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中国服务业能源消费CO2排放及其因素分解
引用本文:王 凯,李 娟,席建超.中国服务业能源消费CO2排放及其因素分解[J].环境科学研究,2013,26(5):576-582.
作者姓名:王 凯  李 娟  席建超
作者单位:1.湖南师范大学旅游学院, 湖南 长沙 410081
基金项目:国家重点基础研究发展计划(973)项目,湖南省哲学社会科学基金项目
摘    要:基于IPCC温室气体排放清单指南中的CO2排放因子与核算方法,估算了1995—2010年中国服务业能源消费与CO2排放量,并对其总体变化趋势进行时间序列分析;以LMDI(对数平均迪氏指数)法辨识与分解3个时段(1995—2000年、2000—2005年和2005—2010年)中影响中国服务业CO2排放量变动的关键因素及其对CO2排放量的贡献值. 结果表明:1995—2010年中国服务业能源消费CO2排放量增长态势明显,累计排放总量为853197.55×104t;服务业能源消费主要依赖于高碳化能源燃料,各年度油品和煤品分别占能源消费总量的67%~74%和5%~27%;LMDI分析结果显示,1995—2010年产业规模和人口效应引起CO2排放增加量分别为133357.10×104和7691.25×104t,能源效率和能源结构引起CO2排放减少量分别为59034.50×104和23898.60×104t. 提出CO2减排对策:①以经济、政策和监管手段促进服务业节能减排;②依托科技创新提高能源综合利用效率,降低服务业CO2排放量. 

关 键 词:服务业    能源消费    CO2排放    对数平均迪氏指数(LMDI)法    中国
收稿时间:2012/9/18 0:00:00
修稿时间:2013/3/11 0:00:00

Emissions of CO2from Energy Consumption and Decomposition Analysis in the Service Industry of China
WANG Kai,LI Juan and XI Jian-chao.Emissions of CO2from Energy Consumption and Decomposition Analysis in the Service Industry of China[J].Research of Environmental Sciences,2013,26(5):576-582.
Authors:WANG Kai  LI Juan and XI Jian-chao
Institution:1.Tourism College of Hunan Normal University, Changsha 410081, China2.Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Abstract:The energy consumption and CO2emissions in the service industry of China was calculated based on the CO2emission coefficients and the methods introduced by IPCC in the Guidelines for National Green Gas Inventories (GNGGI) during 1995-2010. The methods of logarithmic mean Divisia index (LMDI) analysis was applied to identify key factors that cause the change of CO2emission, whose contributions to CO2emissions during three periods (1995-2000,0-2005and 2005-2010) were also estimated respectively. Time-series analysis showed a strong overall rising trend for the service industry, in which total CO2emission was 853,7.55×104t during 1995-2010. The energy consumption of the service industry mainly relied on the energy fuels with high carbonation such as oil and coal products which contributed for 67%-74% and 5%-27% of all energy consumption respectively; Industry scale and population effect raised CO2emissions by 133,7.10×104and 7,1.25×104t, while energy efficiency and structure reduced CO2emissions by 59,4.50×104and 23,8.60×104t. Relevant recommendations were suggested according to the analyzed results, which mainly includes the establishment of a “reversed mechanism” by economic, political and regulatory controls, and the full support to technological innovation to enhance the energy efficiency and to reduce CO2emission of the service industry of China. 
Keywords:service sector  energy consumption  CO2emissions  logarithmic mean Divisia index (LMDI)  China
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