Sustainable Cities and Society最新文献

{"title":"Dynamic effect of climate change on flood damage cost in the Andean region of Colombia using an ARDL-ECM model and climate change projections","authors":"Camilo A. Rodriguez-Espinosa ,&nbsp;Carlos F. Valencia ,&nbsp;Carlos G. Ramirez ,&nbsp;Carlos D. Valencia","doi":"10.1016/j.scs.2025.106309","DOIUrl":"10.1016/j.scs.2025.106309","url":null,"abstract":"<div><div>Natural disasters in Colombia related with climate fluctuations have high economic and social impacts, including the loss of human lives, infrastructure, agricultural production and livestock. Areal and riverine floods are the most common natural disasters in the country given its topography and heavy rainfall seasons. Flooding occurrences have increased during the last decades due the climate change and climate variability with prolonged periods with precipitation also affected by global phenomena as El Niño and La Niña. This research uses an Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM) to describe the dynamics of meteorological variables, such as precipitation, relative humidity and air temperature, and their effects on floods in the Colombian Andean region by using monthly flood damage costs during the period from October 2006 to December 2023. The ARDL-ECM cointegration test indicates a statistically significant relationship among the meteorological variables analyzed and flood damage cost. With the estimated parameters for long and short-run effects, flood cost is projected for future decades using two climate change scenarios for meteorological variables. These simulations provide a perspective of the future impact of floods in the study region and allow discussion of current flood prevention and action policies to mitigate these results. The imminent increase of flood events under climate change found in this study provides even more evidence on the necessity of strengthening current efforts to reduce emissions and deforestation in Colombia.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106309"},"PeriodicalIF":10.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777583","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":"Enhancing public transportation sustainability: Insights from electric bus scheduling and charge optimization","authors":"Foroogh Behnia ,&nbsp;Seyyed Sajad Mousavi Nejad Souq ,&nbsp;Beth-Anne Schuelke-Leech ,&nbsp;Mitra Mirhassani","doi":"10.1016/j.scs.2025.106298","DOIUrl":"10.1016/j.scs.2025.106298","url":null,"abstract":"<div><div>This study presents a joint optimization model for optimizing the scheduling and charging of electric buses in urban transit systems, integrating fleet size determination, trip scheduling, and charging infrastructure planning. The model is solved using a genetic algorithm and validated through constrained particle swarm optimization. Results demonstrate that by efficiently incorporating time-of-use pricing, optimized partial charging, and dynamic speed variations, the model achieves a 2.5% cost reduction compared to full charging and improves operational efficiency by over 7% within changing speed scenarios. Sensitivity analyses confirm the model’s robustness, identifying the minimum charge duration of 15 min and discharge depth of 90% as economically optimal. The study provides valuable insights for transit agencies seeking to optimize electric bus fleet operations and transition to more sustainable and cost-effective public transportation.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106298"},"PeriodicalIF":10.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Compact City on Carbon Emission Reduction Based on Urban Size: A Spatial Analysis Using Satellite Imagery","authors":"Hansol Mun ,&nbsp;Juchul Jung","doi":"10.1016/j.scs.2025.106326","DOIUrl":"10.1016/j.scs.2025.106326","url":null,"abstract":"<div><div>Cities contribute significantly to global carbon emissions, making sustainable development through efficient spatial planning essential for achieving carbon neutrality. Compact cities are crucial for urban planning because of concerns regarding the impact of dispersed development on society and the environment. However, researchers hold differing opinions, and debates continue. As cities expand, they develop different influences, underscoring the need to consider regional characteristics rather than rely on a single assessment framework. Understanding the interaction between urban size and spatial structure is vital because the relationship between compact city elements and carbon emissions is complex. This study aimed to clarify the carbon emission reduction effects of compact cities by considering their urban size and analyzing cities in the United States and South Korea. For example, although the compactness in larger cities may reduce emissions, the opposite effect may occur in smaller cities. Using satellite imagery, this study not only measured carbon emissions and three compact city characteristics but also conducted a panel threshold regression analysis to assess how these elements influenced emissions across various urban sizes. The results indicated that the impact of compact city characteristics on emissions varied with urban size, highlighting the significant interaction between urban size and spatial structure. For example, the analysis confirmed that reducing per capita VKT is essential despite environmental regulations and technological advancements. In small cities, greater compactness tends to increase carbon emissions, but beyond a certain size it contributes to their reduction. Likewise, while polycentricity increase emissions in smaller cities, it helps lower them as the city grows. This underscores the importance of understanding emission trends in relation to urban size and structure, while advocating for tailored spatial planning and strategic approaches rather than uniform policies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106326"},"PeriodicalIF":10.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852223","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 Digitization and Artificial Intelligence on Carbon Emissions Considering Variable Interaction and Heterogeneity: An Interpretable Deep Learning Modeling Framework","authors":"Gongquan Zhang ,&nbsp;Shenglin Ma ,&nbsp;Mingxing Zheng ,&nbsp;Cheng Li ,&nbsp;Fangrong Chang ,&nbsp;Fangbing Zhang","doi":"10.1016/j.scs.2025.106333","DOIUrl":"10.1016/j.scs.2025.106333","url":null,"abstract":"<div><div>With growing attention on digitization and artificial intelligence (D&amp;AI), interpreting their impact on urban carbon emissions has become critical. This study proposes an interpretable deep learning modeling framework considering variable feature interaction and heterogeneity to explore D&amp;AI's carbon impacts. Random Forest (RF) is used to explore variable importance and optimize model parameters, followed by constructing a Decision Deep &amp; Cross Feature-Transformation Network (DDCFTN) for high-fitting accuracy. SHapley Additive exPlanations (SHAP) and causal inference are employed to interpret variable effects and heterogeneity. Using data from 275 Chinese cities (2000–2021), DDCFTN outperforms traditional statistical or machine learning models (RMSE=579.88, MAE=440.91, <span><math><msubsup><mi>R</mi><mrow><mi>a</mi><mi>d</mi><mi>j</mi></mrow><mn>2</mn></msubsup></math></span>=0.994). Key findings include: 1) D&amp;AI's contribution to carbon emissions is underestimated—interactive effects increase the carbon impact by 665.569%. 2) Staged interaction patterns are observed: Digitization initially suppresses AI-related emissions (-34.017% to -96.361%) but later enhances them (13.191% to 45.353%). 3) Asymmetrical interactions exist, with AI's impact on digitization's emissions peaking at just 0.119%, following an inverted U-shaped trend due to retro-regulation effects. 4) City characteristics (e.g., Location Conditions and Urban Scale) reshape the heterogeneity of D&amp;AI emissions through chain reactions, acting as key antecedents. This study introduces a novel analytical paradigm for interpreting D&amp;AI's carbon impact and guiding urban decarbonization policies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106333"},"PeriodicalIF":10.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768454","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 Role of Geographic Scale of Weather Data in Urban Building Energy Models","authors":"Indira Adilkhanova,&nbsp;Ji Hwan Jeong,&nbsp;Geun Young Yun","doi":"10.1016/j.scs.2025.106339","DOIUrl":"10.1016/j.scs.2025.106339","url":null,"abstract":"<div><div>Building energy consumption is sensitive to fluctuations in outdoor climate conditions, making weather data a crucial factor in determining the accuracy of building energy models. However, the influence of spatial scale on weather data and its effect on energy demand simulations has received limited attention. To address this gap, the present study applied an innovative approach integrating urban climate modeling and urban building energy modeling to evaluate the impact of meteorological data at varying spatial scales on buildings' energy consumption in the high-density urban environment of Seoul. This integration enabled an assessment of how weather data at different spatial scales, ranging from city-wide to individual grid levels, influenced buildings' energy use intensity. The results revealed that using generalized weather data, such as in the Typical Meteorological Year (TMY) files, underestimated energy demand. In contrast, applying localized weather data, particularly at the subdistrict level, led to a significant increase in monthly energy use intensity and predictive accuracy compared to the TMY case, which reached 56.46 % and 14.54 %, respectively. These results highlight the importance of considering finer-scale weather data in urban energy simulations to better capture the effects of local climate variations on cooling energy demand.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106339"},"PeriodicalIF":10.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768418","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 a decision-support tool to estimate the cooling potential and economic savings of urban vegetation in Singapore","authors":"Emma E. RAMSAY ,&nbsp;Yuan WANG ,&nbsp;Mahyar MASOUDI ,&nbsp;Min Wei CHAI ,&nbsp;Tiangang YIN ,&nbsp;Perrine HAMEL","doi":"10.1016/j.scs.2025.106337","DOIUrl":"10.1016/j.scs.2025.106337","url":null,"abstract":"<div><div>To mitigate the growing threat of urban heat, cities are implementing greening strategies such as tree planting or the development of parks. Effectively integrating these solutions into planning requires quantitative information on the cooling effect of urban vegetation. Here we examined the performance of an open-source decision-support tool, the Integrated Valuation of Ecosystem Services and Tradeoffs Urban Cooling model, to estimate the cooling effect and economic benefits of urban vegetation in a tropical city context, using Singapore as an exemplar case study. Using observed temperature data, we calibrated the model to estimate the spatial distribution of annual average day- and night-time temperature at 10 m spatial resolution and validated the results using leave-one-out cross validation. The calibrated models performed well to estimate annual average daily mean and maximum (day), and minimum (night) temperatures (R² of 0.78, 0.65, and 0.52 respectively). We estimated that urban cooling in Singapore provides economic savings of $47.14 million SGD annually from reduced energy consumption in public residential buildings, based on the relationship between energy consumption and mean temperature. Our results give confidence in the model as a decision-support tool to estimate urban heat island effects and evaluate heat mitigation strategies in tropical cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106337"},"PeriodicalIF":10.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scaling law in a stable urban system of Japan","authors":"Gang Xu ,&nbsp;Mengyan Zhu ,&nbsp;Liran Liu ,&nbsp;Shiyi Guo ,&nbsp;Yuji Murayama ,&nbsp;Muhammad Salem ,&nbsp;Limin Jiao","doi":"10.1016/j.scs.2025.106335","DOIUrl":"10.1016/j.scs.2025.106335","url":null,"abstract":"<div><div>Scaling law quantifies nonlinear relationships between urban indicators and population size. Japan typifies populous Asian countries with a high urbanization level, but the applicability of scaling law in Japan was underexplored. Here, we address this gap by analyzing over 50 urban indicators across more than 1700 municipalities, encompassing education, dwelling, economy, health, and employment sectors. Results revealed a distinctive stability in Japan's urban system, with minimal variations in scaling exponents and residuals over time (2005–2020), which is different from rapidly urbanizing systems. Despite most indicators following expected scaling regimes, the sub-linear scaling relationship for financial revenue deviated from the theoretical expectation due to unique local tax policies. Furthermore, scale-adjusted indicators reveal significant spatial clusters, with central Japan showing enhanced performance in employment and northeastern cities excelling in taxpayer income. Our findings contributed to understanding urban scaling law in highly urbanized mature systems. The stability of urban systems promises predictability, highlighting scaling law as a theoretical guide in urban governance, but should also be combined with local policies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106335"},"PeriodicalIF":10.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768455","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":"Recent progress in optimization of RANS turbulence models for accurate urban airflow and contaminant dispersion simulations","authors":"Sumei Liu ,&nbsp;Longhui Xu ,&nbsp;Bingqian Chen ,&nbsp;Zhipeng Deng ,&nbsp;Pin Li ,&nbsp;Runmin Zhao ,&nbsp;Qingyan Chen","doi":"10.1016/j.scs.2025.106336","DOIUrl":"10.1016/j.scs.2025.106336","url":null,"abstract":"<div><div>Modeling urban airflow and contaminant dispersion is critical for maintaining a healthy and safe outdoor environment. Reynolds-Averaged Navier-Stokes (RANS) turbulence models, while computationally efficient, often lack accuracy, limiting the reliability of airflow and pollutant dispersion predictions. This literature review explores the latest optimization techniques for improving RANS model accuracy in urban airflow and contaminant dispersion simulations. The paper discusses the challenges with traditional RANS models, including uncertainties in the model structure, parameters, and dispersion sub-models. It also reviews recent optimization strategies, such as machine learning methods, surrogate models, hybrid approaches, and Physics-informed neural networks (PINNs). Through case studies and applications, the paper assesses the effectiveness of these techniques and highlights future research directions. By synthesizing existing research, this review offers insights into optimizing RANS models and enhancing urban environmental modeling.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106336"},"PeriodicalIF":10.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban heat island mitigation in Tehran: District-based mapping and analysis of key drivers","authors":"Ali Aslani ,&nbsp;Maryam Sereshti ,&nbsp;Ayyoob Sharifi","doi":"10.1016/j.scs.2025.106338","DOIUrl":"10.1016/j.scs.2025.106338","url":null,"abstract":"<div><div>Urban Heat Islands (UHIs) result in higher urban temperatures than rural areas, increasing energy use for cooling, degrading air quality, and worsening heat-related health issues. Although previous studies have explored UHIs in Tehran, a comprehensive analysis remains lacking. This study adopts a macro-scale approach, combining linear, non-linear, and location-based methods to investigate key factors influencing UHI intensity (UHII) in detail. It uses remote sensing and machine learning to analyze UHIs in Tehran by examining Land Surface Temperature and Land Cover data within GIS. The study compares urban temperatures with those in surrounding rural areas and identifies key factors influencing UHIs through ordinary least squares (OLS), random forest (RF), and geographically weighted regression. The findings reveal substantial UHII variations across Tehran, with central districts showing temperature differences greater than 5 °C compared to rural areas. The OLS model identifies aerosol optical depth (0.38), population density (0.45), and Normalized Difference Built-up Index (NDBI) (0.17) as contributors to higher UHII, while relative humidity (-0.51) and terrain slope (-0.22) are linked to lower UHII. The RF model, with a Mean Squared Error of 0.33 and an R-squared value of 0.84, highlights population density, relative humidity, and NDBI as primary factors influencing UHII. Results can inform planning and design strategies to foster climate change adaptation and enhance urban resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106338"},"PeriodicalIF":10.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792622","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":"Assessment of Spatio-temporal Variability of Winter Land Surface Temperature in Industrial City of Durgapur, India: Trend and Drivers","authors":"Juel Sk. ,&nbsp;Sasanka Ghosh ,&nbsp;Arijit Das","doi":"10.1016/j.scs.2025.106334","DOIUrl":"10.1016/j.scs.2025.106334","url":null,"abstract":"<div><div>Durgapur Municipal Corporation (DMC) is an important growing smart city in Eastern India mainly dependent on an industry-based economy. This growing urbanization negatively affected its thermal condition over the last few decades. This study identified the changing thermal environment of the study area through the Mann-Kendal test and also identified major factors for the spatial variation of Land Surface Temperature (LST) over the region through collecting opinions of the residents, existing literature, multicollinearity test and Geodetector analysis. Results revealed that overall temperature has increased for the study area, but there are some pocket areas where temperatures have increased rapidly, and some areas have shown negative temperature change. This temperature variation is mainly influenced by the location of power-plant (q = 0.144), Distance from Settlement (q = 0.114), Distance from Road (q = 0.105), Distance from Waterbody (q = 0.096), Distance from Vegetation (q = 0.099), and Distance from Industries (q = 0.089). Similarly, interaction between two factors is more responsible than individual factors for this spatial variation of LST over the region. This identification of temperature variations and their influencing factors will help to develop a planning strategy to minimize the negative thermal effect for the study area.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106334"},"PeriodicalIF":10.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777585","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}