碳交易政策对高污染工业企业经济绩效的影响——基于多重中介效应模型的实证分析

doi:10.18402/resci.2020.09.10

摘要/Abstract

摘要：

以中国2011年出台的碳交易政策作为一次天然外生冲击,选取2010—2018年微观企业数据构造准自然实验,分别采用三重差分模型及多重中介效应模型研究该政策对高污染工业企业经济绩效的影响,验证碳交易政策能否实现经济红利及其传导机制。结果发现：①碳交易政策显著提升了高污染工业企业的经济绩效,实现了经济红利。②机制检验表明：碳交易政策可通过低碳补助扶持效应、企业获益激励效应和研发创新动力效应间接影响高污染工业企业经济绩效,其中企业获益激励效应和研发创新动力效应两类机制表现出促进作用,而低碳补助扶持效应呈现出遮掩效果。③异质性分析显示：就总效应而言,碳交易政策对非国有企业和小规模企业经济绩效的提升效果更优;就中介效应而言,碳交易政策对国有企业和大规模企业经济绩效的总体中介效应明显,对非国有企业和小规模企业经济绩效只存在部分显著的并行及链式中介效应。本文从微观视角对中国碳市场理论构建作了有益补充,为碳交易政策的差异化实施和高污染工业企业减排增效机制设计提供了数据支持和政策建议。

关键词: 碳交易政策, 企业经济绩效, 三重差分模型, 多重中介效应模型

Abstract:

Taking China’s carbon trading policy introduced in 2011 as a natural exogenous shock, this study used the micro-enterprise data from 2010 to 2018 to construct a quasi-natural experiment. The triple difference model and multiple mediating effect models were used to examine the impact of this policy on the economic performance of highly polluting industrial enterprises, and to verify whether carbon trading policies can achieve economic dividends and its transmission mechanisms. The results show that: (1) Carbon trading policy significantly improves the economic performance of high-pollution industrial enterprises and realizes economic dividends. (2) Mechanism test shows that the carbon trading policy can indirectly affect the economic performance of highly polluting industrial enterprises through the low-carbon subsidy support effect, corporate benefit incentive effect and R&D innovation dynamic effect. Enterprise benefit incentive effect and innovation motivation effect show positive effects, while the support effect of low-carbon subsidies shows a concealing effect. (3) The heterogeneity analysis shows that, in terms of the overall effect, carbon trading policy has a better effect on the economic performance of non-state-owned enterprises and small-scale enterprises. As for the mediating effect, the whole mediation effect of carbon trading policy on the economic performance of state-owned enterprises and large-scale enterprises is obvious, and there are only some significant parallel and chain-mediated mediation effects on the economic performance of non-state-owned enterprises and small-scale enterprises. This study makes a beneficial supplement to the theoretical construction of China’s carbon market from a micro perspective, and provides data support and policy suggestions for the differentiated implementation of carbon trading policies and the design of emission reduction and efficiency enhancement mechanism for highly polluting industrial enterprises.

Key words: carbon emissions trading, corporate economic performance, Difference-in-difference-in-difference Model, Multiple Mediating Effect Model

任晓松, 马茜, 刘宇佳, 赵国浩. 碳交易政策对高污染工业企业经济绩效的影响——基于多重中介效应模型的实证分析[J]. 资源科学, 2020, 42(9): 1750-1763.

REN Xiaosong, MA Qian, LIU Yujia, ZHAO Guohao. The impact of carbon trading policy on the economic performance of highly polluting industrial enterprises：Empirical analysis based on multiple mediating effect model[J]. Resources Science, 2020, 42(9): 1750-1763.

图/表 7

图1

表1

表2

表3

碳交易政策对高污染工业企业经济绩效的三重差分估计结果"

	Ea				Roa
	（1）	（2）	（3）	（4）	（5）	（6）	（7）	（8）
$are a r × tim e t × industr y j$	1.994*** (4.58)	2.211*** (4.14)	1.602*** (3.56)	1.935*** (3.38)	2.274*** (5.45)	2.414*** (4.26)	1.890*** (4.18)	2.037*** (3.46)
控制变量	Y	Y	Y	Y	Y	Y	Y	Y
个体固定效应	Y	Y		Y	Y	Y		Y
时间固定效应	Y	Y	Y	Y	Y	Y	Y	Y
地区 $×$ 时间趋势固定效应		Y	Y	Y		Y	Y	Y
行业 $×$ 时间趋势固定效应			Y	Y			Y	Y
常数项	-43.17*** (-7.45)	135.6 (0.42)	-27.8*** (-5.81)	222.0 (0.31)	-43.65*** (-7.85)	81.01 (0.22)	-29.0*** (-6.02)	-125.9 (-0.18)
R-square	0.153	0.002	0.251	0.001	0.177	0.001	0.285	0.001
N	3114	3114	3114	3114	3114	3114	3114	3114

表3

表4

碳交易政策对高污染工业企业经济绩效的多重中介效应机制分析"

变量		Ea	Roa
总体中介效应		0.235** [0.017,0.450]	0.226** [0.002,0.443]
并行中介效应	Lcs ( $a 1 b 1$ )	-0.058** [-0.101,-0.020]	-0.071** [-0.122,-0.031]
	Cash ( $a 2 b 2$ )	0.210** [0.001,0.426]	0.211** [0.001,0.433]
	R&D ( $a 3 b 3$ )	0.034** [0.005,0.079]	0.037** [0.005,0.078]
链式中介效应	Lcs→ Cash ( $a 1 d 1 b 2$ )	0.049** [0.026,0.080]	0.049** [0.027,0.082]
	Lcs→ R&D ( $a 1 d 2 b 3$ )	0.004** [0.001,0.011]	0.004** [0.001,0.011]
	Cash→ R&D ( $a 2 d 3 b 3$ )	-0.003** [-0.010,-0.001]	-0.004** [-0.10,-0.001]
	Lcs→ Cash→ R&D ( $a 1 d 1 d 3 b 3$ )	-0.001** [-0.002,-0.001]	-0.001** [-0.002,-0.001]

表4

表5

碳交易政策对高污染工业企业经济绩效的异质性分析结果"

变量	企业所有制		企业规模
变量	国有企业	非国有企业	大规模企业	小规模企业
$are a r × tim e t × industr y j$	1.782*** (3.43)	2.618** (2.27)	1.485** (2.31)	2.655*** (3.07)
控制变量	Y	Y	Y	Y
个体固定效应	Y	Y	Y	Y
时间固定效应	Y	Y	Y	Y
地区 $×$ 时间趋势固定效应	Y	Y	Y	Y
行业 $×$ 时间趋势固定效应	Y	Y	Y	Y
常数项	-43.16 (-0.09)	-68.33 (-0.04)	145.2 (0.22)	-347.0 (-0.30)
R-square	0.014	0.031	0.010	0.014
N	1903	1211	1422	1692

表5

表6

碳交易政策对高污染工业企业经济绩效的多重中介效应异质性分析结果"

变量		企业所有制		企业规模
变量		国有企业	非国有企业	大规模企业	小规模企业
总体中介效应		0.370** [0.147,0.617]	0.277 [-0.140,0.620]	0.534** [0.257,0.847]	0.187 [-0.180,0.547]
总体中介效应	Lcs ( $a 1 b 1$ )	-0.119** [-0.120,-0.058]	0.114** [0.033,0.240]	-0.126** [-0.238,-0.059]	0.050** [0.001,0.143]
	Cash ( $a 2 b 2$ )	0.443** [0.213,0.679]	-0.024 [-0.425,0.314]	0.619** [0.353,0.935]	-0.090 [-0.426,0.245]
	R&D ( $a 3 b 3$ )	0.002 [-0.010,0.027]	0.161** [0.062,0.329]	0.004 [-0.007,0.044]	0.206** [0.113,0.355]
链式中介效应	Lcs→ Cash ( $a 1 d 1 b 2$ )	0.045** [0.017,0.082]	0.028** [0.008,0.066]	0.036 [-0.001,0.082]	0.011** [0.001,0.031]
	Lcs→ R&D ( $a 1 d 2 b 3$ )	0.003 [-0.001,0.012]	-0.003 [-0.024,0.006]	-0.001 [-0.006,0.001]	0.008** [0.001,0.020]
	Cash→ R&D ( $a 2 d 3 b 3$ )	-0.004 [-0.012,0.002]	-0.001 [-0.011,0.004]	0.002 [-0.004,0.010]	0.003 [-0.007,0.016]
	Lcs→ Cash→ R&D ( $a 1 d 1 d 3 b 3$ )	-0.001 [-0.001,0.01]	0.001 [-0.001,0.002]	0.001 [-0.001,0.001]	-0.001** [-0.001,-0.0002]

表6

参考文献 39

[1]	中华人民共和国国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2019.
	[ National Bureau of Statistics of the People’s Republic of China. China Statistical Yearbook[M]. Beijing: China Statistics Press, 2019.]
[2]	BP. BP世界能源统计年鉴[M]. 北京: 中国统计出版社, 2019.
	[ BP. BP World Energy Statistical Yearbook[M]. Beijing: China Statistics Press, 2019.]
[3]	中华人民共和国生态环境部. 2018中国生态环境状况公报[EB/OL]. (2019-05-22) [2020-01-29]. http://www.mee.gov.cn/hjzl/zghjzkgb.
	[ Ministry of Ecology and Environment of the People’s Republic of China. The State of China’s Ecological Environment Bulletin 2018 [EB/OL]. (2019-05-22) [2020-01-29]. http://www.mee.gov.cn/hjzl/zghjzkgb.]
[4]	Coase R H. The problem of social cost[J]. The Journal of Law and Economics, 1960,3(4):1-44.
[5]	李百兴, 王博. 新环保法实施增大了企业的技术创新投入吗?基于PSM-DID方法的研究[J]. 审计与经济研究, 2019,34(1):87-96.
	[ Li B X, Wang B. Has the implementation of the new environmental protection law increased the company’s technological innovation investment? Research based on PSM-DID method[J]. Journal of Audit & Economics, 2019,34(1):87-96.]
[6]	Porter M E, Van der Linde C. Toward a new conception of the environment-competitiveness relationship[J]. Journal of Economic Perspectives, 1995,9(4):97-118.
[7]	宋晓玲, 孔垂铭. 中国碳交易市场对地区经济结构影响的实证分析[J]. 宏观经济研究, 2018, (9):98-108.
	[ Song X L, Kong C M. An empirical analysis of the impact of China’s carbon trading market on regional economic structure[J]. Macroeconomics, 2018, (9):98-108.]
[8]	沈洪涛, 黄楠. 碳排放权交易机制能提高企业价值吗?[J]. 财贸经济, 2019,40(1):144-161.
	[ Shen H T, Huang N. Will the carbon emission trading scheme improve firm value?[J]. Finance & Trade Economics, 2019,40(1):144-161.]
[9]	Feng D, Dai Y J, Zhang S N, et al. Can a carbon emission trading scheme generate the Porter effect? Evidence from pilot areas in China[J]. The Science of the Total Environment, 2019,653:565-577. pmid: 30414586
[10]	任亚运, 傅京燕. 碳交易的减排及绿色发展效应研究[J]. 中国人口·资源与环境, 2019,29(5):11-20.
	[ Ren Y Y, Fu J Y. Research on the effect of carbon emissions trading on emission reduction and green development[J]. China Population, Resources and Environment, 2019,29(5):11-20.]
[11]	Han W, Chen Z P, Wu X Y, et al. Can a carbon trading system promote the transformation of a low-carbon economy under the framework of the porter hypojournal? Empirical analysis based on the PSM-DID method[J]. Energy Policy, 2019,129:930-938. doi: 10.1016/j.enpol.2019.03.007
[12]	任胜钢, 郑晶晶, 刘东华, 等. 排污权交易机制是否提高了企业全要素生产率: 来自中国上市公司的证据[J]. 中国工业经济, 2019, (5):5-23.
	[ Ren S G, Zheng J J, Liu D H, et al. Does emissions trading system improve firm’s total factor productivity: Evidence from Chinese listed companies[J]. China Industrial Economics, 2019, (5):5-23.]
[13]	赵璨, 王竹泉, 杨德明, 等. 企业迎合行为与政府补贴绩效研究: 基于企业不同盈利状况的分析[J]. 中国工业经济, 2015, (7):130-145.
	[ Zhao C, Wang Z Q, Yang D M, et al. Research on the catering behavior of enterprise and government subsidy performance: Based on the analysis of the enterprise’s profitability[J]. China Industrial Economics, 2015, (7):130-145.]
[14]	李雪婷, 宋常, 郭雪萌. 碳信息披露与企业价值相关性研究[J]. 管理评论, 2017,29(12):175-184.
	[ Li X T, Song C, Guo X M. Enterprise-value effect of carbon disclosure[J]. Management Review, 2017,29(12):175-184.]
[15]	刘传明, 孙喆, 张瑾. 中国碳排放权交易试点的碳减排政策效应研究[J]. 中国人口·资源与环境, 2019,29(11):49-58.
	[ Liu C M, Sun Z, Zhang J. Research on the effect of carbon emission reduction policy in China’s carbon emissions trading pilot[J]. China Population, Resources and Environment, 2019,29(11):49-58.]
[16]	Abadie A, Drukker D, Herr J L, et al. Implementing matching estimators for average treatment effects in Stata[J]. The Stata Journal, 2004,4(3):290-311. doi: 10.1177/1536867X0400400307
[17]	唐清泉, 罗党论. 政府补贴动机及其效果的实证研究: 来自中国上市公司的经验证据[J]. 金融研究, 2007, (6):149-163.
	[ Tang Q Q, Luo D L. An empirical study on the motivation and effect of government subsidy: Empirical evidence from Chinese listed companies[J]. Journal of Financial Research, 2007, (6):149-163.]
[18]	范子英, 王倩. 财政补贴的低效率之谜: 税收超收的视角[J]. 中国工业经济, 2019, (12):23-41.
	[ Fan Z Y, Wang Q. The inefficiency of governmental subsidies: Perspective of tax over-collection[J]. China Industrial Economics, 2019, (12):23-41.]
[19]	张杰, 陈志远, 杨连星, 等. 中国创新补贴政策的绩效评估: 理论与证据[J]. 经济研究, 2015,50(10):4-17.
	[ Zhang J, Chen Z Y, Yang L X, et al. On evaluating China’s innovation subsidy policy: Theory and evidence[J]. Economic Research Journal, 2015,50(10):4-17.]
[20]	Cook A. Emission rights: From costless activity to market operations[J]. Accounting Organizations & Society, 2009,34(3):456-468.
[21]	Rosenbaum P R, Rubin D B. Constructing a control group using multivariate matched sampling methods that incorporate the pro-pensity score[J]. The American Statistician, 1985,39(1):33-38.
[22]	Oestreich A M, Tsiakas I. Carbon emissions and stock returns: Evidence from the EU emissions trading scheme[J]. Journal of Banking & Finance, 2015,58:294-308.
[23]	邵敏, 包群. 政府补贴与企业生产率: 基于我国工业企业的经验分析[J]. 中国工业经济, 2012, (7):70-82.
	[ Shao M, Bao Q. Government subsidies and firm’s productivity: An empirical study based on Chinese industrial plants[J]. China Industrial Economics, 2012, (7):70-82.]
[24]	Da Silva P P, Moreno B, Figueiredo N C. Firm specific impacts of CO2 prices on the stock market value of the Spanish power industry[J]. Energy Policy, 2016,94:492-501. doi: 10.1016/j.enpol.2016.01.005
[25]	Brouwers R, Schoubben F, Van H, et al. The initial impact of EU ETS verification events on stock prices[J]. Energy Policy, 2016,94:138-149. doi: 10.1016/j.enpol.2016.04.006
[26]	Liang X Y, Lu X W, Wang L H. Outward internationalization of private enterprises in China: The effect of competitive advantages and disadvantages compared to home market rivals[J]. Journal of World Business, 2012,47(1):134-144. doi: 10.1016/j.jwb.2011.02.002
[27]	Goodchild A, Toy J. Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO₂ emissions in the delivery service industry[J]. Transportation Research Part D: Transport and Environment, 2018,61(6):58-67. doi: 10.1016/j.trd.2017.02.017
[28]	傅京燕, 司秀梅, 曹翔. 排污权交易机制对绿色发展的影响[J]. 中国人口·资源与环境, 2018,28(8):12-21.
	[ Fu J Y, Si X M, Cao X. Research on the influence of emission trading mechanism on green development[J]. China Population, Resources and Environment, 2018,28(8):12-21.]
[29]	涂正革, 谌仁俊. 排污权交易机制在中国能否实现波特效应?[J]. 经济研究, 2015,50(7):160-173.
	[ Tu Z G, Shen R J. Can emissions trading scheme achieve the Porter effect in China?[J]. Economic Research Journal, 2015,50(7):160-173.]
[30]	Cai X Q, Lu Y, Wu M Q, et al. Does environmental regulation drive away inbound foreign direct investment? Evidence from a quasi-natural experiment in China[J]. Journal of Development Economics, 2016,123:73-85. doi: 10.1016/j.jdeveco.2016.08.003
[31]	齐绍洲, 林屾, 崔静波. 环境权益交易市场能否诱发绿色创新?基于我国上市公司绿色专利数据的证据[J]. 经济研究, 2018,53(12):129-143.
	[ Qi S Z, Lin S, Cui J B. Do environmental rights trading schemes induce green innovation? Evidence from listed firms in China[J]. Economic Research Journal, 2018,53(12):129-143.]
[32]	Preacher K J, Hayes A F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models[J]. Behavior Research Methods, 2008,40(3):879-891. doi: 10.3758/brm.40.3.879 pmid: 18697684
[33]	杨洋, 魏江, 罗来军. 谁在利用政府补贴进行创新?所有制和要素市场扭曲的联合调节效应[J]. 管理世界, 2015, (1):75-86.
	[ Yang Y, Wei J, Luo L J. Who innovate from the government subsidies? The joint moderate effects of the ownership and the factor market distortions[J]. Management World, 2015, (1):75-86.]
[34]	逯进, 王恩泽. 新能源示范城市建设对区域环境污染治理的影响[J]. 资源科学, 2019,41(11):2107-2118.
	[ Lu J, Wang E Z. Impact of new energy demonstration city construction on regional environmental pollution control[J]. Resources Science, 2019,41(11):2107-2118.]
[35]	龙小宁, 万威. 环境规制、企业利润率与合规成本规模异质性[J]. 中国工业经济, 2017, (6):155-174.
	[ Long X N, Wan W. Environmental regulation, corporate profit margins and compliance cost heterogeneity of different scale enterprises[J]. China Industrial Economics, 2017, (6):155-174.]
[36]	刘晔, 张训常. 碳排放交易制度与企业研发创新: 基于三重差分模型的实证研究[J]. 经济科学, 2017, (3):102-114.
	[ Li Y, Zhang X C. Carbon emission trading system and enterprise R&D innovation: An empirical study based on triple difference model[J]. Economic Science, 2017, (3):102-114.]
[37]	Kleer R. Government R&D subsidies as a signal for private investors[J]. Research Policy, 2010,39(10):1361-1374. doi: 10.1016/j.respol.2010.08.001
[38]	Hussinger K. R&D and subsidies at the firm level: An application of parametric and semiparametric two step selection models[J]. Journal of Applied Econometrics, 2008,23:729-747. doi: 10.1002/jae.v23:6
[39]	任晓松, 刘宇佳, 赵国浩. 经济集聚对碳排放强度的影响及传导机制[J]. 中国人口·资源与环境, 2020,30(4):95-106.
	[ Ren X S, Liu Y J, Zhao G H. The impact and transmission mechanism of economic agglomeration on carbon intensity[J]. China Population, Resources and Environment, 2020,30(4):95-106.]

变量		均值		误差率 %	误差变动率 %	T检验
变量		对照组	实验组	误差率 %	误差变动率 %	t value	p>\|t\|
lnass	匹配前	22.986	22.635	23.2	92.0	5.14	0.000
lnass	匹配后	22.998	22.969	1.9	92.0	0.31	0.758
asslia	匹配前	48.200	50.182	-10.2	96.1	-2.14	0.033
asslia	匹配后	48.219	48.141	0.4	96.1	0.07	0.945
share	匹配前	35.944	37.955	-12.4	73.7	-2.62	0.009
share	匹配后	36.044	35.514	3.3	73.7	0.60	0.551
tbq	匹配前	2.246	1.941	17.7	87.0	4.19	0.000
tbq	匹配后	2.160	2.120	-2.3	87.0	-0.46	0.644
owner	匹配前	0.560	0.632	-14.6	87.0	-3.11	0.002
owner	匹配后	0.562	0.571	-1.9	87.0	-0.33	0.742

	DID				PSM-DID
	Ea		Roa		Ea		Roa
	（1）	（2）	（3）	（4）	（5）	（6）	（7）	（8）
did	1.959*** （3.69）	1.911*** （3.86）	2.174*** （4.37）	2.117*** （4.60）	2.024*** （2.98）	1.915*** （3.10）	2.314*** （3.66）	2.208*** （3.87）
lnass		2.577*** （7.54）		2.512*** （7.91）		3.630*** （9.01）		3.450*** （9.27）
asslia		-0.190*** （-15.65）		-0.186*** （-16.49）		-0.202*** （-13.46）		-0.197*** （-14.23）
share		0.033* （1.73）		0.030* （1.69）		0.034 （1.47）		0.030 （1.39）
tbq		0.846*** （7.70）		0.841*** （8.24）		1.655*** （9.93）		1.617*** （10.51）
owner		0.591 （0.49）		0.133 （0.12）		1.099 （0.82）		0.658 （0.53）
个体固定效应	Y	Y	Y	Y	Y	Y	Y	Y
时间固定效应	Y	Y	Y	Y	Y	Y	Y	Y
常数项	4.672*** （14.70）	-47.15*** （-6.21）	5.278*** （17.72）	-44.86*** （-6.35）	4.925*** （11.99）	-72.98*** （-8.13）	5.529*** （14.44）	-68.05*** （-8.21）
R-square	0.020	0.131	0.027	0.159	0.020	0.125	0.025	0.152
N	2214	2214	2214	2214	1688	1688	1688	1688