中国碳排放权交易政策对地区清洁生产意愿的影响
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摘要
碳排放权交易政策的实施对于企业开展清洁生产,推动地区经济、社会、环境的可持续发展具有重要意义。基于2011—2017年中国省际面板数据,针对国家发改委"两省五市"碳排放权交易试点政策,运用双重差分法实证分析碳排放权交易对中国各地自愿开展清洁生产的影响。研究结果表明:(1)试点地区企业自愿开展清洁生产的比例平均比非试点地区要高出22个百分点;(2)碳排放权交易政策对当前自愿开展清洁生产比例较低的地区影响程度更显著;(3)碳排放权交易试点有利于地区环境的改善,试点地区PM_(10)、S0_2和NO_2的年平均浓度与比非试点地区相比,分别平均下降了0.0288、0.0170和0.0105 mg/m~3。这些结论有助于我国政府科学有序地实施碳排放权交易政策,提升各地企业开展清洁生产的意愿,改善地区空气环境质量。
The implementation of carbon emissions trading policy is important so that enterprises can both carry out cleaner production and promote the sustainable development of regional economies, societies, and environments. The present study used the trial carbon emissions trading market(CETM) policies of five provinces and two cities, which had been proposed by the National Development and Reform Commission in 2012, and a Difference-in-Differences approach to analyse the effect of the CETM on voluntary clean production by enterprises in various regions of China. This pilot project, which established an account of carbon emission in total volume control and quota trading, will facilitate the formulation of scientific, targeted, and differentiated carbon emission reduction policies and estimation of appropriate emission reduction space throughout China. The findings of this study indicate that 22% more enterprises willingly conduct clean production in pilot areas than in non-pilot areas. The annual mean concentrations of PM_(10), SO_2, and NO_2 in pilot areas were 0.0288, 0.0170, and 0.0105 mg/m~3 lower, respectively, than those of the non-pilot areas. In addition, quantile regression and differential decomposition analyses indicated that CETM trial policies are more effective in regions where lower proportions of enterprises voluntarily carry out cleaner production. These conclusions will help the Chinese government implement CETM policy in a scientifically and orderly manner, so as to promote the will of enterprises to conduct clean production and improve air quality. These findings support several suggestions. First, priority should be given to the promotion of Clean development mechanism(CDM) projects in more populated areas(e.g., Sichuan, Yunnan, inner Mongolia, Gansu, and Hebei). Second, in areas where relatively fewer enterprises carry out cleaner production(e.g., Hainan, Xinjiang, Qinghai, Guizhou, and Shanxi), the government should improve the enterprise awareness of energy conservation, environmental protection, and cooperation with developed industrial countries about the propaganda of CDM projects. Finally, each region should accelerate its industrial and economic restructuring, alter the mode of its economic growth, optimize the energy structure, reduce energy consumption, and increase investments in environmental governance, in order to promote sustainable development.
The implementation of carbon emissions trading policy is important so that enterprises can both carry out cleaner production and promote the sustainable development of regional economies, societies, and environments. The present study used the trial carbon emissions trading market(CETM) policies of five provinces and two cities, which had been proposed by the National Development and Reform Commission in 2012, and a Difference-in-Differences approach to analyse the effect of the CETM on voluntary clean production by enterprises in various regions of China. This pilot project, which established an account of carbon emission in total volume control and quota trading, will facilitate the formulation of scientific, targeted, and differentiated carbon emission reduction policies and estimation of appropriate emission reduction space throughout China. The findings of this study indicate that 22% more enterprises willingly conduct clean production in pilot areas than in non-pilot areas. The annual mean concentrations of PM_(10), SO_2, and NO_2 in pilot areas were 0.0288, 0.0170, and 0.0105 mg/m~3 lower, respectively, than those of the non-pilot areas. In addition, quantile regression and differential decomposition analyses indicated that CETM trial policies are more effective in regions where lower proportions of enterprises voluntarily carry out cleaner production. These conclusions will help the Chinese government implement CETM policy in a scientifically and orderly manner, so as to promote the will of enterprises to conduct clean production and improve air quality. These findings support several suggestions. First, priority should be given to the promotion of Clean development mechanism(CDM) projects in more populated areas(e.g., Sichuan, Yunnan, inner Mongolia, Gansu, and Hebei). Second, in areas where relatively fewer enterprises carry out cleaner production(e.g., Hainan, Xinjiang, Qinghai, Guizhou, and Shanxi), the government should improve the enterprise awareness of energy conservation, environmental protection, and cooperation with developed industrial countries about the propaganda of CDM projects. Finally, each region should accelerate its industrial and economic restructuring, alter the mode of its economic growth, optimize the energy structure, reduce energy consumption, and increase investments in environmental governance, in order to promote sustainable development.
引文
[1] 刘莉娜,曲建升,黄雨生,王莉,曾静静,边悦.中国居民生活碳排放的区域差异及影响因素分析.自然资源学报,2016,31(8):1365- 1377.
[2] Hu Z,Huang D,Rao C J,Xu X L.Innovative allocation mechanism design of carbon emission permits in China under the background of a low-carbon economy.Environment and Planning B:Planning and Design,2016,43(2):419- 434.
[3] 靳云汇,刘学,杨婉华.清洁发展机制与中国环境技术引进.数量经济技术经济研究,2001,18(2):24- 28.
[4] 李荣刚,张小宁.清洁发展机制(CDM)与环境经济可持续发展.农业工程技术(新能源产业),2007,(5):41- 44.
[5] 李挚萍.《京都议定书》与温室气体国际减排交易制度.环境保护,2004,(2):58- 60.
[6] 潘竟虎,赵轩茹.基于空间回归模型的中国碳排放空间差异模拟.环境科学学报,2018,38(7):2894- 2901.
[7] 钟小剑,黄晓伟,范跃新,王莹莹,杨智杰,杨玉盛.中国碳交易市场的特征、动力机制与趋势——基于国际经验比较.生态学报,2017,37(1):331- 340.
[8] Grubb M,Sha F,Spencer T,Hughes N,Zhang Z X.A review of Chinese CO2 emission projections to 2030:the role of economic structure and policy.Climate Policy,2015,15(S):S7-S39.
[9] 颜艳梅,王铮,吴乐英,刘昌新.中国碳排放强度影响因素对区域差异的作用分析.环境科学学报,2016,36(9):3436- 3444.
[10] 姚亮,刘晶茹,王如松.中国居民消费隐含的碳排放量变化的驱动因素.生态学报,2011,31(19):5632- 5637.
[11] Jones C,Kammen D M.Spatial distribution of U.S.household carbon footprints reveals suburbanization undermines greenhouse gas benefits of urban population density.Environmental Science & Technology,2014,48(2):895- 902.
[12] 崔琦,杨军,董琬璐.中国碳排放量估计结果及差异影响因素分析.中国人口·资源与环境,2016,26(2):35- 41.
[13] 曾静静,张志强,曲建升,李燕,刘莉娜,董利苹.家庭碳排放计算方法分析评价.地理科学进展,2012,31(10):1341- 1352.
[14] 邓吉祥,刘晓,王铮.中国碳排放的区域差异及演变特征分析与因素分解.自然资源学报,2014,29(2):189- 200.
[15] 胡初枝,黄贤金,钟太洋,谭丹.中国碳排放特征及其动态演进分析.中国人口·资源与环境,2008,18(3):38- 42.
[16] 陈彦玲,王琛.影响中国人均碳排放的因素分析.北京石油化工学院学报,2009,17(2):54- 58.
[17] 谭显春,赖海萍,顾佰和,涂唐奇,李辉.主体功能区视角下的碳排放核算——以广东省为例.生态学报,2018,38(17):6292- 6301.
[18] 刘晓,王铮,邓吉祥.配额目标约束下区域减排的最优控制率.生态学报,2016,36(5):1380- 1390.
[19] 陈林,伍海军.国内双重差分法的研究现状与潜在问题.数量经济技术经济研究,2015,32(7):133- 148.
[20] Card D,Krueger A B.Minimum wages and employment:a case study of the fast food industry in New Jersey and Pennsylvania.The American Economic Review,1994,18(4):772- 793.
[21] Eissa N,Liebman J B.Labor supply response to the earned income tax credit.The Quarterly Journal of Economics,1996,111(2):605- 637.
[22] 周黎安,陈烨.中国农村税费改革的政策效果:基于双重差分模型的估计.经济研究,2005,40(8):44- 53.
[23] 李永友,陆晨晨.基层分权改革与农村社会公共品供给——基于DID方法的经验证据.经济学家,2012,(7):78- 87.
[24] 肖浩,孔爱国.融资融券对股价特质性波动的影响机理研究:基于双重差分模型的检验.管理世界,2014,30(8):30- 43.
[25] Machado P J A F,Mata J.Counterfactual Decomposition of changes in wage distributions using quantile regression.Journal of Applied Econometrics,2005,20(4):445- 465.
[26] 蒋晶晶,叶斌,计军平,马晓明.中国碳强度下降和碳排放增长的行业贡献分解研究.环境科学,2014,35(11):4378- 4386.
[27] 杜雯翠,朱松,张平淡.我国工业化与城市化进程对环境的影响及对策.财经问题研究,2014,(5):22- 29.
[28] 张亚斌,金培振,沈裕谋.两化融合对中国工业环境治理绩效的贡献——重化工业化阶段的经验证据.产业经济研究,2014,(1):40- 50.
[29] Liu J R,Wang R S,Yang J X,Shi Y.The relationship between consumption and production system and its implications for sustainable development of China.Ecological Complexity,2010,7(2):212- 216.
[30] 徐国泉,刘则渊,姜照华.中国碳排放的因素分解模型及实证分析:1995—2004.中国人口·资源与环境,2006,16(6):158- 161.
[31] 刘志雄.能源消费是否引起环境污染?——基于我国1991—2011年数据的实证研究.生态经济,2015,31(3):19- 24.
[32] Apergis N,Payne J E.CO2 emissions,energy usage,and output in Central America.Energy Policy,2009,37(8):3282- 3286.
[33] 王鹏,谢丽文.污染治理投资、企业技术创新与污染治理效率.中国人口·资源与环境,2014,24(9):51- 58.
[34] 石龙宇,孙静.中国城市低碳发展水平评估方法研究.生态学报,2018,38(15):5461- 5472.
[35] 孙秀锋,施开放,吴健平.县级尺度的重庆市碳排放时空格局动态.环境科学,2018,39(6):2971- 2981.
[36] Zhang M,Mu H L,Ning Y D.Accounting for energy-related CO2 emission in China,1991- 2006.Energy Policy,2009,37(3):767- 773.
[37] Koenker R,Bassett G Jr.Regression quantiles.Econometrica,1978,46(1):33- 50.
[38] Zhao J Z.Ecological and Environmental Science & Technology in China:A Roadmap to 2050.Berlin,Heidelberg:Springer,2010.
[2] Hu Z,Huang D,Rao C J,Xu X L.Innovative allocation mechanism design of carbon emission permits in China under the background of a low-carbon economy.Environment and Planning B:Planning and Design,2016,43(2):419- 434.
[3] 靳云汇,刘学,杨婉华.清洁发展机制与中国环境技术引进.数量经济技术经济研究,2001,18(2):24- 28.
[4] 李荣刚,张小宁.清洁发展机制(CDM)与环境经济可持续发展.农业工程技术(新能源产业),2007,(5):41- 44.
[5] 李挚萍.《京都议定书》与温室气体国际减排交易制度.环境保护,2004,(2):58- 60.
[6] 潘竟虎,赵轩茹.基于空间回归模型的中国碳排放空间差异模拟.环境科学学报,2018,38(7):2894- 2901.
[7] 钟小剑,黄晓伟,范跃新,王莹莹,杨智杰,杨玉盛.中国碳交易市场的特征、动力机制与趋势——基于国际经验比较.生态学报,2017,37(1):331- 340.
[8] Grubb M,Sha F,Spencer T,Hughes N,Zhang Z X.A review of Chinese CO2 emission projections to 2030:the role of economic structure and policy.Climate Policy,2015,15(S):S7-S39.
[9] 颜艳梅,王铮,吴乐英,刘昌新.中国碳排放强度影响因素对区域差异的作用分析.环境科学学报,2016,36(9):3436- 3444.
[10] 姚亮,刘晶茹,王如松.中国居民消费隐含的碳排放量变化的驱动因素.生态学报,2011,31(19):5632- 5637.
[11] Jones C,Kammen D M.Spatial distribution of U.S.household carbon footprints reveals suburbanization undermines greenhouse gas benefits of urban population density.Environmental Science & Technology,2014,48(2):895- 902.
[12] 崔琦,杨军,董琬璐.中国碳排放量估计结果及差异影响因素分析.中国人口·资源与环境,2016,26(2):35- 41.
[13] 曾静静,张志强,曲建升,李燕,刘莉娜,董利苹.家庭碳排放计算方法分析评价.地理科学进展,2012,31(10):1341- 1352.
[14] 邓吉祥,刘晓,王铮.中国碳排放的区域差异及演变特征分析与因素分解.自然资源学报,2014,29(2):189- 200.
[15] 胡初枝,黄贤金,钟太洋,谭丹.中国碳排放特征及其动态演进分析.中国人口·资源与环境,2008,18(3):38- 42.
[16] 陈彦玲,王琛.影响中国人均碳排放的因素分析.北京石油化工学院学报,2009,17(2):54- 58.
[17] 谭显春,赖海萍,顾佰和,涂唐奇,李辉.主体功能区视角下的碳排放核算——以广东省为例.生态学报,2018,38(17):6292- 6301.
[18] 刘晓,王铮,邓吉祥.配额目标约束下区域减排的最优控制率.生态学报,2016,36(5):1380- 1390.
[19] 陈林,伍海军.国内双重差分法的研究现状与潜在问题.数量经济技术经济研究,2015,32(7):133- 148.
[20] Card D,Krueger A B.Minimum wages and employment:a case study of the fast food industry in New Jersey and Pennsylvania.The American Economic Review,1994,18(4):772- 793.
[21] Eissa N,Liebman J B.Labor supply response to the earned income tax credit.The Quarterly Journal of Economics,1996,111(2):605- 637.
[22] 周黎安,陈烨.中国农村税费改革的政策效果:基于双重差分模型的估计.经济研究,2005,40(8):44- 53.
[23] 李永友,陆晨晨.基层分权改革与农村社会公共品供给——基于DID方法的经验证据.经济学家,2012,(7):78- 87.
[24] 肖浩,孔爱国.融资融券对股价特质性波动的影响机理研究:基于双重差分模型的检验.管理世界,2014,30(8):30- 43.
[25] Machado P J A F,Mata J.Counterfactual Decomposition of changes in wage distributions using quantile regression.Journal of Applied Econometrics,2005,20(4):445- 465.
[26] 蒋晶晶,叶斌,计军平,马晓明.中国碳强度下降和碳排放增长的行业贡献分解研究.环境科学,2014,35(11):4378- 4386.
[27] 杜雯翠,朱松,张平淡.我国工业化与城市化进程对环境的影响及对策.财经问题研究,2014,(5):22- 29.
[28] 张亚斌,金培振,沈裕谋.两化融合对中国工业环境治理绩效的贡献——重化工业化阶段的经验证据.产业经济研究,2014,(1):40- 50.
[29] Liu J R,Wang R S,Yang J X,Shi Y.The relationship between consumption and production system and its implications for sustainable development of China.Ecological Complexity,2010,7(2):212- 216.
[30] 徐国泉,刘则渊,姜照华.中国碳排放的因素分解模型及实证分析:1995—2004.中国人口·资源与环境,2006,16(6):158- 161.
[31] 刘志雄.能源消费是否引起环境污染?——基于我国1991—2011年数据的实证研究.生态经济,2015,31(3):19- 24.
[32] Apergis N,Payne J E.CO2 emissions,energy usage,and output in Central America.Energy Policy,2009,37(8):3282- 3286.
[33] 王鹏,谢丽文.污染治理投资、企业技术创新与污染治理效率.中国人口·资源与环境,2014,24(9):51- 58.
[34] 石龙宇,孙静.中国城市低碳发展水平评估方法研究.生态学报,2018,38(15):5461- 5472.
[35] 孙秀锋,施开放,吴健平.县级尺度的重庆市碳排放时空格局动态.环境科学,2018,39(6):2971- 2981.
[36] Zhang M,Mu H L,Ning Y D.Accounting for energy-related CO2 emission in China,1991- 2006.Energy Policy,2009,37(3):767- 773.
[37] Koenker R,Bassett G Jr.Regression quantiles.Econometrica,1978,46(1):33- 50.
[38] Zhao J Z.Ecological and Environmental Science & Technology in China:A Roadmap to 2050.Berlin,Heidelberg:Springer,2010.