Environmental improvement or industry enhancement? A case study on the impact of environmental regulations on Chinese automobile industry

doi:10.1016/j.regsus.2021.11.002

Abstract

Abstract:

Developing countries are encouraged to pursue environmental protection and industrial development simultaneously, although it is a big challenge to reach win-win situation. This study explored the relationship between environmental regulations and the industry enhancement in developing countries through conducting an empirical analysis of the impact of environmental regulations on Chinese automobile industry. In the empirical analysis, a total of 64 Chinese automobile enterprises were investigated, the Malmquist index of total factor productivity was adopted to determine the productivity of Chinese automobiles, and the two-step system Generalised Method of Moments (GMM) estimation was used for the regression analysis. The results showed that environmental regulations negatively affected the productivity and technology of Chinese automobile industry during 2004-2018. The production cost of automobile industry in China was rising sharply, and the productivity and technological progress of Chinese automobile industry were impeded significantly. The case of Chinese automobile industry illustrated that strict environmental requirements can negatively impact industry productivity by increasing production costs and squeezing industry profits.

Key words: Environmental regulations, Environmental policies, Industry enhancement, Automobile industry, Malmquist index, Chinese automobile industry

LIANG Xiao, FU Jing. Environmental improvement or industry enhancement? A case study on the impact of environmental regulations on Chinese automobile industry[J]. Regional Sustainability, 2021, 2(3): 256-263.

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References 42

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Year	Carbon monoxide (CO) (g/km)	Nitrogen oxides (NOx) (g/km)	Hydrocarbon (HC)+NOx (g/km)
2001	2.72	-	0.97
2004	2.20	-	0.50
2007	2.30	0.15	-
2011	1.00	0.08	-
2017	1.00	0.06	-

Year	CO₂ emission limit for passenger cars (g/km)
Year	China	EU	US	Japan
2006	189	160	242	147
2015	161	130	140	126
2020	118	95	113	105

Item	Variable	Calculation	Data source
Dependent variables	Total factor productivity (TFP)	Using the DEAP software to estimate the Malmquist index.	Annual company’s reports, Wind Economic database, and CSMAR database.
	Technological (TECH)
	Pure technical efficiency (PTE)
	Scale efficiency (SE)
Explanatory variables	Environmental regulation (ER)	The investment in automobile industrial pollution treatment multiplied by the ratio of the output of the enterprise to the total output of the automobile industry.	Chinese Statistical Yearbooks and Chinese Statistical Yearbooks on Environment.
Control variables	Enterprise’s age (AGE)	The number of years that the automobile enterprise has existed.	Annual company’s reports.
	Return on equity (RE)	The ratio of net income to the value of stakeholders’ equity.	Annual company’s reports, Wind Economic database, and CSMAR database.
	Ratio of operating cash flow to operating profit (CA)	The ratio of operating cash flow to sales revenue in a given period.	Annual company’s reports, Wind Economic database, and CSMAR database.
	Total asset growth rate (DE)	The ratio of asset increases to the total assets.	Annual company’s reports, Wind Economic database, and CSMAR database.
	Receivables turnover ratio (CP)	The ratio of net credit sales to average accounts receivable.	Annual company’s reports, Wind Economic database, and CSMAR database.
	Debt asset ratio (AB)	Total liabilities is divided by the total assets.	Annual company’s reports, Wind Economic database, and CSMAR database.
	Enterprise scale (SC)	The assets of an enterprise.	Annual company’s reports, Wind Economic database, and CSMAR database.

Period	Malmquist index
Period	TFP	TECH	PTE	SE
2004-2005	1.327	1.883	0.889	0.825
2005-2006	1.130	0.777	1.189	1.236
2006-2007	1.420	0.872	1.235	1.543
2007-2008	1.125	0.981	0.857	1.372
2008-2009	1.363	0.974	1.342	0.977
2009-2010	1.391	1.207	1.169	1.165
2010-2011	1.288	4.689	0.617	0.835
2011-2012	0.912	1.007	1.081	0.863
2012-2013	1.069	0.917	1.055	1.137
2013-2014	0.942	0.925	0.969	1.060
2014-2015	1.064	0.662	1.064	1.551
2015-2016	1.155	0.826	1.182	1.179
2016-2017	1.213	0.947	1.006	1.316
2017-2018	1.220	0.919	1.090	1.266
Mean value	1.175	1.239	1.050	1.155

Variable	TFP	TECH	PTE	SE
TFP_i,t_-1	0.428^* (1.770)	-	-	-
TECH_i,t_-1	-	0.050 (0.199)	-	-
PTE_i,t_-1	-	-	0.212^* (1.710)	-
SE_i,t_-₁	-	-	-	0.004 (0.113)
ER	-1.060^* (-1.704)	-1.171^** (-2.443)	-1.212^** (-2.131)	-0.306^* (-1.895)
ER²	0.164^* (1.755)	0.078^**(2.213)	0.111^*** (2.957)	0.028^** (2.164)
AGE	0.067 (1.514)	-0.023 (-1.121)	0.048^*** (2.789)	0.017^** (2.062)
RE	-6.072^** (-2.316)	1.178^** (2.187)	0.941 (0.775)	-1.536^* (-1.775)
CA	-0.028^** (-2.196)	-0.024 (-0.900)	-0.011 (-1.055)	0.070^** (2.096)
CP	-0.003 (-1.269)	-0.001 (-0.452)	0.000 (0.271)	0.002 (0.866)
DE	0.944^*** (5.381)	0.743^* (1.665)	-0.565 (-0.947)	-0.069 (-0.278)
AB	-0.539^** (-2.546)	0.739 (1.421)	3.192^* (1.774)	-0.884^** (-2.546)
SC	-3.213 (-1.625)	0.184 (0.229)	-1.364^** (-2.220)	-0.427 (-1.215)
Constant	20.361^* (1.765)	3.707 (0.968)	10.072^*** (2.835)	4.751^** (2.234)
Observations	944	944	899	899
AR(1) test	0.002	0.029	0.034	0.014
AR(2) test	0.194	0.542	0.240	0.346
Sargan test	0.860	0.957	0.990	0.980
Hansen test	0.992	0.997	0.976	1.000