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Position: Home > Articles > Carbon footprint of buildings in the urban agglomeration of central Liaoning,China Chinese Journal of Applied Ecology 2017 (6) 2040-2046

辽宁中部城市群建筑碳足迹

作  者：
石羽;运迎霞;刘冲;初亚奇
单  位：
沈阳大学建筑工程学院;天津大学建筑学院;中国科学院沈阳应用生态研究所森林生态与管理重点实验室
关键词：
碳足迹;高分遥感影像;辽宁中部城市群
摘  要：
随着我国城市化进程的不断深入,建筑消费大量的材料和能源,如何计算建筑碳足迹具有重要意义.本文应用碳足迹的方法,结合地理信息系统和高分遥感手段对辽宁中部城市群进行碳足迹评估.结果表明:辽宁中部城市群的建筑相关碳足迹系数为269.16 kg·m~(-2).基于遥感影像提取建筑面积的方法能够有效提取建筑面积总量和空间分布,提取精度达到89%,对累积碳足迹计算和空间分布评估起到重要作用.辽宁中部7个城市建筑面积和累积碳足迹由高到低依次为沈阳、鞍山、抚顺、辽阳、营口、铁岭和本溪;2011—2013年,年均碳足迹增长由高到低依次为沈阳、本溪、抚顺、鞍山、铁岭、营口和辽阳.准确计算建筑碳足迹总量并分析其空间分布,对于碳减排和空间格局优化或规划具有重要意义.
译  名：
Carbon footprint of buildings in the urban agglomeration of central Liaoning,China
作  者：
SHI Yu;YUN Ying-xia;LIU Chong;CHU Ya-qi;School of Architecture,Tianjin University;Key Laboratory of Forest Ecology and Management,Institute of Applied Ecology,Chinese Academy of Sciences;School of Architectural Engineering,Shenyang University;
关键词：
carbon footprint;;high resolution remote sensing;;central Liaoning agglomeration
摘  要：
With the development of urbanization in China,buildings consumed lots of material and energy. How to estimate carbon emission of buildings is an important scientific problem. Carbon footprint of the central Liaoning agglomeration was studied with carbon footprint approach,geographic information system( GIS) and high-resolution remote sensing( HRRS) technology. The results showed that the construction carbon footprint coefficient of central Liaoning urban agglomeration was 269.16 kg·m~(-2). The approach of interpreting total building area and spatial distribution with HRRS was effective,and the accuracy was 89%. The extraction approach was critical for total carbon footprint and spatial distribution estimation. The building area and total carbon footprint of central Liaoning urban agglomeration in descending order was Shenyang,Anshan,Fushun,Liaoyang,Yingkou,Tieling and Benxi. The annual average increment of footprint from 2011 to 2013 in descending order was Shenyang,Benxi,Fushun,Anshan,Tieling,Yingkou and Liaoyang. The accurate estimation of construction carbon footprint spatial and its distribution was of significance for the planning and optimization of carbon emission reduction.

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