Sustainable Cities and Society最新文献_第10页

Housing Passport knowledge graph: Promoting a circular economy in urban residential buildings

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106050

Naomi Keena , Avi Friedman , Mojtaba Parsaee , Madeline Mussio , Ava Klein , Martha Pomasonco-Alvis , Paulo Pinheiro

This paper introduces the Housing Passport knowledge graph (HPKG) as a novel digital standardization framework with a robust semantic data infrastructure to promote a circular economy in the home-building industry. Unstandardized and dispersed housing data impedes a comprehensive assessment of housing stock characteristics and life cycle impacts, hindering the implementation of circular economy principles. The HPKG addresses this challenge by providing (1) a standardized framework for integrated analysis of residential buildings’ affordability and circularity across various spatiotemporal scales and socioeconomic contexts, and (2) a scalable semantic infrastructure using web ontologies that enhances the sharability, searchability, readability, and interoperability of housing-related data. A case study involving five Canadian cities demonstrates the HPKG's effectiveness in semantically linking and standardizing approximately 62 million data points representing over 1.2 million residential buildings. The results show how the HPKG enables a multi-scale integrated assessment of Canadian housing stock, focusing on affordability, energy efficiency, and environmental footprints. As a key conclusion, the HPKG supports informed decisions regarding housing stock by enabling the exploration of circular economy scenarios that prioritize the reuse and recycling of residential building materials. The HPKG empowers stakeholders to develop residential typologies that promote affordability, circularity, and sustainability across diverse socioeconomic contexts.

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引用次数: 0

Enhanced whale optimization algorithms with source proximity indicators: Locating gaseous pollutants with time-varying release rates in weak airflow indoors

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106112

Jiamin Qiu , Hongyi Mao , Yaohua Jiang , Boyuan Zhang , Hao Cai

This study enhances the localization of stationary pollutant sources with time-varying release rates in indoor environments with weak airflow, addressing limitations of previous methods that were only effective for constantly released sources and dependent on concentration gradients. We refined the traditional whale optimization algorithm (WOA), based on mean concentration, by incorporating three novel source proximity indicators (SPIs): Bout, introduced by other researchers, and our newly developed modified proximity indicator (MPI) and source confidence (SC). These enhancements resulted in the development of three advanced methods: WOA_Bout, WOA_MPI, and WOA_SC. Using a custom-built multi-robot system, we conducted a two-stage experimental framework involving 120 trials across 8 scenarios to ensure statistical reliability. Our results demonstrate significant improvements in source localization, with WOA_SC achieving an impressive 90 % success rate, surpassing WOA_Bout at 83 %, WOA_MPI at 77 %, and significantly outperforming the traditional WOA at 60 %. Notably, in complex periodic source scenarios, WOA_SC maintained an 87 % success rate compared to WOA’s 40 %, demonstrating enhanced adaptability to variations in source release rates. This research underscores the effectiveness of integrating SPIs to improve localization strategies in indoor environments characterized by weak airflow.

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引用次数: 0

Towards multi-scale and context-specific heat health risk assessment - A systematic review

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106102

Jiaxing Ye , Feng Yang

Frequent and continuous heatwaves cause significant heat stress on human health. The Heat Health Risk Index (HHRI) is a comprehensive assessment tool that helps identify heat risk levels across various regions. In this study, we systematically review 57 papers using PRISMA method. Our findings reveal limited attention to three key aspects: (1) consistency in indicator classification and selection, (2) context-specific subjects, and (3) adequate consideration of local-scale conditions. To bridge these gaps, we provide detailed explanations of how indicators can be classified based on their functional relationships with heat health. We also summarize methods for assessing indoor thermal environment at the local level and offer guidance for refining context-specific subjects. Additionally, we propose a cyclical framework for sustained HHRI assessments. This study concludes with implications for future HHRI assessments, calling for greater accuracy in technical methods and tailored assessments for specific spatiotemporal scales and contexts. It also encourages interdisciplinary collaboration, community engagement, and attention to compound climate risks.

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引用次数: 0

A vehicle trajectory-based parking location recognition and inference method: Considering both travel action and intention

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106088

Zhihan Su, Xiaochen Liu, Hao Li, Tao Zhang, Xiaohua Liu, Yi Jiang

Vehicle mobility impacts urban infrastructure planning, e.g., surging electric vehicle chargers in building parking lots. Existing methods for recognizing vehicle mobility patterns often ignore either drivers’ travel actions or intentions, thus misunderstanding their travel and parking behaviors. This study proposes a new Global Positioning System (GPS) trajectory-based machine learning method to reveal travel actions and intentions. It adopts DBSCAN clustering to recognize vehicles’ high-frequency access locations and then uses principal components analysis to infer building categories of their intended destinations. A dataset containing 10.37 million trips of 11,590 vehicles in Beijing was used to validate the method. Compared with conventional point-of-interest analysis, our method reveals a significant difference between drivers’ intended destinations and actual parking locations, resulting from the destinations with multiple functions or limited parking spaces. By travel intention analysis, 9,625 vehicles are identified as private vehicles, further distinguished into commuters (52.9 %) and non-commuters (47.1 %). The commuters have smaller travel ranges around their homes and workplaces (

\overline{R_{g}}

=11.0 km and

\overline{R_{g}^{2}}

=4.4 km), whereas the non-commuters have larger travel ranges and more chances of long trips (

\overline{R_{g}}

=12.7 km and

\overline{R_{g}^{2}}

=2.9 km). The proposed method extracts travel intention information from GPS trajectories, contributing to comprehensively understanding vehicle mobility and informing urban planning.

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引用次数: 0

Thermal environment control units for multi-objective urban optimization in territorial spatial planning

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106119

Genyu Xu , Huihui Zhao , Jinglei Li , Chengfang Wang , Yurong Shi , Lingye Yao , Yaoning Yang , Jian Xu , Ruiqu Ma

Urban heat island phenomenon significantly impacts urban thermal conditions, human well-being, and energy consumption, necessitating innovative urban thermal environment management approaches. This study introduces thermal environment control units (TECUs) to optimize urban thermal environments in territorial spatial planning, addressing the gap between current assessment tools and actionable, data-driven planning solutions. By leveraging the urban weather generator (UWG) model, we developed an optimization framework, dividing the central urban area of Guangzhou into 234 TECUs. The methodology involves UWG-based simulations and multi-objective parametric optimization. Analysis revealed significant spatial heterogeneity in thermal performance indicators: urban heat island intensity (0.87 °C-2.42 °C), universal thermal climate index (17.1 °C-27.0 °C), and cooling energy demand (5.32–28.76 W/m²). A case study optimization of a representative TECU demonstrated simultaneous improvements in all indicators through parameter tuning, including building height, density, and green coverage. The TECU approach bridges micro-scale thermal phenomena and macro-scale urban planning, providing a quantitative basis for incorporating thermal performance into the planning system. This methodology offers a pathway toward climate-resilient and thermally comfortable urban environments, addressing the challenges of rapid urbanization and climate change. The findings have significant implications for urban policy, design practices, and future research in creating sustainable and livable cities.

{"title":"Thermal environment control units for multi-objective urban optimization in territorial spatial planning","authors":"Genyu Xu , Huihui Zhao , Jinglei Li , Chengfang Wang , Yurong Shi , Lingye Yao , Yaoning Yang , Jian Xu , Ruiqu Ma","doi":"10.1016/j.scs.2024.106119","DOIUrl":"10.1016/j.scs.2024.106119","url":null,"abstract":"<div><div>Urban heat island phenomenon significantly impacts urban thermal conditions, human well-being, and energy consumption, necessitating innovative urban thermal environment management approaches. This study introduces thermal environment control units (TECUs) to optimize urban thermal environments in territorial spatial planning, addressing the gap between current assessment tools and actionable, data-driven planning solutions. By leveraging the urban weather generator (UWG) model, we developed an optimization framework, dividing the central urban area of Guangzhou into 234 TECUs. The methodology involves UWG-based simulations and multi-objective parametric optimization. Analysis revealed significant spatial heterogeneity in thermal performance indicators: urban heat island intensity (0.87 °C-2.42 °C), universal thermal climate index (17.1 °C-27.0 °C), and cooling energy demand (5.32–28.76 W/m²). A case study optimization of a representative TECU demonstrated simultaneous improvements in all indicators through parameter tuning, including building height, density, and green coverage. The TECU approach bridges micro-scale thermal phenomena and macro-scale urban planning, providing a quantitative basis for incorporating thermal performance into the planning system. This methodology offers a pathway toward climate-resilient and thermally comfortable urban environments, addressing the challenges of rapid urbanization and climate change. The findings have significant implications for urban policy, design practices, and future research in creating sustainable and livable cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"119 ","pages":"Article 106119"},"PeriodicalIF":10.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146019","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}

引用次数: 0

Heat exposure assessment and comfort path recommendations for leisure jogging based on street view imagery and GPS trajectories

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106099

Wei Yang, Guangyu Zhang, Yong Liu, Zihao An

Heat exposure significantly affects outdoor leisure activities and health. However, limited research has investigated the dynamic heat exposure assessments and comfort-based route recommendations for leisure jogging. This study addresses this gap by developing a framework that assesses real-time heat exposure and recommends comfortable routes for joggers based on street view images and GPS tracks. Specifically, we utilize Baidu street view images to dynamically estimate the mean radiant temperature (MRT). Individual joggers’ heat exposure is then assessed using real-time MRT measurements and jogging trajectories. To enhance jogger comfort, we developed a dynamic route recommendation method that combines the jogging comfort index with user preferences using a genetic algorithm, offering personalized jogging routes. Empirical analysis using actual data in Chengdu reveals that (1) MRT and heat risk areas exhibit significant spatiotemporal heterogeneity and clustering. (2) About 33.8 % of jogging tracks overlap with thermal risk regions, with 11 % intersecting high-risk areas, indicating substantial heat exposure for joggers in July. (3) The recommended jogging routes reduce heat exposure risk by 10 % compared to actual routes and a baseline model, demonstrating the approach's effectiveness. (4) The recommended routes, tailored to various needs, outperform the benchmark model, particularly in avoiding dynamic heat risk and providing personalized experiences. These insights can help guide leisure joggers to routes that offer a more comfortable and individualized environment.

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引用次数: 0

High spatio-temporal and continuous monitoring of outdoor thermal comfort in urban areas: A generic and modular sensor network and outreach platform

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.105991

Gregor Feigel , Marvin Plein , Matthias Zeeman , Swen Metzger , Andreas Matzarakis , Dirk Schindler , Andreas Christen

This study presents an operational citywide monitoring network designed to measure meteorological and human biometeorological variables at a high spatio-temporal resolution. The network is based on an in-house developed, generic data logging and monitoring platform, with 13 stations strategically placed at the pedestrian level on public street lights within the urban canopy layer of Freiburg im Breisgau, Germany. Over the first year of deployment (August 2022 to August 2023), the stations continuously collected high-resolution data (30 s intervals) with a minimal data loss rate of 2% for half of the stations which underscores the robustness of the network. The collected data includes Black Globe temperature, used to calculate the Physiologically Equivalent Temperature (PET) and other thermal comfort indices such as Tropical Night. A case study focused on a July 2023 heatwave showed that residential mid- to low-density areas experienced 16.5 to 18.7 h of extreme heat stress, while inner-city sites recorded the highest number of tropical nights (

T_{a, m i n (1 h)}

\geq

20 °C), with 5 to 6 nights, compared to 3 in outer areas. The findings demonstrate significant spatial variability in thermal stress across urban microclimates, particularly during extreme weather events. To address the gap in real-time data dissemination and science communication, we developed the uniWeather outreach platform and app, providing end-users and the public with free access to real-time data, following FAIR principles. This continuous data set is invaluable for urban climate modelers, offering real-time monitoring and insights into localized thermal stress, and can inform urban heat mitigation strategies and adaptation planning for policymakers and city planners.

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引用次数: 0

Potential causal links of long-term PM2.5 components exposure with diabetes incidence and mortality in the United States

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106071

Gonghua Wu , Shenghao Wang , Wenjing Wu , Tarik Benmarhnia , Shao Lin , Kai Zhang , Xiaobo Xue Romeiko , Haogao Gu , Yanji Qu , Jianpeng Xiao , Xinlei Deng , Ziqiang Lin , Zhicheng Du , Wangjian Zhang , Yuantao Hao

There is limited evidence on the relationship of diabetes burden with fine particulate matter (PM_2.5) and its components, which is not conducive to sustainable development in the context of rapid urbanization. To obtain relevant clues in the United States (US), we collected annual county-level diabetes incidence and mortality, concentrations of PM_2.5 and five major components (including elemental carbon, organic carbon, sulfate, nitrate, and ammonium), temperature, and socioeconomic factors during 2008–2017. Through an integrating method of difference-in-differences approach and quantile G-computation, we observed that (i) long-term PM_2.5 components mixture exposure was associated with diabetes mortality, but not incidence, with percent risk increase (IR%) of 3.58 % (95 %CI: 1.84 %, 5.36 %); (ii) among the five components of PM_2.5, sulfate was estimated to have the largest weight (0.519); (iii) the effect of PM_2.5 and its components mixture was higher when the summer mean temperature was 2 or 3° below the 10-year average temperature; (iv) in counties with higher health insurance coverage, nitrate was the most important component (with the greatest weight of 0.829). Our findings suggest that long-term PM_2.5 exposure is associated with increased diabetes mortality, and reducing sulfate and nitrate emission could effectively alleviate the burden of PM_2.5-related diabetes mortality in the US.

{"title":"Potential causal links of long-term PM2.5 components exposure with diabetes incidence and mortality in the United States","authors":"Gonghua Wu , Shenghao Wang , Wenjing Wu , Tarik Benmarhnia , Shao Lin , Kai Zhang , Xiaobo Xue Romeiko , Haogao Gu , Yanji Qu , Jianpeng Xiao , Xinlei Deng , Ziqiang Lin , Zhicheng Du , Wangjian Zhang , Yuantao Hao","doi":"10.1016/j.scs.2024.106071","DOIUrl":"10.1016/j.scs.2024.106071","url":null,"abstract":"<div><div>There is limited evidence on the relationship of diabetes burden with fine particulate matter (PM<sub>2.5</sub>) and its components, which is not conducive to sustainable development in the context of rapid urbanization. To obtain relevant clues in the United States (US), we collected annual county-level diabetes incidence and mortality, concentrations of PM<sub>2.5</sub> and five major components (including elemental carbon, organic carbon, sulfate, nitrate, and ammonium), temperature, and socioeconomic factors during 2008–2017. Through an integrating method of difference-in-differences approach and quantile G-computation, we observed that (i) long-term PM<sub>2.5</sub> components mixture exposure was associated with diabetes mortality, but not incidence, with percent risk increase (IR%) of 3.58 % (95 %CI: 1.84 %, 5.36 %); (ii) among the five components of PM<sub>2.5</sub>, sulfate was estimated to have the largest weight (0.519); (iii) the effect of PM<sub>2.5</sub> and its components mixture was higher when the summer mean temperature was 2 or 3° below the 10-year average temperature; (iv) in counties with higher health insurance coverage, nitrate was the most important component (with the greatest weight of 0.829). Our findings suggest that long-term PM<sub>2.5</sub> exposure is associated with increased diabetes mortality, and reducing sulfate and nitrate emission could effectively alleviate the burden of PM<sub>2.5</sub>-related diabetes mortality in the US.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"119 ","pages":"Article 106071"},"PeriodicalIF":10.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146085","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}

引用次数: 0

A high-performance multi-scale modular-based green design studio platform for building and urban environmental quality and energy simulations

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106078

Lu Li , Shayan Mirzabeigi , Sameeraa Soltanian-Zadeh , Bing Dong , Bess Krietemeyer , Peng Gao , Nina Wilson , Jianshun Zhang

Buildings play a significant role in global energy consumption and carbon emissions, representing 40% of energy use and 36% of CO₂ emissions. The indoor environmental quality (IEQ) within these structures is crucial for ensuring occupant health and comfort. However, achieving a healthy and comfortable indoor environment frequently results in increased energy demands. In response to this deficiency, this study builds a modular-based integrated platform called Green Design Studio (GDS) designed for the preliminary phases of multi-scale building design and urban planning. The GDS platform combines Rhino-Grasshopper and Computational Fluid Dynamics (CFD), embedded with various plugins and parallel computing to facilitate rapid assessments and visualizations of IEQ, outdoor air pollution, and energy efficiency. It adopts a modular strategy for analyzing building and urban components across different scales, including site analysis, space planning, occupancy patterns, building enclosures, and service systems. Measurement data derived from complex building scales are employed to calibrate the platform's effectiveness. This calibration is subsequently scaled up to urban extents and integrated with parallel computing to achieve rapid feedback. Such capabilities underscore the efficacy of the GDS platform, marking a significant advancement in high-performance building design tools that effectively combine ease of use with comprehensive and scalable performance analysis.

{"title":"A high-performance multi-scale modular-based green design studio platform for building and urban environmental quality and energy simulations","authors":"Lu Li , Shayan Mirzabeigi , Sameeraa Soltanian-Zadeh , Bing Dong , Bess Krietemeyer , Peng Gao , Nina Wilson , Jianshun Zhang","doi":"10.1016/j.scs.2024.106078","DOIUrl":"10.1016/j.scs.2024.106078","url":null,"abstract":"<div><div>Buildings play a significant role in global energy consumption and carbon emissions, representing 40% of energy use and 36% of CO<sub>2</sub> emissions. The indoor environmental quality (IEQ) within these structures is crucial for ensuring occupant health and comfort. However, achieving a healthy and comfortable indoor environment frequently results in increased energy demands. In response to this deficiency, this study builds a modular-based integrated platform called Green Design Studio (GDS) designed for the preliminary phases of multi-scale building design and urban planning. The GDS platform combines Rhino-Grasshopper and Computational Fluid Dynamics (CFD), embedded with various plugins and parallel computing to facilitate rapid assessments and visualizations of IEQ, outdoor air pollution, and energy efficiency. It adopts a modular strategy for analyzing building and urban components across different scales, including site analysis, space planning, occupancy patterns, building enclosures, and service systems. Measurement data derived from complex building scales are employed to calibrate the platform's effectiveness. This calibration is subsequently scaled up to urban extents and integrated with parallel computing to achieve rapid feedback. Such capabilities underscore the efficacy of the GDS platform, marking a significant advancement in high-performance building design tools that effectively combine ease of use with comprehensive and scalable performance analysis.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"119 ","pages":"Article 106078"},"PeriodicalIF":10.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144911","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}

引用次数: 0

Quantitative analysis of differences in cooling effect and efficiency after changes in Green Infrastructure Types (GIT)

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-01 DOI: 10.1016/j.scs.2024.106101

Haojie Cheng, Yuha Han, Chan Park

Green Infrastructure (GI) is heavily used in urban planning as it can provide better thermal comfort conditions in urban spaces and can mitigate the morbidity and mortality associated with urban heat waves. Later, the concept of Green Infrastructure Typology (GIT) was proposed for urban heat wave assessment. However, we are not aware of studies on the wider benefits of GITs, such as the cooling effect and the cooling efficiency considering water requirement. To fill the gap in prior studies, this study quantitatively analyzed the cooling effect, transpiration rate, and cooling efficiency after GIT type conversion using ENVI-met. The results show that even if the green space location and ratio are the same, there are large differences in cooling effect, transpiration rate, and cooling efficiency between GITs. For PET, the cooling effects of the low, medium, and high vegetation GITs with 60 % green space ratio are 1.2 °C, 1.3 °C, and 2.0 °C, respectively. In the high vegetation GITs with 10 % shrubs and 30 % trees (PV6,8,10), the continued addition of 60 % shrubs and 50 % trees (PV11) resulted in an average reduction of 0.6 °C in Ta and 1.8 °C in PET. The low vegetation GITs have the lowest cooling effect but the highest cooling efficiency.

{"title":"Quantitative analysis of differences in cooling effect and efficiency after changes in Green Infrastructure Types (GIT)","authors":"Haojie Cheng, Yuha Han, Chan Park","doi":"10.1016/j.scs.2024.106101","DOIUrl":"10.1016/j.scs.2024.106101","url":null,"abstract":"<div><div>Green Infrastructure (GI) is heavily used in urban planning as it can provide better thermal comfort conditions in urban spaces and can mitigate the morbidity and mortality associated with urban heat waves. Later, the concept of Green Infrastructure Typology (GIT) was proposed for urban heat wave assessment. However, we are not aware of studies on the wider benefits of GITs, such as the cooling effect and the cooling efficiency considering water requirement. To fill the gap in prior studies, this study quantitatively analyzed the cooling effect, transpiration rate, and cooling efficiency after GIT type conversion using ENVI-met. The results show that even if the green space location and ratio are the same, there are large differences in cooling effect, transpiration rate, and cooling efficiency between GITs. For PET, the cooling effects of the low, medium, and high vegetation GITs with 60 % green space ratio are 1.2 °C, 1.3 °C, and 2.0 °C, respectively. In the high vegetation GITs with 10 % shrubs and 30 % trees (PV6,8,10), the continued addition of 60 % shrubs and 50 % trees (PV11) resulted in an average reduction of 0.6 °C in Ta and 1.8 °C in PET. The low vegetation GITs have the lowest cooling effect but the highest cooling efficiency.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"119 ","pages":"Article 106101"},"PeriodicalIF":10.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144914","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}

引用次数: 0