基于DEA模型的G20国家全要素能源效率比较研究
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摘要
能源不是取之不尽用之不竭的,如何提高能源效率将是一个长久议题。我国目前处于能源结构调整的重要发展时期,以供给侧结构性改革为主线,着力推进清洁能源的开发利用,在此过程中要创新和完善能源治理改革,推动质量变革与效率变革。因此,站在宏观角度,了解中国能源效率在世界主要国家中的位置,通过对比发现可改进之处,对推动中国能源变革和经济社会持续健康发展具有推动意义。本文利用DEA模型,对G20国家进行全要素能源效率分析。通过对各国能源效率进行对比,分析影响各国能源效率差异的动因,进而探讨提高能源效率的可行性方案。作者以能源消费总量、资本存量、劳动力投入为输入指标,以国内生产总值为产出指标,在规模报酬可变和规模报酬不变的情况下测算了G20各国的能源效率相对有效性水平。研究结论如下:(1)2014—2017年间,G20国家的全要素能源效率差异明显;(2)经济发展水平影响全要素能源效率与纯技术效率存在正相关关系;(3)资源禀赋及能源消费结构影响全要素能源效率;(4)中国在G20国家中的全要素能源效率表现存在进一步的完善空间,在纯技术效率和规模效率方面有待提升。
Energy is not inexhaustible, so how to improve energy efficiency will be a long-term issue. At present, China is in an important period of energy structure adjustment, focusing on supply-side structural reform and promoting the development and utilization of clean energy. In this process, we should innovate and improve the reform of energy governance, and promote the reform of quality and efficiency. Therefore, from a macro perspective, it is of great significance to understand the position of China's energy efficiency in major countries in the world and find out what can be improved through comparison, so as to promote China's energy reform and sustainable and healthy economic and social development. In this paper, the total factor energy efficiency analysis of G20 countries will be conducted by using the data envelopment analysis. By comparing the energy efficiency of different countries, this paper analyzes the factors influencing the differences in energy efficiency, and then discusses the feasibility of improving energy efficiency. Taking total energy consumption, capital stock and labor input as input indicators and GDP as output indicator, this paper estimates the relative efficiency of energy efficiency of G20 countries with variable returns to scale and constant returns to scale. Through data analysis and literature research, the influencing factors of energy efficiency in different countries are discussed. The main conclusions of this paper are as follows: 1) from 2014 to 2017, the total factor energy efficiency of G20 countries has displayed obvious differences; 2) the level of economic development affects total factor energy efficiency and has a positive correlation with pure technical efficiency; 3) the resource endowment and energy consumption structure affect total factor energy efficiency; 4) China's performance in total factor energy efficiency among G20 countries is not optimistic, and there is a big gap in pure technical efficiency and scale efficiency.
Energy is not inexhaustible, so how to improve energy efficiency will be a long-term issue. At present, China is in an important period of energy structure adjustment, focusing on supply-side structural reform and promoting the development and utilization of clean energy. In this process, we should innovate and improve the reform of energy governance, and promote the reform of quality and efficiency. Therefore, from a macro perspective, it is of great significance to understand the position of China's energy efficiency in major countries in the world and find out what can be improved through comparison, so as to promote China's energy reform and sustainable and healthy economic and social development. In this paper, the total factor energy efficiency analysis of G20 countries will be conducted by using the data envelopment analysis. By comparing the energy efficiency of different countries, this paper analyzes the factors influencing the differences in energy efficiency, and then discusses the feasibility of improving energy efficiency. Taking total energy consumption, capital stock and labor input as input indicators and GDP as output indicator, this paper estimates the relative efficiency of energy efficiency of G20 countries with variable returns to scale and constant returns to scale. Through data analysis and literature research, the influencing factors of energy efficiency in different countries are discussed. The main conclusions of this paper are as follows: 1) from 2014 to 2017, the total factor energy efficiency of G20 countries has displayed obvious differences; 2) the level of economic development affects total factor energy efficiency and has a positive correlation with pure technical efficiency; 3) the resource endowment and energy consumption structure affect total factor energy efficiency; 4) China's performance in total factor energy efficiency among G20 countries is not optimistic, and there is a big gap in pure technical efficiency and scale efficiency.
引文
(1)国际能源署:《2018年能源效率报告》,2019年1月9日,https://webstore.iea.org/,访问日期:2019年1月10日。
(2)孙巍、刘阳:《能源效率常用测度指标的比较研究》,载《经济纵横》,2014年第10期,第68—71页。
(1)Murray Patterson,"What is energy efficiency?:Concepts,indicators and methodological issues,"Energy policy,Vol.24,No.5,1996,pp.377-390.
(2)Didier Bosseboeuf,Bertrand Chateau,Bruno Lapillonne,"Cross-country comparison on energy efficiency indicators:the on-going European effort towards a common methodology,"Energy Policy,Vol.25,No.7-9,1997,pp.673-682.
(3)David Reister,"The link between energy and GDP in developing countries,"Energy,Vol.12,No.6,1987,pp.427-433.
(4)Murray Patterson,"An accounting framework for decomposing the energy-to-GDP ratio into its structural components of change,"Energy,Vol.18,No.7,1993,pp.741-761.
(5)张少华、蒋伟杰:《能源效率测度方法:演变、争议与未来》,载《数量经济技术经济研究》,2016年第33期,第4—25页。
(6)Gale Boyd,Joseph Pang,"Estimating the linkage between energy efficiency and productivity,"Energy policy,Vol.28,No.5,2000,pp.289-296.
(1)Jin-Li Hu,Shih-Chuan Wang,"Total-factor energy efficiency of regions in China,"Energy policy,Vo1.34,No.17,2006,pp.3206-3217.
(2)师博、沈坤荣:《市场分割下的中国全要素能源效率:基于超效率DEA方法的经验分析》,载《世界经济》,2008年第9期,第49—59页。
(3)屈小娥:《中国省际全要素能源效率变动分解—基于Malmquist指数的实证研究》,载《数量经济技术经济研究》,2009年第8期,第29—43页。
(4)Abraham Charnes,William Cooper,Edwardo Rhodes,"Measuring the efficiency of decision-making units,"European Journal of Operational Research,Vo1.2,No.6,1978,pp.429-444.CC摸型是DEA模型的一种,由A.Charnes及W.W.Cooper和E.Rhodes提出,因此以三人姓氏首字母命名。
(5)Wade Cook,Lawrence Seiford,"Data envelopment analysis(DEA)-Thirty years on,"European Journal of Operational Research,Vol.192,No.1,2009,pp.1-17.
(6)Rajiv Banker,Abraham Charnes,William Cooper,"Some models for estimating technical and scale inefficiencies in data envelopment analysis,”Management Science,Vol.30,No.9,1984,pp.1078-1092.BCC模型是由Rajiv BanKer及Abraham Chames和William Cooper首次提出,也用的是三人姓氏首字母命名。
(7)魏权龄:《评价相对有效性的DEA方法——运筹学的新领域》,中国人民大学出版社1988年版第100—119页。
(1)Rolf Fare,Shawna Grosskopf,James Logan,"The relative efficiency of Illinois electric utilities,"Resource Energy,Vo1.5,1983,pp.349-67.
(2)Atakelty Hailu,"Nonparametric Productivity Analysis with Undesirable Outputs:Reply,"American Journal of Agricultural Econom ics,Vol.85,No.4,2003,pp.1 075-1077.
(3)Kankana Mukherjee,"Energy use efficiency in US manufacturing:a nonparametric analysis,"Energy Economics,Vol.20,2008,pp.76-96.
(4)袁晓玲、张宝山、杨万平:《基于环境污染的中国全要素能源效率研究》,载《中国工业经济》,2009年第2期,第76—86页。
(5)Satoshi Honma,Jin-Li Hu,"Total-factor energy efficiency of regions in Japan,"Energy Policy,Vol.36,2008,pp.821-33.
(6)Peng Zhou,Kim-LengPoh,BengWah Ang,"A non-radial DEA approach to measuring environ-mental performance,"European Journal of Operational Research,Vol.178,2007,pp.1-9.
(7)Jin-Li Hu,Chih-Hung Kao,"Efficient energy-saving targets for APEC economies,"Energy Policy,Vol.35,2007,pp.373-382.
(8)Jiandong Chen,Malin Song,Long Xu,"Evaluation of environmental efficiency in China using data envelopment analysis,"Ecological Indicators,Vol.52,2015,pp.577-583.
(1)黄伟华:《固定资本存量理论方法探讨与核算设计》,广东财经大学博士论文,2017年。
(2)Raymond Goldsmith,A Perpetual Inventory of National Wealth,Studies in Income and Wealth,National Bureau of Economic Research,Cambridge,1951,14,pp.5-74.
(1)David Hawdon,Ian McQueen,Using Lambda for DEA.Surrey Energy Economics Center Technical Paper No.l,https://www.seec.surrey.ac.uk/research/SEEDS/SEEDSTP1.pdf,访问日期:2019年1月15日。
(1)BritishPetroleum. BP Statistical Review of World Energy(67 edition),https://www.bp.com/content,访问日期:2019年1月19日。
(2)《世界银行世界各国劳动力总量》,https://data.worldbank.org/indicator/SL.TLF.TOTL.IN?end=2016&start=1990,访问日期:2019年1月19日。
(3)国际货币基金组织:Investment and Capital Stock Dataset,1960-201 5.https://www.google.com/url,访问日期:2019年1月19日。
(1)国家发改委、国家能源局:《能源发展“十三五”规划》,http://www.ndrc.gov.cn/zcfb/zcfbtz/2 01 701/W02017011733 5278192779.pdf,访问日期:2019年1月25日。
(2)The World Bank,Industry(including Construction),Value Added(%of GDP),https://data.worldbank.org/indicator/N V.IND.TOTL.Z S?locations=US,访问日期:2019年1月25日。
(1)人民网:《二十国集团领导人杭州峰会公报》,http://world.people.com.cn/n1/2016/0906/c1002-28693129.html,访问日期:2019年2月2日。
(1)董娟:《国际能源结构转型趋势及典型路径比较分析》,载《中外能源》,2014年第10期,第3—20页。
(2)孙巍、刘阳:《能源效率常用测度指标的比较研究》,载《经济纵横》,2014年第10期,第68—71页。
(1)Murray Patterson,"What is energy efficiency?:Concepts,indicators and methodological issues,"Energy policy,Vol.24,No.5,1996,pp.377-390.
(2)Didier Bosseboeuf,Bertrand Chateau,Bruno Lapillonne,"Cross-country comparison on energy efficiency indicators:the on-going European effort towards a common methodology,"Energy Policy,Vol.25,No.7-9,1997,pp.673-682.
(3)David Reister,"The link between energy and GDP in developing countries,"Energy,Vol.12,No.6,1987,pp.427-433.
(4)Murray Patterson,"An accounting framework for decomposing the energy-to-GDP ratio into its structural components of change,"Energy,Vol.18,No.7,1993,pp.741-761.
(5)张少华、蒋伟杰:《能源效率测度方法:演变、争议与未来》,载《数量经济技术经济研究》,2016年第33期,第4—25页。
(6)Gale Boyd,Joseph Pang,"Estimating the linkage between energy efficiency and productivity,"Energy policy,Vol.28,No.5,2000,pp.289-296.
(1)Jin-Li Hu,Shih-Chuan Wang,"Total-factor energy efficiency of regions in China,"Energy policy,Vo1.34,No.17,2006,pp.3206-3217.
(2)师博、沈坤荣:《市场分割下的中国全要素能源效率:基于超效率DEA方法的经验分析》,载《世界经济》,2008年第9期,第49—59页。
(3)屈小娥:《中国省际全要素能源效率变动分解—基于Malmquist指数的实证研究》,载《数量经济技术经济研究》,2009年第8期,第29—43页。
(4)Abraham Charnes,William Cooper,Edwardo Rhodes,"Measuring the efficiency of decision-making units,"European Journal of Operational Research,Vo1.2,No.6,1978,pp.429-444.CC摸型是DEA模型的一种,由A.Charnes及W.W.Cooper和E.Rhodes提出,因此以三人姓氏首字母命名。
(5)Wade Cook,Lawrence Seiford,"Data envelopment analysis(DEA)-Thirty years on,"European Journal of Operational Research,Vol.192,No.1,2009,pp.1-17.
(6)Rajiv Banker,Abraham Charnes,William Cooper,"Some models for estimating technical and scale inefficiencies in data envelopment analysis,”Management Science,Vol.30,No.9,1984,pp.1078-1092.BCC模型是由Rajiv BanKer及Abraham Chames和William Cooper首次提出,也用的是三人姓氏首字母命名。
(7)魏权龄:《评价相对有效性的DEA方法——运筹学的新领域》,中国人民大学出版社1988年版第100—119页。
(1)Rolf Fare,Shawna Grosskopf,James Logan,"The relative efficiency of Illinois electric utilities,"Resource Energy,Vo1.5,1983,pp.349-67.
(2)Atakelty Hailu,"Nonparametric Productivity Analysis with Undesirable Outputs:Reply,"American Journal of Agricultural Econom ics,Vol.85,No.4,2003,pp.1 075-1077.
(3)Kankana Mukherjee,"Energy use efficiency in US manufacturing:a nonparametric analysis,"Energy Economics,Vol.20,2008,pp.76-96.
(4)袁晓玲、张宝山、杨万平:《基于环境污染的中国全要素能源效率研究》,载《中国工业经济》,2009年第2期,第76—86页。
(5)Satoshi Honma,Jin-Li Hu,"Total-factor energy efficiency of regions in Japan,"Energy Policy,Vol.36,2008,pp.821-33.
(6)Peng Zhou,Kim-LengPoh,BengWah Ang,"A non-radial DEA approach to measuring environ-mental performance,"European Journal of Operational Research,Vol.178,2007,pp.1-9.
(7)Jin-Li Hu,Chih-Hung Kao,"Efficient energy-saving targets for APEC economies,"Energy Policy,Vol.35,2007,pp.373-382.
(8)Jiandong Chen,Malin Song,Long Xu,"Evaluation of environmental efficiency in China using data envelopment analysis,"Ecological Indicators,Vol.52,2015,pp.577-583.
(1)黄伟华:《固定资本存量理论方法探讨与核算设计》,广东财经大学博士论文,2017年。
(2)Raymond Goldsmith,A Perpetual Inventory of National Wealth,Studies in Income and Wealth,National Bureau of Economic Research,Cambridge,1951,14,pp.5-74.
(1)David Hawdon,Ian McQueen,Using Lambda for DEA.Surrey Energy Economics Center Technical Paper No.l,https://www.seec.surrey.ac.uk/research/SEEDS/SEEDSTP1.pdf,访问日期:2019年1月15日。
(1)BritishPetroleum. BP Statistical Review of World Energy(67 edition),https://www.bp.com/content,访问日期:2019年1月19日。
(2)《世界银行世界各国劳动力总量》,https://data.worldbank.org/indicator/SL.TLF.TOTL.IN?end=2016&start=1990,访问日期:2019年1月19日。
(3)国际货币基金组织:Investment and Capital Stock Dataset,1960-201 5.https://www.google.com/url,访问日期:2019年1月19日。
(1)国家发改委、国家能源局:《能源发展“十三五”规划》,http://www.ndrc.gov.cn/zcfb/zcfbtz/2 01 701/W02017011733 5278192779.pdf,访问日期:2019年1月25日。
(2)The World Bank,Industry(including Construction),Value Added(%of GDP),https://data.worldbank.org/indicator/N V.IND.TOTL.Z S?locations=US,访问日期:2019年1月25日。
(1)人民网:《二十国集团领导人杭州峰会公报》,http://world.people.com.cn/n1/2016/0906/c1002-28693129.html,访问日期:2019年2月2日。
(1)董娟:《国际能源结构转型趋势及典型路径比较分析》,载《中外能源》,2014年第10期,第3—20页。