县域工业终端能源碳排放核算方法研究及应用
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- 英文论文题名：Energy-Induced Carbon Emission Accouting of Manufacturing in Chongming County
- 论文作者：赵亚茹 ; 郭茹
- 英文论文作者：Yaru Zhao ; Ru Guo ; College of Environmental Science and Engineering ; UNEP-Tongji Institute of Environment for Sustainable Development
- 年：2016
- 作者机构：同济大学环境科学与工程学院;同济大学—联合国环境规划署环境与可持续发展学院;
- 论文关键词：工业 ; 碳排放 ; 能源消费 ; 低碳 ; 节能减排
- 英文论文关键词：manufacturing ; carbon emission ; energy consumption ; low-carbon
- 会议召开时间：2016-11-24
- 会议录名称：2016全国环境信息技术与应用交流大会暨中国环境科学学会环境信息化分会年会论文集
- 语种：中文
- 分类号：X322;X24
- 学会代码：ZHKX
- 会议名称：2016全国环境信息技术与应用交流大会暨中国环境科学学会环境信息化分会年会
- 会议地点：中国北京
- 主办单位：中国环境科学学会环境信息化分会、《中国环境管理》杂志
- 学会名称：中国环境科学学会环境信息化分会
- 页数：7
- 文件大小：1024k
- 原文格式：O
- 会议级别：全国
摘要
低碳发展已成为各国应对气候变化的重要路径之一,其中工业节能减排是关键。县级行政单元是我国重要的基层行政单元,但在碳排放核算方面基础数据薄弱,为县域碳排放基准线确定带来困难。本文以上海市崇明县为例,在融合多个数据来源的基础上确定适宜的碳排放因子,核算了该县2005—2014年工业终端能源碳排放,并分析了工业产值、产业结构、能源效率、能源结构等因素的影响。结果表明:①2005年以来,崇明县工业碳排放量呈现先增长后下降的趋势,其中2005—2008年为快速增长期,2008—2014年为下降期;②空间格局呈现向东南方向集中的趋势,2005年崇明县16个乡镇中9个乡镇的碳排放量超过5万t,而2014年崇明县18个乡镇中只有位于东南部的长兴镇工业碳排放量超过5万t;③崇明县工业碳总量减排成效以及碳生产效率的逐步提升主要得益于工业产业结构调整、产业中心转移、节能降耗等政策的有效实施,但电力仍是该县工业终端最主要的能源消费类型,工业能源效率与上海其他区县相比处于末位。研究为数据相对缺乏的县级区域开展工业终端能源碳排放核算研究提供了方法学依据和实证案例。
Low-carbon development has become an important path for countries to mitigate climate change,and manufacturing is the key.County-level administrative unit is the basic administrative unit,however,the carbon emission(CE) accounting data of it is weak,which makes it difficult for decision makers to make low-carbon policies.Taking Chongming county(China) as a case study,energy-induced CE of manufacturing were calculated based on multiple data sources and local coefficients from 2005 to2014.A temporal-spatial variation of CE was observed,owing to the variable change of GDP,industrial structure,energy intensity,energy structure and other factors.A temporal result showed that CE went though a process that 2005—2008 was the period of rapid growth,2008—2014 for the fall.Respectively,a spatial resultshowed that CE tended to concentrate toward the Southeast of Chongmingcounty.It is worth mentioning that electricity remains the main energy type and energy efficiency is in relative lower level so far in Chongmingcounty,compared with other districts of Shanghai.Consequently,the approach of CE accounting and analysis of manufacturing can serve as an additional tool for Counties,which are lack of basic data.
Low-carbon development has become an important path for countries to mitigate climate change,and manufacturing is the key.County-level administrative unit is the basic administrative unit,however,the carbon emission(CE) accounting data of it is weak,which makes it difficult for decision makers to make low-carbon policies.Taking Chongming county(China) as a case study,energy-induced CE of manufacturing were calculated based on multiple data sources and local coefficients from 2005 to2014.A temporal-spatial variation of CE was observed,owing to the variable change of GDP,industrial structure,energy intensity,energy structure and other factors.A temporal result showed that CE went though a process that 2005—2008 was the period of rapid growth,2008—2014 for the fall.Respectively,a spatial resultshowed that CE tended to concentrate toward the Southeast of Chongmingcounty.It is worth mentioning that electricity remains the main energy type and energy efficiency is in relative lower level so far in Chongmingcounty,compared with other districts of Shanghai.Consequently,the approach of CE accounting and analysis of manufacturing can serve as an additional tool for Counties,which are lack of basic data.
引文
[1]Yao X,Zhou H,Zhang A,et al..Regional energy efficiency,carbon emission performance and technology gaps in China:a meta-frontier non-radial directional distance function analysis[J].Energy Policy,2015,84:142-154.
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[4]Wang Z,Yang L.Delinking indicators on regional industry development and carbon emissions:Beijing—Tianjin—Hebei economic band case.Ecological Indicators,2015,48:41-48.
[5]苟少梅,王长建,张利,等.1990--2010年广东省能源消费的碳排放驱动因素分析[J].热带地理,2012,32(4).
[6]吴国华.化石能源消费的二氧化碳排放量计算与分析——以济南市为例[J].理论学刊,2012(3):61-65.
[7]李玉敏,张友国.中国碳排放影响因素的空间分解分析[J].中国地质大学学报:社会科学版,2016,16(3):73-85.
[8]义白璐,韩骥,周翔,等.区域碳源碳汇的时空格局——以长三角地区为例[J].应用生态学报,2015,26(4):973-980.
[9]叶懿安,朱继业,李升峰,等.长三角城市工业碳排放及其经济增长关联性分析[J].长江流域资源与环境,2013,22(3):257-262.
[10]武戈,谢钰思.工业碳排放的驱动因素研究——基于南京市工业35个行业的LMDI模型分析[J].环境污染与防治,2014,36(2):105-110.
[11]Zhao M,Tan L,Zhang W,et al..Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method[J].Energy,2010,35(6):2505-2510.
[12]Li X,Jiang P,Zhang Y,et al..Development of a stationary carbon emission inventory for Shanghai using pollution source census data[J].Frontiers of Earth Science,2016:1-16.
[13]赵荣钦,张帅,黄贤金,等.中原经济区县域碳收支空间分异及碳平衡分区[J].地理学报,2014,69(10):1425-1437.
[14]IPCC.Guidelines for national greenhouse gas inventories,volume 2 energy[EB/OL].http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html
[15]耿涌,焦江波,刘竹,等.城市能源消费碳排放核算方法[J].资源科学,2011,33(7):1325-1330.
[16]国家发展改革委应对气候变化司.中国温室气体清单研究(2005)[M].北京:中国环境出版社,2014.
(1)能源平衡表法:从能源的投入、加工转换及最终消费的角度,基于能源平衡表建立能源消费流程结构图,确定最终的碳排放能源消耗。
(2)分行业能源消费总量法:从能源生产角度,计算煤、石油、天然气等8种一次能源消费产生的碳排放。
(3)分行业终端能源消费法:从能源使用角度,计算分品种能源终端消费产生的碳排放。
[2]Li Q,Guo R,Li F,et al..Integrated inventory-based carbon accounting for energy-induced emissions in Chongming eco-island of Shanghai,China[J].Energy policy,,2012,,49:173-181.
[3]Ouyang X,Lin B.An analysis of the driving forces of energy-related carbon dioxide emissions in China's industrial sector[J].Renewable and Sustainable Energy Reviews,2015,45:838-849.
[4]Wang Z,Yang L.Delinking indicators on regional industry development and carbon emissions:Beijing—Tianjin—Hebei economic band case.Ecological Indicators,2015,48:41-48.
[5]苟少梅,王长建,张利,等.1990--2010年广东省能源消费的碳排放驱动因素分析[J].热带地理,2012,32(4).
[6]吴国华.化石能源消费的二氧化碳排放量计算与分析——以济南市为例[J].理论学刊,2012(3):61-65.
[7]李玉敏,张友国.中国碳排放影响因素的空间分解分析[J].中国地质大学学报:社会科学版,2016,16(3):73-85.
[8]义白璐,韩骥,周翔,等.区域碳源碳汇的时空格局——以长三角地区为例[J].应用生态学报,2015,26(4):973-980.
[9]叶懿安,朱继业,李升峰,等.长三角城市工业碳排放及其经济增长关联性分析[J].长江流域资源与环境,2013,22(3):257-262.
[10]武戈,谢钰思.工业碳排放的驱动因素研究——基于南京市工业35个行业的LMDI模型分析[J].环境污染与防治,2014,36(2):105-110.
[11]Zhao M,Tan L,Zhang W,et al..Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method[J].Energy,2010,35(6):2505-2510.
[12]Li X,Jiang P,Zhang Y,et al..Development of a stationary carbon emission inventory for Shanghai using pollution source census data[J].Frontiers of Earth Science,2016:1-16.
[13]赵荣钦,张帅,黄贤金,等.中原经济区县域碳收支空间分异及碳平衡分区[J].地理学报,2014,69(10):1425-1437.
[14]IPCC.Guidelines for national greenhouse gas inventories,volume 2 energy[EB/OL].http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html
[15]耿涌,焦江波,刘竹,等.城市能源消费碳排放核算方法[J].资源科学,2011,33(7):1325-1330.
[16]国家发展改革委应对气候变化司.中国温室气体清单研究(2005)[M].北京:中国环境出版社,2014.
(1)能源平衡表法:从能源的投入、加工转换及最终消费的角度,基于能源平衡表建立能源消费流程结构图,确定最终的碳排放能源消耗。
(2)分行业能源消费总量法:从能源生产角度,计算煤、石油、天然气等8种一次能源消费产生的碳排放。
(3)分行业终端能源消费法:从能源使用角度,计算分品种能源终端消费产生的碳排放。
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