Sustainable Cities and Society最新文献_第4页

Development of Machine Learning-Aided Rapid CFD Prediction for Optimal Urban Wind Environment Design

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

Sustainable Cities and Society

Pub Date : 2025-02-16 DOI: 10.1016/j.scs.2025.106208

Aiymzhan Baitureyeva , Tong Yang , Hua Sheng Wang

This paper presents a Machine Learning (ML) model based on Computational Fluid Dynamics (CFD), developed to quickly and accurately predict the impact of buildings on the urban wind environment. While CFD simulations are effective for wind studies, such as analyzing wind loads, pedestrian comfort, and pollution dispersion, they require significant computational resources and time. Recently, Machine Learning has demonstrated strong potential in providing accurate and immediate predictions by learning from datasets. By training on CFD-generated data, the ML model can quickly produce accurate and physically consistent results, addressing the limitations of CFD methods. The Reynolds-Averaged Navier-Stokes (RANS) turbulence model was chosen for CFD simulations, which were validated against experimental data, with mesh sensitivity analyzed at a wind speed of 3 m/s. A dataset of 300 cases, involving 100 hypothetical buildings and three wind speeds (3, 4, and 5 m/s), was generated to train the ML model. A multi-output regression model was proposed to effectively predict key parameters—wind velocity, turbulence intensity, and CO₂ mass fraction—in the selected urban domain. The Random Forest algorithm, which best represented the CFD results, was selected for model development. The ML model demonstrated high efficiency on new data, achieving 88-96% accuracy. This work offers a fast and precise prediction tool, valuable for urban design and related applications.

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引用次数: 0

Large-scale building-level electricity consumption estimation for multiple building types: A case study from Dongguan, China

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

Sustainable Cities and Society

Pub Date : 2025-02-15 DOI: 10.1016/j.scs.2025.106224

Geng Liu , Jinpei Ou , Yue Zheng , Yaotong Cai , Xiaoping Liu , Honghui Zhang

Accurate estimation of building electricity consumption (BEC) is essential for sustainable urban development and effective energy management. Existing methods, which rely on using physical models or small-scale surveys, often lack the accuracy and reliability required to provide meaningful insights at the city-scale building level. To address this gap, we introduce a data-driven framework combining electricity consumption data from meters with building footprint data. This framework, implemented in the megacity of Dongguan, China, utilizes five advanced machine learning algorithms to estimate BEC for residential, commercial, and industrial buildings. Our results show that the random forest (RF) model outperforms other algorithms, with building volume identified as the primary predictor. Spatially, residential BEC decreases from urban centers to suburban and rural areas, while commercial BEC exhibits polarization, with high concentrations in central urban areas and key commercial towns. Although industrial BEC is widespread, it shows localized high-consumption clusters. At the community level, BEC patterns exhibit strong spatial autocorrelation, with distinct hot spots and cold spots observed for residential, commercial, and industrial BEC, despite significant variations in their spatial distributions. Both total BEC and BEC intensity exhibit log-normal distribution characteristics across building types. In terms of median BEC intensity, commercial and industrial buildings consume 3.2 times and 5 times more electricity per unit area, respectively, compared to residential buildings. This study advances the accurate estimation of BEC at the building level for multiple building types within a Chinese megacity, providing valuable insights for sustainable urban planning and energy efficiency policies.

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引用次数: 0

Historical changes and driving factors of food-water-energy footprint consumption: A Case study of the Beijing-Tianjin-Hebei city agglomeration

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

Sustainable Cities and Society

Pub Date : 2025-02-14 DOI: 10.1016/j.scs.2025.106222

Ke Yang, Qi Han, Dujuan Yang, Bauke de Vries

Food, water, and energy (FWE) are critical for the development of urban agglomerations, but research on FWE footprints at this scale remains limited. This study addresses this gap by analyzing the FWE footprints of the Beijing-Tianjin-Hebei metropolitan region in 2012 and 2017 at the city level. Using the multi-regional Input-Output model (MRIO) and Structural Decomposition Analysis (SDA), the study identifies the key factors driving changes in these footprints across five dimensions. The analysis evaluates both actual and virtual FWE consumption, focusing on utilization coefficients and inter-industry connections. Key findings include: (1) Agriculture in Chengde and Zhangjiakou plays a vital role in the FWE nexus and requires more attention. (2) Beijing, Tianjin, and Langfang are net inflow areas for FWE, while Tangshan and Chengde act as net outflow zones. (3) In 2017, agriculture was the largest contributor to virtual water outflows, followed by services, manufacturing, construction, energy, and mining. (4) In 2017, the virtual energy footprint was driven mainly by manufacturing and services, with strong links to the construction sector. (5) From 2012 to 2017, population size has the greatest effect on FWE footprints, while demand structure positively influences FWE growth in the construction industry. The study concludes with targeted recommendations for industrial strategies at both regional and city levels to enhance resource efficiency and promote sustainable development within the metropolitan agglomeration.

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引用次数: 0

The impact of innovation-driven industrial clusters on urban carbon emission efficiency: Empirical evidence from China

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

Sustainable Cities and Society

Pub Date : 2025-02-14 DOI: 10.1016/j.scs.2025.106220

Hongyu Lu , Zhuang Yao , Zhao Cheng , Anna Xue

Innovative industrial clusters can leverage economies of scale and synergies, thereby providing new impetus for enhancing carbon emission efficiency. This study, based on panel data from Chinese cities between 2010 and 2021, views the pilot policy of innovative industrial clusters as a quasi-natural experiment, using a difference-in-differences model to identify the causal relationship between innovative industrial clusters and carbon emission efficiency. The findings indicate that: (1) Innovative industrial clusters can significantly improve carbon emission efficiency, and this conclusion holds under multiple robustness checks; (2) Innovative industrial clusters enhance carbon emission efficiency by upgrading industrial structures, accelerating technological progress, and improving transportation efficiency; (3) The positive impact of innovative industrial clusters on carbon emission efficiency is more pronounced in cities with non-resource-based economies, higher administrative levels, greater marketization, relatively well-developed digital infrastructure, and stronger intellectual property protection; (4) Innovative industrial clusters exert a positive spatial spillover effect on carbon emission efficiency. This study is the first to examine the role of innovative industrial cluster policies in improving carbon emission efficiency, providing valuable insights and experiences for advancing sustainable development.

创新型产业集群可以发挥规模经济和协同效应，从而为提高碳排放效率提供新的动力。本研究基于 2010 年至 2021 年中国城市的面板数据，将创新型产业集群试点政策视为准自然实验，采用差分模型识别创新型产业集群与碳排放效率之间的因果关系。研究结果表明(1）创新型产业集群能够显著提高碳排放效率，这一结论在多重稳健性检验下成立；（2）创新型产业集群通过产业结构升级、加快技术进步和提高运输效率来提高碳排放效率；（3）创新型产业集群对碳排放效率的积极影响在非资源型经济、行政级别较高、市场化程度较高、数字基础设施相对发达、知识产权保护较强的城市更为明显；（4）创新型产业集群对碳排放效率具有积极的空间溢出效应。本研究首次考察了创新型产业集群政策在提高碳排放效率方面的作用，为推进可持续发展提供了宝贵的见解和经验。

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引用次数: 0

Parametric analysis of planting strategies and environmental factors for the thermal and aerodynamic effects of indirect green façades

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

Sustainable Cities and Society

Pub Date : 2025-02-14 DOI: 10.1016/j.scs.2025.106213

Meifang Su , Pengyu Jie , Sijie Zhu , Nannan Dong , Francesco Causone , John Grunewald , Xiaoping Xie , Xing Shi

The cavity microenvironment of indirect green façades (IGFs) influences building thermal performance. However, precisely simulating this environment remains a challenge. To address this, we employed OpenFOAM for computational fluid dynamics simulations, vegetation was modeled as a porous medium. A leaf energy balance model was used to solve leaf temperature and heat fluxes. We investigated the effect of leaf area density (LAD), cavity thickness, and solar radiation direction on the thermal performance of IGFs. The daily thermal effects of IGFs during summer in Shanghai, China were also explored. The results showed that (1) The IGFs on the windward side (0.07 m/s) and leeward side (0.14 m/s) achieved the highest wind speed reductions with the highest LAD and largest cavity thickness. (2) An IGF with high LAD and small cavity thickness effectively cooled the wall surface. The maximum wall surface cooling (16.93°C) was observed when the leeward side received the majority of solar radiation. (3) IGFs installed on the west side demonstrated significant cooling, with maximum temperature reductions of 3.21°C and 16.41°C within the cavity and on wall surfaces, respectively, in Shanghai, China. This research developed a new simulation framework for IGFs and provided insights for optimizing IGF design.

间接绿色外墙（IGF）的空腔微环境会影响建筑物的热性能。然而，精确模拟这种环境仍然是一项挑战。为了解决这个问题，我们采用 OpenFOAM 进行计算流体动力学模拟，将植被作为多孔介质建模。叶片能量平衡模型用于求解叶片温度和热通量。我们研究了叶面积密度（LAD）、空腔厚度和太阳辐射方向对 IGF 热性能的影响。我们还探讨了 IGF 在中国上海夏季的日热效应。结果表明：（1）位于迎风面（0.07 米/秒）和背风面（0.14 米/秒）的中空玻璃以最高的 LAD 和最大的空腔厚度实现了最高的风速降低。(2) LAD 高、空腔厚度小的 IGF 能有效冷却墙面。当背风面接受大部分太阳辐射时，墙面降温幅度最大（16.93°C）。(3) 在中国上海，安装在西侧的 IGF 制冷效果显著，空腔内和墙面的最高温度分别降低了 3.21°C 和 16.41°C。这项研究为中空玻璃隔热箱开发了一个新的模拟框架，并为优化中空玻璃隔热箱的设计提供了启示。

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引用次数: 0

Electricity self-sufficiency of off-grid mobile homes as temporary housing: A feasibility study in Japan

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

Sustainable Cities and Society

Pub Date : 2025-02-14 DOI: 10.1016/j.scs.2025.106221

Sihwan Lee , Risa Ito , Hideyo Harada

The increasing frequency of natural disasters caused by climate change, such as typhoons, torrential rain, and earthquakes, highlights the urgent need for effective and sustainable temporary housing. This study explores the potential for off-grid power independence in mobile homes for disaster recovery in Japan, a region vulnerable to seismic and climatic events. Through measurements and numerical analyses, we assessed the electricity self-sufficiency of mobile homes with photovoltaic (PV) panels and storage batteries across various regions, including Hokkaido and the Nansei Islands. Results indicate significant electricity self-sufficiency during cooling periods, especially in areas with lower cooling loads. Mobile homes equipped with eight PV panels (2400 Wp) can produce over 3000 kWh of electricity annually, surpassing heating and cooling energy needs in all studied areas. However, achieving self-sufficiency during heating periods remains difficult due to higher energy demand during non-generating hours. Expanding PV panels and battery capacity can raise the electricity self-sufficiency rate to over 80 % in non-cold regions but offers limited improvements in colder areas. This study highlights the potential of off-grid mobile homes as resilient, energy-efficient post-disaster solutions and points to the need for further optimization of insulation and design guidelines for diverse climates.

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引用次数: 0

Quantification of the impact of street design features on restorative quality in urban settings

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

Sustainable Cities and Society

Pub Date : 2025-02-13 DOI: 10.1016/j.scs.2025.106216

Keundeok Park, Semiha Ergan

This paper investigates the impact of design of urban spaces on restorativeness. It aims to identify the urban street design features that are highly effective in shaping human restorativeness and quantify their impact on restorativeness of urban dwellers. The study employs a suite of data acquisition methods, including crowdsourcing, computer vision (CV), and Geographic Information Systems (GIS), to gather data on people's perceptions of urban environments that feature different configurations of urban street elements. Machine learning was used to identify the influential urban street design elements on human restorativeness and quantify impacts. Our findings reveal that while the amount of greenery generally enhances restorativeness along with sky visibility, an excess beyond a certain threshold diminishes its positive effects- hence indicating a strong non-linear relationship between sky visibility and greenery density in relation to restorativeness impact of such urban spaces. This suggests that a balance of greenery is essential for promoting restorativeness in urban environments. Results also indicate that height-of-buildings, irregular-building-height, building-density, crowdedness, and retail-stores are negatively associated with restorativeness while around urban spaces. Practitioners can benefit from these findings as this study provides one of the comprehensive computational evaluations of urban street design elements towards people's restorativeness in urban settings.

本文研究了城市空间设计对恢复性的影响。其目的是找出在塑造人的恢复性方面非常有效的城市街道设计特征，并量化它们对城市居民恢复性的影响。研究采用了一系列数据采集方法，包括众包、计算机视觉（CV）和地理信息系统（GIS），收集人们对具有不同城市街道元素配置的城市环境的感知数据。机器学习被用来识别对人类恢复能力有影响的城市街道设计元素，并量化其影响。我们的研究结果表明，虽然绿化的数量通常会随着天空能见度的增加而提高人的恢复能力，但超过一定限度后，绿化的积极作用就会减弱--这表明天空能见度和绿化密度对此类城市空间恢复能力的影响之间存在很强的非线性关系。这表明，绿化的平衡对于促进城市环境的恢复性至关重要。研究结果还表明，建筑物高度、不规则建筑物高度、建筑物密度、拥挤程度和零售商店与城市空间周围的恢复性呈负相关。这项研究对城市街道设计元素在城市环境中影响人们恢复力的情况进行了全面的计算评估，实践者可以从这些发现中获益。

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引用次数: 0

Contrasting urban heat disparities across income levels in Seoul and London

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

Sustainable Cities and Society

Pub Date : 2025-02-12 DOI: 10.1016/j.scs.2025.106215

Sarath Raj , Lee Yerim , Geun Young Yun , Mattheos Santamouris

Urban areas face rising heat exposure due to urbanization and climate change, with over half the world's population in cities, projected to reach nearly 70 % by 2050. Marginalized communities often endure higher temperatures, reflecting broader environmental inequalities. Despite this, comprehensive analyses across diverse urban contexts are limited. This study examines Seoul and London using high-resolution satellite data from ECOSTRESS and Landsat (2013–2023) to explore urban heat distribution and socioeconomic status. Our findings show lower-income neighborhoods in London consistently experience higher heat island intensities, while Seoul does not exhibit this pattern. Higher-income areas in London benefit from more green spaces and having more detached houses leading to reduced heat intensities, whereas equitable greenspace distribution and denser housing patterns in rich neighborhoods in Seoul results in less pronounced disparities. Seasonal variations highlight heat exposure disparities in warmer months. This study contributes to strategies for reducing heat exposure and promoting equitable urban environments.

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引用次数: 0

UBEM-SER: Role of sufficiency, efficiency and renewable in the decarbonization of commercial building stock at city scale

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

Sustainable Cities and Society

Pub Date : 2025-02-12 DOI: 10.1016/j.scs.2025.106214

Usama Perwez , Muhammad Haseeb Rasool , Imran Aziz , Usman Zia

The transitional pathway for decarbonization of commercial building stock requires adequately managing the degree of complexity by providing a coordinated effort with the implementation of non-technological mitigation strategies, energy conservation measures and the use of renewable distributed energy resources (DERs). However, there exist significant challenges in facilitating coordination among different methodological characterizations of building stock interventions. These challenges hinder the ability to reveal the quantitative description of sufficiency, efficiency and renewable DERs in achieving a carbon-neutral building stock. To address this challenge, this paper presents a multi-model framework to integrate an urban building energy model (UBEM), that supports the consideration of energy conservation and socio-behavioural effects, with a physical-based approach of BIPV potential estimation to estimate energy demand and supply patterns of commercial building stock at the city scale. A scenario-based simulation procedure is constructed to explore the degree of variability of sufficiency and efficiency dimensions in decarbonization pathways as complementary levers rather than contrasting ones. The analysis of results reveals that: sufficiency provides an additional gain of 15 % to reduce the annual median value of energy use intensity (EUI); 60 % reduction in energy demand is observed with larger energy savings originating from efficiency measures accounting for 46 % reduction potential; and sufficiency supports wider decarbonization with reduction of peak load by 18 % and improvement of self-sufficiency (SS) by 20 % with longer duration of negative net load. Overall, this study provides a context-based perspective of energy conservation and socio-behavioural effects to energy modelers and policymakers for achieving broader decarbonization of commercial building stock at the city scale.

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引用次数: 0

Adapting wind shear coefficients to urban morphology: Unlocking urban wind energy potential

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

Sustainable Cities and Society

Pub Date : 2025-02-12 DOI: 10.1016/j.scs.2025.106217

Shuai Kong , Yuliang Xiao , Junliang Cao , Zhitao Han

The varied roughness of building clusters makes the urban wind rather complex. The current exponential wind profile may not accurately capture the spatial variation of urban wind. This study aims to develop urban morphology-based exponents for the exponential wind profile model to improve the assessment of wind energy in urban areas. The Weather Research and Forecasting (WRF) model, combined with Local Climate Zones (LCZ), was used to simulate wind fields, validated with field data. The study then analyzed wind field distribution urban morphology, introducing wind shear coefficients adapted for various urban morphologies. The results were compared with the existing wind shear coefficients by assessing the wind power potential. The results indicated that wind shear coefficients increase from urban outskirts to centers, with minimal seasonal variation. The annual average wind shear coefficient peaked at 0.50 in Harbin and 0.49 in Guangzhou. Building density, height, and plot ratio (PR), significantly impacts wind fields. PR showed the strongest correlation with the wind shear coefficient as the determining factor. Wind shear coefficients for low (0.0–1.0), medium (1.0–2.0), and high PR zones (2.0+) were 0.33, 0.38, and 0.41 in Harbin, and 0.30,0.35, and 0.40 in Guangzhou, respectively, providing more accurate estimate of urban wind speed.

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引用次数: 0