Environmental Regulation, Enterprises' Bargaining Power, and Enterprises' Total Factor Productivity: Evidence from China

The relationship between environmental regulation and enterprises' total factor productivity (TFP) has been a hot topic in the eld of environmental economics, but the conclusions are still mixed. Employing a sample of 14,110 rm-year observations in China from 2010 to 2018, our research explores whether and when environmental regulation could trigger rms, to enhance TFP. The available evidence leads us to cautiously conclude that: 1) Environmental regulation notably improves enterprises' TFP, the conclusion still holds after a series of robustness tests. 2) Enterprises' bargaining power signicantly weakens the inuence of environmental regulation on enterprises' TFP. 3) Compared with non-state-owned enterprises and non-heavy-polluting industries, environmental regulation has a greater impact on state-owned enterprises and heavy-polluting industries; higher executive compensation does not motivate rms to improve TFP; compared with enterprises headquartered in non-provincial capital cities, environmental regulation has a greater impact on enterprises' TFP in provincial capital cities. Overall, the ndings of our research are extremely relevant for the government, investor, and enterprise's manager, this paper provides micro-rm-level evidence for the Porter hypothesis in practice in China.


Introduction
Since the reform and opening up, China's rapid economic development has been accompanied by a series of environmental issues caused by the ine cient use of social resources, which has caused signi cant losses to people's lives and property safety (Chen et al. 2013a; Wang et al. 2021), and in response to the increasing environmental pollution, the Chinese government has issued a series of environmental regulatory measures (Chang et al. 2015). As the traditional development philosophy of pursuing government plays a leading role in the implementation of environmental regulation policies ). Under China's environmental decentralization system and cadre evaluation system, local government o cials tend to place more emphasis on economic development than on environmental performance in order to gain a greater promotion probability (Xu 2011). Therefore, local government possesses more discretion when implementing environmental regulation policies, which leads to the incomplete implementation of the central government's environmental regulation policies (Chang et al. 2015). One of the most important reasons is that enterprises bargain with local governments by virtue of their contribution to local governments' tax, employment rate and economic growth, so as to exempt from environmental regulation to a certain extent (Feng et al. 2020). As a typical example, in Jiaokou County, Lvliang City, Shanxi Province, Xinfa aluminum plant paid 85% of the local tax revenue at its peak, and by virtue of this strength the plant managed to escape three shutdown penalties from the Ministry of Environmental Protection. [4] And tax protection will exacerbate environmental pollution (Bai et al. 2019).
Researches show that environmental regulation will lead to the decline of labor demand (Rivers 2013;Liu et al. 2017;Liu et al. 2021), limited by the employment rate brought by enterprises to local government, in order to maintain social stability, local government may weaken environmental regulation intensity.
Therefore, when enterprises enter into regulatory capture, the enterprises with greater bargaining power can get more lenient environmental regulation, and the result is that environmental regulation policy lacks real implementation effect (Chang et al. 2015).
Against the backdrop, it is important to provide convincing evidence for the practice of Porter hypothesis in China. Herein, we focus on whether and when environmental regulation could enhance enterprises' TFP.
To solve this issue, our study adopts a sample of 14,110 rm-year observations in China from 2010 to 2018 to study the casual effect of environmental regulation on enterprises' TFP, and further explores the moderating role of enterprises' bargaining power the casual effect. More deeply, this paper discusses the heterogeneity of casual effect from enterprise-, industry-and region-level. Our study nds that, environmental regulation notably enhances enterprises' TFP, enterprises' bargaining power weaken the positive relationship between environmental regulation and enterprises' TFP. The results of the heterogeneity analysis indicate that environmental regulation has a greater impact on enterprises' TFP for state-owned enterprises, enterprises with lower executive compensation, heavy-polluting industries, and enterprises headquartered in provincial capitals. The empirical results of this study can provide reference for the government to formulate reasonable environmental regulation policies.
Our paper adds value in the following ways. First, While there exists a large empirical studies on how environmental regulation affects TFP, most of them focus on the industry, city or provincial level Hou et al. 2020;Xie et al. 2017). This paper provides new evidence for the applicability of Porter hypothesis in developing countries from the perspective of micro enterprises. Second, unfortunately, enterprises' bargaining power has not been paid enough attention by the academic circles, or only measure the bargaining power from a single aspect such as the total output of enterprises (Feng et al. 2020), which can't comprehensively re ect the bargaining behavior between enterprises and local government. This paper creatively measures enterprises' bargaining power from a more comprehensive perspective, and reveal the mystery of how enterprises' bargaining power affects the relationship between environmental regulation and enterprises' TFP. Third, drawing on the top literature in the eld of environmental economics , the environmental regulation variables in this paper are derived from authoritative government documents, which can not only comprehensively re ect the government's environmental regulation intensity, but also effectively alleviate the endogenous problems.
The remainder of our research is organized as follows: Section 2 introduces research hypotheses; Section 3 is the research design, which introduces the research methods, including sample selection, data collection, model establishment and variable de nition; Section 4 gives the empirical results and analysis; Section 5 summarizes the conclusions of our study and puts forward some managerial suggestions.
[1] A typical example is Olympic blue. To prepare for the 2008 Olympic Games, China adopted a number of radical measures to improve air quality, such as vehicle restrictions and factory shutdown. Chen

Research Hypotheses
Environmental regulation, when improving environmental performance, inevitably affects activities such as resource reallocation, capital investment and technological innovation in the production process of rms (Albrizio et al. 2017), which in turn affects enterprises' TFP. On the one hand, enterprises that comply with environmental regulation are more likely to get preferential measures such as nance, tax, price and government procurement (Xu et al. 2020), which will provide essential guarantee for the growth of TFP. On the other hand, environmental regulation provides necessary external conditions for enterprises to improve production e ciency. Although environmental regulation may incur some "compliance costs" (Xie et al. 2017), moderate and strict environmental regulation can promote technological innovation, enhance rms' competitive strength and productivity, and thus partially or even fully compensate rms for the "compliance costs" incurred by environmental regulation. Enterprises' environmental protection behavior is more likely to bring good reputation to enterprises, and this kind of intangible asset will ultimately help to improve enterprise performance (Vanhamme et al. 2012;Li et al. 2019), so as to improve enterprise's TFP and achieve a win-win situation of environmental governance and enterprise pro tability. Numerous studies (e.g., Porter and Linde 1995; Ai et al. 2020) have supported the notion that environmental regulation are more conductive to enterprise's TFP.
In light of the above-mentioned arguments, we put forward our rst hypothesis: Hypothesis 1: Environmental regulation will signi cantly improves enterprise's TFP.
When the central government begins to tackle environmental issues with a heavy hand, local government rarely follows in lockstep (Child et al. 2007), and although the central government has fully recognized the importance of environmental protection, local government has not always given environmental governance an equally high priority ). The reason for this is that under China's current cadre appraisal system and environmental territorial management system, the marginal effect of economic performance is much greater than that of environmental performance in the promotion process of local government o cials (Jiang et al. 2020;Tang et al. 2021). Therefore, limited by the contribution of enterprises to local economic growth, in order to maximize the promotion probability, local government o cials are justi ed in weakening environmental regulation. In addition, according to Porter hypothesis, environmental regulation can promote enterprise innovation and improve production e ciency. Due to the randomness of innovation activities (Noailly and Smeets 2015), large investment and long cycle (Ren et al. 2021), and the improvement of TFP takes a longer time to complete (Lanoie et al. 2008), thus enterprises are not willing to invest their limited capital in innovation activities with high uncertainty.
Enterprises rely on their contribution to local government's employment rate, economic growth and tax revenue, and take it as a bargaining chip with local government to demand local government to weaken environmental regulation. In this case, local governments have strong incentives to become the "umbrella" of polluting enterprises. In order to obtain more tax sources, local government may be captured by enterprises and "open the back door" to polluting enterprises (Ran 2017). At the same time, enterprises lose the incentive of technological innovation, and enterprises' bargaining power will eventually have a negative impact on enterprises' TFP. Given the above-mentioned arguments, we propose our second hypothesis: Hypothesis 2: Enterprises' bargaining power will weaken the positive relationship between environmental regulation and enterprises' TFP. Fig. 1 depicts the conceptual framework based on the above arguments. The impact of environmental regulation on enterprises' TFP is the main line of this study. Along this line, our paper further explores the moderating effect of enterprises' bargaining power on the main effect. Further, this paper explores the moderating effects of rm ownership structure, executive compensation differences, rm pollution levels, and regional political constraints on the relationship between environmental regulation and enterprises' TFP.

Research Design Data and sample
This paper selects Chinese listed companies from 2010-2018 as the initial sample. The above listed companies are also screened according to the following criteria: (1) listed companies with special treatment (ST/PT) and nancial listed companies are eliminated; (2) insolvent listed companies, i.e. those whose debt ratio is already higher than 100%, are eliminated; (3) listed companies that are merged and restructured are excluded; (4) those companies listed in the current year are excluded; (5) listed companies with incomplete data on certain indicators are excluded. Finally, 14,110 observations were obtained, involving 2,282 unique listed companies. To mitigate the in uence of potential outliers, all continuous variables are winsored at the level of 1%. The basic data of the dependent variables (TFP) are from CSMAR database and calculated by LP method and OP method. The independent variables (ER, ER2 and ER3) are constructed according to the frequency of environmental-related vocabulary and the proportion of words in environmental-related paragraphs in annual government work reports at the city level. In addition to regulatory capture from the China Research Data Service Platform (CNRDS), other rm-level data comes from the China Stock Market and Accounting Research (CSMAR) Database. The region-level data such as per capita GDP, industrial structure, population density, foreign direct investment and government science expenditure scale come from the Economy Prediction System (EPS) Database.

Dependent variables
Total factor productivity (TFP). TFP has been a core measure of input-output e ciency, but there has been a lack of academic consensus on how to measure TFP (Syverson 2011). Since non-parametric methods such as data envelopment analysis may not avoid simultaneity and selection bias problems (Gatto et al. 2011), parametric methods such as stochastic frontier analysis rely heavily on the assumption of TFP distribution ). In light of these de ciencies, we employ two semiparametric methods proposed by Levinsohn and Petrin (2003) and Olley and Pakes (1996), called LP and OP methods, respectively, are the most widely used to measure enterprises' TFP (Ai et al. 2020; Peng et al. 2021). The LP method is an improvement of the OP method due to its unbiased consistent estimation results and its superiority in mitigating both the endogenous problems and sample loss (Cai and Ye 2020). Therefore, in the following analysis, only the enterprises' TFP estimated by LP method is analyzed, and the enterprises' TFP obtained by the OP method will be used as the benchmark regression robustness test.

Independent variables
Environmental regulation (ER). At present, there is no uni ed standard on the measurement of environmental regulation intensity in academia. In order to further discuss the impact of environmental regulation on enterprise's TFP, this paper needs to construct appropriate environmental regulation  ). The government work report is an outline for the administration and implementation of decisions and resolutions of organs of power in accordance with the law, it is a programmatic document to guide the government work, so it can re ect the government's environmental governance policy comprehensively. The proportion of environment-related vocabulary in a city's government annual work report is commonly used by the public to measure the amount of actual effort that local government has exerted in environmental governance ). In addition, since the government work report is often prepared at the beginning of the year, it is not affected by the economic development of that year, which can effectively alleviate the endogenous problems (Yang and Song 2019).
For this reason, referring to Chen et al. (2018), this paper selects the frequency of environment-related vocabulary in each city's annual government work report as a proportion of the total frequency of vocabulary as a proxy variable for environmental regulation. However, the above method is not so perfect, because we can not rule out the in uence of vocabulary such as "political ecology", "vicious cycle" which are not related to environmental protection. Therefore, we employ the total number of words in the environment-related paragraphs as a proportion of the total number of words in that annual government work report (ER2) as a robustness test. Furthermore, the environmental-related vocabulary selected in this paper is subjective and random, in order to avoid estimation bias due to subjectivity and randomness, based on the original environmental-related vocabulary, this paper deleted four vocabulary with low frequency, such as "cycle", "sustainable development", "greening", "particulate matter", to construct the independent variable ER3 as the robustness test.
The speci c construction steps of environmental regulation indicators in this research are as follows: First, manual collection of government work reports from prefecture-level and above cities in China for 2010-2018; Secondly, read each government work report in order to pick out the paragraphs devoted to the ecological environment; Finally, use Python 3.8 to calculate ER, ER2 and ER3 according to environmental-related text.

Moderators
Enterprises' bargaining power (BP). There are few studies on enterprises' bargaining power, and most of them measure enterprises' bargaining power unilaterally by the amount of tax paid, the number of employees, and regulatory capture, and lack the organic integration of the three, thus failing to accurately characterize enterprises' bargaining power. Compared with other methods, the entropy evaluation method can eliminate the interference of human factors and make the evaluation results more scienti c and reasonable. Therefore, we use the entropy evaluation method to compute a comprehensive score of enterprises' bargaining power, where corporate tax payments, number of employees, and regulatory capture all positively affect enterprises' bargaining power. Local government o cials realize that political promotion mainly depends on economic performance (Zheng et al. 2015), so they often ignore the environmental violations of enterprises in exchange for local scal revenue, employment rate and economic growth (Jiang et  Chinese listed companies are often used by rms for bribes, seeking government support, etc., and that the item involves expenses for catering, entertainment, and other activities that are strongly associated with corrupt practices such as corporate bribery. And it has been shown that corrupt practices will reduce enterprises' TFP (Wu et al. 2017). Referring to Cai et al. (2011), this paper uses the sum of business entertainment fee and travel expenses to measure enterprises' regulatory capture behavior. Therefore, it is relatively reasonable and scienti c to select the tax amount, the number of employees and regulatory capture to synthesize the bargaining power index.
Corporate ownership structure (Soe), Soe is 1 if it is a state-owned enterprise, 0 otherwise.
Executive compensation incentives (Eci), in this paper, the median of the executive compensation is used as the cut-off point, with the higher group assigns a value of 1 and the lower group assigns a value of 0. Political constraints (PC), the sample cities are divided into provincial capital cities and non-provincial capital cities, and PC is 1 if it is a provincial capital city, and 0 otherwise.

Control variables
To control for other important factors affecting enterprises' TFP, the control variables are selected mainly at the region-and rm-level in this paper. Referring to Hou et al. (2020), the region-level control variables mainly include GDP per capita (Pgdp), industrial structure (Indu), population density (Pden), foreign direct investment (FDI) and science expenditure scale (Sci) to control the effects of economic development level, industrial structure characteristics, human activity scale, foreign trade and R&D investment on enterprises' TFP. Referring to Ai et al. (2020) and Feng et al. (2020), this paper also incorporates rm size (Size), asset liability ratio (Lev), rm performance (Roa), capital labor density(CD), ratio of xed assets(FR), and corporate governance, respectively. Among them, the variables of corporate governance level include the shareholding ratio of the largest shareholder (Shrcr), the duality of chairman and general manager (Duality), and the proportion of independent directors (Bodind).

Model speci cation and model approach
In order to examine the causal in uence of environmental regulation on enterprises' TFP, we construct the following model: Among them, where TFP it is the TFP of rm i in year t; ER mt is the environmental regulation intensity of city m in year t; X imt is a set of control variables that include rm-and region-level; μ i , ν t andγ j represent the rm, year and industry xed effect, respectively; ε it is the random error term.
To examine the moderating role of enterprises' bargaining power, the following model is designed in this paper: BP it is the bargaining power of enterprise i in year t.
In order to explore the boundary when environmental regulation works, the following model is formalized: Moderator imt is the four moderating variables of heterogeneity analysis.

Descriptive statistics
The de nition of variables and descriptive statistics are exhibited in Table 1. The mean value and median of TFP_lp are 7.999 and 7.924 respectively, the mean value and median of TFP_op are 3.572 and 3.492 respectively. Comparing with the results of Cai and Ye (2020) (the mean value of TFP_lp and TFP_op are 8.556 and 4.081 respectively), the mean value of TFP_lp and TFP_op in this paper is relatively small, which may be due to the disappearance of China's "demographic dividend" in recent years, resulting in the decline of enterprises' TFP. The mean value and standard deviation of ER (unit is %) are 0.829 and 0.698 respectively, which indicates that the environmental regulation intensity is slightly different among cities.  Note: T values are presented in parentheses. ***, **, and * are statistically signi cant at the 1%, 5%, and 10% levels, respectively.

Robust tests
To ensure that the benchmark regression results are robust, a series of robustness tests are conducted in this paper. As exhibited in column (1) of Table 3, we use the TFP obtained by OP method as the dependent variable, the value and signi cance of ER do not differ signi cantly from the result of the benchmark regression (see column (4) of Table 2). Column (2) of Table 3, we employ ER2 as the independent variable. Column (3) of Table 3, ER3 as the independent variable. Further, this paper applies robust standard error corrected for heteroskedasticity, and the result is exhibited in column (4) of Table   3. The above robustness test results all present that the benchmark regression results are robust. Note: T values are presented in parentheses. ***, **, and * are statistically signi cant at the 1%, 5%, and 10% levels, respectively.
The moderating role of enterprises' bargaining power The moderating effect of enterprises' bargaining power is exhibited in Table 4. Regardless of whether the region-level or rm-level characteristics have been controlled, ER*BP remains positively pronounced at the level of 5%. Therefore, we con rm that enterprises' bargaining power has a signi cant negative moderating effect on the relationship between environmental regulation and enterprises' TFP. These results support Hypothesis 2. The reason is that enterprises take their contribution to local economic growth and o cials performance evaluation, as "chips" to bargain with local government, so as to exempt from environmental regulation (Feng et al. 2020). Note: T values are presented in parentheses. ***, **, and * are statistically signi cant at the 1%, 5%, and 10% levels, respectively.

Further analysis
The impact of environmental regulation on enterprises' TFP varies with different enterprises, industries and regions (Cai and Ye 2020). Accordingly, the paper will next discuss the causal relationship between environmental regulation and enterprises' TFP in terms of rm-, industry-, and region-level heterogeneity.
The heterogeneity of corporate ownership structure As demonstrated in column (1) of Table 5, whether for state-owned enterprises or non-state-owned enterprises, environmental regulation signi cantly improves enterprises' TFP, but it has a greater effect on state-owned enterprises (coe cient=0.058, t=2.286). One possible reason is that state-owned enterprises undertake important political tasks for the state and are also politically sheltered by the government ). Therefore, state-owned enterprises are more likely to enjoy preferential policies from the government, such as government subsidies and credit preferences, and are less likely to be adversely affected by environmental regulation policies (Peng et al. 2021). Accordingly, compared with non-state-owned enterprises, state-owned enterprises are more motivated to improve TFP.
The heterogeneity of executive compensation incentive As shown in column (2) of Table 5, environmental regulation signi cantly increases enterprises' TFP with lower executive compensation (coe cient=0.066, t=3.754) compared to rms with higher executive compensation (coe cient=0.011, t=608). The possible reason is that the excessive remuneration of senior executives has caused disputes within the company, which leads to the increase of operating costs and the reduction of dividend, even reduce the R&D innovation ability of the enterprise (Yanadori and Cui 2013), which will further have a negative impact on enterprises' TFP (Faleye et al. 2011).
The heterogeneity of enterprise pollution levels As we can see in column (3) of Table 5, environmental regulation signi cantly improves enterprises' TFP, whether for heavy-polluting industries (coe cient=0.056, t=2.771) or non-heavy-polluting industries (coe cient=0.029, t=1.675), but the effect is greater for heavy-polluting enterprises. A possible reason is that heavy-polluting enterprises, as the main polluters of the environment, are more likely to attract the attention of environmental regulatory authorities (Ren et al. 2021). Therefore, heavy-polluting enterprises are more affected by environmental regulation, which need to improve their TFP to cater to the environmental regulation policies of local government.

The heterogeneity of political constraints
As exhibited in column (4) of Table 5, environmental regulation has a greater impact on enterprises' TFP located in provincial capitals (coe cient=0.047, t=2.299) compared to those located in non-capital cities (coe cient=0.035, t=2.021). This is not di cult to understand. On the one hand, the economic development level of provincial capital is often higher, and its industrial structure is also relatively reasonable. Local government can provide good development environment and preferential policies for companies. On the other hand, the political constraints of provincial capitals tend to be stronger, local government's environmental governance activities may be subject to stronger supervision by the superior government , and the social development goals of such cities are often more diversi ed compared to the single goal of economic development and securing scal revenues. Thus environmental quality preferences may be greater in such cities, compared with other cities, the environmental regulation intensity of provincial capital cities is often greater, and more stringent environmental regulation will help   Apart from its theoretical contributions, our research also provides practical guidance for China as well as for other emerging economies. First, considering that there is still a lot of room to improve enterprises' TFP, the central government should further increase the environmental governance, in particular, environmental performance should be incorporated in the performance appraisal of local government o cials to weaken the negative in uence of enterprises' bargaining power. Second, the heterogeneous results suggest that the environmental policy of central government should avoid one-size-ts-all environmental regulation. Speci cally, as the effect of environmental regulation on state-owned enterprises is greater than that of non-state-owned enterprises, thus the government can consider giving some policy support to non-state-owned enterprises; since excessive executive compensation does not enhance enterprises' TFP, appropriate reductions in executive compensation may be considered; it is necessary to strengthen the environmental regulation intensity for non-heavy-polluting enterprises and enterprises in non-provincial capital cities.
Frankly speaking, our results are subject to some limitations. Firstly, for the heterogeneity analysis, this paper does not consider comprehensively, the follow-up research can start from more levels of heterogeneity. Secondly, there does not exist a consensus on how to measure enterprises' bargaining power. Based on the existing literature, this paper uses the entropy method to calculate enterprises' bargaining power, and the results are relatively objective and scienti c. Thirdly, following recent literature (e.g., He et al. 2020), future research can explore this issue from the perspective of political economics such as political incentives. Declarations