Sustainable Cities and Society最新文献_第4页

Future projections of the rainfall intensity-duration-frequency curves in Beijing-Tianjin-Hebei urban agglomeration based on NEX-GDDP CMIP6 simulations

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

Sustainable Cities and Society

Pub Date : 2025-02-17 DOI: 10.1016/j.scs.2025.106227

Lidong Song , Lei Yan , Fuxin Chai , Fan Lu , Jiaqi Zhai , Qinghua Luan , Qiumei Ma , Cong Jiang , Mengjie Zhang , Jian Sun

Intensity-duration-frequency (IDF) curves play a crucial role in urban infrastructure planning. Conventional IDF curves reply on the stationary assumption, which assumes the statistical characteristics of the future extreme rainfall will be identical to historical observations. Global warming, however, has changed the statistical behaviors of extreme rainfall, making conventional IDF curves inadequate for accurately representing future conditions. This study focuses on updating the IDF curves for Beijing-Tianjin-Hebei (BTH) region for future periods (2025–2049, 2050–2074, 2075–2099), using the ensemble simulations of 10 GCMs from NEX-GDDP CMIP6 for different scenarios. Gridded NEX-GDDP CMIP6 simulations were firstly spatially downscaled to station-scale, then the Quantile-Quantile-Mapping method was employed for future temporal downscaling and development of the IDF curves for the 30 BTH stations. In the baseline period, downscaled GCM-based quantiles align well with observations. In future periods, extreme precipitation in BTH region is projected to intensify, with updated IDF curves surpassing stationary ones. Across all scenarios, extreme precipitation intensity increases significantly, particularly for short-duration events with shorter return periods. Notably, under the SSP585 scenario, heavy precipitation in southeastern BTH may rise by over 60 % by century's end, highlighting the urgent need to update IDF curves for safer and sustainable development under a changing climate.

{"title":"Future projections of the rainfall intensity-duration-frequency curves in Beijing-Tianjin-Hebei urban agglomeration based on NEX-GDDP CMIP6 simulations","authors":"Lidong Song , Lei Yan , Fuxin Chai , Fan Lu , Jiaqi Zhai , Qinghua Luan , Qiumei Ma , Cong Jiang , Mengjie Zhang , Jian Sun","doi":"10.1016/j.scs.2025.106227","DOIUrl":"10.1016/j.scs.2025.106227","url":null,"abstract":"<div><div>Intensity-duration-frequency (IDF) curves play a crucial role in urban infrastructure planning. Conventional IDF curves reply on the stationary assumption, which assumes the statistical characteristics of the future extreme rainfall will be identical to historical observations. Global warming, however, has changed the statistical behaviors of extreme rainfall, making conventional IDF curves inadequate for accurately representing future conditions. This study focuses on updating the IDF curves for Beijing-Tianjin-Hebei (BTH) region for future periods (2025–2049, 2050–2074, 2075–2099), using the ensemble simulations of 10 GCMs from NEX-GDDP CMIP6 for different scenarios. Gridded NEX-GDDP CMIP6 simulations were firstly spatially downscaled to station-scale, then the Quantile-Quantile-Mapping method was employed for future temporal downscaling and development of the IDF curves for the 30 BTH stations. In the baseline period, downscaled GCM-based quantiles align well with observations. In future periods, extreme precipitation in BTH region is projected to intensify, with updated IDF curves surpassing stationary ones. Across all scenarios, extreme precipitation intensity increases significantly, particularly for short-duration events with shorter return periods. Notably, under the SSP585 scenario, heavy precipitation in southeastern BTH may rise by over 60 % by century's end, highlighting the urgent need to update IDF curves for safer and sustainable development under a changing climate.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106227"},"PeriodicalIF":10.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454076","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

Online coupling of an urban canopy model with trees and a mesoscale atmospheric model to assess the cooling effects of trees under different urban configurations and varying soil dryness

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

Sustainable Cities and Society

Pub Date : 2025-02-17 DOI: 10.1016/j.scs.2025.106226

Young-Hee Ryu , Seokchan Kim , Moon-Soo Park , Jaehoon Lee , Jihye Kim , Minsoo Kang

Urban trees play a crucial role in mitigating heat stress. Accurately representing the physical processes associated with trees in mesoscale models is essential for reliable simulations of urban environments across city to regional scales. We implement an urban canopy model incorporating trees into the Weather Research and Forecasting model to develop a fully coupled modeling system. The model is evaluated against pedestrian-level temperature, flux tower, and soil moisture measurements from the Seoul metropolitan area. Two heatwave episodes are simulated, and the model demonstrates reasonable performance in reproducing intra-city temperature differences, canyon air temperatures, and sensible and latent heat fluxes. A stronger cooling effect of trees is observed in commercial/industrial areas, with a daily mean temperature reduction of 1.1 °C compared to 0.64 °C in residential areas. The cooling effect is pronounced at night in narrower and deeper canyons, attributed to the greater longwave cooling of leaves. As soil moisture decreases, the cooling effects of trees diminish; however, significant cooling persists under very dry conditions due to tree shading, which is more prominent in commercial/industrial areas than in residential areas. Our findings indicate that comprehensive studies encompassing various tree and urban configurations are necessary to optimize the role of trees in sustainable cities.

{"title":"Online coupling of an urban canopy model with trees and a mesoscale atmospheric model to assess the cooling effects of trees under different urban configurations and varying soil dryness","authors":"Young-Hee Ryu , Seokchan Kim , Moon-Soo Park , Jaehoon Lee , Jihye Kim , Minsoo Kang","doi":"10.1016/j.scs.2025.106226","DOIUrl":"10.1016/j.scs.2025.106226","url":null,"abstract":"<div><div>Urban trees play a crucial role in mitigating heat stress. Accurately representing the physical processes associated with trees in mesoscale models is essential for reliable simulations of urban environments across city to regional scales. We implement an urban canopy model incorporating trees into the Weather Research and Forecasting model to develop a fully coupled modeling system. The model is evaluated against pedestrian-level temperature, flux tower, and soil moisture measurements from the Seoul metropolitan area. Two heatwave episodes are simulated, and the model demonstrates reasonable performance in reproducing intra-city temperature differences, canyon air temperatures, and sensible and latent heat fluxes. A stronger cooling effect of trees is observed in commercial/industrial areas, with a daily mean temperature reduction of 1.1 °C compared to 0.64 °C in residential areas. The cooling effect is pronounced at night in narrower and deeper canyons, attributed to the greater longwave cooling of leaves. As soil moisture decreases, the cooling effects of trees diminish; however, significant cooling persists under very dry conditions due to tree shading, which is more prominent in commercial/industrial areas than in residential areas. Our findings indicate that comprehensive studies encompassing various tree and urban configurations are necessary to optimize the role of trees in sustainable cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106226"},"PeriodicalIF":10.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454078","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}

引用次数: 0

Digital infrastructure construction and urban industrial chain resilience: Evidence from the “Broadband China” strategy

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

Sustainable Cities and Society

Pub Date : 2025-02-17 DOI: 10.1016/j.scs.2025.106228

Yi Chen , Cailou Jiang , Lin Peng , Shuang Zhao , Cheng Chen

Effective governance of industrial chain resilience (ICR) is crucial for urban sustainability, and the development of digital infrastructure provides actionable pathways to achieve this goal. However, limited attention has been paid to the influence of digitalization efforts on ICR. Utilizing panel data from 271 Chinese cities spanning 2009 to 2021, this study adopts the “Broadband China” strategy as a proxy and employs a staggered Difference-in-Differences model combined with machine learning algorithms to evaluate the impacts of digital infrastructure construction (DIC) on urban ICR. The results show that: (1) DIC significantly drives urban ICR. This conclusion shows strong reliability, as it is confirmed by extensive robustness checks. (2) Heterogeneity analysis indicates stronger effects of DIC on eastern cities, non-resource-based cities, and cities with high industrial agglomeration. The positive impact exhibits sustained growth in the eastern region yet gradual attenuation in the central region. (3) Mechanism analysis reveals that DIC improves ICR by bridging the digital divide, fostering digital human capital and elevating innovation quality. These findings provide critical insights for formulating policies to strengthen digital infrastructure development and enhance urban ICR.

{"title":"Digital infrastructure construction and urban industrial chain resilience: Evidence from the “Broadband China” strategy","authors":"Yi Chen , Cailou Jiang , Lin Peng , Shuang Zhao , Cheng Chen","doi":"10.1016/j.scs.2025.106228","DOIUrl":"10.1016/j.scs.2025.106228","url":null,"abstract":"<div><div>Effective governance of industrial chain resilience (ICR) is crucial for urban sustainability, and the development of digital infrastructure provides actionable pathways to achieve this goal. However, limited attention has been paid to the influence of digitalization efforts on ICR. Utilizing panel data from 271 Chinese cities spanning 2009 to 2021, this study adopts the “Broadband China” strategy as a proxy and employs a staggered Difference-in-Differences model combined with machine learning algorithms to evaluate the impacts of digital infrastructure construction (DIC) on urban ICR. The results show that: (1) DIC significantly drives urban ICR. This conclusion shows strong reliability, as it is confirmed by extensive robustness checks. (2) Heterogeneity analysis indicates stronger effects of DIC on eastern cities, non-resource-based cities, and cities with high industrial agglomeration. The positive impact exhibits sustained growth in the eastern region yet gradual attenuation in the central region. (3) Mechanism analysis reveals that DIC improves ICR by bridging the digital divide, fostering digital human capital and elevating innovation quality. These findings provide critical insights for formulating policies to strengthen digital infrastructure development and enhance urban ICR.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106228"},"PeriodicalIF":10.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471316","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

Did the energy transition effectively alleviate multidimensional stresses of the social system? An evidence from a quasi-natural experiment in Chinese cities

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.2025.106225

Zengchuan Wang , Yanling Xi , Li Li , Yalin Lei , Sanmang Wu , Yanfang Cui , Jiabin Chen

Under the urgent demand for global climate governance, energy transition, as a core strategy, had garnered significant attention for its societal impacts. Existing literature primarily focused on the economic and environmental ramifications, while inadequate exploration of its societal implications and incomplete mechanism testing persisted. Based on panel data of 256 Chinese cities from 2010 to 2021, the propensity score matching-difference in difference method was employed to comprehensively examine the multifaceted effects of energy transition on employment, public health and urban-rural income disparities in social systems. The moderation effect model was utilized to verify the moderating roles of resource endowments and technological innovation. Lastly, the spatial spillover effects and regional heterogeneity of impacts were analyzed. The findings indicate that: (1) Energy transition significantly contributed to optimizing employment structures, improving public health, and narrowing urban-rural income gaps. (2) Policy effects were modulated by resource endowments and technological innovation. (3) The influence of energy transition exhibited varying degrees of spatial spillovers across cities with different geographical distances. Additionally, the impacts of energy transition demonstrated regional heterogeneity. It offered a new perspective for understanding the societal effects of energy transition. Recommendations were proposed based on the conclusions.

{"title":"Did the energy transition effectively alleviate multidimensional stresses of the social system? An evidence from a quasi-natural experiment in Chinese cities","authors":"Zengchuan Wang , Yanling Xi , Li Li , Yalin Lei , Sanmang Wu , Yanfang Cui , Jiabin Chen","doi":"10.1016/j.scs.2025.106225","DOIUrl":"10.1016/j.scs.2025.106225","url":null,"abstract":"<div><div>Under the urgent demand for global climate governance, energy transition, as a core strategy, had garnered significant attention for its societal impacts. Existing literature primarily focused on the economic and environmental ramifications, while inadequate exploration of its societal implications and incomplete mechanism testing persisted. Based on panel data of 256 Chinese cities from 2010 to 2021, the propensity score matching-difference in difference method was employed to comprehensively examine the multifaceted effects of energy transition on employment, public health and urban-rural income disparities in social systems. The moderation effect model was utilized to verify the moderating roles of resource endowments and technological innovation. Lastly, the spatial spillover effects and regional heterogeneity of impacts were analyzed. The findings indicate that: (1) Energy transition significantly contributed to optimizing employment structures, improving public health, and narrowing urban-rural income gaps. (2) Policy effects were modulated by resource endowments and technological innovation. (3) The influence of energy transition exhibited varying degrees of spatial spillovers across cities with different geographical distances. Additionally, the impacts of energy transition demonstrated regional heterogeneity. It offered a new perspective for understanding the societal effects of energy transition. Recommendations were proposed based on the conclusions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106225"},"PeriodicalIF":10.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454079","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

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.

{"title":"Large-scale building-level electricity consumption estimation for multiple building types: A case study from Dongguan, China","authors":"Geng Liu , Jinpei Ou , Yue Zheng , Yaotong Cai , Xiaoping Liu , Honghui Zhang","doi":"10.1016/j.scs.2025.106224","DOIUrl":"10.1016/j.scs.2025.106224","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"121 ","pages":"Article 106224"},"PeriodicalIF":10.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489005","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

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.

{"title":"Historical changes and driving factors of food-water-energy footprint consumption: A Case study of the Beijing-Tianjin-Hebei city agglomeration","authors":"Ke Yang, Qi Han, Dujuan Yang, Bauke de Vries","doi":"10.1016/j.scs.2025.106222","DOIUrl":"10.1016/j.scs.2025.106222","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106222"},"PeriodicalIF":10.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548612","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}

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