimensional morphological indicators exerting the strongest effects, followed by landscape-ecological and two-dimensional morphological indicators. Among all variables, NDVI, sky view factor (SVF), building density (SDH), and street compactness (SCD) contribute most to microclimate regulation. Within the LCZ framework, we further identify pronounced spatial heterogeneity in thermal responses across different urban forms. Distinct synergistic interactions are also observed: substantial cooling and humidifying effects occur when NDVI > 0.3 and SVF > 0.3, while blocks characterized by low SCD (< 0.06) and high SDH (> 20) exhibit clear heat-mitigation responses. By combining detailed LCZ information, in-situ observations, and explainable machine learning, this study provides quantitative evidence linking specific morphological configurations to thermal performance and offers a transferable, climate-responsive planning framework for high-density residential environments.
{"title":"How do built environment factors influence urban heat islands across local climate zones? Evidence from an interpretable XGBoost-SHAP model","authors":"Mingzhi Zhang , Dian Zhou , Duo Xu , Keju Liu , Zhaolin Gu , Yujun Yang , Qian Zhang , Liuwei Chen","doi":"10.1016/j.scs.2025.107078","DOIUrl":"10.1016/j.scs.2025.107078","url":null,"abstract":"<div><div>Urban heat islands (UHIs), shaped by both two-dimensional land-cover patterns and three-dimensional urban morphology, pose growing challenges to public health and energy systems. This study investigates microclimate variations in 16 residential areas of Xi’an using synchronous, high-temporal-resolution measurements from 105 sampling points. We develop an LCZ-based spatial machine-learning framework integrating XGBoost and SHAP to quantify the nonlinear impacts of built-environment indicators on near-surface temperature, humidity, and heat index. Results reveal a clear hierarchy of influence, with three-dimensional morphological indicators exerting the strongest effects, followed by landscape-ecological and two-dimensional morphological indicators. Among all variables, NDVI, sky view factor (SVF), building density (SDH), and street compactness (SCD) contribute most to microclimate regulation. Within the LCZ framework, we further identify pronounced spatial heterogeneity in thermal responses across different urban forms. Distinct synergistic interactions are also observed: substantial cooling and humidifying effects occur when NDVI > 0.3 and SVF > 0.3, while blocks characterized by low SCD (< 0.06) and high SDH (> 20) exhibit clear heat-mitigation responses. By combining detailed LCZ information, in-situ observations, and explainable machine learning, this study provides quantitative evidence linking specific morphological configurations to thermal performance and offers a transferable, climate-responsive planning framework for high-density residential environments.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107078"},"PeriodicalIF":12.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.scs.2025.107079
Meng Cai , Linxuan Xie , Luyao Xiang , Jie Chen
Enhancing emotional well-being through public space design is crucial for promoting environmental justice and building sustainable cities. However, most empirical research on the relationship between the built environment and emotions relies on mean-based statistical models. Such approaches obscure the heterogeneous impacts of urban form across different emotional spectrums, particularly neglecting the experiences of individuals in negative emotional states. This study aims to address this gap by exploring how multi-scale urban form affects pedestrian emotional responses, with a specific focus on extreme emotional states. By integrating physiological sensing from wearable devices with self-reported perceptions, this research captures high-resolution spatio-temporal data on real-time emotional states. Built environment indicators at both the neighborhood scale and the pedestrian scale were extracted to assess their emotional impacts. We then employed mean-based linear regression, quantile regression, and an explainable machine learning–based quantile model to identify nonlinear and distributionally heterogeneous effects. The linear regression and machine learning models achieved R² values of 0.578 and 0.852, respectively. Both approaches revealed that individuals in lower emotional states (10th percentile) tend to prefer compact built environments and accessible amenities, whereas those with average emotional states (50th percentile) favor greener and more open spaces. Pedestrian-level visual features exhibited stronger, often nonlinear influences, with several variables demonstrating clear threshold effects.
This study demonstrates that built environments impact emotional well-being in unequal ways, and one-size-fits-all planning approaches may overlook the needs of emotionally vulnerable populations. By integrating explainable machine learning with quantile-based modeling, we provide a novel and interpretable framework for understanding emotional heterogeneity in urban spaces. The findings offer actionable insights for designing emotionally inclusive and restorative environments that support mental health for all urban residents.
{"title":"Beyond the average: Modeling heterogeneous emotional responses to urban form using explainable machine learning and ambulatory technology","authors":"Meng Cai , Linxuan Xie , Luyao Xiang , Jie Chen","doi":"10.1016/j.scs.2025.107079","DOIUrl":"10.1016/j.scs.2025.107079","url":null,"abstract":"<div><div>Enhancing emotional well-being through public space design is crucial for promoting environmental justice and building sustainable cities. However, most empirical research on the relationship between the built environment and emotions relies on mean-based statistical models. Such approaches obscure the heterogeneous impacts of urban form across different emotional spectrums, particularly neglecting the experiences of individuals in negative emotional states. This study aims to address this gap by exploring how multi-scale urban form affects pedestrian emotional responses, with a specific focus on extreme emotional states. By integrating physiological sensing from wearable devices with self-reported perceptions, this research captures high-resolution spatio-temporal data on real-time emotional states. Built environment indicators at both the neighborhood scale and the pedestrian scale were extracted to assess their emotional impacts. We then employed mean-based linear regression, quantile regression, and an explainable machine learning–based quantile model to identify nonlinear and distributionally heterogeneous effects. The linear regression and machine learning models achieved R² values of 0.578 and 0.852, respectively. Both approaches revealed that individuals in lower emotional states (10th percentile) tend to prefer compact built environments and accessible amenities, whereas those with average emotional states (50th percentile) favor greener and more open spaces. Pedestrian-level visual features exhibited stronger, often nonlinear influences, with several variables demonstrating clear threshold effects.</div><div>This study demonstrates that built environments impact emotional well-being in unequal ways, and one-size-fits-all planning approaches may overlook the needs of emotionally vulnerable populations. By integrating explainable machine learning with quantile-based modeling, we provide a novel and interpretable framework for understanding emotional heterogeneity in urban spaces. The findings offer actionable insights for designing emotionally inclusive and restorative environments that support mental health for all urban residents.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107079"},"PeriodicalIF":12.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.scs.2025.107065
Pranav Gupta , Tristan Kershaw
The rising health and ecological consequences of environmental noise remain critically underrepresented in urban policy frameworks across both the Global North and South despite being second only to air pollution in terms of environmental impact. While conventional mapping methods emphasize static exposure metrics, they often overlook vibrational impacts and mitigation pathways. The present study addresses this gap by introducing Cymatic Urbanism, a framework that reconceptualizes noise as a spatial force shaping land-use compatibility and public health exposure. Through the development of a multidimensional Noise Severity Coefficient (NSC) and an Equivalent Source Level (LESL) based buffer model, the study quantifies acoustic stress by integrating absorption (α), reflection (θ), and scaling (κ) factors across built and vegetated surfaces. Applied in Ludhiana, India, the model identified 0.98 km² of high-severity zones, validated against ISO 9613–2 predictions and monitoring data, with error margins within 10 %. Scenario-based simulations revealed a 28–46 % reduction in buffer distance through green façade retrofits along dense traffic corridors. The proposed Noise Severity Index (NSI) not only ensures empirical accuracy through sensitivity testing and calibration but also translates acoustic complexity into spatial intelligence. The study demonstrates that even cities with limited noise regulation can embed vibrational resilience directly into zoning and design, offering a scalable pathway toward health-centric, ecologically responsive urban futures.
{"title":"Cymatic urbanism: A spatial modelling framework for urban noise resilience","authors":"Pranav Gupta , Tristan Kershaw","doi":"10.1016/j.scs.2025.107065","DOIUrl":"10.1016/j.scs.2025.107065","url":null,"abstract":"<div><div>The rising health and ecological consequences of environmental noise remain critically underrepresented in urban policy frameworks across both the Global North and South despite being second only to air pollution in terms of environmental impact. While conventional mapping methods emphasize static exposure metrics, they often overlook vibrational impacts and mitigation pathways. The present study addresses this gap by introducing Cymatic Urbanism, a framework that reconceptualizes noise as a spatial force shaping land-use compatibility and public health exposure. Through the development of a multidimensional Noise Severity Coefficient (NSC) and an Equivalent Source Level (L<sub>ESL</sub>) based buffer model, the study quantifies acoustic stress by integrating absorption (α), reflection (θ), and scaling (κ) factors across built and vegetated surfaces. Applied in Ludhiana, India, the model identified 0.98 km² of high-severity zones, validated against ISO 9613–2 predictions and monitoring data, with error margins within 10 %. Scenario-based simulations revealed a 28–46 % reduction in buffer distance through green façade retrofits along dense traffic corridors. The proposed Noise Severity Index (NSI) not only ensures empirical accuracy through sensitivity testing and calibration but also translates acoustic complexity into spatial intelligence. The study demonstrates that even cities with limited noise regulation can embed vibrational resilience directly into zoning and design, offering a scalable pathway toward health-centric, ecologically responsive urban futures.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107065"},"PeriodicalIF":12.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791767","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号