边际减排成本与区域差异性研究
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摘要
在全球气候变暖的背景下,减少CO_2排放量已经成为世界各国面临的重大挑战,而我国作为CO_2排放大国,承诺到2020年单位GDP的排放量较2005年下降40%～45%.围绕CO_2的减排问题,本文展开了两大方面的研究工作:1)基于我国8种化石能源的面板数据,采用二次型方向性距离函数模型测算我国各省份(直辖市、自治区)的CO_2边际减排成本,并由此分析省际层面和区域层面的CO_2减排成本差异和技术效率.研究结果表明,减排成本最低的省份(山西)是最高的地区(北京)的1/5,而且不同省份之间CO_2减排成本差异较大.还发现,CO_2边际减排成本的大小整体呈现为"东部>中部>西部".这为在全国范围内不同省份之间建立碳排放交易市场提供了理论分析与实证检验的参考依据. 2)深入考察了我国
All the countries in the world are confronted with the challenge of CO_2 abatement. As a nation with a large discharge of CO_2,China has made a commitment to reduce the emissions by 40% ～ 45% per unit of GDP in 2020,compared with those in 2005. In this background,this paper conducts the research from two major aspects. Firstly,using data of eight categories of fossil fuels,the paper calculates the marginal abatement costs of CO_2 among provinces by adopting the quadratic directional distance function model,and then analyzes the differences in abatement costs and technical efficiency at the provincial,municipal and regional level. As the results show,the abatement costs of Shanxi,which has the lowest cost,are 1/5 of Beijing's who has the highest cost. What's more,the marginal abatement costs of CO_2 varies in provinces. The marginal abatement costs differs regionally: it declines progressively from the East to the Middle and then to the West.This provides an objective economic basis for establishing a carbon trading market among various provinces domestically. Secondly,this paper makes a profound analysis on the reasons of the significant differences in the abatement costs among provinces. The results show that the main factors affecting the marginal abatement costs of CO_2 include emission concentration,research and development degree,human resource level,energy structure,carbon emission policy,and urbanization level. Based on the conclusions above,several suggestions for developing low-carbon economy currently are put forward,as gives this paper important academic value and practical significance.
All the countries in the world are confronted with the challenge of CO_2 abatement. As a nation with a large discharge of CO_2,China has made a commitment to reduce the emissions by 40% ～ 45% per unit of GDP in 2020,compared with those in 2005. In this background,this paper conducts the research from two major aspects. Firstly,using data of eight categories of fossil fuels,the paper calculates the marginal abatement costs of CO_2 among provinces by adopting the quadratic directional distance function model,and then analyzes the differences in abatement costs and technical efficiency at the provincial,municipal and regional level. As the results show,the abatement costs of Shanxi,which has the lowest cost,are 1/5 of Beijing's who has the highest cost. What's more,the marginal abatement costs of CO_2 varies in provinces. The marginal abatement costs differs regionally: it declines progressively from the East to the Middle and then to the West.This provides an objective economic basis for establishing a carbon trading market among various provinces domestically. Secondly,this paper makes a profound analysis on the reasons of the significant differences in the abatement costs among provinces. The results show that the main factors affecting the marginal abatement costs of CO_2 include emission concentration,research and development degree,human resource level,energy structure,carbon emission policy,and urbanization level. Based on the conclusions above,several suggestions for developing low-carbon economy currently are put forward,as gives this paper important academic value and practical significance.
引文
[1]张国兴,叶亚琼,管欣,等.京津冀节能减排政策措施的差异与协同研究[J].管理科学学报,2018, 21(5):111-126.Zhang Guoxing, Ye Yaqiong, Guan Xin, et al. Difference and collaboration in Jing-Jin-Ji's energy saving and emission reduction policy measurers[J]. Journal of Management Sciences in China, 2018, 21(5):111-126.(in Chinese)
[2]Kesicki F, Ekins P. Marginal abatement cost curves:A call for caution[J]. Climate Policy, 2012, 12(2):219-236.
[3]石敏俊,袁永娜,周晟吕,等.碳减排政策:碳税、碳交易还是两者兼之?[J].管理科学学报,2013, 16(9):9-19.Shi Minjun, Yuan Yongna, Zhou Shenglii, et al. Carbon tax, cap-and-trade or mixed policy:Which is better for carbon mitigation?[J]. Journal of Management Sciences in China, 2013, 16(9):9-19.(in Chinese)
[4]Edenhofer 0, Kai Land, Kemfert C. Induced technological change:Exploring its implications for the economics of atmospheric stabilization:Synthesis report from the innovation modeling comparison project[J]. Energy Journal, 2006, 27:57-107.
[5]Weyant P, John F C, Chesnaye D L, et al. Overview of EMF-21:Multigas mitigation and climate policy[J]. Energy Journal, 2006, 27:1-32.
[6]Fare R, Grosskopf S, Lovell C A, et al. Derivation of shadow prices for undesirable outputs:A distance function approach[J]. The Review of Economics and Statistics, 1993, 75:374-380.
[7]Lee M. The shadow price of substitutable sulfur in the US electric power plant:A distance function approach[J]. Journal of Environmental Management, 2005, 77(2):104-110.
[8]Rezek J P, Campbell R C. Cost estimates for multiple pollutants:A maximum entropy approach[J]. Energy Economics,2007, 29:503-519.
[9]Hailu A, Veeman T S. Environmentally sensitive productivity analysis of the Canadian pulp and paper industry, 1959-1994:An input distance function approach[J]. Journal of Environmental Economics and Management, 2000, 40(3):251-274.
[10]Fare R, Grosskopf S, Noh D W, et al. Characteristics of a polluting technology:Theory and practice[J]. Journal of Econometrics, 2005, 126:469-492.
[11]Murty M N, Kumar S, Dhavala K K. Measuring environmental efficiency of industry:A case study of thermal power generation in India[J]. Environmental and Resource Economics, 2007, 38:31-50.
[12]Matsushita K, Yamane F. Pollution from the electric power sector in Japan and efficient pollution reduction[J]. Energy Economics,2012,34(4):1124-1130.
[13]Wei C,Loschel A, Liu B. An empirical analysis of the CO2 shadow price in Chinese thermal power enterprises[J]. Energy Economics,2013,40(18):22-31.
[14]Du L, Hanley A, Zhang N. Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China:A parametric meta-frontier analysis[J]. Resource&Energy Economics, 2016, 43:14-32.
[15]Wang K,Wei Y M. China's regional industrial energy efficiency and carbon emissions abatement costs[J]. Applied Energy, 2014, 130:617-631.
[16]魏楚.中国城市CO_2边际减排成本及其影响因素[J].世界经济,2014,(7):115-141.Wei Chu. The marginal abatement costs of CO_2 and its determinants in China[J]. The Journal of World Economy, 2014,(7):115-141.(in Chinese)
[17]Wu J, Ma C. The convergence of China's marginal abatement cost of CO_2:An emission-weighted continuous state space approach[J]. Environmental and Resource Economics, 2018:1-21.
[18]Wang Q,Cui Q, Zhou D, et al. Marginal abatement costs of carbon dioxide in China:A nonparametric analysis[J]. Energy Procedia, 2011, 5:2316-2320.
[19]Wei C, Ni J, Du L. Regional allocation of carbon dioxide abatement in China[J]. China Economic Review, 2012, 23(3):552-565.
[20]Zhang X, Xu Q, Zhang F, et al. Exploring shadow prices of carbon emissions at provincial levels in China[J]. Ecological Indicators, 2014, 46:407-414.
[21]Du L, Hanley A, Wei C. Estimating the marginal abatement cost curve of C02 emissions in China:Provincial panel data analysis[J]. Energy Economics, 2015, 48:217-229.
[22]Du L, Wei C, Cai S. Economic development and carbon dioxide emissions in China:Provincial panel data analysis[J].China Economic Review, 2012, 23(2):371-384.
[23]Wang Q, Chiu Y H, Chiu C R. Driving factors behind carbon dioxide emissions in China:A modified production-theoretical decomposition analysis[J]. Energy Economics, 2015, 51:252-260.
[24]Zhang N, Choi Y. A note on the evolution of directional distance function and its development in energy and environmental studies 1997-2013[J]. Renewable&Sustainable Energy Reviews, 2014, 33(2):50-59.
[25]Perroni C, Rutherford T F. Regular flexibility of nested CES functions[J]. European Economic Review, 1995, 39:335-343.
[26]Vardanyan M, Noh D W. Approximating pollution abatement costs via alternative specifications of a multi-output production technology:A case of the US electric utility industry[J]. Journal of Environmental Management,2006,80(2):177-190.
[27]Fare R, Martins-Filho C, Vardanyan M. On functional form representation of multi-output production technologies[J].Journal of Productivity Analysis, 2010, 33(2):81-96.
[28]陈诗一.工业二氧化碳的影子价格:参数和非参数方法[J].世界经济,2010,(8):93-111.Chen Shiyi. The shadow price of industrial carbon dioxide:Parametric and nonparametric methods[J]. The Journal of World Economy, 2010,(8):93-111.(in Chinese)
[29]Ma C, Hailu A. The marginal abatement cost of carbon emissions in China[J]. The Energy Journal, 2016,37(1):111-127.(China Special Issue)
[30]Yang D, Feng L. Examining the effects of urbanization and industrialization on carbon dioxide emission:Evidence from China's provincial regions[J]. Energy, 2017, 125:533-542.
[31]Bauman Y. The Effects of Environmental Policy on Technological Change in Pollution Control[D]. Seattle:University of Washington, 2003.
[32]Heal G, Tarui N. Technology diffusion, abatement cost, and transboundary pollution[J]. Ssrn Electronic Journal,2008,96(11):1-24.
[33]申萌,李凯杰,曲如晓.技术进步、经济增长与二氧化碳排放:理论和经验研究[J].世界经济,2012,(7):83-100.Shen Meng, Li Kaijie, Qu Ruxiao. Technological progress, economic growth and carbon dioxide emissions:Theoretical and empirical research[J]. The Journal of World Economy, 2012,(7):83-100.(in Chinese)
[34]杨子晖,田磊.“污染天堂”假说与影响因素的中国省际研究[J].世界经济,2017,(5):148-172.Yang Zihui, Tian Lei. A panel co-integration analysis of the pollution haven hypothesis and its determinants in China[J].The Journal of World Economy, 2017,(5):148-172.(in Chinese)
(2)Du等[22]基于1995年-2009年的7种化石能源和水泥生产的二氧化碳排放的省际面板数据,对中国二氧化碳排放的决定性因素,变化趋势和减排潜力进行了探讨,得出经济发展、技术进步和产业结构是影响中国二氧化碳排放的最重要因素,而能源消费结构、贸易开放度和城市化水平的影响可以忽略不计.Wang等[23]使用Shephard距离函数来测算非合意产出(二氧化碳排放)和投入(能耗),同时提出了一种改进的PDA(production-theoretical decomposition analysis)方法,将二氧化碳排放的变化分解为七个驱动因素,根据2005年~2010年的数据,该研究发现,影响二氧化碳排放的三大因素依次为经济发展、能源结构、能源效率.此外,技术进步、能源强度降低和二氧化碳排放效率的提高,降低了二氧化碳排放增长率.此外,中国东部、中部和西部的二氧化碳排放和驱动因素有显著差异.
(3)西藏、内蒙古数据缺失,不含港澳台.
(4)相关研究表明燃烧煤炭产生的CO_2要远高于天然气、石油等燃料.
(5)而从狭义的角度,人力资源数量水平的已有度量已经较为接近人力资源的定义,这也是本文只考虑使用人力资源数量来代表人力资源水平的另一个重要原因.
(6)从理论上来看,人力资源水平与经济发展很可能具有高度的相关性,且相关系数分析结果显示它们的相关系数为0.834,故为了避免多重共线性等问题,本文在考虑之后剔除了原来的经济发展水平变量.
(7)由于本文中政策变量为时间虚拟变量,无法放入截面数据和滚动面板数据中回归,因此后文中只在普通面板中加入了碳排放政策变量进行回归,以考察其对CO2边际减排成本的影响.
(8)相关碳排放政策的参考来源于中国碳排放交易网,网址为http://www.tanpaifang.com.
(9)为了节省论文空间,没有报道出所有影响因素的省际分布图,有兴趣的读者可向作者索取.
(10)篇幅所限,并未列出东部及热点地区逐步回归法的所有结果,在这类只报道考虑所有变量的最终结果.
[2]Kesicki F, Ekins P. Marginal abatement cost curves:A call for caution[J]. Climate Policy, 2012, 12(2):219-236.
[3]石敏俊,袁永娜,周晟吕,等.碳减排政策:碳税、碳交易还是两者兼之?[J].管理科学学报,2013, 16(9):9-19.Shi Minjun, Yuan Yongna, Zhou Shenglii, et al. Carbon tax, cap-and-trade or mixed policy:Which is better for carbon mitigation?[J]. Journal of Management Sciences in China, 2013, 16(9):9-19.(in Chinese)
[4]Edenhofer 0, Kai Land, Kemfert C. Induced technological change:Exploring its implications for the economics of atmospheric stabilization:Synthesis report from the innovation modeling comparison project[J]. Energy Journal, 2006, 27:57-107.
[5]Weyant P, John F C, Chesnaye D L, et al. Overview of EMF-21:Multigas mitigation and climate policy[J]. Energy Journal, 2006, 27:1-32.
[6]Fare R, Grosskopf S, Lovell C A, et al. Derivation of shadow prices for undesirable outputs:A distance function approach[J]. The Review of Economics and Statistics, 1993, 75:374-380.
[7]Lee M. The shadow price of substitutable sulfur in the US electric power plant:A distance function approach[J]. Journal of Environmental Management, 2005, 77(2):104-110.
[8]Rezek J P, Campbell R C. Cost estimates for multiple pollutants:A maximum entropy approach[J]. Energy Economics,2007, 29:503-519.
[9]Hailu A, Veeman T S. Environmentally sensitive productivity analysis of the Canadian pulp and paper industry, 1959-1994:An input distance function approach[J]. Journal of Environmental Economics and Management, 2000, 40(3):251-274.
[10]Fare R, Grosskopf S, Noh D W, et al. Characteristics of a polluting technology:Theory and practice[J]. Journal of Econometrics, 2005, 126:469-492.
[11]Murty M N, Kumar S, Dhavala K K. Measuring environmental efficiency of industry:A case study of thermal power generation in India[J]. Environmental and Resource Economics, 2007, 38:31-50.
[12]Matsushita K, Yamane F. Pollution from the electric power sector in Japan and efficient pollution reduction[J]. Energy Economics,2012,34(4):1124-1130.
[13]Wei C,Loschel A, Liu B. An empirical analysis of the CO2 shadow price in Chinese thermal power enterprises[J]. Energy Economics,2013,40(18):22-31.
[14]Du L, Hanley A, Zhang N. Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China:A parametric meta-frontier analysis[J]. Resource&Energy Economics, 2016, 43:14-32.
[15]Wang K,Wei Y M. China's regional industrial energy efficiency and carbon emissions abatement costs[J]. Applied Energy, 2014, 130:617-631.
[16]魏楚.中国城市CO_2边际减排成本及其影响因素[J].世界经济,2014,(7):115-141.Wei Chu. The marginal abatement costs of CO_2 and its determinants in China[J]. The Journal of World Economy, 2014,(7):115-141.(in Chinese)
[17]Wu J, Ma C. The convergence of China's marginal abatement cost of CO_2:An emission-weighted continuous state space approach[J]. Environmental and Resource Economics, 2018:1-21.
[18]Wang Q,Cui Q, Zhou D, et al. Marginal abatement costs of carbon dioxide in China:A nonparametric analysis[J]. Energy Procedia, 2011, 5:2316-2320.
[19]Wei C, Ni J, Du L. Regional allocation of carbon dioxide abatement in China[J]. China Economic Review, 2012, 23(3):552-565.
[20]Zhang X, Xu Q, Zhang F, et al. Exploring shadow prices of carbon emissions at provincial levels in China[J]. Ecological Indicators, 2014, 46:407-414.
[21]Du L, Hanley A, Wei C. Estimating the marginal abatement cost curve of C02 emissions in China:Provincial panel data analysis[J]. Energy Economics, 2015, 48:217-229.
[22]Du L, Wei C, Cai S. Economic development and carbon dioxide emissions in China:Provincial panel data analysis[J].China Economic Review, 2012, 23(2):371-384.
[23]Wang Q, Chiu Y H, Chiu C R. Driving factors behind carbon dioxide emissions in China:A modified production-theoretical decomposition analysis[J]. Energy Economics, 2015, 51:252-260.
[24]Zhang N, Choi Y. A note on the evolution of directional distance function and its development in energy and environmental studies 1997-2013[J]. Renewable&Sustainable Energy Reviews, 2014, 33(2):50-59.
[25]Perroni C, Rutherford T F. Regular flexibility of nested CES functions[J]. European Economic Review, 1995, 39:335-343.
[26]Vardanyan M, Noh D W. Approximating pollution abatement costs via alternative specifications of a multi-output production technology:A case of the US electric utility industry[J]. Journal of Environmental Management,2006,80(2):177-190.
[27]Fare R, Martins-Filho C, Vardanyan M. On functional form representation of multi-output production technologies[J].Journal of Productivity Analysis, 2010, 33(2):81-96.
[28]陈诗一.工业二氧化碳的影子价格:参数和非参数方法[J].世界经济,2010,(8):93-111.Chen Shiyi. The shadow price of industrial carbon dioxide:Parametric and nonparametric methods[J]. The Journal of World Economy, 2010,(8):93-111.(in Chinese)
[29]Ma C, Hailu A. The marginal abatement cost of carbon emissions in China[J]. The Energy Journal, 2016,37(1):111-127.(China Special Issue)
[30]Yang D, Feng L. Examining the effects of urbanization and industrialization on carbon dioxide emission:Evidence from China's provincial regions[J]. Energy, 2017, 125:533-542.
[31]Bauman Y. The Effects of Environmental Policy on Technological Change in Pollution Control[D]. Seattle:University of Washington, 2003.
[32]Heal G, Tarui N. Technology diffusion, abatement cost, and transboundary pollution[J]. Ssrn Electronic Journal,2008,96(11):1-24.
[33]申萌,李凯杰,曲如晓.技术进步、经济增长与二氧化碳排放:理论和经验研究[J].世界经济,2012,(7):83-100.Shen Meng, Li Kaijie, Qu Ruxiao. Technological progress, economic growth and carbon dioxide emissions:Theoretical and empirical research[J]. The Journal of World Economy, 2012,(7):83-100.(in Chinese)
[34]杨子晖,田磊.“污染天堂”假说与影响因素的中国省际研究[J].世界经济,2017,(5):148-172.Yang Zihui, Tian Lei. A panel co-integration analysis of the pollution haven hypothesis and its determinants in China[J].The Journal of World Economy, 2017,(5):148-172.(in Chinese)
(2)Du等[22]基于1995年-2009年的7种化石能源和水泥生产的二氧化碳排放的省际面板数据,对中国二氧化碳排放的决定性因素,变化趋势和减排潜力进行了探讨,得出经济发展、技术进步和产业结构是影响中国二氧化碳排放的最重要因素,而能源消费结构、贸易开放度和城市化水平的影响可以忽略不计.Wang等[23]使用Shephard距离函数来测算非合意产出(二氧化碳排放)和投入(能耗),同时提出了一种改进的PDA(production-theoretical decomposition analysis)方法,将二氧化碳排放的变化分解为七个驱动因素,根据2005年~2010年的数据,该研究发现,影响二氧化碳排放的三大因素依次为经济发展、能源结构、能源效率.此外,技术进步、能源强度降低和二氧化碳排放效率的提高,降低了二氧化碳排放增长率.此外,中国东部、中部和西部的二氧化碳排放和驱动因素有显著差异.
(3)西藏、内蒙古数据缺失,不含港澳台.
(4)相关研究表明燃烧煤炭产生的CO_2要远高于天然气、石油等燃料.
(5)而从狭义的角度,人力资源数量水平的已有度量已经较为接近人力资源的定义,这也是本文只考虑使用人力资源数量来代表人力资源水平的另一个重要原因.
(6)从理论上来看,人力资源水平与经济发展很可能具有高度的相关性,且相关系数分析结果显示它们的相关系数为0.834,故为了避免多重共线性等问题,本文在考虑之后剔除了原来的经济发展水平变量.
(7)由于本文中政策变量为时间虚拟变量,无法放入截面数据和滚动面板数据中回归,因此后文中只在普通面板中加入了碳排放政策变量进行回归,以考察其对CO2边际减排成本的影响.
(8)相关碳排放政策的参考来源于中国碳排放交易网,网址为http://www.tanpaifang.com.
(9)为了节省论文空间,没有报道出所有影响因素的省际分布图,有兴趣的读者可向作者索取.
(10)篇幅所限,并未列出东部及热点地区逐步回归法的所有结果,在这类只报道考虑所有变量的最终结果.