Sustainable Cities and Society最新文献

{"title":"Causal inference of urban heat island effect and its spatial heterogeneity: A case study of Wuhan, China","authors":"","doi":"10.1016/j.scs.2024.105850","DOIUrl":"10.1016/j.scs.2024.105850","url":null,"abstract":"<div><div>The accelerated urbanization process has exacerbated the urban heat island effect, leading to significant negative impacts on the physical and mental health of urban residents. A deeper understanding of the mechanisms underlying the urban heat island phenomenon is essentially beneficial for providing scientific supports towards improving the urban thermal environment. To address the challenge of effectively depicting the complex interactions among urban environment factors, this study employed the Peter-Clark causal discovery algorithm to analyze the causal structure of urban thermal driving factors, and validated the effectiveness of the 6 key factors directly influencing the surface urban heat island intensity (SUHII). In response to the inadequacy of existing big data causal inference tools in assessing the spatial heterogeneity of causal effects, this study proposed a method for evaluating the causal effects on SUHII and their spatial heterogeneity based on local analysis and geospatial causal principle. The result for 4 different intervention scenarios in this study show that there is obvious spatial heterogeneity in the causal effects of different interventions on SUHII in Wuhan, and that increasing greenery and preserving natural environments is an effective way to mitigate the urban heat island (UHI) effect. This approach, provides a new perspective for studying the phenomenon of UHI and insights of potential approaches for mitigating UHI.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358804","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":"Towards multi-variable tsunami damage modeling for coastal roads: Insights from the application of explainable machine learning to the 2011 Great East Japan Event","authors":"","doi":"10.1016/j.scs.2024.105856","DOIUrl":"10.1016/j.scs.2024.105856","url":null,"abstract":"<div><div>The accurate assessment of tsunami-induced damage to coastal roads is crucial for effective disaster risk management. Traditional approaches, reliant on univariate fragility functions, often fail to capture the complex interplay of variables influencing road damage during tsunami events. This study addresses this limitation by employing machine learning techniques on an extensive dataset compiled after the 2011 Great East Japan tsunami. The dataset, enriched with additional explicative variables accounting for the hydraulic features of the event and the physical characteristics at roads’ location, enables a comprehensive analysis of road damage mechanisms. Results indicate that while inundation depth remains a significant predictor, factors such as wave approach angle, road orientation and potential overflow from inland watercourses also play critical roles.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422613","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":"Optimal representation of tree foliage for local urban climate modeling","authors":"","doi":"10.1016/j.scs.2024.105857","DOIUrl":"10.1016/j.scs.2024.105857","url":null,"abstract":"<div><div>Trees impact the local urban climate, notably at street level by intercepting solar radiation and providing shading. Evapotranspiration in foliage may reduce the air temperature although it may increase relative humidity and leaf drag may reduce wind speed, affecting thermal comfort. To document and quantify this impact, microclimate modeling with Computational Fluid Dynamics (CFD) simulations requires explicit information of the urban configuration, including trees. However, trees are complex individuals with a variety of shapes and a variety of foliage distribution. This study aims to investigate the sensibility to the tree modeling of the urban climate simulations. Starting with terrestrial LiDAR data from trees of different species, ages, and forms, we propose a systematic evaluation of the optimal representation of arboreal configurations in terms of local urban comfort. One way to represent the foliage of trees accurately is to apply Delaunay triangulation on the LiDAR data, which yields a convex envelope model. The resulting foliage shape is very close to the actual tree, but includes a high number of facets leading to complex objects to model numerically. Comparing four species and three maturity level of trees with this method, the paper shows that the size of the zone shadowed by a tree is the parameter with the largest impact on thermal comfort, as the ability of trees to absorb solar radiation is the main asset to improve thermal comfort. The UTCI could be up to 2.1°C lower for a mature ACPL than for a sapling, mainly because the zone covered by the tree is larger. In addition, polyhedron shape rhombicuboctahedron (RBC) produces accurate shadowed zones. Mostly, in literature, tree canopies are modeled with cubic representations while we see that they overestimate the size of the shadowed zone. To have reliable compromise between accuracy and time for conception and computational time, this paper shows that the RBC is the best alternative to common tree models. Despite requiring a good knowledge of the canopy geometry, RBC provides a strong capacity for accurately modelling complex canopy shapes of most tree species and offers large benefits in reduced complexity. We show that the RBC shape, thanks to its simple but flexible geometry, is an efficient and accurate methodological approach to model trees and allows savings in computational time (up to 15% faster than the convex envelope) and costs; and we expect that this method will improve the modeling of further parametric studies on vegetation impact on thermal urban comfort.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422204","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":"Construction and demolition waste management and its impacts on the environment and human health: Moving forward sustainability enhancement","authors":"","doi":"10.1016/j.scs.2024.105855","DOIUrl":"10.1016/j.scs.2024.105855","url":null,"abstract":"<div><div>Rapid population growth with industrialization, modernization, and urbanization has stimulated construction activities. Demolition activities have widely been undertaken to replace old buildings and civil structures with better, bigger, and/or newer ones. The construction and demolition activities generate tremendous amounts of construction waste (CW) and demolition waste (DW) consisting of a large variety of materials with varying properties. Many types of CW and DW contain various types of pollutants such as fine residues, heavy metals, and persistent organic chemicals; thereby having deleterious impacts on the environment and human health. This study comprehensively analyzes various data and survey on the generation and characteristics of CW and DW, their current management methods, as well as the environmental and health effects of CW and DW constituents and their management. Based on the comprehensive analysis, the strategies for sustainably managing CW and DW are devised to alleviate negative impacts of current CW and DW management. Sustainable management of CW and DW should be considered at each stage of a construction project (from planning/design to post-construction). It is hoped that this study will contribute to reducing the burden of CW and DW on the construction industry and local communities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422608","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":"Simulating the impact of ventilation corridors for cooling air temperature in local climate zone scheme","authors":"","doi":"10.1016/j.scs.2024.105848","DOIUrl":"10.1016/j.scs.2024.105848","url":null,"abstract":"<div><div>Climate change and urbanization are impacting urban microclimates, necessitating careful consideration in urban ventilation planning. However, there remains uncertainty regarding the identification and examination of urban ventilation corridors (VCs), and their mechanisms for urban heat mitigation. This study combined ENVI-met and the least-cost path (LCP) algorithm to simulate the VCs within the local climate zone (LCZ) scheme, with Guangzhou as an example. The results show that (1) VCs constructed using the LCP method are primarily distributed along vegetation and rivers, bypassing high-temperature aggregation in high-density/high-rise building areas; (2) The cooling effect of VCs varies across different LCZs, influenced by factors such as land use, vegetation, building, and topography. (3) In low-density, low-rise LCZs, VCs significantly reduce temperatures by about 0.23 °C, while in high-density, high-rise LCZs, the cooling effect is weaker, with a reduction of only around 0.01 °C. This study highlights the importance of preserving hydrological system and optimizing green space layout to enhance urban ventilation thus mitigate heat accumulation.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358800","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":"Multi-objective optimal sizing and techno-economic analysis of on- and off-grid hybrid renewable energy systems for EV charging stations","authors":"","doi":"10.1016/j.scs.2024.105846","DOIUrl":"10.1016/j.scs.2024.105846","url":null,"abstract":"<div><div>Integrating electric vehicle charging stations (EVCSs) with renewable energy systems requires the consideration of several factors during the planning stage, including environmental impact, economic viability, grid reliability, and self-sufficiency. Therefore, this study conducts a multi-objective optimal sizing of on- and off-grid hybrid renewable energy systems for EVCSs. The sizing problem is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II). Subsequently, the best suitable solutions from the obtained non-dominated solutions are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, prioritizing the objective functions based on diverse interests of different stakeholders (large and small private investors and governmental entities). Finally, a techno-economic analysis is made considering payback period, profitability index (PI), and internal rate of return (IRR). The results show that on-grid systems show high economic viability with payback periods between 1.98 and 7.72 years, an average PI of 5.07 and an average IRR of 23.97%. Although off-grid systems present lower economic viability with payback periods between 8.77 and 22.42 years, an average PI of 1.68 and an average IRR of 4.91%, in certain cases they reach investable levels with payback periods below 10 years, PI above 2, and IRR above the interest rate.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358805","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":"Energy signature approach for retrofit prioritization: A proposal for building identification methodology","authors":"","doi":"10.1016/j.scs.2024.105844","DOIUrl":"10.1016/j.scs.2024.105844","url":null,"abstract":"<div><div>Amidst the urgent call for carbon reduction, retrofitting existing buildings for energy efficiency has become imperative. Nonetheless, the challenge of selecting suitable buildings for retrofitting persists. This paper proposes a method for identifying retrofit target buildings using the heating Energy Signature (ES) approach to achieve effective carbon reduction through retrofitting.</div><div>The method involves comparing the actual ES, derived from monthly heating energy usage in existing buildings, with the optimal ES based on the building's physical performance to assess the need for retrofitting or behavioral improvements. It takes into account factors such as building physical characteristics, occupants’ heating behavior, and outdoor environment, integrating ES parameters like balance-point temperature (BPT) and heating sensitivity.</div><div>In practical application, the proposed method involves four steps. Step 1 collects five essential building data inputs from the building user. Step 2 supplements this information using open-source platforms to calculate the optimal ES parameters. Step 3 computes both the actual and optimal ES, and Step 4 compares these to identify energy inefficiencies. The results are then presented in a user-friendly format for intuitive understanding.</div><div>Offering novelty, this method accurately identifies the causes of building energy consumption, streamlines calculation processes, requires minimal input data, and yields intuitive results. It furnishes valuable insights for stakeholders engaged in retrofitting projects and holds potential for the effective implementation of building retrofit policies on a larger scale.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422162","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":"District-wise evaluation of meteorological factors and outdoor thermal comfort in India using UTCI – Insight into future climatic scenario","authors":"","doi":"10.1016/j.scs.2024.105840","DOIUrl":"10.1016/j.scs.2024.105840","url":null,"abstract":"<div><div>The study aims to comprehensively analyze outdoor meteorological conditions and thermal comfort across 592 districts in India, addressing the critical issues of thermal comfort which significantly impacts health, well-being and productivity. Given the anticipated global warming, understanding these factors becomes crucial, particularly for low-income populations who spend considerable time outdoors. While most previous studies in India have focused on indoor environments or on broad climatic regions, our research provides a granular analysis at the district level, incorporating future climate scenarios from 2050 to 2080. Using the Universal Thermal Climate Index (UTCI), our findings reveal that the northwestern part of India experiences ‘Strong’ to ‘Extreme’ heat stress, with temperatures exceeding 50 °C, while the Himalayan regions face ‘Strong’ cold stress, with temperatures dropping below -20 °C. The geographical distribution of UTCI classes shows that the western and central regions suffer from high thermal stress during summer afternoons, whereas coastal areas, benefiting from higher relative humidity and wind speeds, exhibit moderate UTCI values. The Himalayan regions consistently present lower UTCI values, indicating colder conditions. We have developed a district-wise climate atlas of India, mapping key environmental parameters and the outdoor thermal stress of UTCI values. The main objective of this study is to provide localized insights into how climate change will affect outdoor thermal comfort, facilitating informed decision-making for public health planning, energy infrastructure, and climate adaptation strategies across India.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533635","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":"Estimating casualties from urban fires: A focus on building and urban environment information","authors":"","doi":"10.1016/j.scs.2024.105839","DOIUrl":"10.1016/j.scs.2024.105839","url":null,"abstract":"<div><div>This study developed two prediction models for urban fire occurrence and related casualties via a fire accident dataset from Seoul, South Korea, from 2017 to 2021. Our models exhibit improved predictive performance by incorporating built environment features, such as building characteristics and the urban context, alongside weather and demographic data. This approach showed improved predictive performance suitable for public health implementation. Compared with the weather- and demographic-only models, our models had an 18.1 % greater fire occurrence prediction accuracy and a 10.4 % greater casualty prediction accuracy. Major variables affecting fire occurrence include building characteristics, e.g., the floor area ratio (FAR), building age, and commercial building number. Important features affecting casualty occurrence include demographic aspects, e.g., income level and weather, and network-based features, e.g., road connectivity and fire station proximity. These findings suggest that fire prevention strategies and fire casualty prevention strategies may need to differ. Furthermore, we identify high-risk zones by conducting spatial analysis and fire risk and casualty prediction on all buildings by applying our models to Seoul's Gangnam District. These contributions can promote safe and healthy urban environments by improving fire risk prediction accuracy and providing important insights into urban planning for appropriate urban fire accident response and prevention.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358807","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":"Unraveling the effect of electricity price on electric vehicle charging behavior: A case study in Shenzhen, China","authors":"","doi":"10.1016/j.scs.2024.105836","DOIUrl":"10.1016/j.scs.2024.105836","url":null,"abstract":"<div><div>Estimating price elasticity of demand for electric vehicle charging contributes to the accurate determination of charging price, thereby improving electric vehicle adoption and energy sustainability. However, few studies have studied the impact of electricity price on electric vehicle charging behavior, especially the demand spillover effect caused by price fluctuations. To fill the gaps, on a citywide dataset of public charging piles in Shenzhen, China, first, correlation coefficients and hypothesis tests are used to determine the relationship between charging demand and price. A learning model incorporating two-layer graph attention, temporal pattern attention, and knowledge-embedded meta-learning is developed for accurate spatio-temporal regression. Impulse response analysis is conducted to unravel several noteworthy phenomena: (1) public charging demand is inelastic to electricity price, with an average elasticity of <span><math><mrow><mo>−</mo><mn>0</mn><mo>.</mo><mn>76</mn></mrow></math></span>, and distinction between different functional areas and times is revealed; (2) negative price impulses marginally change the elasticity, while positive ones make electric vehicle charging users more price sensitive, and (3) the spillover effects caused by price increases and decreases bring 89.48% and 53.88% of its local demand changes to neighbors, respectively, with a scope of 3.45 kilometer. These findings provide policy implications for promoting electric vehicle charging to facilitate renewable energy transition.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322952","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}