基于DEA的CO_2排放因素分解模型
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摘要
全球变暖已经成为世界主要的环境问题之一,其主要归因于温室气体的排放。因此,寻找二氧化碳减排途径,分析影响二氧化碳排放变化的内在驱动因素成为近年来研究重点。
本文首先利用距离函数和数据包络分析法建立考虑环境影响的DEA模型,并测度投入要素、期望产出和非期望产出要素的相对效率,利用Malmquist指数将效率的变化分解为技术效率变化和技术水平变化。在考虑投入和产出要素的技术效率和技术水平的基础上,建立碳排放的因素分解模型。该模型将驱动碳排放变化的因素分解为9个方面：潜在的碳因素变化、潜在的能源强度变化、潜在的GDP变化以及能源、碳排放和生产技术效率变化以及能源、碳排放和生产技术进步,可以量化不同因素对碳排放变化的贡献。其突出的优点是可以分析不同投入和产出要素的技术效率和技术水平对碳排放的影响机制。
然后,对20个发展中国家1995-2005的碳排放变化的驱动因素进行了实证分析,结果表明：对于20个发展中国家整体来说,1995-2005年,经济发展是促进二氧化碳排放增长的最重要因素,而生产技术进步是二氧化碳减排的最主要驱动因素。同时,该实证结果可以分析每个国家的碳排放变化驱动因素,本文重点分析了中国二氧化碳排放时空演变机制。结果表明：从促进碳排放增加的因素来看,经济增长是增加二氧化碳排放的最主要因素；低碳技术的开发与储备不足,中国的减排技术进步相对缓慢；能源利用效率先增长后降低,对二氧化碳排放增加年均贡献1.29%；碳因素变化、节能技术以及生产技术逐渐好转,后期贡献了二氧化碳排放的降低。从降低碳排放的因素来看,能源强度的降低是最主要的碳减排驱动因素；碳排放技术效率与生产技术效率的提高,对降低二氧化碳排放分别贡献了2.83%和3.39%。
最后,根据实证分析结果,本文分别从能源低碳化、生产低碳化、减碳的制度创新三个方面制定节能减排策略。
Global warming due to greenhouse gas emissions has become one of the major environmental problems. So, looking for decomposition model of CO2emissions and developing low-carbon economy have become a key issue.
The efficiency change of inputs/outputs, which can be decomposed into technical efficiency change and technical change, was measured by using distance function and data envelopment analysis. An approach to decompose the change of aggregate CO2emission over time was proposed by taking technical efficiency change and technical change into account. The proposed model decomposes the CO2emission change into nine components specified in this paper, and several production technology related components are included. The key feature of the proposed approach is the introduction of inputs/outputs factor efficiencies, specified as distance functions, to the decomposition model.
The paper was applied in decomposing the CO2emission for20developing countries during the periods of1995-2005. Empirical results indicate that:
For the20developing counties as a whole, in1995-2005, the economic growth is the most important contributor to CO2emissions increase, while good output technical change is the most important component to CO2emissions reduction between1995and2005.
The empirical results also provide extensive insights into the components driving the CO2emissions for single country between1995and2005.The economic growth is the most important contributor to CO2emissions increase; Energy efficiency contribute1.29%to CO2emissions reduction; Carbon factors change, energy saving technology and production technology gradually improve, and later contribute to CO2emissions reduction. The reduction of energy intensity is the most important component to CO2emissions reduction between1995and2005.The enhancement of carbon emissions technology efficiency and production technology efficiency contributed2.83%and3.39%to reduce carbon dioxide emissions, respectively.
The empirical results are useful for setting policies of energy saving and emission reduction.
本文首先利用距离函数和数据包络分析法建立考虑环境影响的DEA模型,并测度投入要素、期望产出和非期望产出要素的相对效率,利用Malmquist指数将效率的变化分解为技术效率变化和技术水平变化。在考虑投入和产出要素的技术效率和技术水平的基础上,建立碳排放的因素分解模型。该模型将驱动碳排放变化的因素分解为9个方面：潜在的碳因素变化、潜在的能源强度变化、潜在的GDP变化以及能源、碳排放和生产技术效率变化以及能源、碳排放和生产技术进步,可以量化不同因素对碳排放变化的贡献。其突出的优点是可以分析不同投入和产出要素的技术效率和技术水平对碳排放的影响机制。
然后,对20个发展中国家1995-2005的碳排放变化的驱动因素进行了实证分析,结果表明：对于20个发展中国家整体来说,1995-2005年,经济发展是促进二氧化碳排放增长的最重要因素,而生产技术进步是二氧化碳减排的最主要驱动因素。同时,该实证结果可以分析每个国家的碳排放变化驱动因素,本文重点分析了中国二氧化碳排放时空演变机制。结果表明：从促进碳排放增加的因素来看,经济增长是增加二氧化碳排放的最主要因素；低碳技术的开发与储备不足,中国的减排技术进步相对缓慢；能源利用效率先增长后降低,对二氧化碳排放增加年均贡献1.29%；碳因素变化、节能技术以及生产技术逐渐好转,后期贡献了二氧化碳排放的降低。从降低碳排放的因素来看,能源强度的降低是最主要的碳减排驱动因素；碳排放技术效率与生产技术效率的提高,对降低二氧化碳排放分别贡献了2.83%和3.39%。
最后,根据实证分析结果,本文分别从能源低碳化、生产低碳化、减碳的制度创新三个方面制定节能减排策略。
Global warming due to greenhouse gas emissions has become one of the major environmental problems. So, looking for decomposition model of CO2emissions and developing low-carbon economy have become a key issue.
The efficiency change of inputs/outputs, which can be decomposed into technical efficiency change and technical change, was measured by using distance function and data envelopment analysis. An approach to decompose the change of aggregate CO2emission over time was proposed by taking technical efficiency change and technical change into account. The proposed model decomposes the CO2emission change into nine components specified in this paper, and several production technology related components are included. The key feature of the proposed approach is the introduction of inputs/outputs factor efficiencies, specified as distance functions, to the decomposition model.
The paper was applied in decomposing the CO2emission for20developing countries during the periods of1995-2005. Empirical results indicate that:
For the20developing counties as a whole, in1995-2005, the economic growth is the most important contributor to CO2emissions increase, while good output technical change is the most important component to CO2emissions reduction between1995and2005.
The empirical results also provide extensive insights into the components driving the CO2emissions for single country between1995and2005.The economic growth is the most important contributor to CO2emissions increase; Energy efficiency contribute1.29%to CO2emissions reduction; Carbon factors change, energy saving technology and production technology gradually improve, and later contribute to CO2emissions reduction. The reduction of energy intensity is the most important component to CO2emissions reduction between1995and2005.The enhancement of carbon emissions technology efficiency and production technology efficiency contributed2.83%and3.39%to reduce carbon dioxide emissions, respectively.
The empirical results are useful for setting policies of energy saving and emission reduction.
引文
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[30]Charnes A., Cooper W.W, Wei Q.L. A Semi-infinite Multi-criteria Programming Approach to Data Envelopment Analysis with Infinitely Many Decision Making Units[J]. The University of Texas at Austin, Center for Cybernetic Studies Report CCS 551,1986.
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[35]Fare, R., Grosskopf, S., Noh, D., Weber, W.,2005. Characteristics of a polluting technology:theory and practice. Journal of Econometrics 126,469-92.
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[39]Wu, Y.,2004. Openness, productivity and growth in the APEC economies. Empirical Economics 29,593-604.
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[41]赵奥,武春友.中国CO2排放量变化的驱动力和减排界域分析[J].经济与管理研究,2010(10)
[42]陈绍锋,刘扬,邹秀萍.C02排放演变驱动力的理论与实证研究[J].科学管理研 究,2010(1)
[43]Zhou P, Ang B W.Total factor carbon emission performance:A Malmquist index analysis[J]. Energy Economics,2010(32):194-201.
[44]魏梅,曹明福,江金荣.生产中碳排放效率长期决定及其收敛性分析[J].数量经济技术经济研究,2010(9)
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[2]Xia Y, Yang C H., Chen X K. Analysis on determining factors of energy intensity in China based on comparable price input-output table [J]. Systems Engineering-Theory & Practice,2009,29(10):22-27.
[3]Rose, A., Casler, S.,1996. Input-output structural decomposition analysis:a critical appraisal. Economic Systems Research 8,33-62.
[4]Charlita de Freitas L., Kaneko S. Decomposition of CO2 emissions change from energy consumption in Brazil:Challenges and policy implications [J]. Energy Policy,2011,39:1495-1504.
[5]Tunc G. I., Turut-Asik S., Akbostanci, S. A decomposition analysis of CO2 emissions from energy use:Turkish case [J]. Energy Policy,2009,37:4689-4699.
[6]Liu L., Fan Y., Wu G., Wei Y.M. Using LMDI method to analyze the change of China industrial CO2 emissions from final fuel use:An Empirical Analysis [J]. Energy Policy,2007,35:5892—5900.
[7]宋德勇、卢忠宝.中国碳排放影响因素分解及其周期性波动研究[J].中国人口·资源与环境,2009,3：18-24
[8]王锋,吴丽华,杨超.中国经济发展中碳排放增长的驱动因素研究[J].经济研究,2010,2：123-136.
[9]Paul,P., Bhattachary, R. N.,2004. CO2 emission from energy use in India:a decomposition analysis. Energy Policy 32,585-593.
[10]Wang, C, Chen, J., Zou J.,2005. Decomposition of energy related CO2 emission in China:1957-2000. Energy 30,73-83.
[11]Lee, K., Oh, W.,2006. Analysis of CO2 emissions in APEC countries:a time series and a cross sectional decomposition using the log mean Divisia method. Energy Policy 34,2779-2787.
[12]Hatzigeorgiou, E., Polatidis, H., Haralambopoulos, D.,2008. CO2 emissions in Greece for 1990-2002:a decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques. Energy 33,492-499.
[13]Zha, D.L., Zhou D.Q., Zhou, P.,2010. Driving forces of residential CO2 emissions in urban and rural China:an index decomposition analysis. Energy Policy 38,3377-3383
[14]Pasurka, C.A.,2006. Decomposing electric power plant emissions within a joint production framework. Energy Economics 28,26-43
[15]Wang,C.,2007. Decomposing energy productivity change:a distance function approach. Energy 32,1326-1333.
[16]Zhou, P., Ang, B.W., Poh, K.L.,2006. Slacks-based efficiency measures for modeling environmental performance. Ecological Economics 60,111-118
[17]Zhou, P., Ang, B.W.,2008b. Decomposition of aggregate CO2 emissions:a production theoretical approach. Energy Economics 30,1054-1067.
[18]Kaya Y. Impact of carbon dioxide emission control on GNP growth:Interpretation of proposed scenarios [R]. Paris:IPCC Energy and Industry Subgroup, Response Strategies Working Group,1990.
[19]朱勤,彭希哲,陆志明,吴开亚.中国能源消费碳排放变化的因素分解及实证分析[J].资源科学,2009,31(12)：2072-2079
[20]赵奥,武春友.中国C02排放量变化的驱动力与减排界域分析[J].经济与管理研究,2010,10：48-54.
[21]York R., Rosa E.A., Dietz T. STIRPAT, IPAT and ImPACT:Analytic tools for unpacking the driving forces of environmental impacts [J]. Ecological Economics, 2003,46(3):351-365.
[22]陈佳瑛,彭希哲,朱勤.家庭模式对碳排放影响的宏观实证分析[J].中国人口科学,2009,5：68-79.
[23]Stephen D., Adamrose. Carbon dioxide emissions in the U.S. economy:A structural decomposition analysis [J]. Environmental and Resource Economics, 1998,11(3):349-363.
[24]Dalton M., O'Neill B., Prskawetz A. Population aging and future carbon emissions in the United States [J]. Energy Economics,2008,30:642-675.
[25]Sadjadi, S.J., Omrani, H.,2008. Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies[J]. Energy Policy 36, 4247-4254.
[26]Charnes A,Cooper W W, Rhodes E. Measuring the efficiency decision making units[J]. European Journal of Operational Research,1978,2:429-444
[27]Fare R,S.Grosskopf and J.Logan (1983). The relative Efficiency of Illinois Electric Utilities[J]. Resoures and Energy,5,349-367
[28]Banker, R.D, A.Charnes and W.W.Cooper (1984). Some Models for estimating Technical and Scale Inefficiencies in Data Envelopment analysis[J]. Management and Science,30,1078-1092.
[29]Charnes A., Cooper W.W.,Golary B., et al. Foundation of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Function[J]. Journal of Econometrics (Netherlands),1985,30 (1):91-107.
[30]Charnes A., Cooper W.W, Wei Q.L. A Semi-infinite Multi-criteria Programming Approach to Data Envelopment Analysis with Infinitely Many Decision Making Units[J]. The University of Texas at Austin, Center for Cybernetic Studies Report CCS 551,1986.
[31]Charnes A., Cooper W.W., Wei Q.L., et al. Corn-positive Data Envelopment Analysis and Multi-objective Programming [J]. Center for Cybernetic Studies Report CCS 633,1988
[32]施秧秧.DEA方法与Tobit模型相结合的工业用地效率研究——基于浙江省的实证[D].浙江：浙江大学,2009
[33]Fare, R., Grosskopf, S., Hernandez-Sancho, F.,2004. Environmental performance: an index number approach. Resource and Energy Economics 26,343-352.
[34]Ang, B.W., Zhang, F.Q.,2000. A survey of index decomposition analysis in energy and environmental analysis. Energy 25,1149-1176.
[35]Fare, R., Grosskopf, S., Noh, D., Weber, W.,2005. Characteristics of a polluting technology:theory and practice. Journal of Econometrics 126,469-92.
[36]Zhou, P., Ang, B.W., Poh, K.L.,2008a. Measuring environmental performance under different environmental DEA technologies Energy Economics 30,1-14.
[37]Tyteca, D.,1997. Linear programming models for the measurement of environmental performance of firms-concepts and empirical results. Journal of Productivity Analysis 8,183-197.
[38]Zaim, O.,2004. Measuring environmental performance of state manufacturing through changes in pollution intensities:a DEA framework. Ecological Economics 48,37-47.
[39]Wu, Y.,2004. Openness, productivity and growth in the APEC economies. Empirical Economics 29,593-604.
[40]Zha, D.L., Zhou D.Q., Zhou, P.,2010. Driving forces of residential CO2 emissions in urban and rural China:an index decomposition analysis. Energy Policy 38,3377-3383.
[41]赵奥,武春友.中国CO2排放量变化的驱动力和减排界域分析[J].经济与管理研究,2010(10)
[42]陈绍锋,刘扬,邹秀萍.C02排放演变驱动力的理论与实证研究[J].科学管理研 究,2010(1)
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