# Does Stricter Command-and-Control Environmental Regulation Promote Total Factor Productivity? Evidence from China’s Industrial Enterprises

Authors: Wei Chen; Luwen Gao; Xing Xu; Yan Zeng
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

China is implementing increasing stringent command-and-control environmental regulations to achieve high-quality development. However, we have limited understanding about whether such policies are effective. This study selects the policy of China’s National Environmental Protection “Eleventh Five-Year Plan” as a quasi-natural experiment and uses the difference-in-differences (DID) method to analyze the effect of stricter command-controlled environmental regulations on total factor productivity from the enterprise level. Our results indicate that stricter command-and-control environmental regulation promotes TFP of enterprises throughout the country. This effect is even greater in long-established, large-scale, or low capital density enterprises. In addition, stricter command-and-control environmental regulation has a greater positive effect on the TFP of companies in industries with high pollution intensity and fierce competition. Furthermore, government transformation and market green preference will magnify the positive effect of stricter command-and-control environmental regulation on corporate TFP. Moreover, stricter command-and-control environmental regulation promotes the growth of enterprise TFP mainly by improving the efficiency of resource allocation within and between enterprises, rather than stimulating innovation. Local governments should refine pollution control policies, optimize the external environment, and enhance the innovation power of enterprises.


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## Body

## 1. Introduction

The extensive pattern of economic growth in China has resulted in a shortage of core technology and international competitiveness and has been locked in the middle and low end of the global value chain. Even in the more developed eastern provinces, numerous companies continue to produce and provide low-value, energy-inefficient, and environmentally harmful products, a trend that has spread to the central and western regions [1]. Furthermore, the economic growth model has also brought environmental deterioration [2, 3]. The “2020 Global Environmental Performance Index Report,” jointly issued by Yale University and several other prestigious research units, ranked China 120 out of 180 countries for environmental performance.Under the pressure of sluggish economic growth, the report of the 19th National Congress of the Communist Party of China proposed the promotion of economic development quality reforms, efficiency reforms. Improving the total factor productivity (TFP) is essential for China to achieve higher quality, more efficient, and more sustainable economic development [4, 5]. Since the 1990s, China has begun to implement a series of environmental policies from the central government to local governments. However, local governments, the actual executors, believe that environmental governance will stifle economic growth, so they selectively implement the orders of the central government.Albrizio et al. [6] reported that, compared to command-and-control environmental regulations, market-based environmental regulations are more flexible, suggesting that the latter can effectively encourage companies to adopt appropriate technologies to enhance their competitiveness. The regulations are important to reduce environmental emissions of industrial and agricultural sector [7–9]. However, China’s environmental regulations have been largely command and control [10]. Recently, the Chinese government has continuously strengthened its command-and-control environmental regulations instead of completely shifting to market-based environmental regulations. Specifically, the central government decomposed the total pollutant control target to all provinces, autonomous regions, municipalities, and cities separately listed in the plan in China’s National Environmental Protection “Eleventh Five-Year Plan.” Furthermore, the central government adopted the chief responsibility system, which is measuring emission reduction performance based on a detailed evaluation plan and linking the evaluation results with government promotion. Therefore, the initiative and enthusiasm of local governments to implement environmental regulations have been greatly improved. During this period, pollutant emissions reduced significantly, and the total control target was successfully achieved. Consequently, the pollutant emission control target proposed in the “Eleventh Five-Year Plan” represented a turning point where stricter command-and-control environmental regulations come into play.If command-and-control environmental regulation is not effective, then the current environmental policy system will not be able to push China out of the economic and environmental dilemma. Therefore, it is essential to analyze the effect of stricter command-and-control environmental regulation on total factor productivity.This study analyzed the effect of a stricter command-and-control environmental regulation, chemical oxygen demand emission control target (COD), on TFP at the enterprise level. The contributions of this research are summarized as follows.Firstly, as far as we are aware, this study is the first to examine the stricter command-and-control environmental regulation’s economic effects. It is believed that market-based environmental regulations are more effective than command-and-control environmental regulation, but the role of the latter is lack of recognition. We took the total pollutant emission reduction control in the Eleventh Five-Year Plan as an exogenous shock and used the DID method to analyze the economic effects of the policy, which can avoid the endogenous problems and estimation bias.Secondly, we analyze the mechanism of environmental regulation affecting TFP from the perspective of innovation and the resource allocation efficiency within and between enterprises. Companies must choose to continue operating or withdraw from the market due to the costs caused by environmental regulations. Surviving companies can make up for the compliance cost by improving the efficiency of resource allocation within the company. Notably, the exit of enterprises will speed up the flow of factors in the market, thereby improving the efficiency of resource allocation among enterprises and thus improving performance.Thirdly, this study examines the influence of regional characteristics on the effect of environmental regulation from two aspects: local government and social forces. This allows an in-depth and comprehensive understanding of the economic effect boundary of stricter environmental regulations, thereby providing an effective path for policy recommendations.


## 2. Literature Review

Currently, two representative theories exist for the relationship between environmental regulation and TFP. A theory based on the neoclassical framework states that environmental regulations shift resources from production to pollution control, which is not conducive to productivity improvement [11, 12]. Conversely, Porter’s hypothesis suggests that properly designed environmental regulations trigger innovation, thereby compensating for the compliance costs and increasing TFP [13, 14].Scholars have conducted empirical research on the impact of environmental regulations on total factor productivity from the national, industry and corporate levels, but they have not reached a consistent conclusion.Some scholars verified the first theoretical view in a single industry. For example, Gallop and Roberts [15] found that pollution control in the US power industry resulted in an average annual decline in productivity of 0.59%. Aside from this industry, environmental regulations also hindered productivity in some other industries, such as the paper industry [16, 17] and the beer brewing industry [18]. Some scholars have also discovered the adverse effects of environmental regulations on total factor productivity from multiple industries. For example, Gray [12] found that the environmental regulations in the United States from 1958 to 1978 led to an average annual decline of 0.44% in the productivity growth of 450 manufacturing industries. Dufour et al. [19] found that environmental regulations from 1985 to 1988 had a significant negative impact on the productivity growth rate of 19 manufacturing industries.Contrary to the above research, many empirical studies support the second theoretical point of view. Berman and Bui [20] found that the emission reduction expenditure of the petroleum refining industry in the United States during 1979–1993 brought about an increase in productivity. Alpay et al. [21] found that for every 10% increase in the cost of abatement in Mexico, productivity increases by an average of about 2.8%. Domazlicky and Weber [22] found that the productivity of six chemical industries in the United States is higher when considering pollution emissions. Hamamoto [23] found that the regulatory stringency in the US industries of pulp and paper, chemical products, petroleum and coal products, iron and steel, and nonferrous metals and products from 1974 to 1988 had a significant positive effect on the growth rate of TFP.The conflicting evidence may be caused by three factors. First, most studies fail to distinguish between the types of environmental regulations. The form of environmental regulation is important in determining the nature of its relationship with productivity [10, 24]. Second, it is a challenge to measure the stringency of environmental regulations. Traditional individual indicators for measuring environmental regulations, such as abatement costs and a comprehensive index constructed with pollutant emission density, are likely to cause endogenous problems [25]. Thirdly, studies at the regional, industry, and national level ignored the heterogeneity of enterprises, and the results may be affected by aggregation bias [26].Aware of the above-mentioned problems, some scholars have used DID methods to study the effects of China’s command-and-control environmental regulations and market-based environmental regulations on total factor productivity based on corporate data. Tang et al. [27] found that China’s two-control zone policy has increased costs and reduced resource allocation within enterprises, thereby inhibiting productivity gains. Peng et al. [25] found that SO2 Emissions Trading Pilot policy improves the productivity of enterprises. It seems that market-based environmental regulations are more effective than command-and-control environmental regulations, but this is contrary to the reality of tightening command-and-control environmental regulations. Although the two-control zone policy is a typical command-and-control environmental policy in China, neither the scope nor the intensity of the enforcement can compare with the Eleventh Five-Year Plan. Obviously, China's command-and-control environmental regulations have changed from concentration control to pollutant emission control target, constraints range has spread from local areas to the whole country, and constraint strength has also been tightened. Since the previous research is no longer applicable to the current economy, this paper selects the policy of the “Eleventh Five-Year Plan” as a quasi-natural experiment and uses the DID method to analyze the effect of stricter command-controlled environmental regulations on total factor productivity from the enterprise level. This not only conforms to the actual situation in China but also supplements the existing literature.


## 3. Hypotheses Development

The reason for the inconsistent conclusions of the aforementioned studies may be the uncertain effect of environmental regulations on innovation. Generally speaking, as long as the expected revenue from innovation exceeds the compliance cost, the environmental regulatory pressure will trigger corporate innovation [14]. However, environmental regulations will also inhibit innovation in the following two ways. The first is that enterprises transfer element resources originally used in the field of innovation to pollution control, which affects technological input, that is, the “squeeze effect” of innovation [28]. The other is the uncertainty of R&D caused by the inability of the market to provide, identify, and reflect the value of environmental input may delay the R&D investment decision of enterprises.In addition to innovative mechanisms, environmental regulations will also affect TFP by influencing enterprise resource allocation. Surviving companies will outsource low-productivity businesses to improve the efficiency of internal resource allocation. Among enterprises, environmental regulations will eliminate low-efficiency and highly polluting enterprises [29], accelerate the flow of factor resources between enterprises, and improve the efficiency of factor allocation.Therefore, this study proposes following hypothesis.Hypothesis 1.
Environmental regulations affect enterprise TFP by influencing innovation, resource allocation within and between enterprises.
Enterprise characteristics that were overlooked in early research are proved to be important [17, 30]. With the enrichment of data and the deepening of research, scholars have noticed that the effects of environmental regulations on productivity vary at both the industry and enterprise levels. Albrizio et al. [6] found that the effect of regulation stringency on productivity varies with the intensity of pollution within the industry and with the distance to cutting-edge technology. Feng et al. [31] found that the financing constraints and bargaining power of enterprises have a negative moderating effect on the role of China’s SO2 ETP on TFP. All in all, the effect of environmental regulations on the total factor productivity of enterprises varies with industry characteristics and enterprise characteristics.
Besides, the effects of environmental regulations on TFP will also be affected by both government and social factors.
The efficiency and fairness of public policy formulation and implementation are the functional requirements of the government, but it is easily distorted by the government’s self-interested behavior. For example, He et al. [32] found that the government manipulated water quality testing data to a certain extent. Furthermore, a corrupt government could dump market resources, distort market price signals, and disrupt the order of market competition. After a reformation, China has been promoting government transformation (GT), striving to create a fair and competitive market environment, and emphasizing the decisive role of the market in competition. Specifically, China chose the development path of “stabilizing the stock and increasing the increment.” While promoting the reform of state-owned enterprises, “opening up to the outside world” to promote the development of foreign enterprises, and “opening up to the inside” to promote the development of private enterprises. Therefore, the transformation of government governance determines the quality of the market competition environment, which will directly affect innovation and resource allocation, and thus affect the total factor productivity of enterprises.
Market awareness, a market factor, will not directly affect the production behavior of enterprises, but it will affect innovation [33, 34]. It will also then affect total factor productivity. Because innovation is a transmission mechanism, market awareness will indirectly affect the company’s total factor productivity. Specifically, market awareness not only forms the power of external supervision, but also reflects the needs of consumers. On the one hand, market awareness reflects consumers’ tolerance to pollution, and consumers spontaneously form a supervisory force to protect their own interests. On the other hand, market awareness reflects the demand of consumers, which will affect the direction of innovation investment of enterprises from the demand side.
Therefore, we hypothesize the following.Hypothesis 2.
There are differences in the economic effects of stricter command-and-control environmental regulation at the enterprise and industry levels. Moreover, government transformation and green market preferences have a positive moderating effect on the role of stricter command-and-control environmental regulation on TFP.


## 4. Data and Methodology

### 4.1. Data

In this study, we used the Annual Survey of Industrial Firms from 2001 to 2009 maintained by the National Bureau of Statistics of China (NBS) and followed the approach of Brandt et al. and Cai and Liu [35, 36] to process these enterprise data. The data on pollution reduction targets were collected from a document named “Reply to Pollution Control Plan During the Eleventh Five-Year Plan,” issued by the China State Council in 2006. Variables at the city level were obtained from the annual “City Statistical Yearbook” and “National Statistical Yearbook.”


### 4.2. Model

The pollutant emission control target was first proposed in China’s National Environmental Protection “Ninth Five-Year Plan” (1995–2000) approved by the State Council. However, the target was not delegated to provinces, autonomous regions, or municipalities directly under the central government. Although the “Tenth Five-Year Plan for National Environmental Protection” (2001–2005) decomposed the total control target to all provinces, autonomous regions, municipalities, and cities separately listed in the plan, there was no clear evaluation plan for the performance of the target. Therefore, the expected outcome has not been achieved. In 2005, COD emissions were reduced by only 2% compared to 2000, far from the 10% control target.To avoid policy failure, the government strengthened the pollutant emissions control target in the “Eleventh Five-Year Plan” from 2006 to 2010. Specifically, the measuring of emission reduction performance based on a detailed evaluation plan and linking the evaluation results with government promotion was proposed and implemented for the first time in the “Eleventh Five-Year Plan.” During this period, pollutant emissions reduced significantly, and the total control target was successfully achieved. Compared with the previous command-and-control environmental regulations, the pollutant emission control target proposed in the Eleventh Five-Year Plan has a stronger binding force, wider coverage, and better emission reduction effects. Therefore, analyzing the economic effects of this stricter command-and-control environmental regulation not only conforms to the path of China’s environmental regulation but also has practical significance.Due to the different pollutant emission reduction targets set by the central government, local governments with higher targets have implemented stricter environmental regulations. The differences in pollutant emission reduction targets among local governments can be used to demonstrate the relationship between command-and-control environmental regulations and corporate productivity. Furthermore, the TFP of companies situated in cities with stricter environmental regulation were implemented before and after 2006 can be compared with those of cities with less stringent environmental regulations using the following equation:(1)TFPift=β1ERit∗TIMEt+λX+μi+μf+νt+εit,where the dependent variable, TFPift, represents the TFP of firm j at time t in city i, ERi is the intensity of environmental regulation of city i, TIME is a dummy variable equal to 0 for the years before 2006 and equal to 1 from 2006 onward, X is a set of control variables, and μi is city fixed effect used to control time-invariant factors that may affect the corporate TFP. Similarly, μf is the firm fixed effect. νt is the year-fixed effects capturing common economic factors affecting all cities, and εit is the standard error.


### 4.3. Variables

#### 4.3.1. Explanatory Variables

The COD emission reduction targets could only be obtained at the provincial level. We followed the method presented in the study of Chen et al. [37] to construct a city-level environmental regulation intensity indicator ERi:(2)ERi=CODp∗∑j=137μjoutput value of industryjin cityioutput value of industryjin provincep,where CODp is the COD emission reduction target of province p, the second term μj on the right side of equation (2) represents industry j’s proportion of COD emissions of the total COD emissions from all 37 industries, and the third term on the right side is the proportion of the output value of industry j in each city in a single province, calculated based on the output value data of industrial enterprises in 2005.


#### 4.3.2. Explained Variable

OP and LP are two common methods of calculating enterprise TFP. The difference between these two methods is that OP uses enterprise investment as the proxy variable of TFP, whereas LP uses the enterprise’s intermediate investment as the TFP proxy variable. Because many companies did not invest in some years, some companies were excluded when applying the OP method to calculate TFP. Fortunately, no data were missing on the intermediate input of the enterprises. Therefore, using the LP method maximized the use of data and ensured the accuracy of the calculation results. The variables required to calculate the TFP of an enterprise using the LP method were added value, capital stock, labor input, and intermediate input.


#### 4.3.3. Other Variables

Several firm-level variables were selected based on the literature, including the export density of the enterprise (EXPORT), the subsidy received (SUBSIDY), the capital-labor ratio (KL), size, and age. Industry-level data includes the industry pollution intensity classification(IC) and industry competition intensity (HHI). City-level variables include regional economic development (PGDP), population (POP), government transformation (GT), and local environmental preferences (MP). The statistical descriptions of each variable are presented in Table1.Table 1
Descriptive statistics.
VariablesInterpretationMean valueStandard deviationMinimum valueMaximum valueTFPTotal factor productivity5.3071.0140.00212.568ERCity’s COD emission reduction target0.0215.524020TIMEYear dummy0.5000.48901EXPORTRatio of export value to sales revenue0.1600.33101.4SUBSIDYRatio of subsidy to sales revenue0.0020.02400.009KLLogarithm of the ratio of capital to labor11.2401.362023.580AGELogarithm of firm age plus 11.9030.72303.178SIZELogarithm of the company’s total assets9.8091.425020.156PGDPLogarithm of per capita GDP10.1740.7444.59511.932POPLogarithm of population6.3110.6035.3517.417ICCOD emission intensity of every industry0.5280.49901HHIThe sum of the squares of the market shares of all companies in each industry in a city0.1240.1650.0040.888GTProportion of main business income of state-owned and state-controlled industrial enterprises0.6290.1970.0320.870MPUrban green coverage rate0.3640.0740.1320.580


## 4.1. Data

In this study, we used the Annual Survey of Industrial Firms from 2001 to 2009 maintained by the National Bureau of Statistics of China (NBS) and followed the approach of Brandt et al. and Cai and Liu [35, 36] to process these enterprise data. The data on pollution reduction targets were collected from a document named “Reply to Pollution Control Plan During the Eleventh Five-Year Plan,” issued by the China State Council in 2006. Variables at the city level were obtained from the annual “City Statistical Yearbook” and “National Statistical Yearbook.”


## 4.2. Model

The pollutant emission control target was first proposed in China’s National Environmental Protection “Ninth Five-Year Plan” (1995–2000) approved by the State Council. However, the target was not delegated to provinces, autonomous regions, or municipalities directly under the central government. Although the “Tenth Five-Year Plan for National Environmental Protection” (2001–2005) decomposed the total control target to all provinces, autonomous regions, municipalities, and cities separately listed in the plan, there was no clear evaluation plan for the performance of the target. Therefore, the expected outcome has not been achieved. In 2005, COD emissions were reduced by only 2% compared to 2000, far from the 10% control target.To avoid policy failure, the government strengthened the pollutant emissions control target in the “Eleventh Five-Year Plan” from 2006 to 2010. Specifically, the measuring of emission reduction performance based on a detailed evaluation plan and linking the evaluation results with government promotion was proposed and implemented for the first time in the “Eleventh Five-Year Plan.” During this period, pollutant emissions reduced significantly, and the total control target was successfully achieved. Compared with the previous command-and-control environmental regulations, the pollutant emission control target proposed in the Eleventh Five-Year Plan has a stronger binding force, wider coverage, and better emission reduction effects. Therefore, analyzing the economic effects of this stricter command-and-control environmental regulation not only conforms to the path of China’s environmental regulation but also has practical significance.Due to the different pollutant emission reduction targets set by the central government, local governments with higher targets have implemented stricter environmental regulations. The differences in pollutant emission reduction targets among local governments can be used to demonstrate the relationship between command-and-control environmental regulations and corporate productivity. Furthermore, the TFP of companies situated in cities with stricter environmental regulation were implemented before and after 2006 can be compared with those of cities with less stringent environmental regulations using the following equation:(1)TFPift=β1ERit∗TIMEt+λX+μi+μf+νt+εit,where the dependent variable, TFPift, represents the TFP of firm j at time t in city i, ERi is the intensity of environmental regulation of city i, TIME is a dummy variable equal to 0 for the years before 2006 and equal to 1 from 2006 onward, X is a set of control variables, and μi is city fixed effect used to control time-invariant factors that may affect the corporate TFP. Similarly, μf is the firm fixed effect. νt is the year-fixed effects capturing common economic factors affecting all cities, and εit is the standard error.


## 4.3. Variables

### 4.3.1. Explanatory Variables

The COD emission reduction targets could only be obtained at the provincial level. We followed the method presented in the study of Chen et al. [37] to construct a city-level environmental regulation intensity indicator ERi:(2)ERi=CODp∗∑j=137μjoutput value of industryjin cityioutput value of industryjin provincep,where CODp is the COD emission reduction target of province p, the second term μj on the right side of equation (2) represents industry j’s proportion of COD emissions of the total COD emissions from all 37 industries, and the third term on the right side is the proportion of the output value of industry j in each city in a single province, calculated based on the output value data of industrial enterprises in 2005.


### 4.3.2. Explained Variable

OP and LP are two common methods of calculating enterprise TFP. The difference between these two methods is that OP uses enterprise investment as the proxy variable of TFP, whereas LP uses the enterprise’s intermediate investment as the TFP proxy variable. Because many companies did not invest in some years, some companies were excluded when applying the OP method to calculate TFP. Fortunately, no data were missing on the intermediate input of the enterprises. Therefore, using the LP method maximized the use of data and ensured the accuracy of the calculation results. The variables required to calculate the TFP of an enterprise using the LP method were added value, capital stock, labor input, and intermediate input.


### 4.3.3. Other Variables

Several firm-level variables were selected based on the literature, including the export density of the enterprise (EXPORT), the subsidy received (SUBSIDY), the capital-labor ratio (KL), size, and age. Industry-level data includes the industry pollution intensity classification(IC) and industry competition intensity (HHI). City-level variables include regional economic development (PGDP), population (POP), government transformation (GT), and local environmental preferences (MP). The statistical descriptions of each variable are presented in Table1.Table 1
Descriptive statistics.
VariablesInterpretationMean valueStandard deviationMinimum valueMaximum valueTFPTotal factor productivity5.3071.0140.00212.568ERCity’s COD emission reduction target0.0215.524020TIMEYear dummy0.5000.48901EXPORTRatio of export value to sales revenue0.1600.33101.4SUBSIDYRatio of subsidy to sales revenue0.0020.02400.009KLLogarithm of the ratio of capital to labor11.2401.362023.580AGELogarithm of firm age plus 11.9030.72303.178SIZELogarithm of the company’s total assets9.8091.425020.156PGDPLogarithm of per capita GDP10.1740.7444.59511.932POPLogarithm of population6.3110.6035.3517.417ICCOD emission intensity of every industry0.5280.49901HHIThe sum of the squares of the market shares of all companies in each industry in a city0.1240.1650.0040.888GTProportion of main business income of state-owned and state-controlled industrial enterprises0.6290.1970.0320.870MPUrban green coverage rate0.3640.0740.1320.580


## 4.3.1. Explanatory Variables

The COD emission reduction targets could only be obtained at the provincial level. We followed the method presented in the study of Chen et al. [37] to construct a city-level environmental regulation intensity indicator ERi:(2)ERi=CODp∗∑j=137μjoutput value of industryjin cityioutput value of industryjin provincep,where CODp is the COD emission reduction target of province p, the second term μj on the right side of equation (2) represents industry j’s proportion of COD emissions of the total COD emissions from all 37 industries, and the third term on the right side is the proportion of the output value of industry j in each city in a single province, calculated based on the output value data of industrial enterprises in 2005.


## 4.3.2. Explained Variable

OP and LP are two common methods of calculating enterprise TFP. The difference between these two methods is that OP uses enterprise investment as the proxy variable of TFP, whereas LP uses the enterprise’s intermediate investment as the TFP proxy variable. Because many companies did not invest in some years, some companies were excluded when applying the OP method to calculate TFP. Fortunately, no data were missing on the intermediate input of the enterprises. Therefore, using the LP method maximized the use of data and ensured the accuracy of the calculation results. The variables required to calculate the TFP of an enterprise using the LP method were added value, capital stock, labor input, and intermediate input.


## 4.3.3. Other Variables

Several firm-level variables were selected based on the literature, including the export density of the enterprise (EXPORT), the subsidy received (SUBSIDY), the capital-labor ratio (KL), size, and age. Industry-level data includes the industry pollution intensity classification(IC) and industry competition intensity (HHI). City-level variables include regional economic development (PGDP), population (POP), government transformation (GT), and local environmental preferences (MP). The statistical descriptions of each variable are presented in Table1.Table 1
Descriptive statistics.
VariablesInterpretationMean valueStandard deviationMinimum valueMaximum valueTFPTotal factor productivity5.3071.0140.00212.568ERCity’s COD emission reduction target0.0215.524020TIMEYear dummy0.5000.48901EXPORTRatio of export value to sales revenue0.1600.33101.4SUBSIDYRatio of subsidy to sales revenue0.0020.02400.009KLLogarithm of the ratio of capital to labor11.2401.362023.580AGELogarithm of firm age plus 11.9030.72303.178SIZELogarithm of the company’s total assets9.8091.425020.156PGDPLogarithm of per capita GDP10.1740.7444.59511.932POPLogarithm of population6.3110.6035.3517.417ICCOD emission intensity of every industry0.5280.49901HHIThe sum of the squares of the market shares of all companies in each industry in a city0.1240.1650.0040.888GTProportion of main business income of state-owned and state-controlled industrial enterprises0.6290.1970.0320.870MPUrban green coverage rate0.3640.0740.1320.580


## 5. Empirical Analysis

### 5.1. Baseline Estimations

A key assumption made in this study to satisfy the consistency of the DID identification strategy was that the difference in the TFP of companies in different cities is entirely caused by the various pollutant emission reduction targets set in the “Eleventh Five-Year Plan,” rather than by any preexisting differential time trends across the enterprises. To test this assumption, we used the event research method proposed by Jacobson et al. [38]. Specifically, this study replaced the interaction terms between environmental regulations and postdummy variables in (1), with the interaction terms between environmental regulations and dummy variables for each year. It can be seen from Figure 1 that there was no significant trend before 2006.Figure 1
Dynamics of COD emission reduction targets. Note: the figure plots the estimated coefficients of the city’s environmental regulation and year dummy. The reference year is 2001.To ensure the accuracy of the regression results, we adopt the method of gradually adding variables. The baseline regression results are presented in Table2. The coefficient of ER ∗ TIME is 0.009 (significant at the 1% level), indicating that stricter command-and-control environmental regulation linked to the government’s promotion improved the TFP of companies. This result is in line with theoretical expectations, that is, under the pressure of environmental regulations, companies will avoid compliance costs through technological innovation or reallocation of resources to improve production efficiency.Table 2
Baseline results.
(1)(2)(3)(4)(5)(6)(7)(8)VariablesTFPTFPTFPTFPTFPTFPTFPTFPER∗TIME0.011∗∗∗0.012∗∗∗0.012∗∗∗0.012∗∗∗0.011∗∗∗0.009∗∗∗0.008∗∗∗0.009∗∗∗(0.002)(0.003)(0.003)(0.003)(0.002)(0.002)(0.003)(0.003)KL−0.178∗∗∗−0.178∗∗∗−0.178∗∗∗−0.194∗∗∗−0.221∗∗∗−0.223∗∗∗−0.222∗∗∗(0.008)(0.008)(0.008)(0.008)(0.006)(0.006)(0.006)EXPORT0.047∗∗∗0.047∗∗∗0.030∗∗0.0070.0060.007(0.012)(0.012)(0.012)(0.011)(0.011)(0.011)SUBSIDY−0.494∗∗∗−0.566∗∗∗−0.596∗∗∗−0.594∗∗∗−0.594∗∗∗(0.064)(0.065)(0.064)(0.064)(0.064)AGE0.578∗∗∗0.490∗∗∗0.486∗∗∗0.486∗∗∗(0.015)(0.015)(0.015)(0.015)SIZE0.295∗∗∗0.293∗∗∗0.292∗∗∗(0.009)(0.009)(0.009)PGDP0.183∗∗∗0.340∗∗∗(0.064)(0.092)POP0.193∗∗(0.078)Constant5.180∗∗∗7.020∗∗∗7.010∗∗∗7.010∗∗∗6.441∗∗∗4.100∗∗∗2.391∗∗∗−0.290(0.020)(0.098)(0.096)(0.096)(0.102)(0.081)(0.602)(1.317)Firm FEYESYESYESYESYESYESYESYESYear FEYESYESYESYESYESYESYESYESObservations1,598,8551,598,8551,598,8551,598,8551,598,8551,598,8551,598,8551,598,855R-squared0.1490.1670.1670.1670.1930.2280.2290.229Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.The relationship between the control variables and the enterprise TFP is in line with expectations, as well. Enterprises with low capital density, long-term establishment, and large scale have higher productivity. Enterprises with higher capital densities tend to have relatively higher pollution emissions and require more time and higher costs to implement adjustments. Therefore, such enterprises tend to increase the input of production factors to obtain more profit to offset the cost of environmental regulations. However the coefficient of export density is not significantly positive, which shows that the learning while exporting effect can improve the TFP of enterprises. In addition, subsidies harm the improvement of TFP. Since enterprises need to make “subsidy-seeking” investments to obtain government support, they need to continue to comply with the government’s requirements and allocate resources under governmental control, putting pressure on diverting resources to enhance productivity. Moreover, in areas with higher population densities, the number of people affected by the negative externalities of environmental pollution is higher. The environmental awareness of the population results in an informal environmental regulatory force that encourages companies to improve their productivity. The environmental awareness ultimately increase the adoption of technology in society [39].The baseline regression model identifies the average effect of environmental regulations on the productivity of enterprises in the same city. The different characteristics of enterprises will cause the differential effect of environmental regulations on the TFP of enterprises, but to determine the source of the different effects of stricter command-and-control environmental regulation, this paper adds the interaction terms of environmental regulation and enterprise characteristic variables based on the baseline model. The columns 1, 2, and 3 of Table3 show the results of adding the interactive items of age, scale, and capital density of the company. As shown in the first column, the coefficient of the interaction term of firm age is significantly positive at the 1% level, with a value of 0.010, indicating that under the same environmental regulation intensity, the longer the establishment of the firm, the greater the increase in TFP.Table 3
Regression results containing different firm characteristics.
(1)(2)(3)VariablesTFPTFPTFPER∗ TIME0.010∗∗∗0.026∗∗∗0.009∗∗∗(0.003)(0.006)(0.002)ER∗ TIME ∗ AGE0.006∗∗∗(0.001)ER∗ TIME ∗ SIZE0.002∗∗(0.001)ER∗ TIME ∗ KL−0.004∗∗∗(0.001)KL−0.222∗∗∗−0.221∗∗∗−0.222∗∗∗(0.006)(0.006)(0.006)EXPORT0.0070.0060.007(0.011)(0.011)(0.011)SUBSIDY−0.594∗∗∗−0.595∗∗∗−0.594∗∗∗(0.064)(0.064)(0.064)AGE0.487∗∗∗0.488∗∗∗0.486∗∗∗(0.016)(0.015)(0.015)SIZE0.292∗∗∗0.289∗∗∗0.292∗∗∗(0.009)(0.009)(0.009)PGDP0.340∗∗∗0.339∗∗∗0.340∗∗∗(0.092)(0.093)(0.092)POP0.193∗∗0.190∗∗0.193∗∗(0.078)(0.079)(0.078)Constant−0.296−0.251−0.290(1.313)(1.324)(1.317)Firm FEYESYESYESYear FEYESYESYESObservations1,598,8551,598,8551,598,855R-squared0.1490.2290.229Notes: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.On the contrary, short-established enterprises have many imperfections in resource allocation and social capital accumulation, and often lack experience in policy responses. Larger companies are equipped with more resources and can more calmly deal with environmental regulations. Therefore, the coefficient of the interaction term of company size in the second column is significantly positive. The coefficient of the interaction term of capital intensity in the last column is −0.004 (significant at the 1% level), indicating that companies with higher capital intensity are more likely to be adversely affected by environmental regulations. This may be because the pollution emission intensity of capital-intensive enterprises is high, so they are subject to stricter environmental regulations.In addition to enterprise characteristics, industry characteristics may also contribute to differences in the effects of stricter command-and-control environmental regulation. The first column in Table4 presents the regression result containing the industry pollution category(IC). We calculated the average polluting intensity of 37 industries from 2001 to 2005. The first 18 industries with higher COD emission densities were classified as polluting industries, and the remaining 19 industrial industries were categorized as clean industries. If the industry is a polluting industry, the IC value is 1; otherwise, the IC value is 0. The coefficient of the interaction term is 0.002 (significant at the 1% level), indicating that the TFP of polluting industries has increased more than that of clean industries. In addition, industry competition is a factor affecting the effect of environmental regulations. At the most basic sense, the more competition in the industry, the more effective the allocation of factor resources, and the more positive the environmental regulations can achieve. The interaction coefficient of industry competition in the column 2 of Table 4 is significantly negative at the 1% level, indicating that the lower the degree of industry competition (the higher the HHI index), the more adversely affected by environmental regulations.Table 4
Regression results containing different industry, government, and market characteristics.
(1)(2)(3)(4)VariablesTFPTFPTFPTFPER∗ TIME0.009∗∗∗0.009∗∗∗0.015∗∗∗0.004∗(0.003)(0.003)(0.004)(0.002)ER∗ TIME ∗ IC0.002∗(0.001)ER∗ TIME ∗ HHI−0.003∗∗(0.001)ER∗ TIME ∗ GT−0.029∗(0.015)ER∗ TIME ∗ MP0.001∗∗∗(0.000)KL−0.222∗∗∗−0.223∗∗∗−0.226∗∗∗−0.222∗∗∗(0.006)(0.006)(0.006)(0.006)EXPORT0.0070.0070.0100.007(0.011)(0.011)(0.011)(0.011)SUBSIDY−0.594∗∗∗−0.597∗∗∗−0.591∗∗∗−0.594∗∗∗(0.064)(0.063)(0.062)(0.064)AGE0.486∗∗∗0.485∗∗∗0.478∗∗∗0.486∗∗∗(0.015)(0.015)(0.014)(0.015)SIZE0.292∗∗∗0.291∗∗∗0.288∗∗∗0.292∗∗∗(0.009)(0.009)(0.009)(0.009)PGDP0.340∗∗∗0.329∗∗∗0.179∗∗0.339∗∗∗(0.092)(0.092)(0.079)(0.093)POP0.193∗∗0.190∗∗0.155∗∗0.191∗∗(0.078)(0.077)(0.069)(0.078)Constant−0.291−0.1212.300∗−0.289(1.316)(1.309)(1.180)(1.314)Control variablesYESYESYESYESFirm FEYESYESYESYESYear FEYESYESYESYESObservations1,598,8551,598,8551,598,8551,598,855R-squared0.2290.2300.2340.229Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.In addition to the characteristics of enterprises and industries, two external factors, the market, and the government will also affect the economic effects of stricter command-and-control environmental regulation. Accordingly, based on the baseline model, we add the triple crossover items of GT and MP. GT is the speeding up of the development of private enterprises and foreign enterprises while building a fair and effective market, thereby reducing distortions in environmental regulatory policies. The coefficient of the interaction term of GT is significantly negative at the 10% level, indicating that GT can magnify the effect of environmental regulation. Similarly, the coefficient of the interaction term of local green preference is significantly positive at the level of 1%, indicating that the stronger the green preference is, the more positively the regional enterprises will respond to environmental regulation policies.


### 5.2. Mechanism Channels

#### 5.2.1. Innovation Effect

Researchers often measure innovation from the perspective of input and output. This study uses the proportion of R&D expenditure to total assets to measure innovation input, and the proportion of new products to main business income to measure innovation output. These two indicators of innovation are substituted for the dependent variables in the baseline regression model. The results of the study are shown in the columns 1 and 2 of Table5. The interaction coefficient of environmental regulation is significantly negative. In all, environmental regulations have inhibited innovation, which is not conducive to the improvement of enterprises’ TFP.Table 5
Mechanism regression results.
(1)(2)(3)(4)VariablesInnovation inputInnovation outputResource allocationResource allocationER∗ TIME−0.014∗∗∗−0.005∗∗∗0.003∗0.013∗∗(0.003)(0.001)(0.002)(0.006)ER∗ TIME ∗ ln(RETURNitRETURNit−1)0.005∗∗∗(0.001)KL−0.001−0.001∗∗∗0.109∗∗∗0.006∗(0.001)(0.000)(0.005)(0.003)EXPORT0.009∗∗∗0.026∗∗∗0.004−0.0116(0.001)(0.003)(0.004)(0.011)SUBSIDY0.028∗∗∗0.0080.036−0.0206(0.004)(0.011)(0.025)(0.031)AGE0.004∗∗∗0.010∗∗∗−0.054∗∗∗0.0996∗∗∗(0.001)(0.002)(0.006)(0.017)SIZE0.007∗∗∗0.004∗∗∗0.438∗∗∗0.0126∗∗(0.001)(0.001)(0.011)(0.005)PGDP0.0210.017−0.054∗∗0.593∗∗∗(0.014)(0.020)(0.024)(0.140)POP0.004∗0.013−0.0210.472∗∗∗(0.016)(0.017)(0.018)(0.150)Constant−0.302−0.227−4.446∗∗−5.038∗∗(0.226)(0.293)(0.326)(2.079)Control variablesYESYESYESYESFirm FEYESYESYESYESYear FEYESYESYESYESObservations1,598,8551,598,8551,598,8551,598,855R-squared0.0940.0240.1210.871Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.The optimization of resource allocation efficiency within or between enterprises contributes to improving the enterprise TFP. This study uses the investment-investment opportunity sensitivity model proposed by Wurgler [40] to identify the role of environmental regulations in the allocation of resources within the enterprise. The coefficient of the interaction term in the column 3 of Table 5 is significantly positive, indicating that environmental regulations have indeed improved the allocation of resources within the enterprise. Heish and Klenow [41] believe that the TFP level of all enterprises should be the same when resources are fully allocated. Following this idea, this study uses the proportion of each industrial sector in the city’s total sales revenue for the year to weigh the standard deviation of the TFP of the enterprises in that industry to obtain the efficiency of resource allocation among enterprises. The specific regression results are shown in the column 4 of Table 5. In general, environmental regulations also improve the efficiency of resource allocation within and among enterprises, thereby increasing the TFP of enterprises.


### 5.3. Robustness Tests

#### 5.3.1. Alternative Variable Test

To test the robustness of the regression results, we recalculate TFP in a different way. The OP method is a semi-parametric estimation method, and the issue of simultaneous deviation may occur. Therefore, we applied the generalized moment method proposed by Blundell and Bond [42] to solve the problem of simultaneity deviation and measure the TFP of the analyzed enterprises. In addition, this method uses the difference term and the lag term of the explained variable as instrumental variables, effectively solving the endogenous problem. The regression results of (1) after replacing the variables are shown in the columns 1 and 2 of Table 6. Changing the method and recalculating the explained variable did not change the regression result. After the COD emission reduction control targets proposed in the “Eleventh Five-Year Plan,” the enterprise TFP increased significantly. Therefore, stricter command-and-control environmental regulation can help increase the TFP of industrial enterprises.Table 6
Robustness tests results.
(1)(2)(3)(4)(5)(6)ER∗ TIME0.012∗∗∗0.009∗∗∗0.015∗∗∗0.011∗∗∗0.010∗∗∗0.008∗∗∗(0.003)(0.002)(0.002)(0.003)(0.002)(0.003)KL−0.028∗∗∗−0.212∗∗∗−0.224∗∗∗(0.005)(0.006)(0.006)EXPORT0.0002−0.0080.006(0.011)(0.012)(0.011)SUBSIDY−0.581∗∗∗−0.633∗∗∗−0.571∗∗∗(0.064)(0.084)(0.069)AGE0.414∗∗∗0.519∗∗∗0.484∗∗∗(0.015)(0.018)(0.015)SIZE0.245∗∗∗0.283∗∗∗0.293∗∗∗(0.009)(0.130)(0.009)PGDP0.332∗∗∗0.339∗∗∗0.351∗∗∗(0.091)(0.122)(0.091)POP0.184∗∗0.169∗∗0.204∗∗∗(0.077)(0.088)(0.076)Constant4.937∗∗∗−2.464∗5.188∗∗∗−0.1765.180∗∗∗−0.466(0.021)(1.290)(0.028)(1.693)(0.020)(1.287)Firm FEYesYesYesYesYesYesYear FEYesYesYesYesYesYesObservations1,598,8551,598,855859,057859,0571,525,3801,525,380R-squared0.1430.1920.0740.1380.1500.231Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.


#### 5.3.2. Concurrent Events

If other events affected different industries in different regions during the national environmental protection “Eleventh Five-Year Plan” period, the regression results of this article will be biased. From 2006 to 2009, events that might have affected our results include the pilot emissions trading in 2007 and the Beijing Olympics in 2008. In addition to command-and-control environmental regulations, China is also actively exploring market-based environmental regulations. In 2007, the Ministry of Finance, the former Ministry of Environmental Protection and the National Development and Reform Commission approved Tianjin, Hebei, Shanxi, Inner Mongolia, Jiangsu, Zhejiang, Henan, Hubei, Chongqing, Shaanxi, and Qingdao to carry out emission trading pilot projects. To eliminate the possible interference of this policy, this study deletes companies in these areas since 2007. The regression results are shown in the columns 3 and 4 of Table6.To ensure good air quality in Beijing during the 2008 Olympic Games, the Ministry of Environmental Protection jointly formulated the “Beijing Air Quality Assurance Measures for the 29th Olympic Games.” During this time, more stringent pollution control and emission reduction measures were implemented by regulating coal-burning, motor vehicles, industry, and dust pollution. Additionally, specific companies were ordered to suspend or partially suspend production to control pollution. Therefore, this study removed the samples from Beijing, Hebei, Tianjin, Shanxi, Inner Mongolia, and Shandong from 2007 to 2008 to eliminate the impact of the 2008 Beijing Olympics. The regression results are shown in the columns 5 and 6 of Table6. Irrespective of the pilot emissions trading and Beijing Olympic games, the stricter command-and-control environmental regulation has resulted in a higher TFP for industrial enterprises.


### 5.4. Heterogeneity Analysis

Due to the differences in emission reduction targets and implementation methods, the effect of stricter command-and-control environmental regulation may also be heterogeneous. Therefore, this study further observed the influence of stricter command-and-control environmental regulation on the enterprise TFP from two aspects: the internal characteristics of corporate ownership and the external characteristics of geographic location.(1)
Corporate ownership heterogeneity: the columns 1, 2, and 3 of Table7 report the effects of stricter command-and-control environmental regulation on the TFP of state-owned enterprises, private enterprises, and foreign-funded enterprises, respectively. The results in Table 7 indicate that the pollution reduction targets have significantly improved the TFP of private enterprises. However, this result was not observed for state-owned enterprises and foreign-funded enterprises. State-owned enterprises assume more social responsibilities and act as emission reduction pioneers, so they may invest more in emission reduction. Therefore, the economic effects of environmental regulations are not reflected in state-owned enterprises. With better environmental performances, foreign-funded enterprises can easily get rid of being restricted by environmental regulations. Therefore, stricter command-and-control environmental regulation has no significant effect on the TFP of foreign-funded enterprises.(2)
Geographic heterogeneity: the columns 4, 5, and 6 in Table7 list the differences in the effects of stricter command-and-control environmental regulation on the TFP of enterprises in eastern, central, and western China, respectively. It can be seen from the table that the COD emission reduction targets have significantly improved the TFP of enterprises across the country. This is possibly due to the stricter environmental regulations that incorporate environmental performance into the evaluation of official performance have improved the executive power of local government officials. In addition, economic performance and environmental performance are both important in official performance, so local officials will implement environmental regulations more comprehensively and effectively.Table 7
Heterogeneity analysis results.
(1)(2)(3)(4)(5)(6)ER∗ TIME0.0070.010∗∗∗0.0020.007∗∗0.058∗0.008∗∗∗(0.004)(0.003)(0.003)(0.003)(0.030)(0.002)KL−0.185∗∗∗−0.214∗∗∗−0.237∗∗∗−0.228∗∗∗−0.204∗∗∗−0.214∗∗∗(0.015)(0.005)(0.011)(0.007)(0.008)(0.010)EXPORT0.0600.014−0.034∗0.006−0.0190.054(0.067)(0.009)(0.020)(0.012)(0.024)(0.039)SUBSIDY−0.860∗∗∗−0.735∗∗∗−0.286∗∗−0.597∗∗∗−0.724∗∗∗−0.466∗∗∗(0.126)(0.095)(0.129)(0.081)(0.094)(0.156)AGE0.201∗∗∗0.487∗∗∗0.581∗∗∗0.478∗∗∗0.549∗∗∗0.433∗∗∗(0.037)(0.017)(0.020)(0.019)(0.025)(0.024)SIZE0.287∗∗∗0.260∗∗∗0.378∗∗∗0.311∗∗∗0.223∗∗∗0.268∗∗∗(0.017)(0.009)(0.019)(0.010)(0.014)(0.014)PGDP0.0850.357∗∗∗0.272∗0.373∗∗0.366∗∗−0.013(0.052)(0.100)(0.144)(0.149)(0.149)(0.042)POP−0.0420.191∗∗0.284∗∗∗0.260∗∗0.281−0.059(0.044)(0.086)(0.098)(0.109)(0.233)(0.044)Constant3.145∗∗∗−0.216−0.829−1.195−0.4594.631∗∗∗(0.615)(1.441)(1.909)(2.091)(2.459)(0.602)Observations70,5831,175,384352,8881,186,908267,116144,831R-squared0.1570.2040.2580.2410.2050.208Firm FEYesYesYesYesYesYesYear FEYesYesYesYesYesYesNote: FE stands for fixed effect;∗, ∗∗, ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. Standard errors are reported in parentheses.


## 5.1. Baseline Estimations

A key assumption made in this study to satisfy the consistency of the DID identification strategy was that the difference in the TFP of companies in different cities is entirely caused by the various pollutant emission reduction targets set in the “Eleventh Five-Year Plan,” rather than by any preexisting differential time trends across the enterprises. To test this assumption, we used the event research method proposed by Jacobson et al. [38]. Specifically, this study replaced the interaction terms between environmental regulations and postdummy variables in (1), with the interaction terms between environmental regulations and dummy variables for each year. It can be seen from Figure 1 that there was no significant trend before 2006.Figure 1
Dynamics of COD emission reduction targets. Note: the figure plots the estimated coefficients of the city’s environmental regulation and year dummy. The reference year is 2001.To ensure the accuracy of the regression results, we adopt the method of gradually adding variables. The baseline regression results are presented in Table2. The coefficient of ER ∗ TIME is 0.009 (significant at the 1% level), indicating that stricter command-and-control environmental regulation linked to the government’s promotion improved the TFP of companies. This result is in line with theoretical expectations, that is, under the pressure of environmental regulations, companies will avoid compliance costs through technological innovation or reallocation of resources to improve production efficiency.Table 2
Baseline results.
(1)(2)(3)(4)(5)(6)(7)(8)VariablesTFPTFPTFPTFPTFPTFPTFPTFPER∗TIME0.011∗∗∗0.012∗∗∗0.012∗∗∗0.012∗∗∗0.011∗∗∗0.009∗∗∗0.008∗∗∗0.009∗∗∗(0.002)(0.003)(0.003)(0.003)(0.002)(0.002)(0.003)(0.003)KL−0.178∗∗∗−0.178∗∗∗−0.178∗∗∗−0.194∗∗∗−0.221∗∗∗−0.223∗∗∗−0.222∗∗∗(0.008)(0.008)(0.008)(0.008)(0.006)(0.006)(0.006)EXPORT0.047∗∗∗0.047∗∗∗0.030∗∗0.0070.0060.007(0.012)(0.012)(0.012)(0.011)(0.011)(0.011)SUBSIDY−0.494∗∗∗−0.566∗∗∗−0.596∗∗∗−0.594∗∗∗−0.594∗∗∗(0.064)(0.065)(0.064)(0.064)(0.064)AGE0.578∗∗∗0.490∗∗∗0.486∗∗∗0.486∗∗∗(0.015)(0.015)(0.015)(0.015)SIZE0.295∗∗∗0.293∗∗∗0.292∗∗∗(0.009)(0.009)(0.009)PGDP0.183∗∗∗0.340∗∗∗(0.064)(0.092)POP0.193∗∗(0.078)Constant5.180∗∗∗7.020∗∗∗7.010∗∗∗7.010∗∗∗6.441∗∗∗4.100∗∗∗2.391∗∗∗−0.290(0.020)(0.098)(0.096)(0.096)(0.102)(0.081)(0.602)(1.317)Firm FEYESYESYESYESYESYESYESYESYear FEYESYESYESYESYESYESYESYESObservations1,598,8551,598,8551,598,8551,598,8551,598,8551,598,8551,598,8551,598,855R-squared0.1490.1670.1670.1670.1930.2280.2290.229Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.The relationship between the control variables and the enterprise TFP is in line with expectations, as well. Enterprises with low capital density, long-term establishment, and large scale have higher productivity. Enterprises with higher capital densities tend to have relatively higher pollution emissions and require more time and higher costs to implement adjustments. Therefore, such enterprises tend to increase the input of production factors to obtain more profit to offset the cost of environmental regulations. However the coefficient of export density is not significantly positive, which shows that the learning while exporting effect can improve the TFP of enterprises. In addition, subsidies harm the improvement of TFP. Since enterprises need to make “subsidy-seeking” investments to obtain government support, they need to continue to comply with the government’s requirements and allocate resources under governmental control, putting pressure on diverting resources to enhance productivity. Moreover, in areas with higher population densities, the number of people affected by the negative externalities of environmental pollution is higher. The environmental awareness of the population results in an informal environmental regulatory force that encourages companies to improve their productivity. The environmental awareness ultimately increase the adoption of technology in society [39].The baseline regression model identifies the average effect of environmental regulations on the productivity of enterprises in the same city. The different characteristics of enterprises will cause the differential effect of environmental regulations on the TFP of enterprises, but to determine the source of the different effects of stricter command-and-control environmental regulation, this paper adds the interaction terms of environmental regulation and enterprise characteristic variables based on the baseline model. The columns 1, 2, and 3 of Table3 show the results of adding the interactive items of age, scale, and capital density of the company. As shown in the first column, the coefficient of the interaction term of firm age is significantly positive at the 1% level, with a value of 0.010, indicating that under the same environmental regulation intensity, the longer the establishment of the firm, the greater the increase in TFP.Table 3
Regression results containing different firm characteristics.
(1)(2)(3)VariablesTFPTFPTFPER∗ TIME0.010∗∗∗0.026∗∗∗0.009∗∗∗(0.003)(0.006)(0.002)ER∗ TIME ∗ AGE0.006∗∗∗(0.001)ER∗ TIME ∗ SIZE0.002∗∗(0.001)ER∗ TIME ∗ KL−0.004∗∗∗(0.001)KL−0.222∗∗∗−0.221∗∗∗−0.222∗∗∗(0.006)(0.006)(0.006)EXPORT0.0070.0060.007(0.011)(0.011)(0.011)SUBSIDY−0.594∗∗∗−0.595∗∗∗−0.594∗∗∗(0.064)(0.064)(0.064)AGE0.487∗∗∗0.488∗∗∗0.486∗∗∗(0.016)(0.015)(0.015)SIZE0.292∗∗∗0.289∗∗∗0.292∗∗∗(0.009)(0.009)(0.009)PGDP0.340∗∗∗0.339∗∗∗0.340∗∗∗(0.092)(0.093)(0.092)POP0.193∗∗0.190∗∗0.193∗∗(0.078)(0.079)(0.078)Constant−0.296−0.251−0.290(1.313)(1.324)(1.317)Firm FEYESYESYESYear FEYESYESYESObservations1,598,8551,598,8551,598,855R-squared0.1490.2290.229Notes: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.On the contrary, short-established enterprises have many imperfections in resource allocation and social capital accumulation, and often lack experience in policy responses. Larger companies are equipped with more resources and can more calmly deal with environmental regulations. Therefore, the coefficient of the interaction term of company size in the second column is significantly positive. The coefficient of the interaction term of capital intensity in the last column is −0.004 (significant at the 1% level), indicating that companies with higher capital intensity are more likely to be adversely affected by environmental regulations. This may be because the pollution emission intensity of capital-intensive enterprises is high, so they are subject to stricter environmental regulations.In addition to enterprise characteristics, industry characteristics may also contribute to differences in the effects of stricter command-and-control environmental regulation. The first column in Table4 presents the regression result containing the industry pollution category(IC). We calculated the average polluting intensity of 37 industries from 2001 to 2005. The first 18 industries with higher COD emission densities were classified as polluting industries, and the remaining 19 industrial industries were categorized as clean industries. If the industry is a polluting industry, the IC value is 1; otherwise, the IC value is 0. The coefficient of the interaction term is 0.002 (significant at the 1% level), indicating that the TFP of polluting industries has increased more than that of clean industries. In addition, industry competition is a factor affecting the effect of environmental regulations. At the most basic sense, the more competition in the industry, the more effective the allocation of factor resources, and the more positive the environmental regulations can achieve. The interaction coefficient of industry competition in the column 2 of Table 4 is significantly negative at the 1% level, indicating that the lower the degree of industry competition (the higher the HHI index), the more adversely affected by environmental regulations.Table 4
Regression results containing different industry, government, and market characteristics.
(1)(2)(3)(4)VariablesTFPTFPTFPTFPER∗ TIME0.009∗∗∗0.009∗∗∗0.015∗∗∗0.004∗(0.003)(0.003)(0.004)(0.002)ER∗ TIME ∗ IC0.002∗(0.001)ER∗ TIME ∗ HHI−0.003∗∗(0.001)ER∗ TIME ∗ GT−0.029∗(0.015)ER∗ TIME ∗ MP0.001∗∗∗(0.000)KL−0.222∗∗∗−0.223∗∗∗−0.226∗∗∗−0.222∗∗∗(0.006)(0.006)(0.006)(0.006)EXPORT0.0070.0070.0100.007(0.011)(0.011)(0.011)(0.011)SUBSIDY−0.594∗∗∗−0.597∗∗∗−0.591∗∗∗−0.594∗∗∗(0.064)(0.063)(0.062)(0.064)AGE0.486∗∗∗0.485∗∗∗0.478∗∗∗0.486∗∗∗(0.015)(0.015)(0.014)(0.015)SIZE0.292∗∗∗0.291∗∗∗0.288∗∗∗0.292∗∗∗(0.009)(0.009)(0.009)(0.009)PGDP0.340∗∗∗0.329∗∗∗0.179∗∗0.339∗∗∗(0.092)(0.092)(0.079)(0.093)POP0.193∗∗0.190∗∗0.155∗∗0.191∗∗(0.078)(0.077)(0.069)(0.078)Constant−0.291−0.1212.300∗−0.289(1.316)(1.309)(1.180)(1.314)Control variablesYESYESYESYESFirm FEYESYESYESYESYear FEYESYESYESYESObservations1,598,8551,598,8551,598,8551,598,855R-squared0.2290.2300.2340.229Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.In addition to the characteristics of enterprises and industries, two external factors, the market, and the government will also affect the economic effects of stricter command-and-control environmental regulation. Accordingly, based on the baseline model, we add the triple crossover items of GT and MP. GT is the speeding up of the development of private enterprises and foreign enterprises while building a fair and effective market, thereby reducing distortions in environmental regulatory policies. The coefficient of the interaction term of GT is significantly negative at the 10% level, indicating that GT can magnify the effect of environmental regulation. Similarly, the coefficient of the interaction term of local green preference is significantly positive at the level of 1%, indicating that the stronger the green preference is, the more positively the regional enterprises will respond to environmental regulation policies.


## 5.2. Mechanism Channels

### 5.2.1. Innovation Effect

Researchers often measure innovation from the perspective of input and output. This study uses the proportion of R&D expenditure to total assets to measure innovation input, and the proportion of new products to main business income to measure innovation output. These two indicators of innovation are substituted for the dependent variables in the baseline regression model. The results of the study are shown in the columns 1 and 2 of Table5. The interaction coefficient of environmental regulation is significantly negative. In all, environmental regulations have inhibited innovation, which is not conducive to the improvement of enterprises’ TFP.Table 5
Mechanism regression results.
(1)(2)(3)(4)VariablesInnovation inputInnovation outputResource allocationResource allocationER∗ TIME−0.014∗∗∗−0.005∗∗∗0.003∗0.013∗∗(0.003)(0.001)(0.002)(0.006)ER∗ TIME ∗ ln(RETURNitRETURNit−1)0.005∗∗∗(0.001)KL−0.001−0.001∗∗∗0.109∗∗∗0.006∗(0.001)(0.000)(0.005)(0.003)EXPORT0.009∗∗∗0.026∗∗∗0.004−0.0116(0.001)(0.003)(0.004)(0.011)SUBSIDY0.028∗∗∗0.0080.036−0.0206(0.004)(0.011)(0.025)(0.031)AGE0.004∗∗∗0.010∗∗∗−0.054∗∗∗0.0996∗∗∗(0.001)(0.002)(0.006)(0.017)SIZE0.007∗∗∗0.004∗∗∗0.438∗∗∗0.0126∗∗(0.001)(0.001)(0.011)(0.005)PGDP0.0210.017−0.054∗∗0.593∗∗∗(0.014)(0.020)(0.024)(0.140)POP0.004∗0.013−0.0210.472∗∗∗(0.016)(0.017)(0.018)(0.150)Constant−0.302−0.227−4.446∗∗−5.038∗∗(0.226)(0.293)(0.326)(2.079)Control variablesYESYESYESYESFirm FEYESYESYESYESYear FEYESYESYESYESObservations1,598,8551,598,8551,598,8551,598,855R-squared0.0940.0240.1210.871Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.The optimization of resource allocation efficiency within or between enterprises contributes to improving the enterprise TFP. This study uses the investment-investment opportunity sensitivity model proposed by Wurgler [40] to identify the role of environmental regulations in the allocation of resources within the enterprise. The coefficient of the interaction term in the column 3 of Table 5 is significantly positive, indicating that environmental regulations have indeed improved the allocation of resources within the enterprise. Heish and Klenow [41] believe that the TFP level of all enterprises should be the same when resources are fully allocated. Following this idea, this study uses the proportion of each industrial sector in the city’s total sales revenue for the year to weigh the standard deviation of the TFP of the enterprises in that industry to obtain the efficiency of resource allocation among enterprises. The specific regression results are shown in the column 4 of Table 5. In general, environmental regulations also improve the efficiency of resource allocation within and among enterprises, thereby increasing the TFP of enterprises.


## 5.2.1. Innovation Effect

Researchers often measure innovation from the perspective of input and output. This study uses the proportion of R&D expenditure to total assets to measure innovation input, and the proportion of new products to main business income to measure innovation output. These two indicators of innovation are substituted for the dependent variables in the baseline regression model. The results of the study are shown in the columns 1 and 2 of Table5. The interaction coefficient of environmental regulation is significantly negative. In all, environmental regulations have inhibited innovation, which is not conducive to the improvement of enterprises’ TFP.Table 5
Mechanism regression results.
(1)(2)(3)(4)VariablesInnovation inputInnovation outputResource allocationResource allocationER∗ TIME−0.014∗∗∗−0.005∗∗∗0.003∗0.013∗∗(0.003)(0.001)(0.002)(0.006)ER∗ TIME ∗ ln(RETURNitRETURNit−1)0.005∗∗∗(0.001)KL−0.001−0.001∗∗∗0.109∗∗∗0.006∗(0.001)(0.000)(0.005)(0.003)EXPORT0.009∗∗∗0.026∗∗∗0.004−0.0116(0.001)(0.003)(0.004)(0.011)SUBSIDY0.028∗∗∗0.0080.036−0.0206(0.004)(0.011)(0.025)(0.031)AGE0.004∗∗∗0.010∗∗∗−0.054∗∗∗0.0996∗∗∗(0.001)(0.002)(0.006)(0.017)SIZE0.007∗∗∗0.004∗∗∗0.438∗∗∗0.0126∗∗(0.001)(0.001)(0.011)(0.005)PGDP0.0210.017−0.054∗∗0.593∗∗∗(0.014)(0.020)(0.024)(0.140)POP0.004∗0.013−0.0210.472∗∗∗(0.016)(0.017)(0.018)(0.150)Constant−0.302−0.227−4.446∗∗−5.038∗∗(0.226)(0.293)(0.326)(2.079)Control variablesYESYESYESYESFirm FEYESYESYESYESYear FEYESYESYESYESObservations1,598,8551,598,8551,598,8551,598,855R-squared0.0940.0240.1210.871Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.The optimization of resource allocation efficiency within or between enterprises contributes to improving the enterprise TFP. This study uses the investment-investment opportunity sensitivity model proposed by Wurgler [40] to identify the role of environmental regulations in the allocation of resources within the enterprise. The coefficient of the interaction term in the column 3 of Table 5 is significantly positive, indicating that environmental regulations have indeed improved the allocation of resources within the enterprise. Heish and Klenow [41] believe that the TFP level of all enterprises should be the same when resources are fully allocated. Following this idea, this study uses the proportion of each industrial sector in the city’s total sales revenue for the year to weigh the standard deviation of the TFP of the enterprises in that industry to obtain the efficiency of resource allocation among enterprises. The specific regression results are shown in the column 4 of Table 5. In general, environmental regulations also improve the efficiency of resource allocation within and among enterprises, thereby increasing the TFP of enterprises.


## 5.3. Robustness Tests

### 5.3.1. Alternative Variable Test

To test the robustness of the regression results, we recalculate TFP in a different way. The OP method is a semi-parametric estimation method, and the issue of simultaneous deviation may occur. Therefore, we applied the generalized moment method proposed by Blundell and Bond [42] to solve the problem of simultaneity deviation and measure the TFP of the analyzed enterprises. In addition, this method uses the difference term and the lag term of the explained variable as instrumental variables, effectively solving the endogenous problem. The regression results of (1) after replacing the variables are shown in the columns 1 and 2 of Table 6. Changing the method and recalculating the explained variable did not change the regression result. After the COD emission reduction control targets proposed in the “Eleventh Five-Year Plan,” the enterprise TFP increased significantly. Therefore, stricter command-and-control environmental regulation can help increase the TFP of industrial enterprises.Table 6
Robustness tests results.
(1)(2)(3)(4)(5)(6)ER∗ TIME0.012∗∗∗0.009∗∗∗0.015∗∗∗0.011∗∗∗0.010∗∗∗0.008∗∗∗(0.003)(0.002)(0.002)(0.003)(0.002)(0.003)KL−0.028∗∗∗−0.212∗∗∗−0.224∗∗∗(0.005)(0.006)(0.006)EXPORT0.0002−0.0080.006(0.011)(0.012)(0.011)SUBSIDY−0.581∗∗∗−0.633∗∗∗−0.571∗∗∗(0.064)(0.084)(0.069)AGE0.414∗∗∗0.519∗∗∗0.484∗∗∗(0.015)(0.018)(0.015)SIZE0.245∗∗∗0.283∗∗∗0.293∗∗∗(0.009)(0.130)(0.009)PGDP0.332∗∗∗0.339∗∗∗0.351∗∗∗(0.091)(0.122)(0.091)POP0.184∗∗0.169∗∗0.204∗∗∗(0.077)(0.088)(0.076)Constant4.937∗∗∗−2.464∗5.188∗∗∗−0.1765.180∗∗∗−0.466(0.021)(1.290)(0.028)(1.693)(0.020)(1.287)Firm FEYesYesYesYesYesYesYear FEYesYesYesYesYesYesObservations1,598,8551,598,855859,057859,0571,525,3801,525,380R-squared0.1430.1920.0740.1380.1500.231Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.


### 5.3.2. Concurrent Events

If other events affected different industries in different regions during the national environmental protection “Eleventh Five-Year Plan” period, the regression results of this article will be biased. From 2006 to 2009, events that might have affected our results include the pilot emissions trading in 2007 and the Beijing Olympics in 2008. In addition to command-and-control environmental regulations, China is also actively exploring market-based environmental regulations. In 2007, the Ministry of Finance, the former Ministry of Environmental Protection and the National Development and Reform Commission approved Tianjin, Hebei, Shanxi, Inner Mongolia, Jiangsu, Zhejiang, Henan, Hubei, Chongqing, Shaanxi, and Qingdao to carry out emission trading pilot projects. To eliminate the possible interference of this policy, this study deletes companies in these areas since 2007. The regression results are shown in the columns 3 and 4 of Table6.To ensure good air quality in Beijing during the 2008 Olympic Games, the Ministry of Environmental Protection jointly formulated the “Beijing Air Quality Assurance Measures for the 29th Olympic Games.” During this time, more stringent pollution control and emission reduction measures were implemented by regulating coal-burning, motor vehicles, industry, and dust pollution. Additionally, specific companies were ordered to suspend or partially suspend production to control pollution. Therefore, this study removed the samples from Beijing, Hebei, Tianjin, Shanxi, Inner Mongolia, and Shandong from 2007 to 2008 to eliminate the impact of the 2008 Beijing Olympics. The regression results are shown in the columns 5 and 6 of Table6. Irrespective of the pilot emissions trading and Beijing Olympic games, the stricter command-and-control environmental regulation has resulted in a higher TFP for industrial enterprises.


## 5.3.1. Alternative Variable Test

To test the robustness of the regression results, we recalculate TFP in a different way. The OP method is a semi-parametric estimation method, and the issue of simultaneous deviation may occur. Therefore, we applied the generalized moment method proposed by Blundell and Bond [42] to solve the problem of simultaneity deviation and measure the TFP of the analyzed enterprises. In addition, this method uses the difference term and the lag term of the explained variable as instrumental variables, effectively solving the endogenous problem. The regression results of (1) after replacing the variables are shown in the columns 1 and 2 of Table 6. Changing the method and recalculating the explained variable did not change the regression result. After the COD emission reduction control targets proposed in the “Eleventh Five-Year Plan,” the enterprise TFP increased significantly. Therefore, stricter command-and-control environmental regulation can help increase the TFP of industrial enterprises.Table 6
Robustness tests results.
(1)(2)(3)(4)(5)(6)ER∗ TIME0.012∗∗∗0.009∗∗∗0.015∗∗∗0.011∗∗∗0.010∗∗∗0.008∗∗∗(0.003)(0.002)(0.002)(0.003)(0.002)(0.003)KL−0.028∗∗∗−0.212∗∗∗−0.224∗∗∗(0.005)(0.006)(0.006)EXPORT0.0002−0.0080.006(0.011)(0.012)(0.011)SUBSIDY−0.581∗∗∗−0.633∗∗∗−0.571∗∗∗(0.064)(0.084)(0.069)AGE0.414∗∗∗0.519∗∗∗0.484∗∗∗(0.015)(0.018)(0.015)SIZE0.245∗∗∗0.283∗∗∗0.293∗∗∗(0.009)(0.130)(0.009)PGDP0.332∗∗∗0.339∗∗∗0.351∗∗∗(0.091)(0.122)(0.091)POP0.184∗∗0.169∗∗0.204∗∗∗(0.077)(0.088)(0.076)Constant4.937∗∗∗−2.464∗5.188∗∗∗−0.1765.180∗∗∗−0.466(0.021)(1.290)(0.028)(1.693)(0.020)(1.287)Firm FEYesYesYesYesYesYesYear FEYesYesYesYesYesYesObservations1,598,8551,598,855859,057859,0571,525,3801,525,380R-squared0.1430.1920.0740.1380.1500.231Note: standard errors are clustered at the city level, andt statistics are shown in the respective parentheses. ∗∗∗P<0.01; ∗∗P<0.05; ∗P<0.1.


## 5.3.2. Concurrent Events

If other events affected different industries in different regions during the national environmental protection “Eleventh Five-Year Plan” period, the regression results of this article will be biased. From 2006 to 2009, events that might have affected our results include the pilot emissions trading in 2007 and the Beijing Olympics in 2008. In addition to command-and-control environmental regulations, China is also actively exploring market-based environmental regulations. In 2007, the Ministry of Finance, the former Ministry of Environmental Protection and the National Development and Reform Commission approved Tianjin, Hebei, Shanxi, Inner Mongolia, Jiangsu, Zhejiang, Henan, Hubei, Chongqing, Shaanxi, and Qingdao to carry out emission trading pilot projects. To eliminate the possible interference of this policy, this study deletes companies in these areas since 2007. The regression results are shown in the columns 3 and 4 of Table6.To ensure good air quality in Beijing during the 2008 Olympic Games, the Ministry of Environmental Protection jointly formulated the “Beijing Air Quality Assurance Measures for the 29th Olympic Games.” During this time, more stringent pollution control and emission reduction measures were implemented by regulating coal-burning, motor vehicles, industry, and dust pollution. Additionally, specific companies were ordered to suspend or partially suspend production to control pollution. Therefore, this study removed the samples from Beijing, Hebei, Tianjin, Shanxi, Inner Mongolia, and Shandong from 2007 to 2008 to eliminate the impact of the 2008 Beijing Olympics. The regression results are shown in the columns 5 and 6 of Table6. Irrespective of the pilot emissions trading and Beijing Olympic games, the stricter command-and-control environmental regulation has resulted in a higher TFP for industrial enterprises.


## 5.4. Heterogeneity Analysis

Due to the differences in emission reduction targets and implementation methods, the effect of stricter command-and-control environmental regulation may also be heterogeneous. Therefore, this study further observed the influence of stricter command-and-control environmental regulation on the enterprise TFP from two aspects: the internal characteristics of corporate ownership and the external characteristics of geographic location.(1)
Corporate ownership heterogeneity: the columns 1, 2, and 3 of Table7 report the effects of stricter command-and-control environmental regulation on the TFP of state-owned enterprises, private enterprises, and foreign-funded enterprises, respectively. The results in Table 7 indicate that the pollution reduction targets have significantly improved the TFP of private enterprises. However, this result was not observed for state-owned enterprises and foreign-funded enterprises. State-owned enterprises assume more social responsibilities and act as emission reduction pioneers, so they may invest more in emission reduction. Therefore, the economic effects of environmental regulations are not reflected in state-owned enterprises. With better environmental performances, foreign-funded enterprises can easily get rid of being restricted by environmental regulations. Therefore, stricter command-and-control environmental regulation has no significant effect on the TFP of foreign-funded enterprises.(2)
Geographic heterogeneity: the columns 4, 5, and 6 in Table7 list the differences in the effects of stricter command-and-control environmental regulation on the TFP of enterprises in eastern, central, and western China, respectively. It can be seen from the table that the COD emission reduction targets have significantly improved the TFP of enterprises across the country. This is possibly due to the stricter environmental regulations that incorporate environmental performance into the evaluation of official performance have improved the executive power of local government officials. In addition, economic performance and environmental performance are both important in official performance, so local officials will implement environmental regulations more comprehensively and effectively.Table 7
Heterogeneity analysis results.
(1)(2)(3)(4)(5)(6)ER∗ TIME0.0070.010∗∗∗0.0020.007∗∗0.058∗0.008∗∗∗(0.004)(0.003)(0.003)(0.003)(0.030)(0.002)KL−0.185∗∗∗−0.214∗∗∗−0.237∗∗∗−0.228∗∗∗−0.204∗∗∗−0.214∗∗∗(0.015)(0.005)(0.011)(0.007)(0.008)(0.010)EXPORT0.0600.014−0.034∗0.006−0.0190.054(0.067)(0.009)(0.020)(0.012)(0.024)(0.039)SUBSIDY−0.860∗∗∗−0.735∗∗∗−0.286∗∗−0.597∗∗∗−0.724∗∗∗−0.466∗∗∗(0.126)(0.095)(0.129)(0.081)(0.094)(0.156)AGE0.201∗∗∗0.487∗∗∗0.581∗∗∗0.478∗∗∗0.549∗∗∗0.433∗∗∗(0.037)(0.017)(0.020)(0.019)(0.025)(0.024)SIZE0.287∗∗∗0.260∗∗∗0.378∗∗∗0.311∗∗∗0.223∗∗∗0.268∗∗∗(0.017)(0.009)(0.019)(0.010)(0.014)(0.014)PGDP0.0850.357∗∗∗0.272∗0.373∗∗0.366∗∗−0.013(0.052)(0.100)(0.144)(0.149)(0.149)(0.042)POP−0.0420.191∗∗0.284∗∗∗0.260∗∗0.281−0.059(0.044)(0.086)(0.098)(0.109)(0.233)(0.044)Constant3.145∗∗∗−0.216−0.829−1.195−0.4594.631∗∗∗(0.615)(1.441)(1.909)(2.091)(2.459)(0.602)Observations70,5831,175,384352,8881,186,908267,116144,831R-squared0.1570.2040.2580.2410.2050.208Firm FEYesYesYesYesYesYesYear FEYesYesYesYesYesYesNote: FE stands for fixed effect;∗, ∗∗, ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. Standard errors are reported in parentheses.


## 6. Conclusions and Recommendations

Although scholars believe that market-based environmental regulations are more effective than command-and-control environmental regulations, China is still dominated by command-and-control environmental regulations and the strength of command-and-control environmental regulations is increasing, which seems to be contradictory. The existing literature does not analyze the role of stricter command-and-control environmental regulation, so it is no longer applicable to the current economy. This paper selects the policy of China’s National Environmental Protection “Eleventh Five-Year Plan” as a quasi-natural experiment, and uses the DID method to analyze the effect of stricter command-controlled environmental regulations on the total factor productivity of enterprises.Our results indicate that stricter command-and-control environmental regulation promotes the TFP of industrial enterprises, and this effect is even greater in long-established, large scale, and low capital density enterprises. In addition, industry characteristics such as pollution intensity and competition level will also affect the economic effects of stricter command-and-control environmental regulation. Specifically, the greater the pollution density and the fiercer market competition, the more environmental regulations will improve the enterprise TFP. Furthermore, government transformation and market awareness will magnify the positive effect of environmental regulations on corporate TFP. Although stricter command-and-control environmental regulation has achieved positive economic effects across the whole country, only private companies are the beneficiaries. Moreover, stricter command-and-control environmental regulation promotes enterprise TFP mainly by improving the efficiency of resource allocation within and between enterprises rather than stimulating innovation.Based on these research conclusions, this article proposes the following policy implications. First, it is necessary to coordinate command-and-control environmental regulations with market-based environmental regulations to maximize economic and environmental effects. Local governments should refine their pollution control policies. Specifically, implementing different emission reduction plans for industries and enterprises with different characteristics. Second, optimize the external environment, such as speeding up government transformation and strengthening local green preferences to amplify the economic effects of stricter command-and-control environmental regulation. Since private companies are the main beneficiaries of environmental regulations, we should improve the environment that is conducive to private companies. Third, improving technological innovation is the key to magnifying the role of stricter command-and-control environmental regulation in promoting TFP. In terms of stimulating technological innovation, local governments can use tax reductions, subsidies, and preferential policies to reduce the uncertainty in the technological innovation process, thereby stimulating enterprises’ technological innovation power.Despite the above contributions, this study also has limitations. First, this paper cannot address the question on whether the stricter environmental policy is too radical or too lenient, as it lacks an investigation of enterprise costs caused by the environmental policy. Second, this article does not discuss the impact of two important internal factors, the characteristics of corporate managers and the structure of human capital, on the economic effects of stricter environmental regulation. If data are available, they are all important for further research.



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*Source: 2197260-2022-03-10.xml*