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产业结构对能源消费的影响分析——以江苏省为例
引用本文:程文荣,姚天祥.产业结构对能源消费的影响分析——以江苏省为例[J].能源研究与信息,2015,31(3):162-167.
作者姓名:程文荣  姚天祥
作者单位:南京信息工程大学经济管理学院, 南京 210044,南京信息工程大学经济管理学院, 南京 210044
基金项目:国家自然科学基金资助项目(71171116);教育部人文社会科学青年基金资助项目(09YJC630129)
摘    要:研究了2000年、2002—2012年江苏省的产业结构与能源消费之间的关系.首先,使用灰色关联分析法分析了江苏省三大产业及生活用能与能源消费之间的关系,获得了灰色绝对关联度、灰色相对关联度、灰色综合关联度.分析结果表明,第二产业与能源消费的关联度最大.然后,使用对数回归分析法分析了各大产业对能源消费具体的影响程度,得出对能源消费总量影响最大的是第二产业.优化产业结构,提高能源使用效率,是实现江苏省可持续发展的重要任务.

关 键 词:产业结构  灰色关联分析  能源消耗  对数回归分析
收稿时间:2014/9/24 0:00:00

Analysis of the impact of industrial structure on energy consumption: Taking Jiangsu Province for example
CHENG Wenrong and YAO Tianxiang.Analysis of the impact of industrial structure on energy consumption: Taking Jiangsu Province for example[J].Energy Research and Information,2015,31(3):162-167.
Authors:CHENG Wenrong and YAO Tianxiang
Affiliation:School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing 210044, China and School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing 210044, China
Abstract:The relationship between the industrial structure and energy consumption in Jiangsu Province in 2000 and from 2002 to 2012 was investigated.Firstly,gray incidence analysis was used to analyze the relationship between three major industries and energy consumption in Jiangsu Province.Their gray absolute correlation,gray relative correlation,and gray comprehensive correlation were attained.The results shown that the largest correlation degree between the second industry and energy consumption was achieved.Secondly,logarithmic regression analysis was used to analyze the impact of three major industries on energy consumption.It could be concluded that the impact of the second industry on total energy consumption was the greatest.It is of significance for Jiangsu Province to optimize the industrial structure and improve energy efficiency to achieve the goal of sustainable development.
Keywords:industrial structure  gray incidence analysis  energy consumption  logarithmic regression analysis
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