环境规制与中国企业全要素生产率——基于“节能减碳”政策的检验
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摘要
本文利用中国政府2011年开始实施的《万家企业节能低碳行动实施方案》这一准自然实验,采用广义倾向得分匹配法(GPSM)对环境规制与中国工业企业全要素生产率之间的因果关系进行了识别。研究发现":节能减碳"政策强度与制造业企业全要素生产率之间存在"N"形关系。适度的"减碳"政策强度下,创新的促进效应超过成本增加效应,有利于企业全要素生产率的增长;创新的促进效应随着时间的推移得到逐步释放,表现为"节能减碳"政策促进制造业企业全要素生产率增长的政策区间逐步扩大;对于不同样本上述结论依然保持稳健。本文的研究结果表明,适度的环境规制并不会损害中国企业的竞争力,能实现环境质量提高和生产率增长的"双赢"。
It took advantages of the quasi-natural experiment of the implementation of energy saving and low-carbon plan of ten thousand enterprises, which was implemented by the Chinese government in 2011 and used the generalized propensity score matching method(GPSM)to recognize the cause and effect relationship between the "energy saving and carbon reduction"policy and total factor productivity of Chinese manufacturing enterprises. It finds that the "Nshape"relationship exists between the policy intensity of energy saving and carbon reduction and the total factor productivity of manufacturing enterprises. The promotion effect of innovation under the moderate "carbon reduction"policy is more beneficial to the growth of total factor productivity of enterprises, which is gradually released over time.The interval of the policy has been gradually expanded in promoting total factor productivity growth of manufacturing enterprises. The above conclusions are still robust under different samples. The results show that the implementation of a moderate environmental control policy will not undermine the competitiveness of Chinese enterprises, which will help to achieve a "win-win"for improving environmental quality and productivity growth.
It took advantages of the quasi-natural experiment of the implementation of energy saving and low-carbon plan of ten thousand enterprises, which was implemented by the Chinese government in 2011 and used the generalized propensity score matching method(GPSM)to recognize the cause and effect relationship between the "energy saving and carbon reduction"policy and total factor productivity of Chinese manufacturing enterprises. It finds that the "Nshape"relationship exists between the policy intensity of energy saving and carbon reduction and the total factor productivity of manufacturing enterprises. The promotion effect of innovation under the moderate "carbon reduction"policy is more beneficial to the growth of total factor productivity of enterprises, which is gradually released over time.The interval of the policy has been gradually expanded in promoting total factor productivity growth of manufacturing enterprises. The above conclusions are still robust under different samples. The results show that the implementation of a moderate environmental control policy will not undermine the competitiveness of Chinese enterprises, which will help to achieve a "win-win"for improving environmental quality and productivity growth.
引文
[1] BP GROUP. BP statistical review of world energy 2016[R]. London:BP, 2016.
[2]克鲁克斯.迈向环境可持续的未来:中华人民共和国国家环境分析[M].张庆丰,译.北京:中国财政经济出版社, 2012.
[3]林伯强,姚昕,刘希颖.节能和碳排放约束下的中国能源结构战略调整[J].中国社会科学, 2010(1):58-71.
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[16]李斌,彭星,陈柱华.环境规制、FDI与中国治污技术创新———基于省际动态面板数据的分析[J].财经研究, 2011, 37(10):92-102.
[17]李小平,卢现祥,陶小琴.环境规制强度是否影响了中国工业行业的贸易比较优势[J].世界经济, 2012, 35(4):62-78.
[18]张成,陆旸,郭路,等.环境规制强度和生产技术进步[J].经济研究, 2011, 46(2):113-124.
[19]王杰,刘斌.环境规制与企业全要素生产率———基于中国工业企业数据的经验分析[J].中国工业经济,2014(3):44-56.
[20]刘和旺,郑世林,左文婷.环境规制对企业全要素生产率的影响机制研究[J].科研管理, 2016, 37(5):33-41.
[21] ALPAY E, KERKVLIET J, BUCCOLA S. Productivity growth and environmental regulation in Mexican and US food manufacturing[J]. American Journal of Agricultural Economics, 2002, 84(4):887-901.
[22] LANOIE P, PATRY M, LAJEUNESSE R. Environmental regulation and productivity:testing the Porter hypothesis[J]. Journal of Productivity Analysis, 2008, 30(2):121-128.
[23] ESTY D C, PORTER M E. National environmental performance:an empirical analysis of policy results and determinants[J]. Environment and Development Economics, 2005, 10(4):391-434.
[24]李树,陈刚.环境管制与生产率增长———以APPCL2000的修订为例[J].经济研究, 2013, 48(1):17-31.
[25] HIRANO K, IMBENS G W. The propensity score with continuous treatmens[M]//GELMAN A, MENG X L.Applied bayesian modeling and causal inference from incomplete-data perspectives. Chichester:Wiley, 2004:73-84.
[26] BARBARA G, MARCO V. Estimating the dose-response function through a generalized linear model approach[J]. Stata Journal, 2014,14(1):141-158.
(1)万家企业是指年综合能源消费量1万吨标准煤以上以及有关部门指定的年综合能源消费量5 000吨标准煤以上的重点用能单位。
(2)以大写字母表示观测值,Yi=y i(T i),即Y i是处理变量取Ti时的产出值。
(3)例如,如果存在“能者多劳”选择机制,受到较强的减碳政策的就可能是那些(潜在)效率水平高的企业。
(4)在拓展分析“节能减碳”政策的滞后影响时,分别保留2010年和2011年、2010年和2012年、2010年和2013年都在数据集中的企业样本,避免样本损失过大。
(5)OLLEY和PAKES于1996年提出了一种半参数的估计生产函数的方法,该方法能有效克服企业层面数据估计生产函时带来同性偏误(simultaneity bias)和样本选择性偏误(selection bias)。Stata提供的官方命令opreg,能够完成生产函数估计和分离生产率信息。
[2]克鲁克斯.迈向环境可持续的未来:中华人民共和国国家环境分析[M].张庆丰,译.北京:中国财政经济出版社, 2012.
[3]林伯强,姚昕,刘希颖.节能和碳排放约束下的中国能源结构战略调整[J].中国社会科学, 2010(1):58-71.
[4] ZHU X. Understand China’s growth:past, present, and future[J]. Economic Perspective, 2012, 26(4):103-124.
[5] KRUGMAN P. The myth of Asia’s miracle[J]. Foreign Affairs, 1994, 73(6):62-78.
[6] AMBEC S, COHEN M A, ELGIE S, et al. The Porter hypothesis at 20:can environmental regulation enhance innovation and competitiveness?[J]. Review of Environmental Economics and Policy, 2013, 7(1):2-22.
[7] BLACKMAN A, LAHIRI B, PIZER W, et al. Voluntary environmental regulation in developing countries:Mexico’s clean industry program[J]. Journal of Environmental Economics&Management, 2010, 60(3):182-192.
[8] HAVEMAN R H, CHRISTAINSEN G B. Environmental regulations and productivity growth[J]. Natural Resources Journal, 1981, 21(3):489-509.
[9] SMITH J B, SIMS W A. The impact of pollution charges on productivity growth in canadian brewing[J].Rand Journal of Economics, 1985, 16(3):410-423.
[10] BARBERA A J, MCCONNELL V D. The impact of environmental regulations on industry productivity:direct and indirect effects[J]. Journal of Environmental Economics and Management, 1990, 18(1):50-65.
[11] PORTER M E. America’s green strategy[J]. Scientific America, 1991, 264(4):96.
[12] PORTER M E, LINDE C V D. Toward a new conception of the environment-competitiveness relationship[J].Journal of Economic Perspectives, 1995, 9(4):97-118.
[13] BERMAN E, BUI L T M. Environmental regulation and productivity:evidence from Oilrefineries[J]. Review of Economics and Statistics, 2001, 83(3):498-510.
[14] CARRION-FLORES C, INNES R, SAM A G. Do voluntary pollution reduction programs(VPRs)spur innovation in environmental technology[C]. California:American Agricultural Economics Association Annual Meeting,2006.
[15] YANG C H, TSENG Y H, CHEN C P. Environmental regulations, induced R&D, and productivity:evidence from Taiwan’s manufacturing industries[J]. Resource and Energy Economics, 2012, 34(4):514-532.
[16]李斌,彭星,陈柱华.环境规制、FDI与中国治污技术创新———基于省际动态面板数据的分析[J].财经研究, 2011, 37(10):92-102.
[17]李小平,卢现祥,陶小琴.环境规制强度是否影响了中国工业行业的贸易比较优势[J].世界经济, 2012, 35(4):62-78.
[18]张成,陆旸,郭路,等.环境规制强度和生产技术进步[J].经济研究, 2011, 46(2):113-124.
[19]王杰,刘斌.环境规制与企业全要素生产率———基于中国工业企业数据的经验分析[J].中国工业经济,2014(3):44-56.
[20]刘和旺,郑世林,左文婷.环境规制对企业全要素生产率的影响机制研究[J].科研管理, 2016, 37(5):33-41.
[21] ALPAY E, KERKVLIET J, BUCCOLA S. Productivity growth and environmental regulation in Mexican and US food manufacturing[J]. American Journal of Agricultural Economics, 2002, 84(4):887-901.
[22] LANOIE P, PATRY M, LAJEUNESSE R. Environmental regulation and productivity:testing the Porter hypothesis[J]. Journal of Productivity Analysis, 2008, 30(2):121-128.
[23] ESTY D C, PORTER M E. National environmental performance:an empirical analysis of policy results and determinants[J]. Environment and Development Economics, 2005, 10(4):391-434.
[24]李树,陈刚.环境管制与生产率增长———以APPCL2000的修订为例[J].经济研究, 2013, 48(1):17-31.
[25] HIRANO K, IMBENS G W. The propensity score with continuous treatmens[M]//GELMAN A, MENG X L.Applied bayesian modeling and causal inference from incomplete-data perspectives. Chichester:Wiley, 2004:73-84.
[26] BARBARA G, MARCO V. Estimating the dose-response function through a generalized linear model approach[J]. Stata Journal, 2014,14(1):141-158.
(1)万家企业是指年综合能源消费量1万吨标准煤以上以及有关部门指定的年综合能源消费量5 000吨标准煤以上的重点用能单位。
(2)以大写字母表示观测值,Yi=y i(T i),即Y i是处理变量取Ti时的产出值。
(3)例如,如果存在“能者多劳”选择机制,受到较强的减碳政策的就可能是那些(潜在)效率水平高的企业。
(4)在拓展分析“节能减碳”政策的滞后影响时,分别保留2010年和2011年、2010年和2012年、2010年和2013年都在数据集中的企业样本,避免样本损失过大。
(5)OLLEY和PAKES于1996年提出了一种半参数的估计生产函数的方法,该方法能有效克服企业层面数据估计生产函时带来同性偏误(simultaneity bias)和样本选择性偏误(selection bias)。Stata提供的官方命令opreg,能够完成生产函数估计和分离生产率信息。