Sustainable Cities and Society最新文献

{"title":"Socioeconomic disparities in hurricane-induced power outages: Insights from multi-hurricane data in Florida using XGBoost","authors":"Alexys H. Rodríguez-Avellaneda ,&nbsp;Ryan Rodriguez ,&nbsp;Abdollah Shafieezadeh ,&nbsp;Alper Yilmaz","doi":"10.1016/j.scs.2025.106362","DOIUrl":"10.1016/j.scs.2025.106362","url":null,"abstract":"<div><div>This study explores the importance of socioeconomic factors in hurricane-induced power outages in Florida. An XGBoost regression framework that incorporates a comprehensive feature set, including diverse socioeconomic factors, hurricane hazards, and physical exposure, is introduced. To reduce random deviations in importance observed in prior single hurricane studies, data for 11 Florida hurricanes is processed and analyzed, sourced from various state and federal agencies. To further enhance the robustness of model findings, analysis was conducted on 66 independent repetition runs filtered from 250 model iterations to control for overfitting. An extended formulation of SHAP values across iterations is introduced to enable a nuanced assessment of feature importance. Results show that socioeconomic variables account for 19% of the model prediction. This finding underscores the presence and significance of social inequities in hurricane outages. The unemployment rate, percentage of disabled, and racial/ethnic minorities are found as the most important predictors. Two new variables – flooding and substations per county – are assessed in this study, but they are found to have no notable contribution to power outages. The findings of this study provide new insights into the interplay between socioeconomic conditions and power system performance, aiding outage prevention efforts by identifying socioeconomic inequalities in pre-existing conditions and system operations. The findings of this study highlight systemic socioeconomic vulnerabilities in power grid resilience, offering critical insights for policymakers to allocate resources and improve disaster response strategies. While the model is tailored for Florida, its structure could be adapted to assess power outage disparities in other hurricane-prone regions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106362"},"PeriodicalIF":10.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838618","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":"Collaborative governance for urban waste management: A case study using evolutionary game theory","authors":"Wenxuan Song ,&nbsp;Ehsan Elahi ,&nbsp;Guisheng Hou ,&nbsp;Pengmin Wang","doi":"10.1016/j.scs.2025.106380","DOIUrl":"10.1016/j.scs.2025.106380","url":null,"abstract":"<div><div>The rapid escalation in municipal solid waste generation presents a critical challenge, compounded by delays in implementing source-separated household waste management systems. This study addresses the lack of comprehensive stakeholder-oriented planning in government-led waste classification efforts—characterized by overreliance on administrative enforcement, imbalanced resource allocation, and inadequate coordination mechanisms—which has led to rising administrative costs and suboptimal outcomes. By employing an evolutionary game model, this study explores the dynamic interactions between key stakeholders—governments, property service enterprises, and residents—while incorporating subsidy and transfer payment mechanisms to establish a collaborative governance framework. The empirical analysis, grounded in data from Shanghai, China, reveals that a multi-agent collaborative model improves cost efficiency by 25 % compared to a government-only approach. The research findings indicate that when government subsidies exceed an 80 % distribution ratio to real estate service enterprises, a cooperative strategy is achieved, although it may lead to a non-cooperative state among residents. Reducing the cooperation costs of property service enterprises enhances their willingness to cooperate but has limited influence on residents. In contrast, residents' strategies are more sensitive to changes in government subsidies, and increasing subsidies led to evolutionary stability points characterized by dual and tripartite cooperation modes. These findings demonstrate how properly designed subsidy structures can address macro-level planning deficiencies by creating incentive alignment among stakeholders. The study also finds that reducing urban household garbage sorting costs by 30 % contributes to optimal tripartite cooperation. Although the study focuses on Shanghai, its findings apply to other cities with similar waste management policies, offering universal insights for improving urban waste classification systems. The study advocates for robust regulations and comprehensive monitoring systems to sustain long-term collaborative waste management practices.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106380"},"PeriodicalIF":10.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850526","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 urban food production: A framework for optimal site selection and policy development","authors":"Mohammad Ismaeil Kamali ,&nbsp;Rouzbeh Nazari ,&nbsp;Maryam Karimi ,&nbsp;Mohammad Reza Nikoo","doi":"10.1016/j.scs.2025.106375","DOIUrl":"10.1016/j.scs.2025.106375","url":null,"abstract":"<div><div>According to the urbanization process, urban food production is a viable means of ensuring urban food security, and socio-economic development. This paper synthesized and reviewed the existing urban agriculture studies to identify the trends and gaps and suggested future research directions for increasing the efficiency of urban agriculture initiatives. We have also critically analyzed food accessibility using standards defined by the USDA. We investigate our query through a critical review of the historical research regarding urban agriculture. Also, this review discusses the integration of MCDM models with GIS in urban agriculture, considering its synergies, applications, and findings. Finally, we defined a framework to determine a suitable area for urban agriculture by considering the influential criteria. Drawing on several cases of spatial data and a variety of strict analytical frameworks, this study looks forward to offering the necessary insights needed for efficient practice in urban agriculture that will excite a paradigm shift toward more inclusive and sustainable urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106375"},"PeriodicalIF":10.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868667","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":"Novel spatiotemporal nonlinear regression approach for unveiling the impact of urban spatial morphology on carbon emissions","authors":"Lei Li ,&nbsp;Shujie Sun ,&nbsp;Leyuan Zhong ,&nbsp;Ji Han ,&nbsp;Xuepeng Qian","doi":"10.1016/j.scs.2025.106381","DOIUrl":"10.1016/j.scs.2025.106381","url":null,"abstract":"<div><div>Understanding the impacts of urban spatial morphology on carbon emissions is crucial for promoting sustainable urban development. However, traditional models have limitations when analyzing complex spatiotemporal heterogeneity and nonlinear relationships. Therefore, we proposed an Integrated Spatiotemporal Nonlinear Regression (ISTNR) model to explore the complex relationship between urban spatial morphology and carbon emissions. This model combines Geographically and Temporally Weighted Regression (GTWR) to capture spatial and temporal dependencies, the Random Forest (RF) model to address nonlinear relationships, and the game theory-based Shapley Additive Explanations (SHAP) tool to enhance the interpretability of the results. The data encompassed urban morphology and carbon emissions across specific regions and periods, and the robustness and adaptability of the model were validated in various urban morphology environments. The ISTNR model demonstrated significant superiority over traditional regression models, achieving an R² of 0.924, a substantially lower MSE (18.06×10⁶), and higher predictive accuracy and stability in complex urban environments. Additionally, bootstrap uncertainty analysis indicated that the model's prediction intervals were relatively narrow, suggesting low prediction uncertainty and high stability. The SHAP analysis quantified the specific contributions of various urban morphological features to carbon emissions, further revealing their mechanisms impacting emission predictions. This study presents an effective quantitative tool for urban planning and carbon emissions control, offering practical support for future urban policymaking.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106381"},"PeriodicalIF":10.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839409","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 flood and waterlogging vulnerability and community governance in urban villages in the context of climate change: A case study of 89 urban villages in Shanghai","authors":"Shijun Chen ,&nbsp;Jiayue Lin ,&nbsp;Tuolei Wu ,&nbsp;Zhe Yuan ,&nbsp;Wenting Cao","doi":"10.1016/j.scs.2025.106377","DOIUrl":"10.1016/j.scs.2025.106377","url":null,"abstract":"<div><div>Climate change has increased the vulnerability of urban villages to flooding and waterlogging, making it a major challenge for government community management. This study assesses flood-waterlogging vulnerability in Shanghai's urban villages under current conditions and long-term SSP-RCP scenarios using data from 89 redevelopment communities. We identify development patterns based on clustering models, evaluate vulnerability using an ensemble learning model, and analyze climate policy attention with Python-based text mining. Results highlight significant variations in infrastructure and flood risk across urban villages with different development patterns. Suburban Growth Zones and Ecological Agricultural Zones show higher vulnerability. In the long term, most villages become more vulnerable under both SSP245 and SSP585 scenarios, and the uncertainties and risks will increase due to cumulative effects. The study emphasizes the necessity of thorough governmental management in mitigating climate-induced waterlogging risks in urban villages and suggests specific policy recommendations customized for various categories of urban villages.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106377"},"PeriodicalIF":10.5,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838617","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":"Unleashing the power of artificial intelligence: A game changer for urban energy efficiency in China","authors":"Weike Zhang ,&nbsp;Hongxia Fan ,&nbsp;Ming Zeng","doi":"10.1016/j.scs.2025.106372","DOIUrl":"10.1016/j.scs.2025.106372","url":null,"abstract":"<div><div>Energy consumption in China is predominantly concentrated in urban areas, improving urban energy efficiency (UEE) is a crucial step towards mitigating energy pressure and achieving sustainable energy practices. However, it remains uncertain how artificial intelligence (AI) affects UEE, as it is both a promoter of energy conservation and a consumer of large amounts of energy. Given this context, we explore the effect of AI on UEE in China using data from 282 cities spanning 2006 to 2019. We find that AI benefits the improvement of UEE. Specifically, the installation (stock) of one additional standard deviation of industrial robots per hundred workers is associated with a 3.18 % (3.30 %) increase in energy efficiency in Chinese cities. These conclusions remain valid even when subjected to a suite of robustness tests. Furthermore, we reveal that the positive influence of AI on UEE is particularly pronounced in resource-dependent cities, eastern-central cities, northern cities of the Qinling Mountains-Huaihe River line, as well as mega-sized and super-sized cities. Additionally, we demonstrate that AI has a positive spatial spillover effect on UEE, that is, the UEE of local cities can be improved through the influence of neighboring cities' AI systems. Our findings not only improve the cognition of the link between AI and UEE but also guide government efforts to enhance UEE and achieve energy sustainability.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106372"},"PeriodicalIF":10.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847528","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":"Modelling urban carbon emissions for multiple sectors in high spatial resolution for achieving sustainable & net-zero cities","authors":"Lily Purcell ,&nbsp;Anna C. O'Regan ,&nbsp;Conor McGookin ,&nbsp;Marguerite M. Nyhan","doi":"10.1016/j.scs.2025.106370","DOIUrl":"10.1016/j.scs.2025.106370","url":null,"abstract":"<div><div>Many largescale initiatives and networks have been established to support city efforts and leadership in decarbonisation. An essential first step in these initiatives is developing a Baseline Emissions Inventory (BEI) to understand drivers of current emissions and provide a benchmark that progress can be measured against. There has been increasing interest in emission inventory methods. However, previous research has focused on single sectors, has neglected emissions other than CO₂, or has not followed a spatial approach. The latter is particularly important to support policy planning and decision-making. This study investigates the development of a novel BEI for a medium-sized city in Ireland to address the methodological knowledge gap in existing literature for a detailed methodology using mainly open-source and spatially resolved data for developing a multi-sectoral BEI in high spatial resolution. Greenhouse Gas (GHG) emissions including CO₂, CH₄, and N₂O, represented as kilotonnes CO₂-equivalent (ktCO₂-eq), were modelled for the Residential; Transport; Commercial &amp; Industrial; Public; Agriculture, Land Use &amp; Fishing (ALUF); and Waste sectors. Total annual emissions were 987 ktCO₂-eq, with emissions per capita of 4.7 tCO₂-eq. The Residential sector accounted for 34 % of emissions followed by the Transport (29 %), Commercial &amp; Industrial (22 %), Public (7 %), ALUF (6 %), and Waste (2 %) sectors. The fine-resolution spatial outputs facilitate the investigation of socioeconomic factors alongside GHG emissions helping to elucidate local drivers and produce equitable mitigation strategies. The findings will contribute to effective policy development and the methodologies, developed in accordance with the Global Covenant of Mayors, can be replicated by cities globally.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106370"},"PeriodicalIF":10.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850525","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":"Analysis of charging demands, influencing factors and spatial effects of electric vehicles based on multi-source data and local spatial models","authors":"Xiaoqi Zhang ,&nbsp;Fang Yang ,&nbsp;Chunyan Shuai ,&nbsp;Jie Liu ,&nbsp;Kaiwen Zhang ,&nbsp;Xin Ouyang","doi":"10.1016/j.scs.2025.106371","DOIUrl":"10.1016/j.scs.2025.106371","url":null,"abstract":"<div><div>In order to alleviate the difficulty of charging caused by the popularization of electric vehicles (EVs), this paper conducts an in-depth analysis of the charging demands and influencing factors. According to the spatial distribution of charging demands, the target area is divided into more Voronoi unequal polygons, and the spatial characteristics of the built environment and socio-economic factors is qualitatively analyzed. Then, multicollinearity and spatial correlation tests are employed to eliminate redundant factors and explore the spatial clustering and correlation of charging demands and impact elements. A multi-scale geographically weighted regression and spatial autoregressive (MGWR-SAR) model are proposed to investigate such complex properties. An empirical study in Chongqing, China has shown that the charging demands in adjacent units exhibit obvious high-high and low-low clustering patterns, and are significantly influenced by the EVs with low SOCs, population density, parking lots density, transportation conditions, etc. The spatial impact degrees and scales vary with the factors and intervals, wherein the spatial scales of population density and road network density are local, with strong spatial heterogeneity; EVs with low SOCs, land use mixing and housing prices are close to global impacts. The spatial dependence of charging demand in high demand areas and charging peak periods is stronger than that in low demand and off-peak. There are spatial dependence and heterogeneity in charging demands and influencing factors, which makes MGWR-SAR superior to other models. These findings will provide support for predicting charging demands and optimizing charging stations.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106371"},"PeriodicalIF":10.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828273","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":"Corrigendum to “Assessing the potential for green roof retrofitting: A systematic review of methods, indicators and data sources” [Sustainable Cities and Society, Volume 123, 1 April 2025, 106261]","authors":"Jing Dong ,&nbsp;Chunli Li ,&nbsp;Ruonan Guo ,&nbsp;Fei Guo ,&nbsp;Xing Zheng","doi":"10.1016/j.scs.2025.106359","DOIUrl":"10.1016/j.scs.2025.106359","url":null,"abstract":"","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106359"},"PeriodicalIF":10.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864809","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":"Multilevel social vulnerability and urban health in sub-Saharan Africa: Implications for adaptation across household, community, and city levels","authors":"Huijoo Shon","doi":"10.1016/j.scs.2025.106368","DOIUrl":"10.1016/j.scs.2025.106368","url":null,"abstract":"<div><div>As rapidly urbanizing settlements in sub-Saharan Africa face vulnerability to environmental hazards across spatial levels, understanding the multilevel structure of vulnerability is critical for advancing urban health and climate adaptation. This paper develops a conceptual framework to examine multilevel social vulnerability and its health impacts across cities in 28 African countries. By integrating household survey and spatial datasets, this study constructs vulnerability indicators at household, community, and city levels, employing principal component analysis to quantify social vulnerability at each level. Logistic regression models estimate the effects of vulnerabilities on child health outcomes, including under-five mortality, underweight, diarrhea, and acute respiratory infection (ARI). The analysis reveals substantial variations in vulnerabilities across the three spatial levels, each of which significantly impacts health. Household and community vulnerabilities are related to increased risks of underweight and diarrhea while household vulnerability is strongly associated with mortality. In large cities with populations over one million, the effects of city vulnerability become more pronounced across morbidity outcomes, particularly for severe ARI and diarrhea. These findings suggest that the health implications of vulnerabilities differ according to specific outcomes and urban settings, highlighting the importance of incorporating a multilevel perspective into urban health and adaptation planning.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106368"},"PeriodicalIF":10.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873144","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}