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建筑材料生命周期CO_2排放研究——以天津市住宅建筑为例
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  • 英文篇名:Life Cycle CO_2 Emissions of Building Materials:Taking Residential Buildings in Tianjin as Examples
  • 作者:毛希凯 ; 王立雄 ; 李纪伟 ; 全孝莉
  • 英文作者:MAO Xi-kai;WANG Li-xiong;LI Ji-wei;QUAN Xiao-li;School of Architecture,Tianjin Key Laboratory of Architectural Physics and Environmental Technology,Tianjin University;
  • 关键词:建筑材料 ; CO_2排放 ; 生命周期评价 ; 预测模型
  • 英文关键词:building materials;;carbon dioxide emissions;;Life Cycle Assessment(LCA);;prediction model
  • 中文刊名:建筑节能
  • 英文刊名:Building Energy Efficiency
  • 机构:天津大学建筑学院天津市建筑物理环境与生态技术重点实验室;
  • 出版日期:2019-02-25
  • 出版单位:建筑节能
  • 年:2019
  • 期:02
  • 基金:国家重点研发计划课题资助项目(2016YFC0700201)
  • 语种:中文;
  • 页:133-138
  • 页数:6
  • CN:21-1540/TU
  • ISSN:1673-7237
  • 分类号:TU50;TU241;X799.1
摘要
建筑材料CO_2排放是建筑碳排放的重要组成部分。利用生命周期评价理论,将建筑材料生命周期进行了划分,总结出了建筑材料生命周期碳排放的核算模型。利用该核算模型,对天津市38栋住宅的建筑材料生命周期碳排放量进行了计算,并对计算结果进行了分析。发现住宅建筑的单位面积建筑材料碳排放平均值为366. 70 kgCO_2/m~2,其中,钢材、混凝土和砂浆的碳排放量可占80%以上;住宅建筑的建筑面积、标准层面积和层数等参数均与其单位面积碳排放量有着较好的正相关性。之后,分别以建筑面积、标准层面积及层数为自变量,通过统计学方法构建了建筑材料生命周期碳排放的预测模型,并验证了各预测模型的科学性和准确性。
        CO_2 emissions from building materials are important part of building life cycle carbon emissions. Based on LCA theory,the life cycle of building materials was divided into following stages:production,transportation,repair,demolition and processing. And a quantitative life cycle CO_2 emission accounting model was established. Based on this accounting model,life cycle carbon emissions of building materials from 38 residential buildings in Tianjin were calculated( at an average value of 366. 70 kg CO_2/m~2).The results show that carbon emissions from steel,concrete and mortar could account for more than 80%of the total; parameters such as construction area,standard layer area and building layers all show good positive correlation with carbon emissions. Based on the statistical method,prediction models of carbon emissions of building materials were constructed by using construction area,standard layer area and building layers as independent variables respectively,then the accuracy of those prediction models are verified.
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