Sustainable Cities and Society最新文献_第10页

Novel spatiotemporal nonlinear regression approach for unveiling the impact of urban spatial morphology on carbon emissions 新型时空非线性回归方法揭示城市空间形态对碳排放的影响

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-14 DOI: 10.1016/j.scs.2025.106381

Lei Li , Shujie Sun , Leyuan Zhong , Ji Han , Xuepeng Qian

{"title":"Novel spatiotemporal nonlinear regression approach for unveiling the impact of urban spatial morphology on carbon emissions","authors":"Lei Li , Shujie Sun , Leyuan Zhong , Ji Han , Xuepeng Qian","doi":"10.1016/j.scs.2025.106381","DOIUrl":"10.1016/j.scs.2025.106381","url":null,"abstract":"<div><div>Understanding the impacts of urban spatial morphology on carbon emissions is crucial for promoting sustainable urban development. However, traditional models have limitations when analyzing complex spatiotemporal heterogeneity and nonlinear relationships. Therefore, we proposed an Integrated Spatiotemporal Nonlinear Regression (ISTNR) model to explore the complex relationship between urban spatial morphology and carbon emissions. This model combines Geographically and Temporally Weighted Regression (GTWR) to capture spatial and temporal dependencies, the Random Forest (RF) model to address nonlinear relationships, and the game theory-based Shapley Additive Explanations (SHAP) tool to enhance the interpretability of the results. The data encompassed urban morphology and carbon emissions across specific regions and periods, and the robustness and adaptability of the model were validated in various urban morphology environments. The ISTNR model demonstrated significant superiority over traditional regression models, achieving an R² of 0.924, a substantially lower MSE (18.06×10<sup>6</sup>), and higher predictive accuracy and stability in complex urban environments. Additionally, bootstrap uncertainty analysis indicated that the model's prediction intervals were relatively narrow, suggesting low prediction uncertainty and high stability. The SHAP analysis quantified the specific contributions of various urban morphological features to carbon emissions, further revealing their mechanisms impacting emission predictions. This study presents an effective quantitative tool for urban planning and carbon emissions control, offering practical support for future urban policymaking.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106381"},"PeriodicalIF":10.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multidomain approach to neighbourhood typology for urban environmental studies 城市环境研究中社区类型学的多领域研究方法

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-13 DOI: 10.1016/j.scs.2025.106378

Kun Lyu , Dusan Licina , Jan Wienold , Hanieh Khodaei Tehrani , Dolaana Khovalyg

{"title":"A multidomain approach to neighbourhood typology for urban environmental studies","authors":"Kun Lyu , Dusan Licina , Jan Wienold , Hanieh Khodaei Tehrani , Dolaana Khovalyg","doi":"10.1016/j.scs.2025.106378","DOIUrl":"10.1016/j.scs.2025.106378","url":null,"abstract":"<div><div>Cities are facing unprecedented challenges to their environments and residents due to continuous growth, including urban overheating, daylight accessibility, noise, and air pollution. This requires a holistic approach to the research and implementation of mitigation strategies, aiming at improving overall multidomain environmental quality. This study aims to develop a method for classifying neighbourhoods into identifiable types accounting for their distinctive multidomain environmental characteristics. The method uses a data-driven approach based on parameters describing urban morphology, land cover, and road network. A <em>K</em>-means algorithm was used to cluster 1583 neighbourhood units at the resolution of 250 <em>m</em> × 250 m in <em>Geneva</em> and <em>Zurich, Switzerland.</em> Performance-based comparisons were conducted to determine the optimal <em>k</em>-value and the most suitable clustering approach, evaluating separate versus combined clustering. Fifteen distinct neighbourhood types emerged from the analysis, spanning from high-density urban centres with extensive primary road networks to low-density suburban residential areas. These neighbourhood types exhibited distinctive environmental characteristics in the domains of thermal environment, air quality, daylight and acoustics (Kruskal-Wallis tests, <em>p</em> < 0.001 for all indicators). The Multidomain Neighbourhood Typology supports research on effective mitigation strategies by considering the broader multidomain environmental context to maximise co-benefits and minimise trade-offs. It also serves as a framework for context-specific implementations of mitigation measures, addressing the intrinsic multidomain environmental challenges of each neighbourhood type to enhance overall environmental quality.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"128 ","pages":"Article 106378"},"PeriodicalIF":10.5,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138509","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

Assessing flood and waterlogging vulnerability and community governance in urban villages in the context of climate change: A case study of 89 urban villages in Shanghai 气候变化背景下城中村洪水内涝脆弱性评估与社区治理——以上海89个城中村为例

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-12 DOI: 10.1016/j.scs.2025.106377

Shijun Chen , Jiayue Lin , Tuolei Wu , Zhe Yuan , Wenting Cao

{"title":"Assessing flood and waterlogging vulnerability and community governance in urban villages in the context of climate change: A case study of 89 urban villages in Shanghai","authors":"Shijun Chen , Jiayue Lin , Tuolei Wu , Zhe Yuan , Wenting Cao","doi":"10.1016/j.scs.2025.106377","DOIUrl":"10.1016/j.scs.2025.106377","url":null,"abstract":"<div><div>Climate change has increased the vulnerability of urban villages to flooding and waterlogging, making it a major challenge for government community management. This study assesses flood-waterlogging vulnerability in Shanghai's urban villages under current conditions and long-term SSP-RCP scenarios using data from 89 redevelopment communities. We identify development patterns based on clustering models, evaluate vulnerability using an ensemble learning model, and analyze climate policy attention with Python-based text mining. Results highlight significant variations in infrastructure and flood risk across urban villages with different development patterns. Suburban Growth Zones and Ecological Agricultural Zones show higher vulnerability. In the long term, most villages become more vulnerable under both SSP245 and SSP585 scenarios, and the uncertainties and risks will increase due to cumulative effects. The study emphasizes the necessity of thorough governmental management in mitigating climate-induced waterlogging risks in urban villages and suggests specific policy recommendations customized for various categories of urban villages.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106377"},"PeriodicalIF":10.5,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling urban carbon emissions for multiple sectors in high spatial resolution for achieving sustainable & net-zero cities 以高空间分辨率模拟多个部门的城市碳排放，以实现可持续和净零城市

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-11 DOI: 10.1016/j.scs.2025.106370

Lily Purcell , Anna C. O'Regan , Conor McGookin , Marguerite M. Nyhan

{"title":"Modelling urban carbon emissions for multiple sectors in high spatial resolution for achieving sustainable & net-zero cities","authors":"Lily Purcell , Anna C. O'Regan , Conor McGookin , Marguerite M. Nyhan","doi":"10.1016/j.scs.2025.106370","DOIUrl":"10.1016/j.scs.2025.106370","url":null,"abstract":"<div><div>Many largescale initiatives and networks have been established to support city efforts and leadership in decarbonisation. An essential first step in these initiatives is developing a Baseline Emissions Inventory (BEI) to understand drivers of current emissions and provide a benchmark that progress can be measured against. There has been increasing interest in emission inventory methods. However, previous research has focused on single sectors, has neglected emissions other than CO<sub>2</sub>, or has not followed a spatial approach. The latter is particularly important to support policy planning and decision-making. This study investigates the development of a novel BEI for a medium-sized city in Ireland to address the methodological knowledge gap in existing literature for a detailed methodology using mainly open-source and spatially resolved data for developing a multi-sectoral BEI in high spatial resolution. Greenhouse Gas (GHG) emissions including CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O, represented as kilotonnes CO<sub>2</sub>-equivalent (ktCO<sub>2</sub>-eq), were modelled for the Residential; Transport; Commercial & Industrial; Public; Agriculture, Land Use & Fishing (ALUF); and Waste sectors. Total annual emissions were 987 ktCO<sub>2</sub>-eq, with emissions per capita of 4.7 tCO<sub>2</sub>-eq. The Residential sector accounted for 34 % of emissions followed by the Transport (29 %), Commercial & Industrial (22 %), Public (7 %), ALUF (6 %), and Waste (2 %) sectors. The fine-resolution spatial outputs facilitate the investigation of socioeconomic factors alongside GHG emissions helping to elucidate local drivers and produce equitable mitigation strategies. The findings will contribute to effective policy development and the methodologies, developed in accordance with the Global Covenant of Mayors, can be replicated by cities globally.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106370"},"PeriodicalIF":10.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unleashing the power of artificial intelligence: A game changer for urban energy efficiency in China 释放人工智能的力量：改变中国城市能源效率的游戏规则

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-11 DOI: 10.1016/j.scs.2025.106372

Weike Zhang , Hongxia Fan , Ming Zeng

{"title":"Unleashing the power of artificial intelligence: A game changer for urban energy efficiency in China","authors":"Weike Zhang , Hongxia Fan , Ming Zeng","doi":"10.1016/j.scs.2025.106372","DOIUrl":"10.1016/j.scs.2025.106372","url":null,"abstract":"<div><div>Energy consumption in China is predominantly concentrated in urban areas, improving urban energy efficiency (UEE) is a crucial step towards mitigating energy pressure and achieving sustainable energy practices. However, it remains uncertain how artificial intelligence (AI) affects UEE, as it is both a promoter of energy conservation and a consumer of large amounts of energy. Given this context, we explore the effect of AI on UEE in China using data from 282 cities spanning 2006 to 2019. We find that AI benefits the improvement of UEE. Specifically, the installation (stock) of one additional standard deviation of industrial robots per hundred workers is associated with a 3.18 % (3.30 %) increase in energy efficiency in Chinese cities. These conclusions remain valid even when subjected to a suite of robustness tests. Furthermore, we reveal that the positive influence of AI on UEE is particularly pronounced in resource-dependent cities, eastern-central cities, northern cities of the Qinling Mountains-Huaihe River line, as well as mega-sized and super-sized cities. Additionally, we demonstrate that AI has a positive spatial spillover effect on UEE, that is, the UEE of local cities can be improved through the influence of neighboring cities' AI systems. Our findings not only improve the cognition of the link between AI and UEE but also guide government efforts to enhance UEE and achieve energy sustainability.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106372"},"PeriodicalIF":10.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of charging demands, influencing factors and spatial effects of electric vehicles based on multi-source data and local spatial models 基于多源数据和局部空间模型的电动汽车充电需求、影响因素及空间效应分析

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-11 DOI: 10.1016/j.scs.2025.106371

Xiaoqi Zhang , Fang Yang , Chunyan Shuai , Jie Liu , Kaiwen Zhang , Xin Ouyang

{"title":"Analysis of charging demands, influencing factors and spatial effects of electric vehicles based on multi-source data and local spatial models","authors":"Xiaoqi Zhang , Fang Yang , Chunyan Shuai , Jie Liu , Kaiwen Zhang , Xin Ouyang","doi":"10.1016/j.scs.2025.106371","DOIUrl":"10.1016/j.scs.2025.106371","url":null,"abstract":"<div><div>In order to alleviate the difficulty of charging caused by the popularization of electric vehicles (EVs), this paper conducts an in-depth analysis of the charging demands and influencing factors. According to the spatial distribution of charging demands, the target area is divided into more Voronoi unequal polygons, and the spatial characteristics of the built environment and socio-economic factors is qualitatively analyzed. Then, multicollinearity and spatial correlation tests are employed to eliminate redundant factors and explore the spatial clustering and correlation of charging demands and impact elements. A multi-scale geographically weighted regression and spatial autoregressive (MGWR-SAR) model are proposed to investigate such complex properties. An empirical study in Chongqing, China has shown that the charging demands in adjacent units exhibit obvious high-high and low-low clustering patterns, and are significantly influenced by the EVs with low SOCs, population density, parking lots density, transportation conditions, etc. The spatial impact degrees and scales vary with the factors and intervals, wherein the spatial scales of population density and road network density are local, with strong spatial heterogeneity; EVs with low SOCs, land use mixing and housing prices are close to global impacts. The spatial dependence of charging demand in high demand areas and charging peak periods is stronger than that in low demand and off-peak. There are spatial dependence and heterogeneity in charging demands and influencing factors, which makes MGWR-SAR superior to other models. These findings will provide support for predicting charging demands and optimizing charging stations.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106371"},"PeriodicalIF":10.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum to “Assessing the potential for green roof retrofitting: A systematic review of methods, indicators and data sources” [Sustainable Cities and Society, Volume 123, 1 April 2025, 106261] “评估绿色屋顶改造的潜力：对方法、指标和数据来源的系统审查”的勘误表[可持续城市与社会，第123卷，2025年4月1日，106261]

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-10 DOI: 10.1016/j.scs.2025.106359

Jing Dong , Chunli Li , Ruonan Guo , Fei Guo , Xing Zheng

引用次数: 0

Multilevel social vulnerability and urban health in sub-Saharan Africa: Implications for adaptation across household, community, and city levels 撒哈拉以南非洲多层次社会脆弱性和城市卫生：对家庭、社区和城市各级适应的影响

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-10 DOI: 10.1016/j.scs.2025.106368

Huijoo Shon

{"title":"Multilevel social vulnerability and urban health in sub-Saharan Africa: Implications for adaptation across household, community, and city levels","authors":"Huijoo Shon","doi":"10.1016/j.scs.2025.106368","DOIUrl":"10.1016/j.scs.2025.106368","url":null,"abstract":"<div><div>As rapidly urbanizing settlements in sub-Saharan Africa face vulnerability to environmental hazards across spatial levels, understanding the multilevel structure of vulnerability is critical for advancing urban health and climate adaptation. This paper develops a conceptual framework to examine multilevel social vulnerability and its health impacts across cities in 28 African countries. By integrating household survey and spatial datasets, this study constructs vulnerability indicators at household, community, and city levels, employing principal component analysis to quantify social vulnerability at each level. Logistic regression models estimate the effects of vulnerabilities on child health outcomes, including under-five mortality, underweight, diarrhea, and acute respiratory infection (ARI). The analysis reveals substantial variations in vulnerabilities across the three spatial levels, each of which significantly impacts health. Household and community vulnerabilities are related to increased risks of underweight and diarrhea while household vulnerability is strongly associated with mortality. In large cities with populations over one million, the effects of city vulnerability become more pronounced across morbidity outcomes, particularly for severe ARI and diarrhea. These findings suggest that the health implications of vulnerabilities differ according to specific outcomes and urban settings, highlighting the importance of incorporating a multilevel perspective into urban health and adaptation planning.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106368"},"PeriodicalIF":10.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revealing the impact of urban land use patterns on land surface temperature through graph attention networks 利用图关注网络揭示城市土地利用模式对地表温度的影响

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-10 DOI: 10.1016/j.scs.2025.106369

Hongbin Xu , Siyi Zhang , Chong Wu

{"title":"Revealing the impact of urban land use patterns on land surface temperature through graph attention networks","authors":"Hongbin Xu , Siyi Zhang , Chong Wu","doi":"10.1016/j.scs.2025.106369","DOIUrl":"10.1016/j.scs.2025.106369","url":null,"abstract":"<div><div>A considerable body of literature focuses on the intensification of urban heat island effects (UHI) due to urbanization. However, the impacts of interactions between parcels on land surface temperature (LST) remains unclear. In this study, Graph Attention Networks model represented the land use (LU) pattern as a graph structure to quantify the interaction between parcels, and further investigated its impact on LST. The results elucidated the strong influence of LU on LST not only comes from itself but also adjacent parcels. Roadway land, industrial/storage land, and water significantly affect the LST of adjacent parcels. In contrast, administrative land, park/green space, arable land, forest land, and grassland had a stronger impact on themselves. This strong influence varies with the topological distance (namely, order). For instance, The influence of commercial land on the focal parcel' LST within the four orders increases with the rise in topological distances. Finally, it was further clarified which types of parcels were more affected by specific LUs. These findings would provide the implementation of the LU layout to mitigate the UHI and promote sustainable urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"125 ","pages":"Article 106369"},"PeriodicalIF":10.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833909","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

Spatiotemporal assessment and monitoring of urban heat islands in metropolitan areas using machine learning and downscaling 基于机器学习和降尺度的大城市热岛时空评价与监测

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-04-10 DOI: 10.1016/j.scs.2025.106365

Rafael João Sampaio , Daniel Andrés Rodriguez , Rogério Pinto Espíndola , Fabricio Polifke da Silva

{"title":"Spatiotemporal assessment and monitoring of urban heat islands in metropolitan areas using machine learning and downscaling","authors":"Rafael João Sampaio , Daniel Andrés Rodriguez , Rogério Pinto Espíndola , Fabricio Polifke da Silva","doi":"10.1016/j.scs.2025.106365","DOIUrl":"10.1016/j.scs.2025.106365","url":null,"abstract":"<div><div>Urban heat islands (UHI) are a significant phenomenon that results from human modification of the land. Numerical models that are used to study the impacts of UHIs on air temperature require high resolution to estimate spatial fields accurately.This study employs both statistical and dynamic downscaling methods to estimate urban heat islands (UHI) from 1 km-resolution air temperature fields from outputs of the Weather Research and Forecasting (WRF) model, initially generated at a coarser resolution of 5 km, for the Metropolitan Area of Rio de Janeiro (MARJ). In the dynamic method, a grid with 1 km spatial resolution is obtained through a nesting system with three domains of the WRF model of 25–5–1 km. The latter domain couples to the Single-Layer Urban Canopy Model (SLUCM). The statistical approach introduces a novel methodology based on the extreme gradient boosting machine learning algorithm, which correlates air temperature with physiographic landscape variables on a scale of 1 km by 1 multiple nonlinear regression. Additionally, SHAP analysis is applied to assess individual feature contributions in the machine learning model. The performance of theses downscaling methods is evaluated using atmospheric temperature measured at meteorological stations and estimated from remote sensing data. Both methods satisfactorily simulate the temporal and spatial behavior of UHIs in the metropolitan region of Rio de Janeiro, however, the statistical approach demonstrates significantly lower computational costs. This result demonstrates the feasibility of this machine learning-based approach as an alternative for studying and monitoring UHI with limited computational resources.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"126 ","pages":"Article 106365"},"PeriodicalIF":10.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894820","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