城市轨道交通基础设施隐含碳排放研究综述
Review of Research on Embodied Carbon Emissions in Urban Rail Transit Infrastructure
陈湘生 1陈坤阳 1邱桐 1王雷 1何秋凤 1赖亚妮 1陈武雄 2付艳斌1
作者信息
- 1. 深圳大学土木与交通工程学院未来地下城市研究院,深圳 518061;滨海城市韧性基础设施教育部重点实验室(深圳大学),深圳 518060;深圳市地铁地下车站绿色高效智能建造重点实验室,深圳 518060
- 2. 深圳中建院建筑科技有限公司,深圳 518057
- 折叠
摘要
城市轨道交通基础设施碳排放的定量研究已经成为交通运输领域低碳建设的关注焦点.以Web of Science(WOS)核心数据库和中国知网数据库为基础,对城市轨道交通基础设施碳排放的定量研究进行系统性综述与分析,从研究方法和影响因素2个方面对样本文献进行深入分析与归纳,共涉及4种类型基础设施的碳排放,具体包括地铁系统(包括隧道和车站)、轻轨系统、市域快速轨道交通系统和有轨电车系统.首先对关于碳排放计算的文献的研究方法、目标与范围界定、功能单位和清单数据进行比较,确定研究方法的适用范围,并对数据来源进行分类;其次,确定并分析影响城市轨道交通基础设施隐含碳排放的关键因素;最后,讨论现有研究的不足.研究结果可为城市轨道交通基础设施碳排放核算标准化提供参考.
Abstract
Quantitative research on carbon emissions of urban rail transit infrastructure has become a focal point in the field of low-carbon construction in transportation.This study provides a systematic review and analysis of the quantitative research on carbon emissions of urban rail transit infrastructure,based on the Web of Science(WOS)core database and the China National Knowledge Infrastructure(CNKI)database.This literature review delves into re-search methods and influencing factors,covering four types of infrastructure:subway systems(including tunnels and stations),light rail systems,urban rapid transit systems,and tram systems.Firstly,the research methods,objectives and scope definition,functional units and inventory data of the literature on carbon emission calculation are com-pared,the scope of application of the research methods is determined,and the data sources are classified.Secondly,the key factors affecting the embodied carbon emissions of urban rail transit infrastructure are identified and analyzed.Finally,discuss the shortcomings of existing research.The research results provide reference for the standardization of carbon emission accounting for urban rail transit infrastructure.
关键词
城市轨道交通/基础设施/生命周期评价/隐含碳排放/影响因素Key words
Urban rail transit/Infrastructure/LCA/Embodied carbon emissions/Influencing factors引用本文复制引用
基金项目
国家自然科学基金(51938008)
国家自然科学基金(52090084)
国家自然科学基金(52308410)
出版年
2024
NETL
NSTL
万方数据