Environmental Regulation, Technological Innovation and Industrial Environmental Efficiency: An Empirical Study Based on Chinese Cement Industry

Abstract

China’s cement production has been the highest worldwide for decades and contributes significant environmental pollution. Using the DEA-Tobit model, the paper empirically analyzes the impact of environmental regulation and technological innovation on industrial environmental efficiency with data from the Chinese Cement Industry. The results show that both environmental regulation and technological innovation have a significant role in promoting the environmental efficiency of the cement industry. Among all the influencing factors, the improvement of pollution disposal capacity has the biggest positive effect on environmental efficiency, while the energy-saving effect caused by environmental regulation is not obvious, the factor endowment structure has no substantial impact on environmental efficiency. Adhering to the strategy of “reducing emissions mainly and saving energy as auxiliary”, continuously optimizing the energy consumption structure, raising the level of industrialization and industrial agglomeration are conducive to the sustainable development of the Chinese cement industry.

1. Introduction

It is a complex process for environmental regulation to affect the economic system. Reducing environmental pollution, improving environmental quality and improving environmental efficiency are the original intentions of the government to implement environmental regulation. The micro effect of environmental regulation affects the decision-making behavior of enterprises through at least two ways. First, in order to meet the regulatory requirements, enterprises actively carry out pollution control activities to reduce pollution emissions; Second, enterprises can purchase more pollution emission permits in the open trading market when they are unwilling to increase investment in pollution control. However, either way will increase the economic cost of the enterprise. When the cost of compliance becomes very high, as the enterprise organization pursuing profit maximization, it has to re-examine its compliance behavior. So, can we actively carry out technological innovation to deal with the risks brought by environmental regulation? Theoretically, environmental regulation “leads to innovation hypothesis” as its inherent logic and rationality, and it challenges the theoretical framework of traditional neoclassical economics on environmental protection. Once this viewpoint was put forward, it aroused great interest in scholars. Among them, the most representative is Michael Porter, known as the “father of competitive strategy” in the early 1990s; the “Porter Hypothesis” proposed by him well explains the impact of environmental regulation on technological innovation [1,2]. Porter’s hypothesis infers the influence effect of environmental regulation on technological innovation from a speculative perspective, which has strong vitality in theory, but the empirical test results are not consistent. Some empirical studies have found that there is a positive correlation between environmental regulation intensity and technological innovation [3,4,5]. Other studies believe that environmental regulation has no significant efficiency in technological innovation [6,7,8]. If environmental regulation can really stimulate enterprises to reduce the cost of compliance, it will have great and far-reaching practical significance for the top-level design of environmental regulation policies.

For a long time, the Chinese government has implemented mandatory measures such as “closing down, stopping and transferring” for heavy polluting enterprises and, given policy support to the environmental protection industry, that is encouraged to develop. These regulatory measures will inevitably affect the output and price changes of relevant industries and may eventually become an external pressure and internal force to stimulate enterprises to develop innovative activities. The cement industry is inseparable from national economic development, production and construction and people’s life, and its output value accounts for 40% of the building materials industry. China is not only a large cement producer but also a large consumer. In 2019, China’s cumulative cement output was 2.33 billion tons, accounting for more than 50% of the total global cement output. However, as a traditional industrial sector, the cement manufacturing industry has typical production characteristics of high energy consumption, high emission and resource dependence, which will inevitably bring a series of environmental pollution problems. Dust particles are the most important pollutants produced in the process of cement preparation, followed by SO₂, NO_x, CO₂ and other harmful gas emissions. NO_x is an important reason for the formation of photochemical smoke and acid rain and is also an important source of PM2.5. Existing statistical data show that the dust emission per ton of cement clinker produced in China is 2746 g/T, which is about 176 times that of Germany (15.6 g/T) and more than 10 times higher than the emission standard of China (GB4915-2013); SO₂ emission is 4.6 times that of Germany, one times higher than the national standard; NO_x emission is 2.2 times that of Germany, 32.6% higher than the new national standard [9]; In 2020, the direct CO₂ emission of China’s cement industry will be about 1.375 billion tons, accounting for 13.14% of the country’s total emission of 10.251 billion tons. It can be seen that the pollutant emission of China’s cement manufacturing industry in reality is seriously exceeding the standard.

In October 2021, the Chinese government formulated the “opinions on fully, accurately and comprehensively implementing the new development concept and doing a good job in carbon peak and carbon neutralization” and the “action plan for carbon peak by 2030”. In these two documents, it was clearly proposed that “strictly increase the cement clinker production capacity, promote the normalization of cement off peak production, and reasonably shorten the operation time of cement clinker plant”. This means that China’s cement industry has become a zero-growth industry. It has become the key field of environmental regulation of the Chinese government. Based on the above background, this paper selects the heavily polluting cement manufacturing industry in China as the research object, empirically analyzes the main factors affecting the environmental efficiency of the cement manufacturing industry and their impact degree, systematically displays the process and results of environmental regulation on environmental efficiency and provides decision support for the government departments to formulate scientific regulation measures and promote the sustainable development of the cement industry in China.

This study gives contributions to the existing literature in two ways: Firstly, the paper adopts the reverse thinking mode to study the economic consequences of environmental regulation from the perspective of environmental technical efficiency, and integrates the structural analysis and the result analysis, which expands the in-depth space of the existing theoretical research of environmental regulation and has certain innovation; Secondly, in this paper, the pollution data set of micro-enterprises is used, and the data used are rarely seen in the existing literature. The measurement method used is also helpful to identify the causal effect, which enriches the existing literature research.

The rest of this article is organized as follows: Section 2 combs the relevant theories and literature; Section 3 establishes the analytic models and sample selection and describes variables; Section 4 follows with empirical results; Section 5 provides concluding remarks; and, finally, Section 6 summarizes the full text and puts forward future prospects.

2. Literature Review

The structural characteristics of environmental technology determine that, with a certain input, the reduction in unexpected output will occupy limited innovation resources, and the result will lead to a reduction in expected output. According to the constraint conditions of the output set function, there are four different ways to improve environmental efficiency for enterprises to choose: (1) under the condition that the unexpected output is certain, maximize the expected output and minimize the factor input; (2) Under the condition of a certain factor input, maximize the expected output and minimize the unexpected output; (3) Under the condition that the expected output is fixed, minimize the unexpected output and input factors; (4) Minimize factor input and unexpected output while maximizing expected output. No matter which method is adopted, in order to meet the environmental standards regulated by the government, the enterprises either passively increase the investment in pollution control or actively carry out technological upgrading to achieve the set goal of increasing production and reducing pollution. On the surface, pollution end treatment can significantly improve the environmental risks faced by enterprises, but at the same time, it also increases the production costs of enterprises, resulting in a reduction in good output. Therefore, as a rational producer, one may be more inclined to choose the latter, improve the output efficiency of resources, reduce the discharge of hazardous wastes and fundamentally improve the environmental efficiency of enterprises through the technological transformation of existing production processes. This truly reflects the policy effect of environmental regulation and is also the original intention of environmental regulation policy design. It can be seen that environmental regulation and technological innovation are two major factors affecting environmental efficiency. On the one hand, the government can control the discharge of harmful pollutants by strengthening environmental regulation and promote enterprises to passively improve environmental efficiency; on the other hand, enterprises can optimize their environmental behavior by actively carrying out technological innovation activities to avoid the environmental risks brought by government regulation.

Environmental regulation is an effective means to coordinate economic development and environmental protection. It has gradually become an indispensable part of the functions of governments and has been widely used in environmental protection practice. Many studies have shown that environmental regulation can significantly promote the improvement of environmental efficiency. Boyd et al. [10] used Shephard’s input distance function to conduct empirical study, which showed that environmental control can reduce environmental pollution and improve the environmental efficiency of enterprises. Picazo et al. [11] used the output distance function to find that strict environmental regulation has an obvious promoting effect on environmental efficiency. Tu and Xiao [12] also found that the environmental efficiency under the assumption of non-expected output weak disposal technology is significantly better than that under the assumption of free disposal technology, and environmental regulation has a significant role in promoting the improvement of environmental efficiency. The studies of Shen [13] and Yang [14] also obtained similar conclusions. It can be seen that environmental regulation can improve the environmental efficiency of enterprises, and the improvement of environmental efficiency can also bring some potential economic benefits to the regulation, thus realizing the indirect transmission mechanism of environmental regulation.

As an externally imposed mandatory constraint, environmental regulation will inevitably increase the private cost of the manufacturer to a certain extent. When this cost reaches the threshold that the manufacturer can bear, it may stimulate the manufacturer to actively engage in environmental technology innovation to reduce the compliance cost, especially the development and utilization of new energy, renewable energy and clean energy, and greatly reduce the dependence on primary energy such as coal and oil. A large number of studies have also confirmed that technical factors are the most direct and effective way to improve environmental efficiency. The research of Tu et al. [15] shows that independent innovation and technology introduction are effective ways to improve the environmental efficiency of SBM. Wang et al. [16] found that technological progress is the main reason for the improvement of the total factor productivity of the manufacturing industry. Song et al. [17] used the SBM-based environmental efficiency decomposition model to study the impact of technological factors on environmental efficiency and found that technological progress was significantly positively correlated with environmental efficiency. The studies of Zeng [18] and Li [19] also support the above conclusions. Therefore, in addition to environmental regulation variables, technological innovation is another important factor to promote the improvement of environmental efficiency.

As a traditional industrial sector, the cement manufacturing industry has obvious production process characteristics of high energy consumption, high emission and resource dependence, which will inevitably bring a series of environmental pollution problems. Unfortunately, the existing literature research rarely involves the environmental efficiency of the cement industry. For example, Oggioni et al. [20] analyzed the eco-efficiency of 21 prototypes of the cement industry using a DDF approach. Riccardi et al. [21] assessed the efficiency of the high energetic and CO₂ emissions-intensive cement production processes in 21 countries using the distance function and directional distance function. Long et al. [22] investigated total factor productivity eco-efficiency and the determinants of Malmquist in China’s cement manufacturers. Zhang et al. [23] analyzed the environmental efficiency of China’s listed cement companies using a non-radical DEA model with slacks-based measures. Tu et al. [12] studied the environmental efficiency of the Chinese cement industry based on the undesirable output DEA model. From the discussion above, we understand that more research focuses on the 3E problem at the macro level. However, enterprise organizations are the objects of national environmental regulation and the executors of regulatory policies. Therefore, it is necessary to analyze the problem of environmental efficiency from the level of industry and enterprise.

Based on the above theoretical analysis, this paper believes that environmental regulation and technological innovation can help to improve the environmental efficiency of the cement manufacturing industry as a whole. Following the environmental regulation can improve environmental efficiency, and the improvement of environmental efficiency itself can bring some potential economic benefits to the regulation, so as to realize the indirect transmission mechanism of environmental regulation. Therefore, this article needs to study the complex relationship between environmental regulation, technological innovation and environmental efficiency. Figure 1 shows the relationship between these variables.

3. Methods and Data

3.1. DEA-Tobit Model

Since the environmental efficiency score calculated by us is between 0 and 1, the explained variable becomes a restricted dependent variable due to truncation. At this time, it will be biased and inconsistent to estimate the parameters using the ordinary least squares method, and the Tobit model following the maximum likelihood estimation method becomes a better choice. Many studies have used the Tobit regression model to capture the influencing factors of DEA efficiency on the basis of solving DEA efficiency [24,25]. Since the unconditional fixed effect Tobit model is biased, this paper will use the maximum likelihood estimation method of the Tobit random effect model to investigate the factors affecting the environmental efficiency of China’s cement manufacturing industry and the degree of impact.

Firstly, we take environmental efficiency (ETE) as a restricted dependent variable to establish the following Tobit basic model:

$$ET{E}_{i,t}^{*}=C+{\displaystyle \sum \beta X_{i,t}}+{\epsilon }_{i,t};\hspace{1em}ET{E}_{i,t}=Max\left(0,ET{E}_{i,t}^{*}\right)$$

(1)

In the above formula, $ET{E}_{i,t}^{*}$ is the latent variable, ETE_i,t represents the environmental efficiency of the cement manufacturing industry in the region i of period t, X_i,t is the various influencing factors of environmental efficiency and ε_i,t is the random error term subject to the independent identical distribution of $N\left(0,{\sigma }^2\right)$.

Secondly, on the basis of Equation (1), we build the Tobit regression models as follows:

$$\begin{array}{l}ET{E}_{i,t}^{\ast }=C+{\beta }_{1}ERI{\left(j\right)}_{i,t}+{\beta }_{2}ERI{\left(j\right)}_{i,t-1}+{\beta }_{3}R\&D-M_{i,t-1}+{\beta }_{4}R\&D-P_{i,t-1}\\ \hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{5}E{C}_{i,t-1}+{\beta }_{6}E{S}_{i,t-1}+{\beta }_{7}D{I}_{i,t-1}+{\beta }_{8}Siz{e}_{i,t-1}+{\beta }_9{\displaystyle \sum Region}\\ \hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{10}{\displaystyle \sum Year}+{\epsilon }_t;\hspace{0.33em}\hspace{1em}\hspace{1em}ET{E}_{i,t}=Max\left(0,ET{E}_{i,t}^{\ast }\right)\end{array}$$

(2)

$$\begin{array}{l}ET{E}_{i,t}^{\ast }=C+{\beta }_{1}ERI{\left(j\right)}_{i,t}+{\beta }_{2}ERI{\left(j\right)}_{i,t-1}+{\beta }_{3}R\&D-M_{i,t-1}+{\beta }_{4}R\&D-P_{i,t-1}\\ \hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{5}E{C}_{i,t-1}+{\beta }_{6}E{S}_{i,t-1}+{\beta }_{7}D{I}_{i,t-1}+{\beta }_{8}Siz{e}_{i,t-1}\\ \hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_9{\displaystyle \sum Year}+{\epsilon }_t;\hspace{0.33em}\hspace{1em}\hspace{1em}ET{E}_{i,t}=Max\left(0,ET{E}_{i,t}^{\ast }\right)\end{array}$$

(3)

Equation (2) is the Tobit regression equation for estimating the national panel data, and Equation (3) is the Tobit regression equation for estimating the regional panel data. In the above formula, j is equal to 1 or 2, which is used to characterize the intensity of environmental regulation. When j = 1, it indicates the removal rate of dust (smoke); when j = 2, it indicates the emission rate of SO₂, which is used for a robustness test. In order to avoid the influence of endogenous problems on the estimation results, the relevant variables in Equations (2) and (3) enter the equation with a lag of 1 period.

3.2. Variables Choice

(1)

Environmental efficiency. The action process of environmental regulation on the economic system is extremely complex, which means it is extremely difficult to measure the economic consequences of environmental regulation effectively. Until the environmental efficiency theory is put forward, this problem can be reasonably solved. Environmental efficiency is an important indicator to describe the coordinated development of energy, environment and economy. To evaluate environmental efficiency, we should not only pay attention to the damage of the evaluation object to the environment in the production process but also evaluate the economic value of its production activities, taking into account economic efficiency and environmental efficiency, which are indispensable. This paper uses the directional distance function model proposed by chambers and Chung [26,27] for reference to measure the environmental efficiency score of China’s cement manufacturing industry.

In this study, the analysis object is the cement manufacturing industry in 30 provinces of China (i = 1, 2, …, 30), and the research period is 13 years in total (t = 2004, 2005, …, 2016). Each production unit has three inputs (x₁, x₂, x₃, i.e., capital, labor and energy), one expected output y (industrial added value) and two types of unexpected output (b₁, b₂, i.e., dust(smoke) emissions and SO₂ emissions). Then, the directional distance function of the production unit i at time t can be defined as:

$$D_0^{}(x^i,y^i,b^i;g^i)=\sup \left[\beta :(y^i,b^i)+\beta g^i\in p(x^i)\right]$$

(4)

In the above formula, g represents the direction vector of the change of “good” output (y) and “bad” output (b), and β represents the maximum possible proportion of the simultaneous increase and decrease in “good” and “bad” output under a given input level, that is, the value of the directional distance function to be measured. At this time, the environmental efficiency score of the production unit can be expressed as:

$$ETE=1/\left[1+D_0(x^i,y^i,b^i;g^i)\right]=1/\left(1+\beta \right)$$

(5)

When the actual output is infinitely close to the output frontier, $D_0(x^i,y^i,b^i;g^i)$ approaches 0 and ETE approaches 1, and the environmental technology at this time is the most efficient. Due to the influence of various uncontrollable factors, the actual output can only be located below the output front, resulting in the environmental efficiency score between 0 and 1 as a restricted dependent variable.

(2)

Environmental regulation intensity. In the existing literature, there are four commonly used methods to measure the intensity of environmental regulation: the single index method, alternative index method, composite index method and assignment method. At present, China’s environmental regulation departments mainly take the administrative lead and use the means of command and control to forcibly intervene in the external environmental behavior of enterprises. The applicability of market-oriented environmental regulation tools is poor. Existing studies have shown that reducing the pollutant emission concentration is not the most direct way to improve environmental efficiency but controlling the total amount of pollutants and improving the removal rate of harmful substances and the standard emission rate will have the most direct impact on the improvement of environmental efficiency. According to the pollution emission characteristics of the cement manufacturing industry and the new national standard GB4915-2013, this paper selects the dust(smoke) removal rate of the cement manufacturing industry as the proxy variable of environmental regulation intensity. In addition, in order to enhance the reliability of the research conclusion, we also set the SO₂ emission rate for the robustness test of the panel data. The calculation formulas are as follows:

$$ERI\left(1\right)=SDrem/\left(SDrem+SDemi\right)$$

(6)

$$ERI\left(2\right)=S{O}_{2}semi/S{O}_{2}emi$$

(7)

In the above formula, ERI (1) and ERI (2) represent the dust(smoke) removal rate and SO₂ emission rate, respectively. In Formula (6), SDrem and SDemi, respectively, represent the removal amount and total emission amount of dust(smoke); SO₂semi and SO₂emi in Formula (7), respectively, represent the standard emission and total emission amount of SO₂. ERI (1) and ERI (2) are both used to measure the severity of environmental regulation. The higher the value, the more stringent the environmental regulation measures are, the better the environmental regulation effect is, and vice versa.

(3)

Technological innovation ability. Technological innovation is essentially a process of the integration of science and technology and economy. In the technological innovation activities under the dual effects of technology promotion and market demand, research and development is the decisive factor to promote technological innovation. Research and development investment, as the driving force of innovation, directly determines the level of technological innovation. Many existing literature studies show that [8,28] research and development investment intensity is an excellent indicator that can characterize the technological innovation ability and is measurable. Therefore, this paper selects two major indicators of research and development fund input intensity and research and development personnel input intensity to comprehensively reflect the financial and human resources invested in the research and development activities of China’s cement manufacturing industry. See

Table 1. Variable definition and measurement.

	Variables	Code	Measurement
Dependent variable	Environmental efficiency	ETE	Environmental efficiency score, see Formula (5)
Independent variables	Environmental regulation intensity	ERI (1)	Dust(smoke) removal rate, see Formula (6)
		ERI (2)	Standard emission rate of SO2, see Formula (7)
	Technological innovation capability	R&D-M	Input intensity of research and development funds, proportion of research and development funds of cement manufacturing industry in total industrial output value.
		R&D-P	Input intensity of research and development personnel, proportion of personnel engaged in scientific and technological activities in the cement manufacturing industry
Control variables	Energy consumption structure	EC	Proportion of coal consumption of cement manufacturing industry in total energy consumption
	Factor endowment structure	ES	Natural logarithm of capital labor ratio of cement manufacturing industry
	Degree of industrialization	DI	Per capital industrial added value of the cement manufacturing industry
	Economic scale	Size	Natural logarithm of total assets of the cement manufacturing industry
	Region	Region	Dummy variable (1 for the eastern region and 0 for the central and western regions)
	Year	Year	Dummy variable

for the measurement methods.

(4)

Control variables. In addition to the core variables of environmental regulation and technological innovation, we also need to control the impact of other variables. Referring to previous relevant studies [14,29], this paper sets control variables such as energy consumption structure, factor endowment structure, degree of industrialization and economic scale, as well as regional and annual dummy variables. The definitions and measurement methods of each variable are shown in Table 1.

3.3. Data Source and Descriptive Statistics

The paper selects the balanced panel data of the cement manufacturing industry from 2004 to 2016 in 30 provinces of China and uses the data of 2002 and 2003 as the lag term of relevant variables. Among them, the data of research and development funds and research and development personnel input for investigating technological innovation variables are from the statistical data of the cement manufacturing industry in the China cement Yearbook, China Statistical Yearbook and China Science and technology statistical yearbook over the years. The data of dust(smoke) removal and total emission, SO2 emission and standard emission are from the statistical data of the cement manufacturing industry in the China cement Yearbook, China Environment Yearbook and China Statistical Yearbook over the years. The data of coal consumption and total energy consumption are derived from the statistical data of the cement manufacturing industry in the China cement Yearbook and China Energy Statistics Yearbook over the years. The data of total industrial output value, industrial added value, total assets and employees are from the China cement Yearbook and digital cement network over the years.

Table 2. Descriptive statistics.

	Variables
		ETE	ERI (1)	ERI (2)	EC	ES	DI
Nation wide	Mean	0.8884	87.354	76.644	85.544	22.398	11.549
	Std.Dev.	0.143	9.319	21.774	8.317	20.809	9.889
	Maximum	1.00	99.920	100	99.600	147.514	45.903
	Minimum	0.524	61.330	0.580	30.369	1.819	0.873
	N	390	390	390	390	390	390
Eastern	Mean	0.9515	92.712	90.212	93.08	27.181	13.625
	Std.Dev	0.110	5.984	11.472	9.785	21.996	10.983
	Maximum	1.00	99.92	100	98.03	120.87	45.903
	Minimum	0.52	70.17	52.13	30.37	3.633	1.744
	N	143	143	143	143	143	143
Central	Mean	0.9013	84.761	78.595	85.129	21.106	11.073
	Std.Dev.	0.133	9.66	15.432	7.169	22.057	9.538
	Maximum	1.00	98.92	98.63	99.306	147.51	37.563
	Minimum	0.538	64.70	37.16	64.014	2.916	1.40
	N	104	104	104	104	104	104
Western	Mean	0.8159	83.882	61.657	88.309	18.555	9.819
	Std.Dev	0.147	9.403	24.084	6.522	17.619	8.588
	Maximum	1.00	98.70	96.91	99.60	105.17	33.061
	Minimum	0.548	61.33	0.58	47.82	1.819	0.873
	N	143	143	143	143	143	143

gives the descriptive statistics of the main variables. From the national statistical data, the average of environmental efficiency is 0.8884, the maximum is 1, and the minimum is 0.524. The overall level of the environmental efficiency of the cement manufacturing industry is not high, and there are great differences in the environmental efficiency of the cement manufacturing industry in different provinces. The average values of ERI (1) and ERI (2) are 87.35% and 76.64%, respectively, which indicates that the control effect of the cement manufacturing industry on dust(smoke) is better than that on SO₂. As the main pollutant of the cement industry, there is still much room for SO₂ emission reduction. From the regional statistical data, the average environmental efficiency of the cement manufacturing industry in the eastern and central regions has reached more than 0.9, while the average environmental efficiency of the cement manufacturing industry in the western region is only 0.8159, far lower than the national average. The results show that ERI (1) and ERI (2) are gradually increasing in the west, middle and east. The average of the dust (smoke) removal rate and SO₂ emission rate of the cement manufacturing industry in the eastern region are more than 90%. The cement manufacturing industry in the eastern region is obviously better than the central and western regions in pollution control, and the environmental regulation is also more serious Strict.

4. Empirical Results

4.1. Correlation Coefficient Test

Before the regression analysis, the correlation coefficient test of variables is carried out in

Table 3. Correlation coefficient.

Variables	Pearson	Sig. (2-Tailed)	Spearman	Sig. (2-Tailed)
ERI (1)	0.2269 ***	0.000	0.2197 ***	0.000
ERI (2)	0.3410 ***	0.000	0.3743 ***	0.000
R&D-M	0.1068 ***	0.000	0.1154 ***	0.000
R&D-P	0.1632 **	0.015	0.1578 **	0.018
EC	−0.1331 **	0.044	−0.1122 **	0.042
ES	−0.1421 *	0.089	−0.1087 *	0.091
DI	0.3181 **	0.029	0.2985 **	0.026
Size	0.2311 ***	0.000	0.2130 ***	0.000

Note: *, **, and *** denote the significance levels at 10%, 5%, and 1%, respectively.

. We find that, except for the strong correlation between ERI (1) and ERI (2), the correlation coefficients between other variables are small, indicating that the problem of collinearity between independent variables is not serious. In addition, we also use the variance expansion factor method to diagnose the multicollinearity of each variable. The test results show that the tolerance of each variable is greater than 0.264, and the variance expansion factor VIF is controlled within 4, which further shows that the problem of multicollinearity is not serious. In view of the significant positive correlation between ERI (1) and ERI (2), we do not substitute them into the equation at the same time in the empirical study but take ERI (2) as a proxy variable of environmental regulation intensity for the robustness test.

4.2. Regression Analysis of National Panel Data

Because the Tobit model of panel data is extremely complex and difficult to calculate, this paper will directly call the Xttobit package provided by Stata11.0 to complete the parameter estimation process. According to Equations (2) and (3), the Tobit estimation results of national and regional panel data are shown in

Table 4. Tobit regression results of national panel data.

Variables	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)	Model (6)
_Cons	0.9965 *** (8.03)	0.8163 *** (9.28)	0.8480 *** (6.99)	0.7220 *** (8.25)	0.9584 *** (8.01)	0.8289 *** (9.40)
Control variables
ECi,t− 1	−0.0124 * (−1.78)	−0.0906 ** (−2.03)	−0.0482 * (−1.67)	−0.0182 ** (−2.22)	−0.0132 ** (−2.48)	−0.0102 * (−1.74)
ESi,t− 1	−0.0331 (−0.63)	−0.0847 (−0.37)	−0.0504 (−0.89)	−0.0262 (−0.47)	−0.0675 (−1.22)	−0.0436 (−1.08)
DIi,t− 1	0.0485 * (1.67)	0.0384 ** (2.13)	0.0475 ** (2.18)	0.0391 ** (2.05)	0.0290 * (1.86)	0.0208 * (1.93)
Sizei,t− 1	0.0687 *** (2.64)	0.0892 ** (2.35)	0.0589 ** (2.12)	0.0511 *** (2.73)	0.0435 *** (2.95)	0.0364 ** (2.29)
Independent variables
ERI(1)i,t	0.0194 ** (2.13)		0.0142 * (1.84)		0.0191 * (1.94)
ERI(1)i,t− 1	0.0721 (1.32)		0.0516 (1.20)		0.0948 (1.42)
ERI(2)i,t		0.0198 ** (2.47)		0.0208 ** (2.53)		0.0207 *** (2.60)
ERI(2)i,t− 1		0.0914 (1.11)		0.0724 (1.09)		0.0955 (1.12)
R & D − Mi,t − 1	0.0512 *** (2.72)	0.0514 *** (2.81)			0.0417 *** (2.86)	0.0332 *** (2.91)
R & D − Pi,t − 1			0.0169 ** (2.52)	0.0176 * (1.85)	0.0126 * (1.90)	0.0118 * (1.87)
sigma_u	0.1001 ***	0.0987 ***	0.1026 ***	0.1012 ***	0.0997 ***	0.0983 ***
rho	0.8337	0.8039	0.8382	0.8127	0.8286	0.8011
Wald chi2	62.10 ***	78.90 ***	40.16 ***	55.42 ***	66.28 ***	83.12 ***
Log Likelihood	308.26	315.29	298.55	305.32	310.06	317.03
N	390	390	390	390	390	390

Note: *, **, and *** denote the significance levels at 10%, 5% and 1%, respectively. The values in brackets represent Z statistics. sigma_u represents the standard deviation of individual effects; rho indicates the proportion of individual effect fluctuation in the overall fluctuation. Table 5 is the same.

and

Table 5. Tobit regression results of regional panel data.

Variables	Eastern		Central		Western
	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)	Model (6)
_Cons	1.227 *** (6.76)	1.1288 *** (8.84)	0.9689 *** (5.11)	0.7019 *** (4.43)	1.1211 *** (5.23)	0.9160 *** (5.05)
Control variables
ECi,t− 1	−0.0296 (−0.32)	−0.0132 (−0.14)	−0.0162 ** (−2.06)	−0.0142 * (−1.76)	−0.1229 *** (−2.73)	−0.1016 ** (−2.16)
ESi,t− 1	−0.0185 (−0.26)	−0.0117 (−0.17)	−0.0630 * (−1.79)	−0.0482 * (−1.83)	−0.2803 ** (−2.27)	−0.2159 ** (−2.05)
DIi,t− 1	0.0242 * (1.85)	0.0154 ** (2.13)	0.0940 (1.52)	0.0691 (1.29)	0.0192 (1.57)	0.0123 (1.44)
Sizei,t− 1	0.0174 (0.93)	0.0191 (1.06)	0.0877 ** (2.51)	0.0969 *** (2.89)	−0.0104 (−0.36)	−0.0167 (−0.48)
Independent variables
ERI(1)i,t	0.0421 ** (2.13)		0.0913 * (1.87)		0.1592 ** (2.20)
ERI(1)i,t− 1	0.0313 (1.45)		0.0490 * (1.75)		0.1149 * (1.94)
ERI(2)i,t		0.0643 * (1.79)		0.0146 ** (2.07)		0.1367 ** (2.26)
ERI(2)i,t− 1		0.0264 * (1.67)		0.0253 * (1.72)		0.0188 * (1.83)
R & D – Mi,t − 1	0.0289 * (1.69)	0.0302 * (1.71)	0.1504 *** (3.41)	0.1894 *** (3.35)	0.0441 (1.27)	0.0641 (1.33)
R & D – Pi,t − 1	0.0248 ** (2.11)	0.0265 ** (2.22)	0.0799 * (1.82)	0.1156 ** (2.24)	0.0425 (0.93)	0.0475 (1.01)
sigma_u	0.0871 ***	0.0895 ***	0.0966 **	0.1076 **	0.1116 ***	0.1089
rho	0.6210	0.6231	0.7159	0.7393	0.7544	0.7432
Wald chi2	58.77 **	55.36**	62.51 ***	56.80 ***	46.44 ***	51.53 ***
Log Likelihood	164.38	161.08	92.72	89.88	101.31	102.83
N	143	143	104	104	143	143

Note: *, **, and *** denote the significance levels at 10%, 5%, and 1%, respectively.

below. In the models (1), (3) and (5) of the two tables, ERI (1) is selected as the proxy variable of environmental regulation. Under the same conditions, we use the substitution variable ERI (2) and the hierarchical regression method for the robustness test.

It can be seen from the estimation results in Table 4 that the fitting effect of the model is ideal, and the contribution of the panel variance component to the total variance in the six models is more than 0.8, indicating that the change of environmental efficiency is mainly explained by its individual effect. The chi-square value of the Wald test also passed the significance test of 1%, indicating that the explanatory variables we designed have a significant impact on the environmental efficiency of the cement manufacturing industry. In addition, in order to test whether there is a significant difference between the panel estimator and the mixed estimator, we test the likelihood ratio of sigma_u, the test p-value shows that it is necessary to establish a panel data model.

After controlling for other factors, the regression results show that: (1) In various estimates, the coefficients of environmental regulation variables in the current period and lag 1 period are positive, and the coefficients of environmental regulation variables in the current period are statistically significant. This shows that during the study period, environmental regulation has a significant role in promoting environmental efficiency, and strict environmental regulation is conducive to the overall improvement of the environmental efficiency of the cement manufacturing industry. (2) From the hierarchical research results, research and development-M, which reflects the ability of technological innovation, has passed the significance test of 1% level in all models; it is another factor reflecting the ability of technological innovation. The coefficients of research and development-P are all positive and statistically significant. The results show that there is a significant positive correlation between technological innovation and environmental efficiency, and improving the research and development investment of enterprises is another important way to promote the improvement of the environmental efficiency of the cement manufacturing industry. (3) In the control variables, the regression results of each column are relatively stable, and the coefficient and significance of control variables have no substantial change in the robustness test. The regression coefficients of the energy consumption structure are significantly negative. The fundamental reason is that under the existing cement preparation process conditions, the unreasonable energy consumption structure that excessively depends on coal will inevitably increase pollutant emissions, resulting in poor environmental quality and low environmental efficiency. There is a significant positive correlation between the degree of industrialization and environmental efficiency, which indicates that improving the level of industrialization of the cement manufacturing industry can not only increase economic output but also have a positive impact on the control of pollution emissions, thus showing a significant role in promoting the improvement of environmental efficiency. Among all the influencing factors, the variable of economic scale has the largest and most significant effect on environmental efficiency. It may be that the larger the scale, the easier the cement enterprises can obtain the effect of scale economy. Through scale expansion, they can obtain more investment in pollution control and environmental technology, so as to promote the improvement of environmental efficiency. From the regression coefficient value, the negative impact of factor endowment structure on environmental efficiency is very small and not statistically significant. Changing the factor endowment structure in the short term has no substantial impact on the environmental efficiency of the cement manufacturing industry.

4.3. Regression Analysis of Regional Panel Data

According to the estimated results of regional panel data in Table 5, there are significant regional differences in the impact of environmental regulation and technological innovation on environmental efficiency during the sample period. (1) The regression coefficients of current environmental regulation variables in the models (1), (3) and (5) are 0.0421, 0.0913 and 0.1592, respectively, which are statistically significant. The regression coefficients of environmental regulation variables in the lag 1 period are positive, and the coefficients of environmental regulation variables in central and western regions are statistically significant. This shows that environmental regulation has a significant positive impact on the environmental efficiency of the cement manufacturing industry in the three regions and has the greatest promotion effect on the environmental efficiency of the cement manufacturing industry in the western region. (2) Except for the western region, the regression coefficients of research and development-M and research and development-P are significantly positive. Among them, the intensity of the research and development personnel input in the eastern region and research and development funds input in the central region have the most significant impact on the environmental efficiency of the cement manufacturing industry, which shows that technological innovation can indeed improve the environmental efficiency of the cement manufacturing industry to a certain extent. However, due to various objective factors, it is difficult for the backward western regions to obtain the positive role of technological innovation in promoting environmental efficiency at least at present, from the regional perspective. (3) In the control variables, there are great differences between different regions. The energy consumption structure and factor endowment structure are negatively correlated with environmental efficiency in the central and western regions, while in the economically developed eastern regions, they have no significant impact on environmental efficiency. There is a significant positive correlation between the degree of industrialization and environmental efficiency in the eastern region, while the industrialization level has no substantial impact on environmental efficiency in the economically backward central and western regions. The size of the central region has the largest and most significant effect on environmental efficiency. However, in the economically backward western region, the expansion of economic scale is not conducive to the improvement of the environmental efficiency of the cement manufacturing industry.

In order to enhance the reliability of research conclusions and avoid estimation bias caused by single indicator measurement, we use the surrogate variable ERI (2) to carry out the robustness test in the models (2), (4) and (6) in the above Table 4 and Table 5 under the same conditions. After repeating the above research steps, we find that the variables of environmental regulation and technological innovation still show a significant positive correlation with environmental efficiency, and the obtained research conclusions are consistent. Due to the limitation of the length of the article, we will not list the specific regression results here.

5. Discussions and Policy Implication

Environmental regulation plays a significant role in improving the environmental efficiency of the cement manufacturing industry. The design of environmental regulation policy to control pollution emission is an effective way to improve the environmental efficiency of the cement manufacturing industry. Therefore, when designing environmental regulation policies, the regulatory department should closely combine the pollution characteristics of the cement manufacturing industry and focus on monitoring the industrial dust(smoke), SO₂, NO_X and other harmful emissions generated in the process of cement preparation. Secondly, while strictly controlling the emission standards, it will be a major innovative measure to play the guiding role of financial funds and drive social capital into the pollution control of cement enterprises to improve the environmental efficiency of the cement manufacturing industry.

There is a significant positive correlation between technological innovation and the environmental efficiency of the cement manufacturing industry, and technological innovation has become an important means to improve environmental efficiency. Existing statistics show that less than 2% of research and development investment intensity [30] hinders the positive amplification effect of technological innovation on environmental efficiency and also restricts the positive impact of research and development personnel investment on environmental efficiency. In view of this practical problem, on the one hand, we should continue to increase research and development investment in the cement manufacturing industry, and timely introduce venture capital investment in the case of relatively insufficient self-owned funds; on the other hand, to establish a new incentive mechanism and risk incentive mechanism for scientific and technological talents, we can improve the output efficiency of research and development personnel through an equity option incentive, patent evaluation and other reform measures. Only by combining the two, can the positive amplification effect of technological innovation on environmental efficiency be highlighted.

The national panel regression results show that the energy consumption structure has a significant negative correlation with the environmental efficiency of the cement manufacturing industry. The level of industrialization has a significant positive effect on the environmental efficiency, while the factor endowment structure has no substantial impact on the environmental efficiency. Compared with other factors, the scale factor has the greatest contribution to improving the environmental efficiency of the cement manufacturing industry. Excessive dependence on coal and other primary energy causes the imbalance of the energy consumption structure, which hinders the improvement of the environmental efficiency of the cement manufacturing industry. It can be seen that adhering to the strategy of “emission reduction first, energy saving as a supplement” is the future environmental regulation design direction of the cement manufacturing industry. On the basis of mandatory emission reduction, continuously optimizing the energy consumption structure, improving the level of industrialization and industrial agglomeration are the realistic choices to realize the sustainable development of the cement industry in the future.

The regional panel regression results show that environmental regulation has a significant positive effect on the environmental efficiency of the cement manufacturing industry in the three regions and has the greatest impact on the environmental efficiency of the cement manufacturing industry in the western region. Except for the western region, technological innovation plays a significant role in promoting the environmental efficiency of the cement manufacturing industry in the eastern and central regions. According to the economic development of different regions, the implementation of differentiated control measures is more conducive to the healthy and sustainable development of the cement industry. For the economically developed eastern region, we should strengthen environmental regulation, guide cement manufacturing enterprises to actively carry out technological innovation activities, reduce external environmental damage by encouraging the development of high-end cement products and promote the continuous improvement of environmental efficiency. For the economically underdeveloped central and western regions, we should adopt steadily strengthened environmental regulation policies to force cement manufacturing enterprises to take the initiative to adjust unreasonable energy consumption and factor endowment structure. In the process of undertaking industrial transfer, the cement manufacturing enterprises in the central and western regions should actively rely on the technical advantages of the eastern region and gradually improve their own industrialization level and industrial agglomeration degree, so as to obtain the long-term mechanism of environmental regulation on environmental efficiency.

6. Conclusions and Outlook

Based on the DEA Tobit two-stage analysis method, this paper empirically investigates the impact of environmental regulation and technological innovation on the environmental efficiency of the cement manufacturing industry by using the Tobit model with limited dependent variables. The empirical results show that environmental regulation and technological innovation can significantly promote the environmental efficiency of the cement manufacturing industry. Among all the influencing factors, economic scale has the largest and most significant contribution to the improvement of the environmental efficiency of the cement manufacturing industry. There is a significant negative correlation between energy consumption structure and environmental efficiency, while the negative effect of factor endowment structure on environmental efficiency is not obvious. On this basis, this paper also discusses the regional differences of environmental efficiency factors, and we find that there are great differences in the impacts of different factors on the cement manufacturing industry in different regions. These research results systematically show the impact process and results of environmental regulation on environmental efficiency and provide decision support for promoting the sustainable development of China’s cement manufacturing industry.

Due to the influence of many subjective and objective factors, this study still has some limitations which need to be further explored in the follow-up research. In view of the current development situation of China’s cement manufacturing industry, how to find the optimal regulation path between maintaining growth and reducing emissions will be an important direction of future environmental regulation theory research. There are many factors affecting environmental efficiency. However, this study only examines the two main factors, environmental regulation and technological innovation; the other factors need to be further studied. Despite these limitations, this paper gives an important contribution to the research on the environmental efficiency of China’s cement manufacturing industry.