# Nonlinear Effects of Governmental and Civil Environmental Regulation on Green Total Factor Productivity in China

Authors: Xubin Lei; Shusheng Wu
Journal: Advances in Meteorology
 (2019)

---

## Abstract

This paper employs metafrontier Malmquist-Luenberger index to measure green total factor productivity and then builds panel model to investigate the nonlinear effects of both governmental and civil environmental regulation on green total factor productivity in 30 provinces of China in 2007–2016, where the threshold variables are environmental awareness and regulatory foundation. The results show that green total factor productivity takes the characteristic of cyclical fluctuation, and the magnitude and its growth rate in the eastern region are higher than those in the midwestern region. The degrees of the governmental and civil environmental regulation and green total factor productivity display single environmental awareness threshold and regulatory foundation threshold. It should be noted that the sign of governmental and civil environmental regulation on green total factor productivity will transform from negative to positive, if and only if threshold variables ascend and surpass the threshold value. Under the condition of metafrontier technology, governmental environmental awareness threshold value, based on the investigated corruption and malpractice cases by the procuratorates among every hundred thousand people, reaches 0.2158, and civil environmental awareness threshold value based on the per capita education level will attain 12.2330 years, and the corresponding regulatory foundation index threshold values are 0.0163 and 0.0154. These findings show clear policy implications: rather than continually promoting the level of governmental environmental regulation, civil performance, environmental awareness, and regulatory foundation should be considered.


---

## Body

## 1. Introduction

Since China’s reform and opening-up, great achievements have been achieved based on China’s rapid economic development, and residents received huge bonuses from the reform and development in the spiritual and material aspects. But backward production technology and lack of human resources lead to new growth model whose major characteristics are “high energy consumption and low energy efficiency, high input and low output,” which cause extreme decrease in production resources and ecological environment deterioration. Outline of China’s 13th five-year plan points out key to promote sustainable environment performance is increasing productivity, which is also the core strategy for enterprise. Market competition and government regulation are major motivations for enterprises to improve total factor productivity. In this paper, we will endeavor to explore afresh the underlying nonlinear relationship between environmental regulation and total factor productivity.Governmental law enforcement should be primarily through discharging standards for pollutants, levying pollution tax, providing technical innovation subsidies, and improving the pollutant emission property right transaction market to carry out the regulation on enterprise [1–4]. It should be pointed out that most of researches assume that environmental regulation is equal to government-led environmental regulation, which ignores the personal role in that behavior. In fact, personal utility becomes more and more important role in environmental protection in information-based or legalization-based society, especially in China. As basic public resource, excellent environmental protection becomes scarce resource, while the elasticity of demand for civil healthy living tends to be lower level. Moreover, scholars presume that environmental protection law enforcement in different areas is homogeneous, which means they hold the same law enforcement utility. Taking China as an example, environmental awareness and regulatory foundation are significantly heterogeneous, which can affect the above law enforcement utility [5]. Among them, regulatory foundation reflects the regulation resistance determined by local economic structure. These pose questions for us: as for region heterogeneity of environmental regulation and regulatory foundation, can it affect the influence of environmental regulation on the green total factor productivity effect and how does one resolve the dilemma of economy growth and environmental regulation?The remainder of this paper is organized as follows. Section2 presents the corresponding literature review and innovation points. Section 3 denotes methodology. Section 4 is data. Section 5 presents the empirical analysis results. Section 6 concludes this paper and provides some constructive policy implications.


## 2. Literature Review

Since Porter and Linde [1] put forward Porter Hypothesis, relationship between environmental regulation and productivity becomes an important and hot study topic. Based on different research methods, different research objects, and different research data, three distinct research statements have been obtained [6–10]. (1) Hypothesis of cost competition: Becker [6] found that industries with higher environmental regulation cost have no statistically remarkable influential effect in manufacturing industries, which support the hypothesis of cost competition that enterprise innovation does not offset cost with environmental regulation. (2) Porter hypothesis: Porter and Linde [1] took the dynamic characteristic of technology innovation in econometric model and insisted that compensation earnings derived from technology innovation in the long term are bigger than the competition effect. Zhang et al. [11] used Malmquist-Luenberger index to calculate productivity and established econometric model to explore correlation between the strictness of environmental regulation and productivity and then found that positive relationships are tested in empirical analysis which support Porter Hypothesis. (3) Uncertainty hypothesis: Wang and Shen [12] adopted regulatory intension as threshold variable to analyze the nonlinear relationship between environmental regulation and total factor productivity. Results show that an “inverted U-shaped” correlation exists. Xie et al. [13] employed a panel threshold model and a province-level panel dataset during 2000–2012 to examine different types of environmental regulations and heterogeneous influence on green productivity. Research results show that both command-and-control regulation and market-based regulation have a nonlinear relationship and can be positively related to green productivity but with different constrains on regulation stringency.These above research papers provide meaningful study perspectives to understand and analyze the correlation between governmental environmental regulation and productivity, while they seldom go to study regulatory utility of civil environmental regulation. Ebenstein [14] found that abominable environment triggered by the production pollution contributes to the enhancement of the demand and supply of civil society to protect the environment or promote the total factor productivity. Zhang et al. [15] formulated the Hybrid Luenberger index to measure regional industrial green productivity and explored whether civil environmental protection forces the growth of China’s industrial green productivity or not, which shows that, compared with the intensity of environmental pollution and other passive environmental protection incentives, the public are more sensitive to per capita income level, education quality, health concern, and other rational environmental protection incentives. But, the above scholars did not take the heterogeneity of the environmental awareness and regional regulatory foundation, which is not comprehensive. In order to fill these gaps, we make the following improvements. Civil environmental regulation, environmental awareness, and regulatory foundation should be taken into account as the core explanatory variables or threshold variables.The effective measurement of green total factor productivity is the premise and guarantee to estimate the correlation between regulation and productivity. The stochastic frontier approach (SFA) and the data envelopment analysis (DEA) are mainstream research methods. What should be highlighted is SFA hinge on model parameters, while DEA can overcome the potential error derived from the above parameters. Since Charnes et al. [16] creatively proposed linear programming to calculate efficiency or productivity, the DEA method has been far-ranging used or optimized with nonparametric measurement [17–20]. Charnes et al. [16] employed new directional distance function to enhance the measurement accuracy for productivity based on the unexpected output. DEA model contains radial situation and nonradial situation, while both of them hold information loss under the condition of using separately. Epsilon based measure (EBM) effectively covers the advantages of both radial situation and nonradial situation [21]. Moreover, most researchers assume that homogeneity is normal for enterprise productivity. Oh and Lee [22] put forward metafrontier Malmquist-Luenberger (MML) index to propose grouping frontier to measure productivity combined with metafrontier framework [23]. Reference for scholar’s research, MML index is the effective and reasonable method to measure green total factor productivity.


## 3. Methodology

### 3.1. Econometric Model

The purpose of this paper is to study potential nonlinear relationship between environmental regulation and total factor productivity, whose method is using the cross terms of the following two aspects: environmental regulation with environmental awareness or regulation foundation to construct the threshold econometric model. What should be stated is that environmental awareness contains governmental environmental awareness, civil environmental awareness, enterprise environmental awareness, and social environmental awareness, while regulation foundation reflects the cost factors or marker factors, which may influence the government on how to implement environmental regulation decision-making. Then, infrastructure level, industrial structure, marketization level, and opening degree are chosen to construct comprehensive index to measure the regulation foundation. Moreover, studies have shown that technology research and development strength, domestic capital stock of R&D, and foreign direct investment have significantly influence on productivity [24–26]. Considering that the green total factor productivity may have the certain continuity, a dynamic econometric model would be adopted. Taking significant volatility of economy into consideration, four econometric models are established as follows:(1)MMLit=α0+α1MMLit−1+α2GERit+α3PERit+α4GER∗FUBit+α5QYJit+α6RKMit+α7RDit+α8CRDit+α9FDIit+εt+γi+μit,(2)MMLit=β0+β1MMLit−1+β2GERit+β3PERit+β4PER∗RELit+β5QYJit+β6RKMit+β7RDit+β8CRDit+β9FDIit+εt+γi+μit,(3)MMLit=χ0+χ1MMLit−1+χ2GERit+χ3PERit+χ4GER∗REFit+χ5QYJit+χ6RKMit+χ7RDit+χ8CRDit+χ9FDIit+εt+γi+μit,(4)MMLit=δ0+δ1MMLit−1+δ2GERit+δ3PERit+δ4PER∗REFit+δ5QYJit+δ6RKMit+δ7RDit+δ8CRDit+δ9FDIit+εt+γi+μit,where i is the province, t stands for year, MML represents the green total factor productivity, GER denotes the governmental environmental regulation, PER shows the civil environmental regulation, FUB is the governmental environmental awareness, REL stands for the civil environmental awareness, REF represents the regulation foundation, QYJ denotes the enterprise environmental awareness, RKM is the social environmental awareness, RD denotes the technology research and development strength, CRD represents the domestic capital stock of R&D, and FDI shows the foreign direct investment. Moreover, εt shows the time fixed effect, γi stands for the space fixed effect, and μit is the error term. To weaken the volatility of the data, all variables in model (1)–(4) are in logarithmic form.As for the parameter estimation method, least square method, fixed effect method, or random effects method will get error estimation results in the dynamic model, while generalized moment method (GMM) can effectively solve the endogenous problems of dynamic model [27]. In terms of this method, it is divided into system GMM or difference GMM based on instrumental variable and can also be decomposed into one-step GMM or two-step GMM. Windmeijer [28] found that one-step system GMM would result in effective and unbiased estimates under the circumstance of using more information. Finally, Stata 12.0 software will be used to estimate the sign and magnitude of the econometric parameters.


### 3.2. Method of Green Total Factor Productivity

Since scholars take undesirable output into consideration in DEA model, more and more researchers created and employed direction distance function to maximize the desirable output or minimize the undesirable output [9, 18, 21, 29]. This paper supposes that there are N kinds of production resources, M kinds of desirable outputs, and L kinds of undesirable outputs for each decision-making unit (DMU). Firstly, we refer to the definitions by Färe et al. [30]; environmental technology function is introduced as in the following production set:(5)Px=X,Υe,Υux≥Xλ,ye≤Υe,yu=Υuλ,λ≥0,(6)X=x1,x2,…,xN,Υe=y1e,y2e,…,yMe,Υu=y1u,y2u,…,yLu,where Px is the environmental technology function, x is the inputs of DMU, ϒe stands for desirable outputs, and ϒu denotes the undesirable outputs. Direction distance function is preferable to change the composition proportion of inputs for getting more desirable outputs and little undesirable outputs. Direction distance function contains radial model and nonradial model. The radial model would create unnecessary waste, and the nonradial model can make loss of useful information, which means using any of the above methods separately will result in biased estimates. Fortunately, Tone and Tsutsui [21] put forward EBM-DEA model, which can effectively solve the above hidden trouble. Secondly, taking the input orientation and variable return scale (VRS) as an example, we introduce the EBM linear programming (LP) model as follows:(7)γ∗=minϑ,λ,s−θ−εx∑i=1Nwi−si−/xi0,s.t.θx0−Xλ−s−=0,λY≥y,λ≥0,s−≥0,where the research object includes I DMUs, and each DMU contains M + L outputs and N kinds of inputs. γ represents the optimum efficiency calculated by the above EBM model, θ stands for the radial efficiency value estimated by the CCR model [16], s− denotes the slack variable derived from the nonradial model, λ is the weight of each district section observation, εx represents the weight value of variable, and ωi denotes the parameter based on both radial and nonradial models. Following the above model and methods, the annual total factor productivity of each DMU can be measured. What need to be stated briefly is that the above measure result of productivity is static efficiency, which is unable to directly compare the productivity growth rate for the same DMU. To solve this problem, Chung et al. [29] have set Malmquist–Luenberger (ML) index based on the definition of Malmquist index. But the hypotheses of homogeneity of the production technology in the above researches are not optimal. Oh and Lee [22] combined the metafrontier theory with DEA model to develop the metafrontier Malmquist approach. Scholars have investigated metafrontier Malmquist index or metafrontier Malmquist-Luenberger index to test the efficiency of different industries or regions [31–33]. Thirdly, the specific explanation of MML index is shown as follows. According to the production technology heterogeneity assumption of each DMU, all research objects would be divided into K groups in line with a reasonable standard. As for any specific group, the production possibilities’ technology reference set is Ptx=Xt,Yte,YtuXcan produceY, which is the same as formula (5). It is hinge on all DMUS of one group, and group frontier technology level belongs to the upper limit of Ptx. Then K kinds of group frontier will be established, supposing that they abide by common technical boundary. Finally, metafrontier set can be calculated by the upper limit, which is the enveloping curve of all group frontier curves. Therefore, in line with the definition by Chen and Yang [34], the direction distance function and MML index is shown as follows:(8)Dt⟶Xt,Υet,Υut=supβt:Xt,Υt+βtΥet,Υt+βtYut∈PtX,(9)MML=1+Dt⟶Xt,Υet,Υut1+Dt⟶Xt+1,Υet+1,Υut+1×1+Dt+1⟶Xt,Υet,Υut1+Dt+1⟶Xt+1,Υet+1,Υut+11/2,where MML index belongs to the dynamic value of green total factor productivity, MML > 1 denotes that the green total factor productivity is improving, and MML < 1 denotes that green total factor productivity is decreasing.


## 3.1. Econometric Model

The purpose of this paper is to study potential nonlinear relationship between environmental regulation and total factor productivity, whose method is using the cross terms of the following two aspects: environmental regulation with environmental awareness or regulation foundation to construct the threshold econometric model. What should be stated is that environmental awareness contains governmental environmental awareness, civil environmental awareness, enterprise environmental awareness, and social environmental awareness, while regulation foundation reflects the cost factors or marker factors, which may influence the government on how to implement environmental regulation decision-making. Then, infrastructure level, industrial structure, marketization level, and opening degree are chosen to construct comprehensive index to measure the regulation foundation. Moreover, studies have shown that technology research and development strength, domestic capital stock of R&D, and foreign direct investment have significantly influence on productivity [24–26]. Considering that the green total factor productivity may have the certain continuity, a dynamic econometric model would be adopted. Taking significant volatility of economy into consideration, four econometric models are established as follows:(1)MMLit=α0+α1MMLit−1+α2GERit+α3PERit+α4GER∗FUBit+α5QYJit+α6RKMit+α7RDit+α8CRDit+α9FDIit+εt+γi+μit,(2)MMLit=β0+β1MMLit−1+β2GERit+β3PERit+β4PER∗RELit+β5QYJit+β6RKMit+β7RDit+β8CRDit+β9FDIit+εt+γi+μit,(3)MMLit=χ0+χ1MMLit−1+χ2GERit+χ3PERit+χ4GER∗REFit+χ5QYJit+χ6RKMit+χ7RDit+χ8CRDit+χ9FDIit+εt+γi+μit,(4)MMLit=δ0+δ1MMLit−1+δ2GERit+δ3PERit+δ4PER∗REFit+δ5QYJit+δ6RKMit+δ7RDit+δ8CRDit+δ9FDIit+εt+γi+μit,where i is the province, t stands for year, MML represents the green total factor productivity, GER denotes the governmental environmental regulation, PER shows the civil environmental regulation, FUB is the governmental environmental awareness, REL stands for the civil environmental awareness, REF represents the regulation foundation, QYJ denotes the enterprise environmental awareness, RKM is the social environmental awareness, RD denotes the technology research and development strength, CRD represents the domestic capital stock of R&D, and FDI shows the foreign direct investment. Moreover, εt shows the time fixed effect, γi stands for the space fixed effect, and μit is the error term. To weaken the volatility of the data, all variables in model (1)–(4) are in logarithmic form.As for the parameter estimation method, least square method, fixed effect method, or random effects method will get error estimation results in the dynamic model, while generalized moment method (GMM) can effectively solve the endogenous problems of dynamic model [27]. In terms of this method, it is divided into system GMM or difference GMM based on instrumental variable and can also be decomposed into one-step GMM or two-step GMM. Windmeijer [28] found that one-step system GMM would result in effective and unbiased estimates under the circumstance of using more information. Finally, Stata 12.0 software will be used to estimate the sign and magnitude of the econometric parameters.


## 3.2. Method of Green Total Factor Productivity

Since scholars take undesirable output into consideration in DEA model, more and more researchers created and employed direction distance function to maximize the desirable output or minimize the undesirable output [9, 18, 21, 29]. This paper supposes that there are N kinds of production resources, M kinds of desirable outputs, and L kinds of undesirable outputs for each decision-making unit (DMU). Firstly, we refer to the definitions by Färe et al. [30]; environmental technology function is introduced as in the following production set:(5)Px=X,Υe,Υux≥Xλ,ye≤Υe,yu=Υuλ,λ≥0,(6)X=x1,x2,…,xN,Υe=y1e,y2e,…,yMe,Υu=y1u,y2u,…,yLu,where Px is the environmental technology function, x is the inputs of DMU, ϒe stands for desirable outputs, and ϒu denotes the undesirable outputs. Direction distance function is preferable to change the composition proportion of inputs for getting more desirable outputs and little undesirable outputs. Direction distance function contains radial model and nonradial model. The radial model would create unnecessary waste, and the nonradial model can make loss of useful information, which means using any of the above methods separately will result in biased estimates. Fortunately, Tone and Tsutsui [21] put forward EBM-DEA model, which can effectively solve the above hidden trouble. Secondly, taking the input orientation and variable return scale (VRS) as an example, we introduce the EBM linear programming (LP) model as follows:(7)γ∗=minϑ,λ,s−θ−εx∑i=1Nwi−si−/xi0,s.t.θx0−Xλ−s−=0,λY≥y,λ≥0,s−≥0,where the research object includes I DMUs, and each DMU contains M + L outputs and N kinds of inputs. γ represents the optimum efficiency calculated by the above EBM model, θ stands for the radial efficiency value estimated by the CCR model [16], s− denotes the slack variable derived from the nonradial model, λ is the weight of each district section observation, εx represents the weight value of variable, and ωi denotes the parameter based on both radial and nonradial models. Following the above model and methods, the annual total factor productivity of each DMU can be measured. What need to be stated briefly is that the above measure result of productivity is static efficiency, which is unable to directly compare the productivity growth rate for the same DMU. To solve this problem, Chung et al. [29] have set Malmquist–Luenberger (ML) index based on the definition of Malmquist index. But the hypotheses of homogeneity of the production technology in the above researches are not optimal. Oh and Lee [22] combined the metafrontier theory with DEA model to develop the metafrontier Malmquist approach. Scholars have investigated metafrontier Malmquist index or metafrontier Malmquist-Luenberger index to test the efficiency of different industries or regions [31–33]. Thirdly, the specific explanation of MML index is shown as follows. According to the production technology heterogeneity assumption of each DMU, all research objects would be divided into K groups in line with a reasonable standard. As for any specific group, the production possibilities’ technology reference set is Ptx=Xt,Yte,YtuXcan produceY, which is the same as formula (5). It is hinge on all DMUS of one group, and group frontier technology level belongs to the upper limit of Ptx. Then K kinds of group frontier will be established, supposing that they abide by common technical boundary. Finally, metafrontier set can be calculated by the upper limit, which is the enveloping curve of all group frontier curves. Therefore, in line with the definition by Chen and Yang [34], the direction distance function and MML index is shown as follows:(8)Dt⟶Xt,Υet,Υut=supβt:Xt,Υt+βtΥet,Υt+βtYut∈PtX,(9)MML=1+Dt⟶Xt,Υet,Υut1+Dt⟶Xt+1,Υet+1,Υut+1×1+Dt+1⟶Xt,Υet,Υut1+Dt+1⟶Xt+1,Υet+1,Υut+11/2,where MML index belongs to the dynamic value of green total factor productivity, MML > 1 denotes that the green total factor productivity is improving, and MML < 1 denotes that green total factor productivity is decreasing.


## 4. Data

### 4.1. Inputs and Outputs of Green Total Factor Productivity

Appropriately choosing input factors and output factors is a crucial precondition for calculating green total factor productivity based on metafrontier Malmquist-Luenberger index. Five kinds of variables have been selected. In line with Li and Wu [9], input variables include labor force and capital stock and energy consumption, while output variables contain real gross domestic product (GDP) and undesirable pollutants. The effective labor per unit time is an optimal approach to measure labor force, while because of data deficiency the quantity of labor force is a constructive method. This paper uses the total number of tertiary industries, secondary industries, and primary industries, with the specific industry classification being established by China’s national industries classification (GB/T 4754-2002). Labor force data is collected from the provincial statistical yearbook and China statistical yearbook. In terms of capital stock, perpetual inventory approach is reasonable. The formula is Kt=It+1−aKt−1, where Kt is the capital stock of t period, It denotes the fixed assets investment of t period, and a stands for the rate of depreciation. Moreover, the detailed process of measuring the capital stock is given by Shan [35], and we eliminate the price factor at the constant 2007 prices of fixed assets’ investment. The above relevant data can be gained from China statistical yearbook. As for energy consumption, in view of each DMU’s energy consumption, the structure takes a degree of difference, whose energy consumption can be estimated by the consumption of standard coal based on convert coefficients of fossil energy. The fossil energy contains coal, coke, crude oil, kerosene, fuel oil, gasoline, diesel, and natural gas, whose corresponding convert coefficients are 0.7143, 0.9714, 1.4826, 1.4714, 1.4286, 1.4714, 1.4571, and 1.3300, respectively. Data of 2016 is inquired from China energy statistical yearbook, while other data is collected from China statistical yearbook. The gross domestic product can be directly retrieved from China statistical yearbook, and it is calculated at 2004 prices. Undesirable pollutants conclude discharge of industrial waste solid, discharge of industrial waste gas, and discharge volume of industrial waste water, and the specific data is collected from China statistical yearbook. Based on the data availability and continuity, 30 provinces, municipalities, and autonomous regions are perfect research objects, and the time dimension is from 2007 to 2016. Referring to the Chinese traditional division method, the research object is divided into three groups, which are east region, central region, and west region. Table 1 shows input-output factors’ definitions and descriptive statistics. Table 2 contains the detailed division and measurement of green total factor productivity growth ratio from 2007 to 2016. According to Table 2, green total factor productivity of east region is increasing, while the corresponding productivity of middle region and west region is falling based on the average value. The differentiation of productivity levels and variation tendency enlightens that the government should implement differentiation policy.Table 1
Input-output factors’ definitions and descriptive statistics.
Definition
Observation
Mean
Standard deviation
Minimum
Maximum
Unit
Inputs
Labor force
300
2519.543
1685.913
290.42
6580.4
Ten thousand people
Capital stock
300
20101.67
19910
547.3055
114443.8
Hundred million RMB
Energy consumption
300
11685.08
7708.388
742
39423
Ten thousand tons
Outputs
Real gross domestic product
300
12477.19
11379.99
466.1
62474.79
Million RMB
Discharge of industrial waste gas
300
765232.4
448834.4
22000
2002000
Ton
Discharge of industrial waste water
300
196388.6
156083.9
14287
862471.1
Ton
Discharge of industrial waste solid
300
7316.967
6987.782
112
45575.83
TonTable 2
Green total factor productivity growth ratio in 2007–2016.
Zone
Province
07/08
08/09
09/10
10/11
11/12
12/13
13/14
14/15
15/16
East region
Beijing
0.9943
0.9998
1.0019
1.0014
1.0032
1.0017
1.0037
1.0004
1.0012
Tianjin
1.0000
0.9958
0.9940
1.0103
0.9952
1.0004
1.0043
1.0000
1.0000
Hebei
0.9968
0.9966
0.9998
0.9943
0.9954
1.0060
1.0112
1.0000
1.0000
Liaoning
1.0000
1.0000
1.0000
0.9947
0.9956
0.9979
1.0118
1.0000
1.0000
Shanghai
0.9968
1.0033
1.0000
1.0000
0.9973
1.0027
1.0000
1.0000
1.0000
Jiangsu
1.0000
0.9987
0.9967
1.0000
0.9976
1.0026
1.0043
1.0000
1.0000
Zhejiang
0.9981
1.0004
0.9995
1.0015
0.9987
1.0057
1.0049
1.0005
0.9996
Fujian
0.9928
0.9964
1.0018
1.0011
1.0014
1.0021
0.9994
1.0015
1.0007
Shandong
1.0000
1.0000
0.9949
0.9990
0.9959
1.0024
1.0078
1.0000
1.0000
Guangdong
1.0000
0.9978
1.0022
1.0000
0.9970
1.0030
1.0000
1.0000
1.0000
Hainan
0.9946
0.9984
0.9983
1.0088
0.9921
1.0080
1.0000
1.0000
1.0000
Average
0.9976
0.9988
0.9990
1.0010
0.9972
1.0030
1.0043
1.0002
1.0001
Middle region
Shanxi
1.0000
0.9942
0.9983
0.9994
0.9925
1.0052
1.0105
1.0000
1.0000
Jilin
0.9819
0.9958
0.9996
0.9996
0.9995
1.0066
1.0103
1.0010
1.0003
Heilongjiang
0.9816
0.9889
0.9973
1.0014
0.9938
1.0021
1.0088
0.9984
0.9954
Anhui
0.9856
0.9960
1.0002
1.0038
0.9978
1.0030
1.0146
1.0009
0.9997
Jiangxi
1.0000
1.0000
0.9985
1.0015
0.9926
1.0013
1.0061
1.0000
1.0000
Henan
1.0106
0.9938
0.9989
0.9972
0.9957
1.0009
1.0059
1.0003
1.0000
Hubei
0.9897
0.9969
1.0015
1.0037
0.9992
1.0055
1.0063
0.9985
1.0005
Hunan
0.9807
0.9980
1.0027
1.0012
0.9986
1.0026
1.0084
0.9989
0.9981
Average
0.9913
0.9955
0.9996
1.0010
0.9962
1.0034
1.0089
0.9998
0.9993
West region
Inner Mongolia
1.0000
1.0000
0.9984
1.0016
0.9966
1.0035
1.0000
1.0000
0.9964
Guangxi
1.0000
0.9960
1.0041
1.0000
0.9946
1.0054
0.9828
1.0040
0.9941
Chongqing
0.9917
0.9957
0.9946
1.0020
0.9974
0.9994
0.9969
0.9997
1.0002
Sichuan
0.9888
0.9936
0.9986
0.9987
0.9961
1.0045
0.9984
1.0011
1.0007
Guizhou
1.0000
1.0000
0.9938
0.9936
1.0000
0.9997
0.9930
0.9972
0.9996
Yunnan
0.9750
0.9992
1.0032
1.0035
0.9959
1.0019
1.0408
0.9865
0.9965
Shanxi
1.0008
0.9995
0.9999
1.0039
0.9933
1.0034
1.0118
0.9991
1.0009
Gansu
0.9692
0.9936
0.9967
1.0001
0.9970
1.0075
1.0150
0.9959
0.9962
Qinghai
1.0000
0.9949
0.9988
1.0064
1.0000
1.0000
1.0000
1.0000
1.0000
Ningxia
1.0000
1.0000
1.0000
1.0000
0.9955
1.0045
1.0000
1.0000
1.0000
Xinjiang
0.9913
0.9967
1.0031
1.0018
0.9972
1.0037
1.0072
0.9980
0.9984
Average
0.9924
0.9972
0.9992
1.0011
0.9967
1.0030
1.0042
0.9983
0.9985


### 4.2. Core Explanatory Variables and Control Explanatory Variables

Core explanatory variables contain environmental regulation level, environmental awareness, and environmental regulation foundation. In terms of regulation level, governmental environmental regulation can be calculated by the ratio of pollution abatement cost or investment to the industrial value added [2, 3]. The data can be collected from China environmental statistics yearbook. Civil environmental regulation can be measured by personal income level that is estimated by GDP per capita [9]. The data is obtained from China statistics yearbook. As for environmental awareness, some scholars found that environmental awareness of consumer or manager can affect enterprise decision on innovation [36]. Referring to researches from Simsekli [37], education level of labor force, entrepreneur innovative spirit, and population density can be used to measure civil environmental awareness, enterprise environmental awareness, and social environmental awareness. Corresponding data is derived from China statistics yearbook, China labor statistical yearbook, and China science and technology statistic yearbook. Due to the difficult measurement of governmental environmental awareness, few scholars investigate it in the econometric model. This paper uses substitution variable that holds a high correlation with it, and based on the investigation by the people’s procuratorates, the number of corruption and bribery and malfeasance in one hundred thousand people can calculate the governmental environmental awareness. The data is collected from China procuratorial yearbook and annual work report of the people’s procuratorates. Then, the regulation foundation is measured by comprehensive index, which contains infrastructure level, industrial structure, marketization level, and opening degree. This paper adopts road area per capita, ratio of the second industry output to GDP, rate of private sector employment number to total employment number, and percent of import and export output to GDP, and entropy evaluation method can be employed to measure the variable weight and regulation foundation value. All the relevant data comes from China statistic yearbook.In this paper, three control variables conclude the technology research and development strength, domestic capital stock of R&D, and foreign direct investment. In particular, the cost of technology research and development in government, civil society, or enterprise can improve the technical level, increase customer experience utility, shorten the industrial and regional gap, and enhance residents’ happiness index [24, 25]. This paper uses the cost strength and level of R&D, and the data can be directly retrieved from China statistic yearbook. Moreover, foreign direct investment generates technology spillovers from host country to local country, which is conducive to introduce advanced enterprise management experiences, leading production technology, regional leadership, and market power [38]. It thence appears that FDI should be taken into econometric model, and the data is derived from China statistic yearbook. Table 3 shows the descriptions of all variables.Table 3
Descriptions of all variables.
Definition
Variable
Mean
Standard deviation
Minimum
Maximum
Unit
Green total factor productivity
MML
0.9996
0.0056
0.9692
1.0408
—
Governmental environmental regulation
GER
0.0435
0.0344
0.0036
0.2692
—
Civil environmental regulation
PER
22407.0
14291.2
4070.0
74420.9
RMB
Governmental environmental awareness
FUB
2.7522
0.7056
1.4316
5.9069
—
Civil environmental awareness
REL
8.5728
0.9926
6.3778
12.027
Year
Enterprise environmental awareness
QYJ
0.1701
0.1131
0.0264
0.6777
—
Social environmental awareness
RKM
2523.7
1388.2
186.00
6307.0
—
Infrastructure level
GZJC
12.180
3.8706
4.0400
25.3400
—
Industrial structure
CYJG
48.230
7.6461
22.300
61.500
%
Marketization level
NOP
0.3476
0.4308
0.0357
1.7991
—
Opening degree
WOP
0.2106
0.1162
0.0559
0.6870
—
Technology research and development strength
RD
1.2902
1.0111
0.1700
6.0800
—
Domestic capital stock of R&D
CRD
661.779
930.590
5.0719
5226.24
Hundred million RMB
Foreign direct investment
FDI
0.0253
0.0195
0.0007
0.0819
—


## 4.1. Inputs and Outputs of Green Total Factor Productivity

Appropriately choosing input factors and output factors is a crucial precondition for calculating green total factor productivity based on metafrontier Malmquist-Luenberger index. Five kinds of variables have been selected. In line with Li and Wu [9], input variables include labor force and capital stock and energy consumption, while output variables contain real gross domestic product (GDP) and undesirable pollutants. The effective labor per unit time is an optimal approach to measure labor force, while because of data deficiency the quantity of labor force is a constructive method. This paper uses the total number of tertiary industries, secondary industries, and primary industries, with the specific industry classification being established by China’s national industries classification (GB/T 4754-2002). Labor force data is collected from the provincial statistical yearbook and China statistical yearbook. In terms of capital stock, perpetual inventory approach is reasonable. The formula is Kt=It+1−aKt−1, where Kt is the capital stock of t period, It denotes the fixed assets investment of t period, and a stands for the rate of depreciation. Moreover, the detailed process of measuring the capital stock is given by Shan [35], and we eliminate the price factor at the constant 2007 prices of fixed assets’ investment. The above relevant data can be gained from China statistical yearbook. As for energy consumption, in view of each DMU’s energy consumption, the structure takes a degree of difference, whose energy consumption can be estimated by the consumption of standard coal based on convert coefficients of fossil energy. The fossil energy contains coal, coke, crude oil, kerosene, fuel oil, gasoline, diesel, and natural gas, whose corresponding convert coefficients are 0.7143, 0.9714, 1.4826, 1.4714, 1.4286, 1.4714, 1.4571, and 1.3300, respectively. Data of 2016 is inquired from China energy statistical yearbook, while other data is collected from China statistical yearbook. The gross domestic product can be directly retrieved from China statistical yearbook, and it is calculated at 2004 prices. Undesirable pollutants conclude discharge of industrial waste solid, discharge of industrial waste gas, and discharge volume of industrial waste water, and the specific data is collected from China statistical yearbook. Based on the data availability and continuity, 30 provinces, municipalities, and autonomous regions are perfect research objects, and the time dimension is from 2007 to 2016. Referring to the Chinese traditional division method, the research object is divided into three groups, which are east region, central region, and west region. Table 1 shows input-output factors’ definitions and descriptive statistics. Table 2 contains the detailed division and measurement of green total factor productivity growth ratio from 2007 to 2016. According to Table 2, green total factor productivity of east region is increasing, while the corresponding productivity of middle region and west region is falling based on the average value. The differentiation of productivity levels and variation tendency enlightens that the government should implement differentiation policy.Table 1
Input-output factors’ definitions and descriptive statistics.
Definition
Observation
Mean
Standard deviation
Minimum
Maximum
Unit
Inputs
Labor force
300
2519.543
1685.913
290.42
6580.4
Ten thousand people
Capital stock
300
20101.67
19910
547.3055
114443.8
Hundred million RMB
Energy consumption
300
11685.08
7708.388
742
39423
Ten thousand tons
Outputs
Real gross domestic product
300
12477.19
11379.99
466.1
62474.79
Million RMB
Discharge of industrial waste gas
300
765232.4
448834.4
22000
2002000
Ton
Discharge of industrial waste water
300
196388.6
156083.9
14287
862471.1
Ton
Discharge of industrial waste solid
300
7316.967
6987.782
112
45575.83
TonTable 2
Green total factor productivity growth ratio in 2007–2016.
Zone
Province
07/08
08/09
09/10
10/11
11/12
12/13
13/14
14/15
15/16
East region
Beijing
0.9943
0.9998
1.0019
1.0014
1.0032
1.0017
1.0037
1.0004
1.0012
Tianjin
1.0000
0.9958
0.9940
1.0103
0.9952
1.0004
1.0043
1.0000
1.0000
Hebei
0.9968
0.9966
0.9998
0.9943
0.9954
1.0060
1.0112
1.0000
1.0000
Liaoning
1.0000
1.0000
1.0000
0.9947
0.9956
0.9979
1.0118
1.0000
1.0000
Shanghai
0.9968
1.0033
1.0000
1.0000
0.9973
1.0027
1.0000
1.0000
1.0000
Jiangsu
1.0000
0.9987
0.9967
1.0000
0.9976
1.0026
1.0043
1.0000
1.0000
Zhejiang
0.9981
1.0004
0.9995
1.0015
0.9987
1.0057
1.0049
1.0005
0.9996
Fujian
0.9928
0.9964
1.0018
1.0011
1.0014
1.0021
0.9994
1.0015
1.0007
Shandong
1.0000
1.0000
0.9949
0.9990
0.9959
1.0024
1.0078
1.0000
1.0000
Guangdong
1.0000
0.9978
1.0022
1.0000
0.9970
1.0030
1.0000
1.0000
1.0000
Hainan
0.9946
0.9984
0.9983
1.0088
0.9921
1.0080
1.0000
1.0000
1.0000
Average
0.9976
0.9988
0.9990
1.0010
0.9972
1.0030
1.0043
1.0002
1.0001
Middle region
Shanxi
1.0000
0.9942
0.9983
0.9994
0.9925
1.0052
1.0105
1.0000
1.0000
Jilin
0.9819
0.9958
0.9996
0.9996
0.9995
1.0066
1.0103
1.0010
1.0003
Heilongjiang
0.9816
0.9889
0.9973
1.0014
0.9938
1.0021
1.0088
0.9984
0.9954
Anhui
0.9856
0.9960
1.0002
1.0038
0.9978
1.0030
1.0146
1.0009
0.9997
Jiangxi
1.0000
1.0000
0.9985
1.0015
0.9926
1.0013
1.0061
1.0000
1.0000
Henan
1.0106
0.9938
0.9989
0.9972
0.9957
1.0009
1.0059
1.0003
1.0000
Hubei
0.9897
0.9969
1.0015
1.0037
0.9992
1.0055
1.0063
0.9985
1.0005
Hunan
0.9807
0.9980
1.0027
1.0012
0.9986
1.0026
1.0084
0.9989
0.9981
Average
0.9913
0.9955
0.9996
1.0010
0.9962
1.0034
1.0089
0.9998
0.9993
West region
Inner Mongolia
1.0000
1.0000
0.9984
1.0016
0.9966
1.0035
1.0000
1.0000
0.9964
Guangxi
1.0000
0.9960
1.0041
1.0000
0.9946
1.0054
0.9828
1.0040
0.9941
Chongqing
0.9917
0.9957
0.9946
1.0020
0.9974
0.9994
0.9969
0.9997
1.0002
Sichuan
0.9888
0.9936
0.9986
0.9987
0.9961
1.0045
0.9984
1.0011
1.0007
Guizhou
1.0000
1.0000
0.9938
0.9936
1.0000
0.9997
0.9930
0.9972
0.9996
Yunnan
0.9750
0.9992
1.0032
1.0035
0.9959
1.0019
1.0408
0.9865
0.9965
Shanxi
1.0008
0.9995
0.9999
1.0039
0.9933
1.0034
1.0118
0.9991
1.0009
Gansu
0.9692
0.9936
0.9967
1.0001
0.9970
1.0075
1.0150
0.9959
0.9962
Qinghai
1.0000
0.9949
0.9988
1.0064
1.0000
1.0000
1.0000
1.0000
1.0000
Ningxia
1.0000
1.0000
1.0000
1.0000
0.9955
1.0045
1.0000
1.0000
1.0000
Xinjiang
0.9913
0.9967
1.0031
1.0018
0.9972
1.0037
1.0072
0.9980
0.9984
Average
0.9924
0.9972
0.9992
1.0011
0.9967
1.0030
1.0042
0.9983
0.9985


## 4.2. Core Explanatory Variables and Control Explanatory Variables

Core explanatory variables contain environmental regulation level, environmental awareness, and environmental regulation foundation. In terms of regulation level, governmental environmental regulation can be calculated by the ratio of pollution abatement cost or investment to the industrial value added [2, 3]. The data can be collected from China environmental statistics yearbook. Civil environmental regulation can be measured by personal income level that is estimated by GDP per capita [9]. The data is obtained from China statistics yearbook. As for environmental awareness, some scholars found that environmental awareness of consumer or manager can affect enterprise decision on innovation [36]. Referring to researches from Simsekli [37], education level of labor force, entrepreneur innovative spirit, and population density can be used to measure civil environmental awareness, enterprise environmental awareness, and social environmental awareness. Corresponding data is derived from China statistics yearbook, China labor statistical yearbook, and China science and technology statistic yearbook. Due to the difficult measurement of governmental environmental awareness, few scholars investigate it in the econometric model. This paper uses substitution variable that holds a high correlation with it, and based on the investigation by the people’s procuratorates, the number of corruption and bribery and malfeasance in one hundred thousand people can calculate the governmental environmental awareness. The data is collected from China procuratorial yearbook and annual work report of the people’s procuratorates. Then, the regulation foundation is measured by comprehensive index, which contains infrastructure level, industrial structure, marketization level, and opening degree. This paper adopts road area per capita, ratio of the second industry output to GDP, rate of private sector employment number to total employment number, and percent of import and export output to GDP, and entropy evaluation method can be employed to measure the variable weight and regulation foundation value. All the relevant data comes from China statistic yearbook.In this paper, three control variables conclude the technology research and development strength, domestic capital stock of R&D, and foreign direct investment. In particular, the cost of technology research and development in government, civil society, or enterprise can improve the technical level, increase customer experience utility, shorten the industrial and regional gap, and enhance residents’ happiness index [24, 25]. This paper uses the cost strength and level of R&D, and the data can be directly retrieved from China statistic yearbook. Moreover, foreign direct investment generates technology spillovers from host country to local country, which is conducive to introduce advanced enterprise management experiences, leading production technology, regional leadership, and market power [38]. It thence appears that FDI should be taken into econometric model, and the data is derived from China statistic yearbook. Table 3 shows the descriptions of all variables.Table 3
Descriptions of all variables.
Definition
Variable
Mean
Standard deviation
Minimum
Maximum
Unit
Green total factor productivity
MML
0.9996
0.0056
0.9692
1.0408
—
Governmental environmental regulation
GER
0.0435
0.0344
0.0036
0.2692
—
Civil environmental regulation
PER
22407.0
14291.2
4070.0
74420.9
RMB
Governmental environmental awareness
FUB
2.7522
0.7056
1.4316
5.9069
—
Civil environmental awareness
REL
8.5728
0.9926
6.3778
12.027
Year
Enterprise environmental awareness
QYJ
0.1701
0.1131
0.0264
0.6777
—
Social environmental awareness
RKM
2523.7
1388.2
186.00
6307.0
—
Infrastructure level
GZJC
12.180
3.8706
4.0400
25.3400
—
Industrial structure
CYJG
48.230
7.6461
22.300
61.500
%
Marketization level
NOP
0.3476
0.4308
0.0357
1.7991
—
Opening degree
WOP
0.2106
0.1162
0.0559
0.6870
—
Technology research and development strength
RD
1.2902
1.0111
0.1700
6.0800
—
Domestic capital stock of R&D
CRD
661.779
930.590
5.0719
5226.24
Hundred million RMB
Foreign direct investment
FDI
0.0253
0.0195
0.0007
0.0819
—


## 5. Empirical Analysis Results

### 5.1. Threshold Effect Analysis of Governmental and Civil Environmental Awareness

Table4 shows the regression results of nonlinear effect model whose threshold variables are governmental and civil awareness. In order to present the time effect and individual effect, results of year fixed effect model, results of space fixed effect model, and results of space and year fixed effect model are shown in Table 4. Specifically, M1, M2, and M3 denote the regression results of governmental environmental awareness, and M4 and M5 stand for the results of civil environmental awareness. It is clear that the coefficient signs between LnPER and LnGER × LnFUB, LnPER and LnPER × LnREL are different, which justifies the reasonable choice of nonlinear model. In terms of hypothesis testing results for GMM in the following six models, results of residual serial correlation test (AR (2)) show that there is no second-order serial correlation in model residuals, and statistics of Sargan test show that instrumental variable set is reasonable.Table 4
Regression results of nonlinear effect model (threshold variable: FUB and REL).
Model
GER-M1
GER-M2
GER-M3
PER-M4
PER-M5
PER-M6
Variable
Year fixed effect
Space fixed effect
Space and year fixed effect
Year fixed effect
Space fixed effect
Space and year fixed effect
L. LnMML
0.0268∗∗(2.20)
0.0375∗(1.77)
0.1010∗∗∗(5.46)
0.0475∗∗(2.24)
0.0113 (0.55)
0.0620∗∗∗(3.22)
LnGER
−0.0226∗(−1.76)
−0.0251 (−0.09)
−0.0241∗∗∗(−3.82)
−0.0150∗∗(−2.06)
−0.0156∗∗(−2.31)
−0.0152∗∗∗(−4.73)
LnPER
0.0075 (1.56)
−0.0046∗∗(−2.09)
0.0006 (0.28)
−0.1468∗(−1.69)
−0.1539∗(−1.89)
−0.1699∗(−1.86)
LnGER × LnFUB
0.0054∗∗∗(3.66)
0.0051∗∗∗(2.79)
0.0052∗∗(2.33)
LnPER × LnREL
0.0137∗∗∗(5.78)
0.0136∗∗∗(7.70)
0.0139∗∗∗(5.53)
LnQYJ
−0.0019 (−1.06)
0.0031 (0.02)
−0.0023 (−1.17)
−0.0289 (−0.76)
−0.0112 (−0.97)
−0.0338∗(−1.66)
LnRKM
0.0024∗∗∗(3.57)
−0.0022 (−1.05)
0.0018∗∗∗(4.78)
0.0074∗∗∗(4.31)
−0.0235 (−1.03)
0.0087∗∗∗(4.97)
LnRD
0.0037∗∗∗(4.77)
0.0044∗∗∗(4.65)
0.0048∗∗∗(5.09)
0.0022∗∗∗(2.74)
0.0028∗∗∗(2.64)
0.0046∗∗∗(5.29)
LnCRD
0.0017 (1.07)
0.0035∗∗∗(2.88)
0.0013 (0.98)
0.0011∗(1.84)
0.0031 (1.48)
0.0029∗∗(2.10)
LnFDI
−0.0078 (−0.59)
0.0407 (1.18)
−0.0129 (−0.78)
−0.0148 (−0.17)
0.0346 (1.29)
−0.0173 (−0.70)
CONS
0.7146∗∗∗(32.08)
0.7090∗∗∗(31.24)
0.7080∗∗∗(34.87)
0.6504∗∗∗(26.35)
0.6470∗∗∗(24.85)
0.6800∗∗∗(33.33)
AR (2)
0.1182
0.1901
0.1360
0.1920
0.1801
0.2050
Sargan
0.0937
0.1548
0.1024
0.1174
0.2012
0.1412
Note.∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%; t values are shown in parentheses.As shown in column 2 to column 4 or column 5 to column 7 in Table4, the magnitude and sign of coefficients are remarkably consistent, while the significance levels of coefficients in column 4 or column 7better than others which means that space and year fixed effect is applied. As for the governmental environmental awareness in column 2 to column 4, the coefficients of L. LnMML are 0.0268, 0.0375, and 0.1010 and significant at 10% significance level, which means that 1% increase in green total factor productivity this year will induce 0.0268%, 0.0375%, or 0.1010% increase next year. The implementations of macroeconomic policy have certain cycle and certain hysteresis effect. Once the adjustment change of management institutions occurs, the established macropolicy enforcement will be discounted. So, the effect of L. LnMML on MML enlightens that the management institutions should keep continuity of the policy moderately. Similarly, in terms of civil environmental awareness in column 5 to column 7, coefficients of L. LnMML are 0.0475, 0.0113, and 0.0620, which means that 1% increase in local green total factor productivity this year will lead to 0.0475%, 0.0113%, and 0.0620% increase next year. Government should continue to adhere to the environmental protection education to enhance local productivity.The effects of governmental environmental regulation on green total factor productivity are dependent on the regulation stringency and governmental environmental awareness in column 2 to column 4. The coefficients of LnGER are −0.0241 in column 4 and significant at 1% significance level, and the corresponding coefficient of LnGER × LnFUB is 0.0052 and significant at 5% significance level. The above regression results suggest that the government environmental awareness has single threshold when it comes to consider the effect of governmental environmental regulation on green total factor productivity, and the threshold value is 4.6346; namely, corruption and bribery and malfeasance number based on an investigation by the people’s procuratorates in every one hundred thousand people is 0.2158. According to the measure data of the government environmental awareness, the sample range in various areas of China in corruption and bribery and malfeasance number based on an investigation by the people’s procuratorates in one hundred thousand people is greater than 0.2158, and the minimum is 1.4316 in Shanghai in 2011. The reason is that official controls the allocation of production factors, causing serious rent-seeking. In particular, the fiscal and taxation system reformed in 1994 caused fiscal decentralization between the central government and local government, while local government weaken the environmental protection for achieving political demands.In terms of civil environmental regulation on green total factor productivity, which is also determined by civil regulation stringency and its environmental awareness in column 5 to column 7, coefficient of LnPER is negative (−0.1699) and significant at 10% significance level (t = −1.86), while coefficient of LnPER × LnREL is positive (0.0139) and significant at 1% significance level (t = 5.53). The results show that there is a single threshold of civil environmental awareness for the effect of civil environmental regulation on green total factor productivity, and the threshold value is 12.2230, also known as that per education level should reach 12.2230 years. Unfortunately, the highest education level in data sample is Beijing in 2013, and the level is 12.0276. Before reaching the threshold, enhancing the education level can lower the disadvantageous influence. Ascension of education level can develop personal innovation capability and awareness, which is helpful to realize sustainable development. Moreover, we further study the effect of enterprise and social awareness on productivity. Coefficients of LnQYJ in column 4 and column 7 are −0.0023 and −0.0338, which means that 1% increase in LnQYJ will induce 0.0023% or 0.0338% decrease. Coefficients of LnRKM are positive (0.0018, 0.0087) and significant at 1% significance level (t = 4.78, 4.97), which suggests that it is necessary to plan and control the urban population. Coefficients of control variables (LnRD, LnCRD, and LnFDI) in column 4 are 0.0048, 0.0013, and −0.0129, and they are 0.0046, 0.0029, and −0.0173 in column 7. Results show that increasing the research cost moderately will improve the researchers’ study preferences and investment, while crowding-out effect is greater than positive spillover effect, both created by FDI.


### 5.2. Threshold Effect Analysis of Regulation Foundation

Table5 shows the regression results of nonlinear effect model whose threshold variable is regulation foundation in year fixed effect, space fixed effect, and space and year fixed effect. M7, M8, and M9 present econometric model of governmental environment regulation, and M10, M11, and M12 denote the regression results of civil environmental regulation. In terms of statistical hypothesis testing, tests of AR (2) and Sargan show that dynamic panel threshold models are rational and the endogenous problem is effectively solved.Table 5
Regression results of nonlinear effect model (threshold variable: REF).
Variable
Year fixed effect
Space fixed effect
Space and year fixed effect
Year fixed effect
Space fixed effect
Space and year fixed effect
Model
GER-M7
GER-M8
GER-M9
PER-M10
PER-M11
PER-M12
L. LnMML
0.0994∗∗∗(4.79)
0.0224 (0.09)
0.1070∗∗∗(5.66)
0.1116∗∗(2.16)
0.0469∗∗(2.15)
0.1030∗∗∗(5.04)
LnGER
−0.2100∗∗(−2.02)
−0.2630∗∗(−2.22)
−0.2380∗∗(2.21)
−0.0150∗∗∗(−5.96)
−0.0163∗∗∗(−4.26)
−0.0234∗∗∗(−6.88)
LnPER
−0.0022 (−0.10)
−0.0773∗∗(−2.34)
−0.0017 (−0.07)
−0.0006 (−0.17)
−0.0007 (−0.24)
−0.0006 (−0.23)
LnGER × LnREF
15.4715∗(1.90)
17.1200∗∗(2.46)
14.5580∗(1.80)
LnPER × LnREF
0.0415 (0.96)
0.0441 (1.14)
0.0391∗(1.73)
LnQYJ
−0.0020 (−0.63)
0.0015 (0.85)
−0.0012 (−0.52)
−0.0015 (−0.76)
−0.0017 (−0.75)
−0.0016 (−0.77)
LnRKM
0.0751∗∗(2.19)
−0.0443 (−1.49)
0.0959∗∗∗(4.92)
0.0099∗∗(2.04)
−0.0015 (−0.67)
0.0103∗∗∗(4.11)
LnRD
0.0371∗∗∗(3.80)
0.0470∗∗∗(5.01)
0.0445∗∗∗(3.75)
0.0410∗∗∗(3.66)
0.0433∗∗∗(4.92)
0.0463∗∗∗(5.09)
LnCRD
0.0253 (1.08)
0.0474∗∗∗(2.85)
0.0142 (0.91)
0.0089 (1.10)
0.0170 (1.11)
0.0121 (0.78)
LnFDI
−0.0364 (−0.27)
−0.0896 (−0.28)
−0.0473∗(−1.78)
−0.0741 (−0.32)
0.0192 (0.62)
−0.0109 (−0.61)
CONS
0.6918∗∗∗(46.78)
0.7080∗∗∗(31.62)
0.7220∗∗∗(44.54)
0.6677∗∗∗(37.20)
0.6830∗∗∗(20.85)
0.7100∗∗∗(29.14)
AR (2)
0.2708
0.3362
0.2481
0.2274
0.2451
0.2548
Sargan
0.0955
0.1024
0.0874
0.0728
0.0954
0.0895
Note.∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%; t values are shown in parentheses.As for regression results, coefficients of L. LnMML are 0.0994, 0.0224, 0.1070, 0.1116, 0.0469, and 0.1030, which means that 1% increasing in productivity this year will induce 0.0994%, 0.0224%, 0.1070%, 0.1116%, 0.0469%, or 0.1030% augmentation the following year in the same region. The phenomenon and characteristic of MML index are in line with Table4; namely, reducing the effects of policy changes on economic growth is applied. What should be pointed out is that signs of LnGER and LnGER × LnREF, LnPER and LnPER × LnREF are reversed; namely, regulation foundation can influence marginal effects of governmental or civil environmental regulation on green total factor productivity. Coefficients of LnGER and LnGER × LnREF in column 4 or LnPER and LnPER × LnREF in column 7 in Table 5 are −0.2380, 14.5580, −0.0006, 0.0391 and significant at 10% significance level; namely, the single threshold of regulation foundation is 0.0163 and 0.0154, respectively. Above all, we find that all comprehensive indexes of regulation foundation in 2007–2016 are less than the two threshold values. The reasons are reflected in the following several aspects. Firstly, infrastructure construction is the premise and basis of urbanization, which need a lot of support of capital and labor force. However, the status quo is narrow channels of financial income in the condition of huge population base which makes lower level of road holdings. Secondly, the layout of local industrial structure is unreasonable, which needs transformation and upgrading as soon as possible. Thirdly, the distance of the state-owned enterprise management mode and its administrative levels are not conductive to the local government implement environmental regulation. Moreover, the fiscal decentralization narrows the local government tax. The central government should promote joint-stock reform of state-owned enterprises effectively, improve enterprise efficiency, and deal with the relationship between the local state-owned enterprises and local government. Fourthly, import and export is a main driving force to promote the development of economy, and moderately weakening trade barriers and enhancing the regional economic exchanges will help to reduce transaction costs. When it comes to other control variables, coefficients of LnRD and LnCRD are positive (0.0445, 0.0463; 0.0142, 0.0121) in column 4 and column 7, while corresponding coefficients of LnFDI are negative (−0.0473, −0.0109).


## 5.1. Threshold Effect Analysis of Governmental and Civil Environmental Awareness

Table4 shows the regression results of nonlinear effect model whose threshold variables are governmental and civil awareness. In order to present the time effect and individual effect, results of year fixed effect model, results of space fixed effect model, and results of space and year fixed effect model are shown in Table 4. Specifically, M1, M2, and M3 denote the regression results of governmental environmental awareness, and M4 and M5 stand for the results of civil environmental awareness. It is clear that the coefficient signs between LnPER and LnGER × LnFUB, LnPER and LnPER × LnREL are different, which justifies the reasonable choice of nonlinear model. In terms of hypothesis testing results for GMM in the following six models, results of residual serial correlation test (AR (2)) show that there is no second-order serial correlation in model residuals, and statistics of Sargan test show that instrumental variable set is reasonable.Table 4
Regression results of nonlinear effect model (threshold variable: FUB and REL).
Model
GER-M1
GER-M2
GER-M3
PER-M4
PER-M5
PER-M6
Variable
Year fixed effect
Space fixed effect
Space and year fixed effect
Year fixed effect
Space fixed effect
Space and year fixed effect
L. LnMML
0.0268∗∗(2.20)
0.0375∗(1.77)
0.1010∗∗∗(5.46)
0.0475∗∗(2.24)
0.0113 (0.55)
0.0620∗∗∗(3.22)
LnGER
−0.0226∗(−1.76)
−0.0251 (−0.09)
−0.0241∗∗∗(−3.82)
−0.0150∗∗(−2.06)
−0.0156∗∗(−2.31)
−0.0152∗∗∗(−4.73)
LnPER
0.0075 (1.56)
−0.0046∗∗(−2.09)
0.0006 (0.28)
−0.1468∗(−1.69)
−0.1539∗(−1.89)
−0.1699∗(−1.86)
LnGER × LnFUB
0.0054∗∗∗(3.66)
0.0051∗∗∗(2.79)
0.0052∗∗(2.33)
LnPER × LnREL
0.0137∗∗∗(5.78)
0.0136∗∗∗(7.70)
0.0139∗∗∗(5.53)
LnQYJ
−0.0019 (−1.06)
0.0031 (0.02)
−0.0023 (−1.17)
−0.0289 (−0.76)
−0.0112 (−0.97)
−0.0338∗(−1.66)
LnRKM
0.0024∗∗∗(3.57)
−0.0022 (−1.05)
0.0018∗∗∗(4.78)
0.0074∗∗∗(4.31)
−0.0235 (−1.03)
0.0087∗∗∗(4.97)
LnRD
0.0037∗∗∗(4.77)
0.0044∗∗∗(4.65)
0.0048∗∗∗(5.09)
0.0022∗∗∗(2.74)
0.0028∗∗∗(2.64)
0.0046∗∗∗(5.29)
LnCRD
0.0017 (1.07)
0.0035∗∗∗(2.88)
0.0013 (0.98)
0.0011∗(1.84)
0.0031 (1.48)
0.0029∗∗(2.10)
LnFDI
−0.0078 (−0.59)
0.0407 (1.18)
−0.0129 (−0.78)
−0.0148 (−0.17)
0.0346 (1.29)
−0.0173 (−0.70)
CONS
0.7146∗∗∗(32.08)
0.7090∗∗∗(31.24)
0.7080∗∗∗(34.87)
0.6504∗∗∗(26.35)
0.6470∗∗∗(24.85)
0.6800∗∗∗(33.33)
AR (2)
0.1182
0.1901
0.1360
0.1920
0.1801
0.2050
Sargan
0.0937
0.1548
0.1024
0.1174
0.2012
0.1412
Note.∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%; t values are shown in parentheses.As shown in column 2 to column 4 or column 5 to column 7 in Table4, the magnitude and sign of coefficients are remarkably consistent, while the significance levels of coefficients in column 4 or column 7better than others which means that space and year fixed effect is applied. As for the governmental environmental awareness in column 2 to column 4, the coefficients of L. LnMML are 0.0268, 0.0375, and 0.1010 and significant at 10% significance level, which means that 1% increase in green total factor productivity this year will induce 0.0268%, 0.0375%, or 0.1010% increase next year. The implementations of macroeconomic policy have certain cycle and certain hysteresis effect. Once the adjustment change of management institutions occurs, the established macropolicy enforcement will be discounted. So, the effect of L. LnMML on MML enlightens that the management institutions should keep continuity of the policy moderately. Similarly, in terms of civil environmental awareness in column 5 to column 7, coefficients of L. LnMML are 0.0475, 0.0113, and 0.0620, which means that 1% increase in local green total factor productivity this year will lead to 0.0475%, 0.0113%, and 0.0620% increase next year. Government should continue to adhere to the environmental protection education to enhance local productivity.The effects of governmental environmental regulation on green total factor productivity are dependent on the regulation stringency and governmental environmental awareness in column 2 to column 4. The coefficients of LnGER are −0.0241 in column 4 and significant at 1% significance level, and the corresponding coefficient of LnGER × LnFUB is 0.0052 and significant at 5% significance level. The above regression results suggest that the government environmental awareness has single threshold when it comes to consider the effect of governmental environmental regulation on green total factor productivity, and the threshold value is 4.6346; namely, corruption and bribery and malfeasance number based on an investigation by the people’s procuratorates in every one hundred thousand people is 0.2158. According to the measure data of the government environmental awareness, the sample range in various areas of China in corruption and bribery and malfeasance number based on an investigation by the people’s procuratorates in one hundred thousand people is greater than 0.2158, and the minimum is 1.4316 in Shanghai in 2011. The reason is that official controls the allocation of production factors, causing serious rent-seeking. In particular, the fiscal and taxation system reformed in 1994 caused fiscal decentralization between the central government and local government, while local government weaken the environmental protection for achieving political demands.In terms of civil environmental regulation on green total factor productivity, which is also determined by civil regulation stringency and its environmental awareness in column 5 to column 7, coefficient of LnPER is negative (−0.1699) and significant at 10% significance level (t = −1.86), while coefficient of LnPER × LnREL is positive (0.0139) and significant at 1% significance level (t = 5.53). The results show that there is a single threshold of civil environmental awareness for the effect of civil environmental regulation on green total factor productivity, and the threshold value is 12.2230, also known as that per education level should reach 12.2230 years. Unfortunately, the highest education level in data sample is Beijing in 2013, and the level is 12.0276. Before reaching the threshold, enhancing the education level can lower the disadvantageous influence. Ascension of education level can develop personal innovation capability and awareness, which is helpful to realize sustainable development. Moreover, we further study the effect of enterprise and social awareness on productivity. Coefficients of LnQYJ in column 4 and column 7 are −0.0023 and −0.0338, which means that 1% increase in LnQYJ will induce 0.0023% or 0.0338% decrease. Coefficients of LnRKM are positive (0.0018, 0.0087) and significant at 1% significance level (t = 4.78, 4.97), which suggests that it is necessary to plan and control the urban population. Coefficients of control variables (LnRD, LnCRD, and LnFDI) in column 4 are 0.0048, 0.0013, and −0.0129, and they are 0.0046, 0.0029, and −0.0173 in column 7. Results show that increasing the research cost moderately will improve the researchers’ study preferences and investment, while crowding-out effect is greater than positive spillover effect, both created by FDI.


## 5.2. Threshold Effect Analysis of Regulation Foundation

Table5 shows the regression results of nonlinear effect model whose threshold variable is regulation foundation in year fixed effect, space fixed effect, and space and year fixed effect. M7, M8, and M9 present econometric model of governmental environment regulation, and M10, M11, and M12 denote the regression results of civil environmental regulation. In terms of statistical hypothesis testing, tests of AR (2) and Sargan show that dynamic panel threshold models are rational and the endogenous problem is effectively solved.Table 5
Regression results of nonlinear effect model (threshold variable: REF).
Variable
Year fixed effect
Space fixed effect
Space and year fixed effect
Year fixed effect
Space fixed effect
Space and year fixed effect
Model
GER-M7
GER-M8
GER-M9
PER-M10
PER-M11
PER-M12
L. LnMML
0.0994∗∗∗(4.79)
0.0224 (0.09)
0.1070∗∗∗(5.66)
0.1116∗∗(2.16)
0.0469∗∗(2.15)
0.1030∗∗∗(5.04)
LnGER
−0.2100∗∗(−2.02)
−0.2630∗∗(−2.22)
−0.2380∗∗(2.21)
−0.0150∗∗∗(−5.96)
−0.0163∗∗∗(−4.26)
−0.0234∗∗∗(−6.88)
LnPER
−0.0022 (−0.10)
−0.0773∗∗(−2.34)
−0.0017 (−0.07)
−0.0006 (−0.17)
−0.0007 (−0.24)
−0.0006 (−0.23)
LnGER × LnREF
15.4715∗(1.90)
17.1200∗∗(2.46)
14.5580∗(1.80)
LnPER × LnREF
0.0415 (0.96)
0.0441 (1.14)
0.0391∗(1.73)
LnQYJ
−0.0020 (−0.63)
0.0015 (0.85)
−0.0012 (−0.52)
−0.0015 (−0.76)
−0.0017 (−0.75)
−0.0016 (−0.77)
LnRKM
0.0751∗∗(2.19)
−0.0443 (−1.49)
0.0959∗∗∗(4.92)
0.0099∗∗(2.04)
−0.0015 (−0.67)
0.0103∗∗∗(4.11)
LnRD
0.0371∗∗∗(3.80)
0.0470∗∗∗(5.01)
0.0445∗∗∗(3.75)
0.0410∗∗∗(3.66)
0.0433∗∗∗(4.92)
0.0463∗∗∗(5.09)
LnCRD
0.0253 (1.08)
0.0474∗∗∗(2.85)
0.0142 (0.91)
0.0089 (1.10)
0.0170 (1.11)
0.0121 (0.78)
LnFDI
−0.0364 (−0.27)
−0.0896 (−0.28)
−0.0473∗(−1.78)
−0.0741 (−0.32)
0.0192 (0.62)
−0.0109 (−0.61)
CONS
0.6918∗∗∗(46.78)
0.7080∗∗∗(31.62)
0.7220∗∗∗(44.54)
0.6677∗∗∗(37.20)
0.6830∗∗∗(20.85)
0.7100∗∗∗(29.14)
AR (2)
0.2708
0.3362
0.2481
0.2274
0.2451
0.2548
Sargan
0.0955
0.1024
0.0874
0.0728
0.0954
0.0895
Note.∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%; t values are shown in parentheses.As for regression results, coefficients of L. LnMML are 0.0994, 0.0224, 0.1070, 0.1116, 0.0469, and 0.1030, which means that 1% increasing in productivity this year will induce 0.0994%, 0.0224%, 0.1070%, 0.1116%, 0.0469%, or 0.1030% augmentation the following year in the same region. The phenomenon and characteristic of MML index are in line with Table4; namely, reducing the effects of policy changes on economic growth is applied. What should be pointed out is that signs of LnGER and LnGER × LnREF, LnPER and LnPER × LnREF are reversed; namely, regulation foundation can influence marginal effects of governmental or civil environmental regulation on green total factor productivity. Coefficients of LnGER and LnGER × LnREF in column 4 or LnPER and LnPER × LnREF in column 7 in Table 5 are −0.2380, 14.5580, −0.0006, 0.0391 and significant at 10% significance level; namely, the single threshold of regulation foundation is 0.0163 and 0.0154, respectively. Above all, we find that all comprehensive indexes of regulation foundation in 2007–2016 are less than the two threshold values. The reasons are reflected in the following several aspects. Firstly, infrastructure construction is the premise and basis of urbanization, which need a lot of support of capital and labor force. However, the status quo is narrow channels of financial income in the condition of huge population base which makes lower level of road holdings. Secondly, the layout of local industrial structure is unreasonable, which needs transformation and upgrading as soon as possible. Thirdly, the distance of the state-owned enterprise management mode and its administrative levels are not conductive to the local government implement environmental regulation. Moreover, the fiscal decentralization narrows the local government tax. The central government should promote joint-stock reform of state-owned enterprises effectively, improve enterprise efficiency, and deal with the relationship between the local state-owned enterprises and local government. Fourthly, import and export is a main driving force to promote the development of economy, and moderately weakening trade barriers and enhancing the regional economic exchanges will help to reduce transaction costs. When it comes to other control variables, coefficients of LnRD and LnCRD are positive (0.0445, 0.0463; 0.0142, 0.0121) in column 4 and column 7, while corresponding coefficients of LnFDI are negative (−0.0473, −0.0109).


## 6. Conclusion and Policy Recommendation

By controlling the technology research and development strength, domestic capital stock of R&D, and foreign direct investment, this paper establishes dynamic panel data threshold econometric model to analyze the influence effect of governmental and civil environmental regulation on green total factor productivity in China’s 30 provincial regions during 2007–2016. By conducting governmental and civil environmental awareness and regulation foundation as thresholds, we found that single threshold transformation effects significantly exist. Environmental awareness and regulation foundation take positive marginal effect on environmental regulation, which means that they can weaken the negative effect of regulation on productivity. Unfortunately, both environmental awareness and regulation foundation in all regions failed to meet the threshold value. The gap between the governmental environmental awareness and its threshold is larger than the corresponding gap between the civil environmental awareness and its threshold value, and the latter is relatively small. In terms of the calculated comprehensive index of regulation foundation and its threshold value, the marginal effect gap in governmental environmental regulation or civil environmental regulation is at a lower level. Based on this article’s main research conclusions, some relevant policy recommendations are suggested as follows:(1)
Governmental environmental regulation has not increased the green total factor productivity, mainly due to the lower level of governmental environmental awareness and regulation foundation of economic structure. Therefore, governmental environmental awareness and regulation foundation should be enhanced and optimized rather than improving the stringency of environmental regulation unilaterally. As for the environmental awareness, both regulating screening and regulating personnel assessment can improve the governmental environmental awareness. Firstly, personnel recruitment should pay attention to educational level, environmental loyalty, and subjective initiative during the government environmental protection department talent introduction. Secondly, regional environment protection efficiency, implementation of welfare, bonus, and travelling benefit should be taken into consideration during the assessment of environmental regulation personnel. Thirdly, the regulatory department should step up the environmental protection personnel corruption government malfeasance dynamic investigation. In terms of regulation foundation, according to its connotation, the construction of industrial park infrastructure, eliminating enterprises of high energy consumption and low yield of inferior quality through technical innovation to optimize local industrial structure, encouraging entrepreneurship, and accelerating the reformation of state-owned enterprise share-holding system should be employed.(2)
Government environmental regulation and civil environmental regulation produce certain effect to the green total factor productivity, and we cannot ignore the role of persons in the process of environmental regulation. Moreover, we should actively guide environmental protection organization and social individuals to actively participate in enterprise pollution control. But the effect of civil environmental regulation mainly depends on the degree of civil regulation ability and environmental awareness. On the one hand, raising personal income level and increasing donation from enterprise or private channels are crucial to enhance the civil environmental regulation cost. On the other hand, offering environmental protection courses, inviting environmental experts and environmentalists to open lecture, paying attention to environmental protection propaganda, strengthening family education, or creating environmental disaster experience service are important to promote the civil environmental awareness. In addition, civil environmental regulation should protect the legal validity of civil environmental regulation and moral binding. On one side, government should realize decentralization step by step, which contains environmental monitoring and environmental supervision and management. Only in this way, universal supervision can be achieved, and political rent-seeking or economic rent-seeking will be severely suppressed. On the other side, the central government should change the promotion mechanism for local government and enhance personal participation in environmental governance.



---

*Source: 8351512-2019-11-03.xml*