Sustainable Cities and Society最新文献

{"title":"Spatial patterns in urban water consumption: The role of local climate zones and temperature dynamics","authors":"Mohammad Maleki ,&nbsp;Amirbahador Damroodi ,&nbsp;Mahsa Mostaghim ,&nbsp;Amir Reza Bakhshi Lomer ,&nbsp;Samira Sadat Saleh ,&nbsp;Junye Wang ,&nbsp;Nabi Moradpour ,&nbsp;Iain D. Stewart ,&nbsp;Kanglin (Connie) Chen ,&nbsp;Fatemeh Kazemi","doi":"10.1016/j.scs.2025.106438","DOIUrl":"10.1016/j.scs.2025.106438","url":null,"abstract":"<div><div>Urban Water Consumption (UWC) is a major challenge in arid regions, intensified by urbanization, population growth, and resource scarcity, prompting debates on relocating Iran's capital to address resource scarcity and sustainability. This study analyzed the relationship between Local Climate Zones (LCZ), Land Surface Temperature (LST), and water usage in Tehran (2015–2019) to inform urban water management. UWC data was spatially matched to urban areas to calculate per capita consumption. An LCZ map for the base year 2017 was generated using the Random Forest (RF) algorithm, achieving an accuracy of 88.88 %. LST data for the five years was derived using the single-channel algorithm. LCZ2 of dense midrise buildings exhibited the largest area, while LCZG of water had the smallest area. Annual per capita UWC showed a consistent upward trend, with 2019 experiencing the most significant increase. The highest UWC was in LCZG and LCZ2, respectively, while LCZ7 of low dense single buildings recorded the lowest. Most of the city's area had neighbourhoods with an average LST ranging between 30 °C and 35 °C throughout the study period. The correlation between population density, LST, and UWC was 10 % to 17 %. Modelling accuracy, measured by Root Mean Square Error (RMSE), ranged from 1.4 to 9.9. This research highlights the need for climate-sensitive urban design and sustainable water management, providing a foundation for policies to address water scarcity in vulnerable urban areas. Additionally, analyzing annual population dynamics and improving UWC modeling will help better reflect future urban water consumption patterns.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106438"},"PeriodicalIF":10.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of high-density urban spatial form on urban vertical ventilation and thermal comfort in extreme cold region","authors":"Di Song ,&nbsp;Ming Lu","doi":"10.1016/j.scs.2025.106451","DOIUrl":"10.1016/j.scs.2025.106451","url":null,"abstract":"<div><div>The influence of the spatial form and roughness of an actual urban area on the distribution of the comfort zone of the wind and thermal environment is crucial for the liveable and sustainable development of urban spaces. Four cases in Harbin, a high-density city in an extremely cold region, were selected as the analysis objects, with six spatial shape parameters and two aerodynamic roughness parameters. The overall urban status was simulated by computational fluid dynamics and the thermal environment. The results showed that in spaces containing buildings, the urban spatial form significantly affected the aerodynamic roughness of the city. The comfortable wind speed decreases by 16.30–23.96 percentage points in the main rough surface and by 6.29 to 7.52 percentage points in a non-thermal stress environment. The prediction and interpretation degree of the influence of the morphological parameters of the comfortable wind and thermal environment was high. The numerical limit of the spatial morphological parameters was also clarified to ensure that the wind comfort was higher than 66.36 %, and the thermal comfort was higher than 22.83 %. Priority was given to controlling the spatial building height and distribution density. These findings provide valuable spatial impact results for the initial layout stage of urban space planning and a set of simulation frameworks for large-scale urban spaces, making it possible to simulate and analyse the wind and thermal environments of high-density urban spaces in extremely cold regions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106451"},"PeriodicalIF":10.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072040","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":"Assessing annual carbon emissions and its peak year in the Yangtze river economic belt (2021-2035) through land use/land cover analysis","authors":"Lin Du,&nbsp;Chuanjing Peng,&nbsp;Hangxing Ren,&nbsp;Zhiyuan Wu,&nbsp;Wei Gao","doi":"10.1016/j.scs.2025.106453","DOIUrl":"10.1016/j.scs.2025.106453","url":null,"abstract":"<div><div>As a major carbon emitting country, China is committed to peak carbon emissions before 2030. The Yangtze River Economic Belt (YREB), a key economic region in China, plays a significant role in national carbon emissions, making it crucial to study its emissions. This study uses the STIRPAT model and the Grey Model (GM) to analyze the carbon emissions of building land from 2021 to 2035; then the Patch-generating Land Use Simulation model (PLUS) is employed to predict the land-use changes over next 15 years, and future emissions from cropland, forest, grassland, water area, and unused land estimated using emission coefficients. Finally, carbon emissions across the YREB are calculated and mapped. The projections reveal that carbon emissions from building land increase and then decrease in the baseline scenario from 2021 to 2035. However, carbon emissions from cropland decline annually. Furthermore, this study identifies 2028 as carbon peak year in YREB area, totaling 8.278 × 10⁸t. Additionally, diminishing total energy consumption and the proportion of some industries may be beneficial for regional carbon reduction. The results of this study are instructive for carbon emission reduction and peak carbon attainment in China.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106453"},"PeriodicalIF":10.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084150","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":"Nonlinear relationships and spatial heterogeneity between geographical environment and mental health among middle-aged and older adults in China","authors":"Xu Lingyi,&nbsp;Han Huiran,&nbsp;Yang Chengfeng","doi":"10.1016/j.scs.2025.106459","DOIUrl":"10.1016/j.scs.2025.106459","url":null,"abstract":"<div><div>The nonlinear dynamics and spatial heterogeneity between the \"geographical environment and mental health\" represent pivotal elements in ongoing theoretical debates and empirical discrepancies. However, existing analyses are often fragmented and constrained by issues such as the predominance of linear models, multicollinearity, and omitted variable bias. This study bridges these gaps by employing the eXtreme Gradient Boosting (XGBoost) model and SHapley Additive exPlanations (SHAP) to examine the non-linear effects, spatial variability, and interactions of the natural, built, and social environments on the mental health of middle-aged and older adults in China. The findings reveal that nearly all geographical environmental factors exhibit non-linear relationships with mental health, with maximum temperature, NDVI, population density, and per capita park green space area showing the most pronounced effects. Their directional impact and marginal effects follow divergent trends. Geographical environmental factors not only exert independent effects on mental health but also interact in complex ways. Moreover, the relationship between geographical environmental variables and mental health displays significant spatial heterogeneity. K-means clustering analysis further identifies four distinct regions where geographical environmental factors differentially influence mental health. These results offer valuable insights for resolving theoretical disputes, understanding empirical variations, and informing urban planning, landscape architecture, and environmental management strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106459"},"PeriodicalIF":10.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new framework for early-warning classification of urban waterlogging risk based on waterlogging risk angle","authors":"Honglin Xiao ,&nbsp;Jinping Zhang ,&nbsp;Fuqiang Wang ,&nbsp;Lingli Kong","doi":"10.1016/j.scs.2025.106460","DOIUrl":"10.1016/j.scs.2025.106460","url":null,"abstract":"<div><div>Early-warning classification of urban waterlogging risk is the main prevention and control measure to improve urban sustainable water security and effectively reduce waterlogging disasters. The concept of waterlogging risk angle (WRA) is proposed and applied to determine the early-warning classification of urban waterlogging risk combing with the constructed InfoWorks ICM model. The results show that (1) with the increase of rainfall duration, the WRA value gradually decreases. And the WRA value of front-peak rainstorm (FPR) tends to be larger, which makes waterlogging disasters easier to occur. (2) The early-warning time of urban waterlogging risk (EWTUWR) in the same duration of level I and level II of FPR is all less than that of behind-peak rainstorm (BPR). Moreover, the average EWTUWR increases with the rainfall duration, with 1 h rainfall duration corresponding to the smallest average EWTUWR. (3) There has a compactly relations between EWTUWRs and rainstorm characteristics in different rainfall durations. The early-warning time is shortened by the increase of front-peak rainstorm intensity (FPRI) and total rainfall volume (TRV). (4) The theoretical verification and practical verification prove that the proposed early-warning time is available and applicable. This study provides a new method for studying the early-warning of urban waterlogging risk in other cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106460"},"PeriodicalIF":10.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072039","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":"Characterizing public response to unforeseen cascading fuel shortage: Through the lens of human mobility-based explainable machine learning models","authors":"Md Ashiqur Rahman ,&nbsp;Runhe Zhu","doi":"10.1016/j.scs.2025.106446","DOIUrl":"10.1016/j.scs.2025.106446","url":null,"abstract":"<div><div>Climate disasters unfold multitudes of effects, from societal and commercial disruptions to fuel and power shortages. These consequences escalate further in cascading disasters, where individuals are more likely to respond unwarrantedly due to the lack of preparation and situational awareness. A noticeable gap exists in comprehending the linkages between public responses to such disasters and socioeconomic and spatial disparities, which are critical to the provision of effective guidance and situational information to those affected. Based on mobile phone data and various socioeconomic, built environment, and geographical variables, this study systematically examines human mobility-based public responses during a cascading fuel shortage crisis. The spatiotemporal analysis uncovered a significant increase in visits to gasoline stations during and after the crisis and a decrease in mean distance traveled at the Census Block Group level. Furthermore, mobility prediction models were constructed using the random forest regression algorithm, which can adequately forecast visits and mean distance traveled to gasoline stations across different crisis stages. The Shapley Additive Explanations analysis reveals how various factors (e.g., educational attainment and distance to the coast) influenced public responses. These findings reinforce the importance of tailored disaster response education and situational awareness to ensure equitable resource access during cascading disasters.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106446"},"PeriodicalIF":10.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069957","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":"Railway network expansion reduces air pollution in Tokyo over 25 years","authors":"Sunbin Yoo ,&nbsp;Junya Kumagai ,&nbsp;Ryosuke Aki ,&nbsp;Shunsuke Managi","doi":"10.1016/j.scs.2025.106408","DOIUrl":"10.1016/j.scs.2025.106408","url":null,"abstract":"<div><div>We explore the long-term impact of railway infrastructure on air pollution by examining Japan’s railway network expansion over a 25-year period. Using Difference-in-Differences, regression analysis with market access, and instrumental variables strategies, we identify a causal link between railway development and improved air quality. Our findings show that railway expansion significantly reduces Suspended Particulate Matter (SPM) by 9.576<span><math><mtext>%</mtext></math></span> to 21.65<span><math><mtext>%</mtext></math></span> and Nitrogen Dioxide (<span><math><msub><mrow><mi>NO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>) by 1.401<span><math><mtext>%</mtext></math></span> to 1.546<span><math><mtext>%</mtext></math></span>. Results also indicate increased ridership and a shift towards railways following these expansions. Improved air quality translates to health benefits valued between 98.271 and 161.515 million USD, or approximately 1.426<span><math><mtext>%</mtext></math></span> to 2.342<span><math><mtext>%</mtext></math></span> of total construction costs. The most significant improvements are seen in areas with initially high SPM levels, with diminishing benefits noted further from central business districts. Our simulations suggest that the benefits of railway expansions will persist, advocating for continued development of railway networks as a sustainable strategy for environmental and public health enhancement.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106408"},"PeriodicalIF":10.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070097","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":"From heat resilience to sustainable co-benefits: Adaptive urban morphology generation based on multimodal data fusion and a novel generative framework","authors":"Shiqi Zhou ,&nbsp;Xiaodong Xu ,&nbsp;Haowen Xu ,&nbsp;Zichen Zhao ,&nbsp;Haojun Yuan ,&nbsp;Yuankai Wang ,&nbsp;Renlu Qiao ,&nbsp;Tao Wu ,&nbsp;Weiyi Jia ,&nbsp;Mo Wang ,&nbsp;Waishan Qiu ,&nbsp;Zhiqiang Wu","doi":"10.1016/j.scs.2025.106452","DOIUrl":"10.1016/j.scs.2025.106452","url":null,"abstract":"<div><div>Rapid urbanization and global climate change have intensified the Urban Heat Island (UHI) effect. However, practical implementation is often constrained by limitations in data availability and computational capacity, overlooking the influence of socioeconomic factors and spatial heterogeneity. This study proposed an end-to-end urban 3D morphology generation framework that leveraged multimodal datasets, including Local Climate Zones (LCZ), Land Surface Temperature (LST), and Population Density (POPH) through a novel CycleGAN-Pix2pix (CP-GAN) model chain. Using six representative LCZ areas in Guangzhou as case studies, the research evaluated the Urban Morphology Indicators (UMI), Land Use and Land Cover Change (LUCC), and Points of Interest (POI) across various responsive generation scenarios to identify urban morphologies that balanced cooling effects with socioeconomic and ecological benefits. The results showed that:(1) The CP-GAN achieved robust performance in urban morphology generation, demonstrating stable convergence and high precision, with an average structural similarity index exceeding 0.811, along with high signal-to-noise ratios and low error metrics. (2) Rising temperatures reshaped urban morphology, with every 3°C increase reducing green space by 5.47% while raising commercial activity and impervious surfaces by 2.38% and 2.84%, respectively; (3) Population density drove POI clustering but exhibited weaker morphological control than temperature gradients. (4) LCZ4, LCZ5, and LCZ6 exhibited spatial heterogeneity in UMI, LUCC, and POI responses to temperature and population density variations, necessitating LCZ-specific adaptive strategies. This generative system offers fine-grained 3D morphological solutions to mitigate UHI effects while establishing a transformative framework for sustainable urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106452"},"PeriodicalIF":10.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072042","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":"Impact of spatial configuration of urban blue spaces in mitigating temperature during summer: a remote sensing and field-based observation","authors":"Manob Das ,&nbsp;Arijit Das ,&nbsp;Priyakshi Saha ,&nbsp;Suman Paul","doi":"10.1016/j.scs.2025.106418","DOIUrl":"10.1016/j.scs.2025.106418","url":null,"abstract":"<div><div>Cities worldwide face heightened vulnerability to extreme heat due to rapid urbanization and the conversion of natural spaces into built-up areas. This challenge is particularly severe in the global south, where rapid urban expansion leads to the loss of green and blue spaces, essential for temperature mitigation. This study investigates how the spatial configuration of urban blue spaces affects summer temperatures, using remote sensing and field data from a rapidly growing urban agglomeration i.e., English Bazar Urban Agglomeration (EBUA) in Eastern India. The study applied ANOVA and Tukey’s HSD tests to assess temperature differences across urban core, transitional, and rural zones, comparing remote sensing and field data. Bayesian regression analyses further explore relationships between temperature and various explanatory factors, including vegetation and built-up density. Results indicate elevated land surface temperature (LST) in the urban core, reaching a peak of 63.08 °C at 80 m from blue spaces. In the transitional zone, LST shows moderate increases, from 62.21 °C at the blue space edge to 63.06 °C at 100 m. In rural areas, LST starts lower at 60.83 °C, showing minimal variation due to cooling effect of vegetation cover. ANOVA results reveal no significant variation across zones of temperature in remote sensing data (<em>p</em> = 0.144), possibly due to spatial averaging, while field data shows significant differences (<em>p</em> = 0.00045), capturing localized temperature changes. Bayesian regression highlights percentage of vegetation cover, normalized difference water index (NDWI), normalized difference built-up index (NDBI), and area of blue space as key LST predictors, with an R² of 0.875. The findings of the study on the investigation the impact of the spatial configuration of blue spaces on temperature mitigation is crucial in order to facilitate sustainable urban planning. Blue spaces that are well-designed have the potential to increase thermal comfort, enhance microclimate regulation, and reduce urban heat island (UHI) effect. This research can contribute to the development of climate-resilient strategies that promote energy-efficient urban development, public health, and biodiversity.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106418"},"PeriodicalIF":10.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936349","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":"Stratified strategies for enhancing thermal comfort through multidimensional compactness optimization in urban built-up areas during heatwaves","authors":"Yifeng Ji ,&nbsp;Ying Liu ,&nbsp;Hongyu Tang ,&nbsp;Zhitao Li ,&nbsp;Yihang Bai ,&nbsp;Tao Feng","doi":"10.1016/j.scs.2025.106445","DOIUrl":"10.1016/j.scs.2025.106445","url":null,"abstract":"<div><div>Thermal comfort (TC) in built-up areas with varying levels of compactness is unevenly affected during heatwaves (HW). However, identifying zones that should prioritize compactness optimization to effectively enhance TC is often overlooked. This study constructed a research framework for enhancing TC through stratified planning strategies by identifying key compactness-optimized areas and patterns during HW. Taking the built-up area of Shenyang, China, as an example, the compactness index containing spatial, functional and socio-economic dimensions and the TC index were first constructed based on multi-source data. Afterwards, different types of compactness-optimized areas, dominant compactness in different regions, and trade-off and synergy patterns among various dimensions of compactness were revealed using a geographically weighted regression model (GWRM) and local bivariate spatial autocorrelation analysis. The results show that compactness decreases from the center to the periphery of the built-up area, while TC follows the opposite trend. A total of 10 types of compactness-optimized areas are identified, including 8 key types covering 40.472 % of the built-up area. Based on the trade-offs and synergies between different compactness dimensions, 8 optimization patterns are revealed, with synergistic optimization across all three dimensions representing the largest share (71.256 %). Furthermore, 4 optimization categories with different priorities are identified, each exhibiting distinct spatial patterns and targeted optimization strategies. These findings support hierarchical resource allocation and strategic intervention to enhance thermal comfort and promote climate-resilient cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"127 ","pages":"Article 106445"},"PeriodicalIF":10.5,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070098","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}