Pub Date : 2026-01-01DOI: 10.1016/j.scs.2025.107102
Lu Chen , Chenyang Shuai , Xi Chen , Jingran Sun , Bu Zhao
With rapid urbanization expansion, China faces urgent challenges of environmental resource overconsumption and severe pollution. While some studies have evaluated environmental efficiency at national or regional scales, few have systematically examined this efficiency across a large, diverse set of enterprises in urban areas. Addressing this gap, this study uses the Super Slacks-Based Measure (SBM) model to analyze environmental efficiency across 85,327 enterprises, covering 341 cities and 18 industries in China. We comprehensively assess enterprise-level environmental efficiency and quantitatively explore industry-specific pathways for improvement. Findings indicate that overall environmental efficiency is relatively low, with significant variation across industries and cities. Benchmark enterprises are largely concentrated in eastern and northeastern China. Improving environmental efficiency for low-performing enterprises hinges on reducing resource inputs and minimizing undesirable outputs. The study highlights substantial differences in the optimization of labor, capital, environmental resources, economic output, and undesirable outputs across industries. These insights offer quantitative guidance for government and industry in formulating environmental management policies and optimizing resource allocation, supporting China’s green transition and sustainable development goals.
{"title":"Urban environmental efficiency and optimization pathways in Chinese enterprises: A cross-industry analysis","authors":"Lu Chen , Chenyang Shuai , Xi Chen , Jingran Sun , Bu Zhao","doi":"10.1016/j.scs.2025.107102","DOIUrl":"10.1016/j.scs.2025.107102","url":null,"abstract":"<div><div>With rapid urbanization expansion, China faces urgent challenges of environmental resource overconsumption and severe pollution. While some studies have evaluated environmental efficiency at national or regional scales, few have systematically examined this efficiency across a large, diverse set of enterprises in urban areas. Addressing this gap, this study uses the Super Slacks-Based Measure (SBM) model to analyze environmental efficiency across 85,327 enterprises, covering 341 cities and 18 industries in China. We comprehensively assess enterprise-level environmental efficiency and quantitatively explore industry-specific pathways for improvement. Findings indicate that overall environmental efficiency is relatively low, with significant variation across industries and cities. Benchmark enterprises are largely concentrated in eastern and northeastern China. Improving environmental efficiency for low-performing enterprises hinges on reducing resource inputs and minimizing undesirable outputs. The study highlights substantial differences in the optimization of labor, capital, environmental resources, economic output, and undesirable outputs across industries. These insights offer quantitative guidance for government and industry in formulating environmental management policies and optimizing resource allocation, supporting China’s green transition and sustainable development goals.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107102"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885079","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 : 2026-01-01DOI: 10.1016/j.scs.2025.107088
Seungjae Lee , Youngsang Kwon
This study quantifies how urban morphology conditions pedestrian level ventilation in Seoul’s Gangnam Business District using CFD simulated wind fields and a normalized ventilation index. Pedestrian level wind speed at 1.5 m is simulated with OpenFOAM using a steady incompressible RANS solver with the standard k epsilon closure, and ventilation performance is evaluated by the Effectiveness of Urban Ventilation(EUV), defined as the ratio of pedestrian level velocity to inflow speed. Morphological descriptors including building density within 50 m and 100 m radii, mean building height, terrain slope, and sky view factor are computed on a 2.5 m grid. To isolate context dependence, grid cells are stratified into empirical density quintiles at each spatial scale, and the SVF effect is tested within density tiers using one way ANOVA with effect size reporting and complementary robustness checks. Results show that higher density and greater mean height consistently suppress ventilation, whereas higher SVF improves ventilation across all density tiers. The SVF benefit is strongest in medium density conditions and remains meaningful even in the highest density tier, indicating that securing sky openness can improve ventilation where density reduction is infeasible. In contrast, the medium high density tier shows the weakest response to SVF alone, suggesting the need for additional form controls. The findings support density conditioned SVF guidance for ventilation oriented design and planning in compact urban corridors.
{"title":"Impact of building density and sky view factor on pedestrian-level wind environment in Seoul's urban street canyons","authors":"Seungjae Lee , Youngsang Kwon","doi":"10.1016/j.scs.2025.107088","DOIUrl":"10.1016/j.scs.2025.107088","url":null,"abstract":"<div><div>This study quantifies how urban morphology conditions pedestrian level ventilation in Seoul’s Gangnam Business District using CFD simulated wind fields and a normalized ventilation index. Pedestrian level wind speed at 1.5 m is simulated with OpenFOAM using a steady incompressible RANS solver with the standard k epsilon closure, and ventilation performance is evaluated by the Effectiveness of Urban Ventilation(EUV), defined as the ratio of pedestrian level velocity to inflow speed. Morphological descriptors including building density within 50 m and 100 m radii, mean building height, terrain slope, and sky view factor are computed on a 2.5 m grid. To isolate context dependence, grid cells are stratified into empirical density quintiles at each spatial scale, and the SVF effect is tested within density tiers using one way ANOVA with effect size reporting and complementary robustness checks. Results show that higher density and greater mean height consistently suppress ventilation, whereas higher SVF improves ventilation across all density tiers. The SVF benefit is strongest in medium density conditions and remains meaningful even in the highest density tier, indicating that securing sky openness can improve ventilation where density reduction is infeasible. In contrast, the medium high density tier shows the weakest response to SVF alone, suggesting the need for additional form controls. The findings support density conditioned SVF guidance for ventilation oriented design and planning in compact urban corridors.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107088"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926948","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 : 2026-01-01DOI: 10.1016/j.scs.2025.107099
Jiacheng Huang , Zhengdong Huang , Wen Liu , Yueer He , Peixin Xu , Renzhong Guo
Understanding microclimatic variations across Local Climate Zones (LCZs) is crucial for optimizing urban morphology to enhance human thermal comfort and promote sustainable urban environments. While numerous studies have examined the spatio-temporal patterns and underlying mechanisms of thermal environments in different LCZ types, temperature variability within identical LCZs remain insufficiently explored. Moreover, research on other microclimate factors—such as relative humidity (RH) and wind speed (WS)—at both inter- and intra-LCZ scales is limited. In this study, a hybrid modeling framework based on the Weather Research and Forecast (WRF) model was proposed to accurately predict near-surface meteorological fields. It was achieved by refining the Urban Canopy Model (UCM) during the preprocessing stage of the WRF model and then integrating ML algorithms during its postprocessing stage. The predictive performance of four WRF-based datasets was evaluated and compared under relatively stable weather conditions across four seasons. The best-performing ML(XGBoost)-enhanced model dataset was applied to identify multivariate microclimate variations both inter- and intra-LCZs. The analysis was conducted in Shenzhen, a coastal hilly city in southern China. The results revealed that: (1) the WRF-ML hybrid models performed significantly better than the Standard and UCM-refined WRF models, with the optimal XGBoost-enhanced model achieving hourly average RMSE values of 0.613 K for AT, 1.131 % for RH, and 0.207 m/s for WS; (2) unique local geographic conditions, such as coastal surroundings and continuous natural landscapes, significantly influence microclimate variations within urban built-up areas; (3) inter-LCZ microclimate differences were generally within 1.5 K for AT, 9 % for RH, and 0.7 m/s for WS, exceeding intra-LCZ differences; and (4) built-up LCZs could be further divided into 2∼4 subcategories with distinct microclimate conditions, some of which showed relatively favorable microclimate environments during the high temperature period.
{"title":"Hybrid WRF-ML modeling for characterizing inter- and intra-LCZ microclimate variability: A case study of Shenzhen, China","authors":"Jiacheng Huang , Zhengdong Huang , Wen Liu , Yueer He , Peixin Xu , Renzhong Guo","doi":"10.1016/j.scs.2025.107099","DOIUrl":"10.1016/j.scs.2025.107099","url":null,"abstract":"<div><div>Understanding microclimatic variations across Local Climate Zones (LCZs) is crucial for optimizing urban morphology to enhance human thermal comfort and promote sustainable urban environments. While numerous studies have examined the spatio-temporal patterns and underlying mechanisms of thermal environments in different LCZ types, temperature variability within identical LCZs remain insufficiently explored. Moreover, research on other microclimate factors—such as relative humidity (RH) and wind speed (WS)—at both inter- and intra-LCZ scales is limited. In this study, a hybrid modeling framework based on the Weather Research and Forecast (WRF) model was proposed to accurately predict near-surface meteorological fields. It was achieved by refining the Urban Canopy Model (UCM) during the preprocessing stage of the WRF model and then integrating ML algorithms during its postprocessing stage. The predictive performance of four WRF-based datasets was evaluated and compared under relatively stable weather conditions across four seasons. The best-performing ML(XGBoost)-enhanced model dataset was applied to identify multivariate microclimate variations both inter- and intra-LCZs. The analysis was conducted in Shenzhen, a coastal hilly city in southern China. The results revealed that: (1) the WRF-ML hybrid models performed significantly better than the Standard and UCM-refined WRF models, with the optimal XGBoost-enhanced model achieving hourly average RMSE values of 0.613 K for AT, 1.131 % for RH, and 0.207 m/s for WS; (2) unique local geographic conditions, such as coastal surroundings and continuous natural landscapes, significantly influence microclimate variations within urban built-up areas; (3) inter-LCZ microclimate differences were generally within 1.5 K for AT, 9 % for RH, and 0.7 m/s for WS, exceeding intra-LCZ differences; and (4) built-up LCZs could be further divided into 2∼4 subcategories with distinct microclimate conditions, some of which showed relatively favorable microclimate environments during the high temperature period.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107099"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927031","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}
The Local Climate Zone (LCZ) system provides a standard framework for systematically studying the urban thermal environment, enabling broader and more precise research. While previous studies have extensively investigated the thermal environment characteristics among different LCZ types (inter-LCZ), the differences within the same LCZ type (intra-LCZ) remain insufficiently understood. This limitation potentially undermines the effectiveness of the LCZ framework in refined urban planning and climate-adaptive design. Therefore, this study focuses on intra-LCZ thermal environment differences and takes Shenyang, a representative city in a severe cold region of China, as a case study. We conducted continuous fixed-point monitoring at multiple sites classified as LCZ 4 (open high-rise) for two summer months to investigate intra-LCZ differences and analyze their associations with multi-scale land cover and urban morphological parameters. The results reveal a significant core-to-suburb gradient in nighttime air temperature and relative humidity, with maximum differences of 3 °C and 20%, respectively. These intra-LCZ thermal environment differences were notably amplified during late summer’s cooler, drier conditions. Ridge regression analysis indicates that morphological parameters explain humidity variations better than temperature variations. The models show that nighttime air temperature is driven significantly by distance to the city center and large-scale (1500–3000 m) pervious surface fractions. Conversely, nighttime humidity is more sensitive to parameters at a smaller scale (600 m), where pervious surfaces demonstrate a humidifying effect. These findings deepen the understanding of urban thermal differences and offer empirical support for targeted optimization strategies and refined urban planning.
{"title":"Thermal environment mechanism across intra local climate zones in summer in a northern city in China: A case study of Shenyang","authors":"Tianyu Xi, Nuannuan Yang, Zheming Liu, Xinyu Liu, Haibo Sun, Jiawei Chen","doi":"10.1016/j.scs.2025.107094","DOIUrl":"10.1016/j.scs.2025.107094","url":null,"abstract":"<div><div>The Local Climate Zone (LCZ) system provides a standard framework for systematically studying the urban thermal environment, enabling broader and more precise research. While previous studies have extensively investigated the thermal environment characteristics among different LCZ types (inter-LCZ), the differences within the same LCZ type (intra-LCZ) remain insufficiently understood. This limitation potentially undermines the effectiveness of the LCZ framework in refined urban planning and climate-adaptive design. Therefore, this study focuses on intra-LCZ thermal environment differences and takes Shenyang, a representative city in a severe cold region of China, as a case study. We conducted continuous fixed-point monitoring at multiple sites classified as LCZ 4 (open high-rise) for two summer months to investigate intra-LCZ differences and analyze their associations with multi-scale land cover and urban morphological parameters. The results reveal a significant core-to-suburb gradient in nighttime air temperature and relative humidity, with maximum differences of 3 °C and 20%, respectively. These intra-LCZ thermal environment differences were notably amplified during late summer’s cooler, drier conditions. Ridge regression analysis indicates that morphological parameters explain humidity variations better than temperature variations. The models show that nighttime air temperature is driven significantly by distance to the city center and large-scale (1500–3000 m) pervious surface fractions. Conversely, nighttime humidity is more sensitive to parameters at a smaller scale (600 m), where pervious surfaces demonstrate a humidifying effect. These findings deepen the understanding of urban thermal differences and offer empirical support for targeted optimization strategies and refined urban planning.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107094"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885087","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 : 2026-01-01DOI: 10.1016/j.scs.2025.107104
Aowei Liu , Miaosen Zhang , Yuxin Jin , Bo Hong
Avenue trees curtail urban heat yet must simultaneously survive wind storms; however, growth-dependent trade-offs between cooling efficacy and structural safety remain poorly quantified. Integrating field morphometrics with fluid–structure-coupled CFD, we tracked five common species from sapling to maturity and established age–response functions for both cooling efficiency and wind-induced loading. Crown diameter and tree height scaled strongly with age (R² ≥ 0.71), driving an S-shaped increase in cooling capacity that plateaued after 25 yr. Concurrently, wind loads rose exponentially; at 30 yr peak bending moment reached 280 kN·m for G. biloba versus 140 kN·m for A. buergerianum. Morphological determinism was high: crown diameter governed cooling (R² = 0.92), whereas canopy volume controlled bending moment (R² = 0.94). C. deodara exhibited the greatest wind stability but modest cooling, whereas G. biloba delivered maximal cooling yet incurred the highest wind risk. Species-specific optima balancing the two objectives occurred at 20–29 yr. The derived dual-objective framework provides quantitative guidance for species selection, rotation scheduling and pruning in wind-exposed, heat-sensitive cities.
{"title":"Optimized growth windows for avenue trees: CFD-based quantification of the cooling and wind-resistance trade-off","authors":"Aowei Liu , Miaosen Zhang , Yuxin Jin , Bo Hong","doi":"10.1016/j.scs.2025.107104","DOIUrl":"10.1016/j.scs.2025.107104","url":null,"abstract":"<div><div>Avenue trees curtail urban heat yet must simultaneously survive wind storms; however, growth-dependent trade-offs between cooling efficacy and structural safety remain poorly quantified. Integrating field morphometrics with fluid–structure-coupled CFD, we tracked five common species from sapling to maturity and established age–response functions for both cooling efficiency and wind-induced loading. Crown diameter and tree height scaled strongly with age (<em>R²</em> ≥ 0.71), driving an S-shaped increase in cooling capacity that plateaued after 25 yr. Concurrently, wind loads rose exponentially; at 30 yr peak bending moment reached 280 kN·m for <em>G. biloba</em> versus 140 kN·m for <em>A. buergerianum</em>. Morphological determinism was high: crown diameter governed cooling (<em>R²</em> = 0.92), whereas canopy volume controlled bending moment (<em>R²</em> = 0.94). <em>C. deodara</em> exhibited the greatest wind stability but modest cooling, whereas <em>G. biloba</em> delivered maximal cooling yet incurred the highest wind risk. Species-specific optima balancing the two objectives occurred at 20–29 yr. The derived dual-objective framework provides quantitative guidance for species selection, rotation scheduling and pruning in wind-exposed, heat-sensitive cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107104"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885090","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}
Financial capacity is critical to implementing local climate actions, including renewable energy deployment, energy efficiency improvements, and transport decarbonisation. This study examines how 203 European cities from 21 EU countries and covering 46 million inhabitants have financed their climate change mitigation efforts in the context of the Covenant of Mayors for climate and energy (CoM) initiative. It uses data submitted by participating cities in their Sustainable Energy and Climate Action Plans (SECAPs), and it analyses three key dimensions: (i) total budget allocation, (ii) funding sources, and (iii) financial instruments. The selected SECAPs, include more than 8500 mitigation actions and an estimated total investment exceeding €100 billion.
The results reveal a strong dependence on public funding, primarily on municipal budgets and grants, while innovative instruments such as green bonds, public–private partnerships (PPPs), third-party financing, and pay-for-performance schemes remain underutilised. Statistical analysis shows that population size and energy demand (measured through heating and cooling degree days) are the strongest predictors of planned investment, while GDP per capita is negatively correlated with investment levels. Climate targets’ ambition does not significantly influence budget allocation.
These findings underscore persistent barriers to financial diversification, especially among small and medium-sized municipalities. By combining quantitative analyses with qualitative insights, this study provides policy-relevant evidence on local climate finance practices and supports efforts to enhance financial capacity for climate neutrality across the EU.
{"title":"Financing climate change mitigation actions in cities: insights from the Covenant of Mayors initiative","authors":"Valeria Todeschi , Gema Hernandez-Moral , Enrico Clementi , Paolo Bertoldi , Giulia Melica","doi":"10.1016/j.scs.2025.107097","DOIUrl":"10.1016/j.scs.2025.107097","url":null,"abstract":"<div><div>Financial capacity is critical to implementing local climate actions, including renewable energy deployment, energy efficiency improvements, and transport decarbonisation. This study examines how 203 European cities from 21 EU countries and covering 46 million inhabitants have financed their climate change mitigation efforts in the context of the Covenant of Mayors for climate and energy (CoM) initiative. It uses data submitted by participating cities in their Sustainable Energy and Climate Action Plans (SECAPs), and it analyses three key dimensions: (i) total budget allocation, (ii) funding sources, and (iii) financial instruments. The selected SECAPs, include more than 8500 mitigation actions and an estimated total investment exceeding €100 billion.</div><div>The results reveal a strong dependence on public funding, primarily on municipal budgets and grants, while innovative instruments such as green bonds, public–private partnerships (PPPs), third-party financing, and pay-for-performance schemes remain underutilised. Statistical analysis shows that population size and energy demand (measured through heating and cooling degree days) are the strongest predictors of planned investment, while GDP per capita is negatively correlated with investment levels. Climate targets’ ambition does not significantly influence budget allocation.</div><div>These findings underscore persistent barriers to financial diversification, especially among small and medium-sized municipalities. By combining quantitative analyses with qualitative insights, this study provides policy-relevant evidence on local climate finance practices and supports efforts to enhance financial capacity for climate neutrality across the EU.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107097"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885092","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 : 2026-01-01DOI: 10.1016/j.scs.2025.106968
A. Moediartianto , H. Montazeri , B. Blocken
Computational Fluid Dynamics (CFD) simulations are widely applied to assess urban microclimate processes and adaptation strategies. However, their accuracy and reliability critically depend on high-quality validation data. Although field measurements and reduced-scale wind-tunnel testing are commonly used for validation, the use of remote sensing methods remains limited despite their potential. This study systematically evaluates UAV-based thermal imaging as a tool for validating microscale CFD predictions of urban thermal conditions. The case study focuses on the city centre of Semarang, Indonesia, where ground-truth field measurements and UAV surveys with visible RGB and infrared (IR) imaging are conducted. CFD simulations are performed for the same area using 3D URANS with the realizable k–ε model on a high-resolution computational grid. Results show that (i) UAV-IR data and ground-truth measurements differed by 0.47 °C – 1.40 °C across asphalt, concrete, and soil–grass surfaces ranged from, confirming the accuracy of UAV thermal imaging; and (ii) CFD simulations deviated by 3.88 °C in surface-averaged temperatures for impervious-based areas compared with UAV-IR data. These findings highlight UAV thermal imaging as a practical and data-driven approach for validating CFD models, enabling more robust analyses and design of sustainable urban thermal environments.
{"title":"On the use of UAV-thermal imaging for CFD validation of urban thermal microclimate","authors":"A. Moediartianto , H. Montazeri , B. Blocken","doi":"10.1016/j.scs.2025.106968","DOIUrl":"10.1016/j.scs.2025.106968","url":null,"abstract":"<div><div>Computational Fluid Dynamics (CFD) simulations are widely applied to assess urban microclimate processes and adaptation strategies. However, their accuracy and reliability critically depend on high-quality validation data. Although field measurements and reduced-scale wind-tunnel testing are commonly used for validation, the use of remote sensing methods remains limited despite their potential. This study systematically evaluates UAV-based thermal imaging as a tool for validating microscale CFD predictions of urban thermal conditions. The case study focuses on the city centre of Semarang, Indonesia, where ground-truth field measurements and UAV surveys with visible RGB and infrared (IR) imaging are conducted. CFD simulations are performed for the same area using 3D URANS with the realizable k–ε model on a high-resolution computational grid. Results show that (i) UAV-IR data and ground-truth measurements differed by 0.47 °C – 1.40 °C across asphalt, concrete, and soil–grass surfaces ranged from, confirming the accuracy of UAV thermal imaging; and (ii) CFD simulations deviated by 3.88 °C in surface-averaged temperatures for impervious-based areas compared with UAV-IR data. These findings highlight UAV thermal imaging as a practical and data-driven approach for validating CFD models, enabling more robust analyses and design of sustainable urban thermal environments.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 106968"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885099","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 : 2026-01-01DOI: 10.1016/j.scs.2026.107119
Zilong Xia , Nan Jia , Bo Yuan , Ruishan Chen , Haowei Mu , Mo Bi , Uchendu Eugene Chigbu , Penghui Jiang
Faced with rapid population growth and increasing rural-to-urban migration, African cities—especially those in drylands which are vulnerable to climate change—are experiencing the unplanned expansion of informal settlements. While remote sensing has been widely used to delineate these settlements, limited attention has been paid to long-term changes in building area and their effects on land surface temperature (LST) in drylands. This study integrates multi-source remote sensing data and machine learning techniques to investigate the long-term dynamics of building development trajectory and effects on LST in informal settlements in Windhoek, Namibia. We developed a random forest–based subpixel regression method to predict historical building density, and it achieved high validation accuracy (R2 ≥ 0.84). Using this approach, we reconstructed the development trajectory of informal settlements. Analyses of satellite-derived LST reveal a strong negative correlation between building density and LST across seasons, stronger than in other income-based areas. The study helps monitor the current scale of informal settlement expansion and their specific influences on LST in drylands, offering practical insights for climate-adaptive planning and inform strategies.
{"title":"Long-term remote sensing reveals the development of informal settlements and their impact on land surface temperature in African drylands: A case study of Windhoek, Namibia","authors":"Zilong Xia , Nan Jia , Bo Yuan , Ruishan Chen , Haowei Mu , Mo Bi , Uchendu Eugene Chigbu , Penghui Jiang","doi":"10.1016/j.scs.2026.107119","DOIUrl":"10.1016/j.scs.2026.107119","url":null,"abstract":"<div><div>Faced with rapid population growth and increasing rural-to-urban migration, African cities—especially those in drylands which are vulnerable to climate change—are experiencing the unplanned expansion of informal settlements. While remote sensing has been widely used to delineate these settlements, limited attention has been paid to long-term changes in building area and their effects on land surface temperature (LST) in drylands. This study integrates multi-source remote sensing data and machine learning techniques to investigate the long-term dynamics of building development trajectory and effects on LST in informal settlements in Windhoek, Namibia. We developed a random forest–based subpixel regression method to predict historical building density, and it achieved high validation accuracy (R<sup>2</sup> ≥ 0.84). Using this approach, we reconstructed the development trajectory of informal settlements. Analyses of satellite-derived LST reveal a strong negative correlation between building density and LST across seasons, stronger than in other income-based areas. The study helps monitor the current scale of informal settlement expansion and their specific influences on LST in drylands, offering practical insights for climate-adaptive planning and inform strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107119"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927036","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 : 2026-01-01DOI: 10.1016/j.scs.2025.107105
Congxiao Yan , Xiangshun Wang , Yi Ren , Yuan Liang , Zheng Gong , Quan Yuan
Previous studies on urban heat island (UHI) and its influencing factors have focused on built environment and urban functional areas. However, freight facilities, large-scale facilities with impervious surfaces and high albedo characteristics widely used in the era of e-commerce, have not been fully examined in terms of their potential impacts on land surface temperature (LST). In this study, we applied satellite image datasets and machine learning techniques to identify freight facilities, and employed K-means clustering, multiple linear regression (MLR), and eXtreme Gradient Boosting (XGBoost) to investigate how different aggregation patterns of freight facilities, in conjunction with built environmental factors, differentially affect LST. The results show that this method can accurately capture the freight building facilities compared to other data forms. Second, freight facilities have a significant positive impact on LST; the existence of freight facilities will significantly increase the annual average LST by approximately 0.25 °C, an effect that amplifies substantially to 0.393 °C during summer. Subsequently, we categorized grids into three groups based on the scale, quantity, agglomeration level, and location of freight facilities, and validated the heterogeneous effects of their combination with built environment on LST. The warming effect of impervious surfaces is substantially amplified in freight facilities areas, while ecological functions are suppressed or even nullified in highly concentrated areas. Additionally, complex and non-linear relationships between the built environment and LST reflect across different freight clusters. This study provides actionable insights for planners and policymakers to develop freight facility planning strategies that prioritize ecological sustainability and long-term development.
{"title":"Freight footprints and urban heat islands: Interactions between freight facilities, built environment and thermal consequences","authors":"Congxiao Yan , Xiangshun Wang , Yi Ren , Yuan Liang , Zheng Gong , Quan Yuan","doi":"10.1016/j.scs.2025.107105","DOIUrl":"10.1016/j.scs.2025.107105","url":null,"abstract":"<div><div>Previous studies on urban heat island (UHI) and its influencing factors have focused on built environment and urban functional areas. However, freight facilities, large-scale facilities with impervious surfaces and high albedo characteristics widely used in the era of e-commerce, have not been fully examined in terms of their potential impacts on land surface temperature (LST). In this study, we applied satellite image datasets and machine learning techniques to identify freight facilities, and employed K-means clustering, multiple linear regression (MLR), and eXtreme Gradient Boosting (XGBoost) to investigate how different aggregation patterns of freight facilities, in conjunction with built environmental factors, differentially affect LST. The results show that this method can accurately capture the freight building facilities compared to other data forms. Second, freight facilities have a significant positive impact on LST; the existence of freight facilities will significantly increase the annual average LST by approximately 0.25 °C, an effect that amplifies substantially to 0.393 °C during summer. Subsequently, we categorized grids into three groups based on the scale, quantity, agglomeration level, and location of freight facilities, and validated the heterogeneous effects of their combination with built environment on LST. The warming effect of impervious surfaces is substantially amplified in freight facilities areas, while ecological functions are suppressed or even nullified in highly concentrated areas. Additionally, complex and non-linear relationships between the built environment and LST reflect across different freight clusters. This study provides actionable insights for planners and policymakers to develop freight facility planning strategies that prioritize ecological sustainability and long-term development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107105"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927037","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 : 2026-01-01DOI: 10.1016/j.scs.2025.107089
Noushad Ahamed Chittoor Mohammed , Misbaudeen Aderemi Adesanya , SoumyaDeep Chowdhury , Sudipta Debnath , Andrew Halliday , Gurjit S. Randhawa , Aitazaz A. Farooque , Kuljeet Singh Grewal
Accurate evaluation of energy performance in neighborhood energy modeling (NEM) has long been challenged by the lack of representative ground-truth data and high-resolution 3D geometry. Traditional approaches often rely on simplified shoebox archetypes, semantic 3D city models, and standardized assumptions for occupancy, internal loads, and envelope characteristics, thereby limiting model fidelity. To overcome these constraints, this study presents a holistic approach to NEM by introducing rapid energy modeling of neighborhoods through split automation (RENSA) – a novel, semi-automated workflow that integrates community engagement, multi-source data fusion, and machine learning to generate various level of detail (LoD) building models from LoD0–LoD3 and enable high-precision, context-sensitive NEM simulations. RENSA is applied to 297 structures in Georgetown, Prince Edward Island (PE), Canada, producing LoD3 models for the entire community and conducting detailed energy simulations for 71 residential buildings with available utility data. A structured, JavaScript object notation (JSON)-based data pipeline automates simulation inputs gathered through community engagement. Buildings are classified into five energy system categories, and model outputs are validated against real utility records. Geometric validation shows mean absolute percentage errors of 4.27 % for footprint area (LoD0), 3.55 % for peak height (LoD1), 7.48 % for bottom chord height (LoD2), 6.80 % for volume (LoD2), and 12.88 % for fenestration areas (LoD3). Further, integrating community-led data into the calibration workflow allowed 65 % of the simulated buildings to meet the ASHRAE normalized mean bias error (NMBE) requirement of ±5 %. The RENSA generalized framework demonstrates a replicable, scalable, and community-driven approach to NEM, enabling user-defined LoD generation and effectively bridging the gap between theory and real-world application in support of net-zero energy transitions.
{"title":"Drones and community-powered rapid neighborhood energy modeling: Demonstrated in a real-world case study","authors":"Noushad Ahamed Chittoor Mohammed , Misbaudeen Aderemi Adesanya , SoumyaDeep Chowdhury , Sudipta Debnath , Andrew Halliday , Gurjit S. Randhawa , Aitazaz A. Farooque , Kuljeet Singh Grewal","doi":"10.1016/j.scs.2025.107089","DOIUrl":"10.1016/j.scs.2025.107089","url":null,"abstract":"<div><div>Accurate evaluation of energy performance in neighborhood energy modeling (NEM) has long been challenged by the lack of representative ground-truth data and high-resolution 3D geometry. Traditional approaches often rely on simplified shoebox archetypes, semantic 3D city models, and standardized assumptions for occupancy, internal loads, and envelope characteristics, thereby limiting model fidelity. To overcome these constraints, this study presents a holistic approach to NEM by introducing rapid energy modeling of neighborhoods through split automation (RENSA) – a novel, semi-automated workflow that integrates community engagement, multi-source data fusion, and machine learning to generate various level of detail (LoD) building models from LoD0–LoD3 and enable high-precision, context-sensitive NEM simulations. RENSA is applied to 297 structures in Georgetown, Prince Edward Island (PE), Canada, producing LoD3 models for the entire community and conducting detailed energy simulations for 71 residential buildings with available utility data. A structured, JavaScript object notation (JSON)-based data pipeline automates simulation inputs gathered through community engagement. Buildings are classified into five energy system categories, and model outputs are validated against real utility records. Geometric validation shows mean absolute percentage errors of 4.27 % for footprint area (LoD0), 3.55 % for peak height (LoD1), 7.48 % for bottom chord height (LoD2), 6.80 % for volume (LoD2), and 12.88 % for fenestration areas (LoD3). Further, integrating community-led data into the calibration workflow allowed 65 % of the simulated buildings to meet the ASHRAE normalized mean bias error (NMBE) requirement of ±5 %. The RENSA generalized framework demonstrates a replicable, scalable, and community-driven approach to NEM, enabling user-defined LoD generation and effectively bridging the gap between theory and real-world application in support of net-zero energy transitions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"136 ","pages":"Article 107089"},"PeriodicalIF":12.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926949","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}
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