不同环境规制政策对中国规模以上工业企业技术创新的影响

doi:10.18402/resci.2020.07.11

Abstract

Abstract:

Exploring the impact of different environmental regulatory tools on technological innovation and path selection of enterprises is of great significance for China. Based on the panel data of industrial enterprises above the designated scale in 30 provinces from 2013 to 2017, this study divided technological innovation into internal independent R&D and external introduction, and used fixed effect regression to examine the impact of different environmental regulatory tools on technological innovation of industrial enterprises. The results show that: The two types of environmental regulatory tools have played different roles in promoting the expenditure on R&D, and showed a trend of change over time, which partly verifies the “weak” version of porter’s hypothesis. From 2013 to 2015, command-and-control tools did not play a significant role in promoting the R&D expenditure, but they did play a significant role in promoting the R&D expenditure under the impetus of a new round of “environmental protection storm” in 2016 to 2017. Since 2013, market incentives had significantly promoted the internal expenditure, external introduction, and total expenditure on R&D, and the impact showed a decreasing trend. Meanwhile, their promoting effect on the internal expenditure on R&D was greater than that on the external expenditure. Carbon trading market had played a promoting role in the R&D of industrial enterprises, which is mainly reflected in the external introduction. Spatially, the results of the eastern and central regions are relatively consistent and robust with the results of the whole country. The results in western China are consistent with the “narrow” version of porter’s hypothesis. It was found that market-driven environmental policy tools had more innovative incentives for industrial enterprises in western China than command-and-control environmental regulations. This study innovatively introduces renewable energy generation subsidies and carbon trading market into the environmental regulation. In refining the mechanism of Porter hypothesis, it provides a scientific basis for the state to further optimize the environmental policy system in order to stimulate the technological innovation of industrial enterprises.

Key words: environmental regulatory tools, Porter’s hypothesis, innovative approach, emissions trading scheme (ETS), command-and-control tools, market incentives, environmental protection storm

XIONG Hang, JING Zheng, ZHAN Jintao. Impact of different environmental regulatory tools on technological innovation of Chinese industrial enterprises above designated size[J].Resources Science, 2020, 42(7): 1348-1360.

Figures/Tables 6

Table 1

Table 2

Table 3

Table 4

National regression results"

	(1)	(2)	(3)	(4)	(5)	(6)
	rdin	rdout	rd	rdin	rdout	rd
mlkz	0.288	-0.199	0.089	0.288	-0.199***	0.089
	(0.417)	(0.158)	(0.844)	(0.170)	(0.007)	(0.567)
2014×mlkz	-0.031	-0.020	-0.050	-0.031	-0.020	-0.050
	(0.743)	(0.613)	(0.665)	(0.644)	(0.362)	(0.562)
2015×mlkz	-0.207	-0.040	-0.247	-0.207*	-0.040**	-0.247*
	(0.197)	(0.500)	(0.222)	(0.058)	(0.042)	(0.054)
2016×mlkz	-0.417*	-0.126	-0.543*	-0.417**	-0.126***	-0.543**
	(0.085)	(0.222)	(0.080)	(0.031)	(0.000)	(0.017)
2017×mlkz	-0.244	-0.055	-0.299	-0.244	-0.055***	-0.299*
	(0.346)	(0.536)	(0.330)	(0.115)	(0.001)	(0.078)
scjl	-0.173	-0.095	-0.268	-0.173	-0.095	-0.268
	(0.434)	(0.630)	(0.433)	(0.467)	(0.383)	(0.437)
2014×scjl	-0.602	-0.009	-0.611	-0.602	-0.009	-0.611
	(0.248)	(0.938)	(0.292)	(0.169)	(0.844)	(0.196)
2015×scjl	-0.160	0.362	0.202	-0.160	0.362***	0.202
	(0.730)	(0.203)	(0.733)	(0.668)	(0.004)	(0.642)
2016×scjl	1.534*	0.146	1.680*	1.534***	0.146*	1.680***
	(0.078)	(0.318)	(0.071)	(0.004)	(0.088)	(0.003)
2017×scjl	1.395***	0.360*	1.755***	1.395**	0.360***	1.755***
	(0.001)	(0.065)	(0.001)	(0.010)	(0.006)	(0.009)
ins	-0.325***	0.024	-0.301**	-0.325***	0.024	-0.301***
	(0.002)	(0.713)	(0.018)	(0.000)	(0.132)	(0.000)
acindust	-0.660**	-0.098	-0.758**	-0.660***	-0.098***	-0.758***
	(0.012)	(0.111)	(0.011)	(0.001)	(0.001)	(0.001)
perGDP	-0.871**	-0.156	-1.027**	-0.871***	-0.156***	-1.027***
	(0.016)	(0.226)	(0.016)	(0.001)	(0.004)	(0.002)
perGDP2	-1.587***	-0.140	-1.727***	-1.587***	-0.140**	-1.727***
	(0.007)	(0.170)	(0.006)	(0.001)	(0.027)	(0.001)
fdi	0.059***	0.005	0.064***	0.059***	0.005***	0.064***
	(0.000)	(0.118)	(0.000)	(0.000)	(0.000)	(0.000)
dummy2014	-8.452**	-0.989	-9.442**	-8.452***	-0.989***	-9.442***
	(0.012)	(0.199)	(0.012)	(0.001)	(0.002)	(0.001)
dummy2015	-1.725	-0.154	-1.879	-1.725***	-0.154	-1.879***
	(0.174)	(0.308)	(0.153)	(0.001)	(0.291)	(0.002)
dummy2016	0.086	0.009	0.095	0.086**	0.009	0.095**
	(0.350)	(0.296)	(0.308)	(0.021)	(0.436)	(0.030)
dummy2017	0.055	-0.030	0.026	0.055	-0.030*	0.026
	(0.559)	(0.410)	(0.796)	(0.499)	(0.052)	(0.777)
常数项	5.917**	0.487	6.405**	5.917***	0.487	6.405***
	(0.043)	(0.223)	(0.040)	(0.000)	(0.105)	(0.000)
N	150	150	150	150	150	150
标准误估计方法	White	White	White	D&K	D&K	D&K

Table 4

Table 5

Table 6

Regional regression results"

	东部地区					中部地区			西部地区
	rdin	rdout		rd		rdin	rdout	rd	rdin	rdout	rd
mlkz	-0.315	-1.064**		-1.380		0.330	-0.140	0.191	0.323	0.017	0.340
	(0.716)	(0.013)		(0.255)		(0.142)	(0.118)	(0.267)	(0.188)	(0.545)	(0.206)
2014×mlkz	-0.241	-0.274***		-0.514*		-0.336**	-0.122***	-0.458***	0.063**	0.016*	0.079***
	(0.242)	(0.002)		(0.071)		(0.014)	(0.005)	(0.005)	(0.032)	(0.099)	(0.004)
2015×mlkz	-0.221	-0.292***		-0.513		-0.275	-0.181***	-0.455*	0.061	0.031*	0.092
	(0.440)	(0.003)		(0.131)		(0.198)	(0.003)	(0.077)	(0.595)	(0.071)	(0.390)
2016×mlkz	-0.370	-0.489***		-0.859		-0.582*	-0.234**	-0.815**	-0.080	0.002	-0.078**
	(0.588)	(0.000)		(0.236)		(0.077)	(0.012)	(0.035)	(0.113)	(0.924)	(0.047)
2017×mlkz	-0.437	-0.327**		-0.764		-0.287	-0.204**	-0.492	-0.074	0.022	-0.052
	(0.556)	(0.014)		(0.298)		(0.413)	(0.039)	(0.209)	(0.131)	(0.322)	(0.186)
scjl	0.150	0.181		0.330		0.971**	0.240	1.210***	-0.165	-0.044*	-0.210
	(0.357)	(0.411)		(0.128)		(0.021)	(0.193)	(0.002)	(0.181)	(0.076)	(0.156)
2014×scjl	0.042	0.494**		0.536		-0.373	0.081	-0.292	-0.377	-0.036	-0.413
	(0.868)	(0.027)		(0.201)		(0.286)	(0.380)	(0.327)	(0.190)	(0.191)	(0.169)
2015×scjl	0.498**	1.298***		1.796***		-0.050	0.229*	0.179	-1.003***	-0.133***	-1.135***
	(0.044)	(0.003)		(0.001)		(0.929)	(0.057)	(0.733)	(0.001)	(0.002)	(0.000)
2016×scjl	2.197	0.843*		3.041*		-2.231***	0.236**	-1.995***	-1.185***	-0.211***	-1.396***
	(0.107)	(0.075)		(0.097)		(0.005)	(0.023)	(0.006)	(0.000)	(0.003)	(0.000)
2017×scjl	1.590**	0.783***		2.373***		1.598*	0.256	1.855**	0.608***	0.017	0.625***
	(0.040)	(0.006)		(0.005)		(0.067)	(0.197)	(0.026)	(0.000)	(0.543)	(0.000)
ins	-0.276	0.484***		0.208		-0.157	0.027	-0.130*	-0.030	0.011	-0.019
	(0.215)	(0.001)		(0.418)		(0.147)	(0.683)	(0.097)	(0.438)	(0.400)	(0.668)
acindust	-2.089**	-0.263**		-2.352**		-0.387*	0.113*	-0.275*	0.279	-0.006	0.273
	(0.029)	(0.013)		(0.023)		(0.055)	(0.066)	(0.068)	(0.130)	(0.807)	(0.133)
perGDP	-2.582***	-0.482**		-3.064***		-0.504*	0.069	-0.435*	0.944***	0.093*	1.037***
	(0.006)	(0.013)		(0.006)		(0.080)	(0.275)	(0.065)	(0.007)	(0.054)	(0.005)
perGDP2	-2.657***	-0.541**		-3.199***		0.312	0.030	0.342*	1.371***	0.156**	1.528***
	(0.002)	(0.014)		(0.003)		(0.100)	(0.554)	(0.056)	(0.003)	(0.028)	(0.001)
fdi	0.064***	0.006**		0.070***		0.036***	0.003	0.040***	0.016*	0.001	0.018*
	(0.002)	(0.013)		(0.002)		(0.001)	(0.120)	(0.001)	(0.079)	(0.334)	(0.071)
dummy2014	-14.097	-4.463***		-18.560		-9.885**	-0.401	-10.287***	-4.790***	-0.021	-4.810***
	(0.268)	(0.007)		(0.146)		(0.026)	(0.697)	(0.008)	(0.008)	(0.932)	(0.003)
dummy2015	-3.107***	0.048		-3.059**		-0.182	-0.861	-1.042	0.944	0.079**	1.024
	(0.006)	(0.910)		(0.029)		(0.846)	(0.151)	(0.346)	(0.137)	(0.035)	(0.122)
dummy2016	0.160**	-0.007		0.153*		0.202	0.104	0.307*	0.000	-0.003	-0.002
	(0.029)	(0.778)		(0.092)		(0.193)	(0.202)	(0.093)	(0.995)	(0.609)	(0.977)
dummy2017	0.266	0.030		0.297		1.644**	0.210**	1.855***	-1.410**	-0.128***	-1.538**
	(0.308)	(0.228)		(0.255)		(0.012)	(0.037)	(0.007)	(0.032)	(0.004)	(0.023)
常数项	14.142**	1.131		15.274**		-0.642	1.465	0.823	-1.155	-0.211*	-1.366
	(0.017)	(0.289)		(0.024)		(0.753)	(0.150)	(0.710)	(0.281)	(0.061)	(0.230)
N	55		55		55	40	40	40	55	55	55

Table 6

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市场工具	非市场工具
ETS（排放交易计划）：CO₂排放交易计划;可再生能源证书交易计划;能源证书排放交易计划;SO_X排放交易计划	ELV（污染物排放限值）：颗粒物（PM）排放限值;SO_X排放限值;NO_X排放限值
Tax(税收)：CO₂税;NO_X税;SO_X税	政府对可再生能源的研发支出
FIT（上网电价补贴）：风力发电电价;风力发电溢价;日光发电电价;日光发电溢价

一级指标	二级指标	二级指标说明
企业创新行为	自主创新	规模以上的工业企业的R&D内部投入/百亿元
	技术引进	规模以上的工业企业的R&D外部投入/百亿元
市场激励型环境工具	环保税（排污费）	排污费与各省GDP的比值/%
	可再生能源发电补贴	光伏发电平均上网电价与燃煤平均上网电价的比值/% 风力发电平均上网电价与燃煤平均上网电价的比值/%
	环境权交易制度	是否实施SO₂废气排污权制度（1为是,0为否）碳交易量/t
命令控制型环境工具	法律法规、行政手段	累积有效的环保法规数累积有效的行政规章数受理行政处罚数

变量	变量解释	均值	标准差	最小值	最大值
rdin	规模以上工业企业的R&D内部支出/百亿元	3.369	4.207	0.065	18.650
rdout	规模以上工业企业的R&D外部支出/百亿元	0.181	0.241	0.003	1.595
rd	规模以上工业企业的R&D总支出/百亿元	3.550	4.417	0.068	20.240
scjl	市场激励型工具综合指标	0.378	0.254	0.000	1.220
mlkz	命令控制型工具综合指标	0.231	0.140	0.030	0.761
ins	实际工业增加值/百亿元	97.930	81.220	5.170	359.000
acindust	工业增加值占GDP比重/%	0.364	0.082	0.118	0.496
perGDP	人均实际GDP/（万元/人）	4.263	1.874	1.970	9.159
perGDP2	人均实际GDP平方	21.660	20.330	3.880	83.890
fdi	外商实际直接投资/百亿美元	0.918	0.769	0.001	3.326

Impact of different environmental regulatory tools on technological innovation of Chinese industrial enterprises above designated size

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	(1)	(2)	(3)
	rdin	rdout	rd
ctrade	0.222	0.092**	0.315*
	(0.160)	(0.030)	(0.076)
mlkz	0.468	-0.107	0.361
	(0.160)	(0.228)	(0.332)
ins	0.060***	0.005***	0.066***
	(0.000)	(0.000)	(0.000)
acindust	-6.183**	-0.382	-6.565**
	(0.016)	(0.572)	(0.022)
perGDP	-2.026***	-0.187	-2.213***
	(0.003)	(0.300)	(0.004)
perGDP2	0.124**	0.013	0.137**
	(0.013)	(0.319)	(0.014)
fdi	-0.070	-0.064**	-0.134
	(0.542)	(0.040)	(0.299)
dummy2014	-0.184*	-0.036	-0.220*
	(0.092)	(0.212)	(0.072)
dummy2015	-0.517***	-0.066	-0.583***
	(0.002)	(0.131)	(0.002)
dummy2016	-0.654***	-0.080	-0.734***
	(0.002)	(0.147)	(0.002)
dummy2017	-0.317	-0.039	-0.356
	(0.172)	(0.529)	(0.171)
常数项	5.908***	0.434	6.342***
	(0.000)	(0.311)	(0.001)
N	150	150	150