The impact of environmental protection tax on green M&amp;As

doi:10.21203/rs.3.rs-3408230/v1

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The impact of environmental protection tax on green M&As

https://doi.org/10.21203/rs.3.rs-3408230/v1

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This article aims to investigate the impact of The Environmental Protection Tax Law of the People’s Republic of China implemented in 2018 on the decision-making process of green M&As, and incorporates heavy-polluting companies listed on the A-share market in Shanghai and Shenzhen in its study. After collecting data on the green M&As among these firms, the study empirically examines the impact of the environmental protection tax on their decision-making. The findings indicate that: (1)The environmental protection tax offers favorable conditions for enterprises to engage in green M&As.(2)From the perspective of heterogeneity, The environmental protection tax has a particularly evident incentive effect in green M&As of enterprises incorporated in regions with high tax standards, state-owned enterprises, and more heavily-polluting enterprises in central and western China.(3)Green M&As contribute to increased green innovation output by enterprises.(4)The moderating role of regional marketization and corporate legitimacy awareness in the impact of environmental protection taxes on green M&As. Based on these empirical results, several policy recommendations are proposed, including the effective utilization of the environmental protection tax system and the promotion of green transformation among enterprises through price mechanism.

Green M&As

Environmental Protection Tax

Probit model

Chinese government emphasizes China's commitment to building a beautiful country by coordinating industrial restructuring, pollution control, ecological protection, and response to climate change. Promoting green and low-carbon economic and social development is considered crucial for achieving high-quality development. However, China has a large number of heavy-polluting and energy-intensive enterprises as a result of past industrialization. For a quite long period of time, these enterprises have prioritized revenue generation over environmental concerns, leading to severe environmental pollution issues. In order to promote high-quality economic development in China, therefore, it is essential to focus on environmental governance and green transformation of these heavy-polluting enterprises. Green mergers and acquisitions (M&As) have attracted increasing attention as a primary means of facilitating the green transformation for these businesses. Compared to long-term and uncertain internal investments in environmental protection, green M&As offer a faster pathway to acquire clean resources from target enterprises, thereby accelerating the green transformation process and offering a better solution in alleviating environmental pollution [1]. Green M&As put the concept of green development into practice throughout the whole M&A process, which is conducive to enterprises' green innovation, improvement of economic performance, and realization of pollution reduction goal [2][3].Exploring the motivation of enterprises for green M&As is conducive to the identification of the driving factors of green transformation, thus promoting the green development of enterprises. Previous studies have mainly investigated green M&As from such perspectives as public opinion pressure, internal corporate culture, environmental awareness of corporate executives, and financing difficulties [4][5][6]. There is relatively limited research on corporate green M&As from a legal system perspective. In China, the environmental protection tax is the primary tax legislation dedicated to establishing a "green tax system" aimed at protecting the environment. his tax law enhances the regulatory responsibilities of government authorities at all levels and establishes environmental governance constraints for polluting enterprises. Therefore, the impact of the implementation of The Environmental Protection Tax Law of the People's Republic of China (hereinafter referred to as the Environmental Protection Tax Law) on the green M&As of heavy-polluting enterprises is analyzed in this paper.

As a typical market-based environmental regulation, environmental protection tax raises the cost of environmental damage for polluting enterprises and clarifies the environmental governance responsibilities of governments and enterprises. In response to resource bottlenecks and environmental pollution problems in economic development, environmental protection tax is levied by the tax authorities on the direct discharge of taxable pollutants into the environment by individuals or units to hold the polluting units liable for the cost and negative externality of the damage caused to the environment by their generating and operating activities. An improved environmental protection tax system will keep regional pollutant emissions under control through clear price signals. The environmental protection tax system will also actively guide the green transformation of enterprises through green production technologies, facilitating the flow of capital to more eco-friendly areas, which is of great significance to the protection and improvement of the regional environment. The Environmental Protection Tax Law of the People's Republic of China has played an essential role in protecting and improving the regional environment, reducing pollutant emissions, and promoting ecological conservation since its official implementation in 2018. The design of China's environmental protection tax system follows the basic principle of "pay more for more emissions and less for less emissions, and nothing for no emissions", which encourages producers to use renewable and cleaner energy sources and reduce pollutant emissions. Moreover, the imposition of the environmental protection tax has forced business managers to consider the significant impact of environmental policies on their operating costs and the costs associated with switching to green production methods [7]. Under the policy pressure, enterprises may choose to rapidly acquire clean technologies and resources through green M&As to enhance their green development capability at a lower cost.

Guided by the new development concept, accelerating green development has become a crucial initiative for China to achieve high-quality economic growth and reshape its comparative advantages. Recognizing that nature is the fundamental foundation for human survival and development, it is imperative to uphold the principles of respecting, adapting to, and protecting nature as inherent requirements for building a modern socialist country in a comprehensive manner. Consequently, it is essential to firmly embrace and implement the concept that lucid waters and lush mountains are invaluable assets, and to plan development in harmony with the coexistence of humanity and nature. As vital components of social and economic development, enterprises must actively embrace the principles of green and sustainable development. Simultaneously, local governments should proactively establish robust policies and institutional frameworks that facilitate and incentivize the green transformation of enterprises. Based on the aforementioned, this paper aims to analyze the impact of the environmental protection tax on the green M&As of heavily-polluting enterprises after the introduction of The Environmental Protection Tax Law of the People's Republic of China. The paper presents two marginal contributions. Firstly, it examines the green investments of enterprises through the lens of M&As. Existing studies have primarily investigated the impact of environmental regulations on green innovation investments, with little attention paid to the impact of environmental regulations on M&As (M&A). In contrast to green innovation investments, green M&As reflect an enterprise's external policies strategically and enables a faster acquisition of necessary capacities and resources for green development. Consequently, this study expands the scope of understanding the impact of environmental regulations on enterprises' green investments. Secondly, in terms of practical implications, this study explores the differentiated effects of the environmental protection tax on enterprises' green M&As, considering factors such as market competition and legitimate awareness. Moreover, this study provides practical and effective recommendations on how to optimize enterprises' green investments through the strategic utilization of environmental protection tax, especially within the context of their respective external market competition environments.

2.1. Factors affecting corporate green investments

M&As are often pursued by enterprises as one of the investment decisions. However, green M&As take it a step further, as they represent a conscious effort by these enterprises to invest in eco-friendly projects. Unlike traditional investments, corporate green investments are specifically targeted towards initiatives that promote resource conservation, alternative energy production, and sustainable practices [8]. Existing studies have examined the factors that drive corporate green investments in both internal and external environments. In terms of internal factors, it has been observed that internal corporate governance factors, such as the level of environmental awareness and values of corporate managers, have a significant impact on corporate green investments. Managers with a heightened environmental awareness are more inclined to recognize the detrimental effects of unsustainable profit models. Consequently, they are more likely to adhere to environmental protection policies, advocate for higher environmental standards within their enterprises, and take proactive measures to address issues such as corporate environmental pollution. This intrinsic motivation contributes to a more responsible and sustainable approach to business operations [9]. From the viewpoint of external factors, external pressures such as government environmental regulation and media opinions have prompted enterprises to engage in green investment activities. Under the environmental regulation of governmental departments, the polluting enterprises may face high fines or even production suspension [10][11]. As a result, heavy-polluting enterprises will actively engage in green development to avoid possible penalties. News media reports, and other public opinions effectively reflect the environmental management status of enterprises, which can quickly spread the information related to environmental pollution of enterprises and influence the public's impression on enterprises, forcing enterprises to improve pollution control [12]. In addition, media reports also provide the basis for government departments to formulate policy guidelines, thereby continuously improving the environmental policy system and encouraging enterprises to better launch green investment activities [13]. Enterprises in green investments can reduce the environmental costs brought about by environmental regulation, ease the pressure by public opinions, and enhance their corporate image, ultimately making them more "green competitive" in the market, thus obtaining more benefits.

2.2. Environmental Governance Effectiveness of Environmental Protection Tax

Over the years, numerous scholars have dedicated their research to examining the micro-governance effectiveness of environmental regulations. Environmental regulations can effectively reduce corporate pollution emissions by promoting greater investment in environmental protection and improved technological innovation [14][15]. However, it has been widely acknowledged in the literature that the conventional approach to enforcing environmental regulations primarily relies on administrative orders. Unfortunately, this rigid enforcement process often fails to inspire enterprises to proactively engage in green development initiatives [16]. The environmental protection tax policy to regulate the economic players of pollution emissions from the perspective of the statutory tax system has realized the transformation from the sewage-based charging system to the environmental protection tax, resulting in a significantly strengthened implementation in comparison with the past, and attracted the attention of a large number of scholars. The current studies on environmental protection tax mainly have two focuses. On the one hand, the focus is on evaluating the environmental protection tax system. Most of the related researches believe that there is still room for further improvement in China's environmental protection tax in terms of tax system and collection and management [17][18]. However, there is no denying that the environmental protection tax has greater flexibility and monitoring effectiveness than previous environmental regulations, able to more strongly encourage companies to actively seek the most effective solutions [19]. On the other hand, the focus mainly centers on the environmental governance effectiveness of environmental protection tax. By increasing the economic cost of polluting enterprises, the environmental protection tax forces heavy-polluting industries to increase their environmental protection investment and compels enterprises to engage in green technological innovation and reduce polluting emissions [20][21]. Furthermore, the environmental protection tax guides the flow of capital into clean industries, drives the upgrading of industrial structure, and helps the economy develop in a high-quality manner [22].

2.3. Environmental protection tax and corporate green M&As

The implementation of the environmental protection tax increases the financial burden on enterprises by imposing taxes on the pollutants emitted during their production processes. This tax, once imposed, exerts pressure on companies to prioritize ecological preservation and influences their decision-making regarding green M&As. Firstly, the environmental protection tax serves as both a fiscal tool and a legal measure. Taxes and fees function as regulatory instruments for pollutant emission control, making the environmental protection tax equivalent to the price companies pay for the external costs resulting from their utilization of environmental resources. This price is determined based on the marginal abatement cost that maximizes profitability for businesses [23]. As profit-making organizations, enterprises can improve the greenness of products, reduce the unit cost of production, and achieve environmental cost compensation through green M&As. Secondly, the environmental protection tax shows the determination and strength of the national environmental governance at the height of legislation on the basis of The Environmental Protection Tax Law. When the government has higher requirements for enterprises in green production, the heavily-polluting enterprises have to consider eliminating outdated production modes [24]. By inviting M&As from companies with high levels of green production or engaged in eco-friendly business activities, companies will improve their own green production and pollution control technologies. Moreover, enterprises will benefit from pollution control technologies with lower emission costs, profiting from the surplus and gaining an advantage in market competition [25][26]. Thirdly, the level of taxation directly indicates the price of pollutants to enterprises. The higher the environmental protection tax paid by a firm, the more it indicates that there may be resource inefficiencies and potential technological advances and that the firm's green productivity needs to be improved [27]. Green M&As can help enterprises break away from resource constraints and rapidly optimize production methods. On the one hand, the "green" feature of green M&As requires enterprises to take environmental production, energy conservation and emission reduction as the goal of M&A, and optimize the combination of green production resources of the target enterprise after M&As [28]. On the other hand, compared with other green investments, green M&As are characteristic of M&As. M&As help enterprises obtain external resources and optimize resource allocation [29], breaks the dependence on the closed green innovation path, and avoids taking the risks of green innovation. Finally, the purpose of the environmental protection tax is to "pay more for more emissions and less for fewer emissions". Enterprises, as organizations aiming to maximize profits, are bound to react quickly. Enterprise green M&As aim to directly acquire the target company's green resources, enabling swift adoption of green production practices and alleviating the pressure imposed by the environmental protection tax. Simultaneously, it sends a signal of commitment to "greenness and environmental protection" to the external world, attracting investments. This, in turn, enhances the enterprise's reputation while reducing the financial burden associated with the process of green transformation.

On the basis of the discussion presented so far, the following hypothesis is put forward:

The environmental protection tax promotes green M&As of enterprises.

3.1. Sample selection and data sources

The Environmental Protection Tax Law of the People's Republic of China came into effect on January 1, 2018, representing China's first tax aimed at environmental protection to be levied according to law. Based on this, the M&A transaction events of listed companies in the heavy-polluting industry from 2018 to 2021 were selected as research samples in this paper. The samples were screened according to the following criteria: (1) The M&A samples where the transaction fails were excluded; (2) The M&A samples of divestitures, asset restructurings, share buybacks, and debt restructurings were excluded; (3) The M&A samples in which the main merging enterprises are people's governments and banks, and trading land use rights and financial products were excluded; (4) The M&A samples with an acquisition amounts less than RMB 1 million were excluded, as well as samples with an equity acquisition ratio less than 30% and samples that already hold more than 30% of the target enterprise’s equity; (5) The M&A samples of the same enterprises with multiple M&As in the same year with the same M&A targets, and the M&A samples of the same enterprises with multiple M&As in the same year with different M&A targets. The M&A transactions, financial indicators, and corporate governance data in this paper are from China Stock Market & Accounting Research Database (CSMAR). The city-level data come from the China City Statistical Yearbook, in which the economic data of prefecture-level cities such as Golmud City, Alashan League, and Shihezi City are seriously missing, thus examples from these areas are deleted. To eliminate the impact of extreme values, the upper and lower 1% bilateral shrinkage treatment is carried out for the main continuous variables.

3.2. Variables

3.2.1. Dependent Variable: Green M&A

Green M&A refer to enterprises to acquire energy-saving and emission-reduction technologies, and to acquire or merge eco-friendly, energy-saving, and low-carbon enterprises or businesses. Drawing on the study by Pan et al, this paper utilizes the content analysis method to browse the transaction overview of the listed companies and the business scope of the M&A parties one by one to identify whether the M&A case is a green M&A transaction event. If the event meets the definition of green M&As, it is determined that the enterprise is making a green M&A, and the variable Green M&A takes the value of 1, otherwise, it takes the value of 0. In addition, this paper utilizes the ratio of the total amount of Green M&As to the total amount of total M&A transactions of the enterprise to measure the size of green M&As (Green_ratio), and further identifies the motivation of green M&As of the enterprise.

3.2.2. Independent Variable: Environmental Protection Tax

The environmental protection tax is a green tax imposed on the direct discharge of pollutants into the environment to protect the environment. This paper uses the total amount of environmental protection tax paid by enterprises to take the logarithmic measure after adding 1. With the lag of the impact of the environmental protection tax on the enterprises' green M&As taken into consideration, the environmental protection tax indicator had a one-phase lag in the model as a whole.

3.2.3. Independent Variable: Environmental Protection Tax

Drawing on the studies of Bi (2016), Liang (2018) and others on corporate environmental governance and M&A[30][31], we select the factors that have an essential impact on the green M&As of heavily-polluting enterprises as control variables from the enterprise characteristics, M&A characteristics, and regional characteristics, including enterprise scale (Scale): the natural logarithm of the total assets of the enterprise; years of listing (Age): the current fiscal year minus the year of listing; enterprise growth capacity (Growth): (total assets of the current period - total assets of the previous period)/total assets of the previous period; management fee rate (MFEE_ratio): the ratio of the management fee to the total revenue of the main business; operational capacity: the ratio of operating income operating income to total assets; nature of enterprise (SOE): 1 for state-owned enterprises, 0 for not state-owned enterprises; operating profit margin (Lr_ratio): ratio of operating profit to operating income; independent director ratio (Ind_ratio): ratio of the number of independent directors to the number of directors; enterprise network location (Network): the total number of enterprises directly linked to the chain of directors; M&A type (Merge_type): the number of enterprises directly linked to the network of directors. Merge_type: 1 for horizontal M&As, 2 for vertical M&As, and 3 for mixed M&As; M&A ratio (Merge_ratio): the equity acquisition ratio of M&As; level of economic development (Lnpergdp): the natural logarithm of the per capita GDP of the location of the enterprise. Descriptive statistics of the variables are shown in Table 1.

Table 1

Descriptive statistics.
Variables	N	Mean	Standard Deviation
Green	328	0.375	0.485
Green_ratio	328	0.349	0.466
Tax	328	8.999	7.253
Scale	328	22.82	1.572
Age	328	13.42	7.763
Growth	328	0.167	0.234
MFEE_ratio	328	0.059	0.055
Op_capacity	328	0.681	0.425
SOE	328	0.503	0.501
Lr_ratio	328	0.099	0.396
Ind_ratio	328	0.312	0.066
Network	328	5.229	3.718
Merge_type	328	2.038	0.819
Merge_ratio	328	79.51	23.11
lnperGDP	328	11.39	0.519

3.3. Econometric Modelling

Considering that the dependent variable corporate green M&A is a dummy variable, this paper mainly uses the Probit model to analyze the impact of environmental protection tax on corporate green M&As. The Probit model is as follows:

$$\text{P}\text{r}\{{\text{G}\text{r}\text{e}\text{e}\text{n}}_{\text{i},\text{t}}=1\} = {\Phi }({{\text{b}}_{0}+{\text{b}}_{1}\text{T}\text{a}\text{x}}_{\text{i},\text{t}-1}+{\text{b}}_{2}{\sum \text{c}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}+\text{Y}\text{e}\text{a}\text{r}}_{\text{t}}+{\text{I}\text{n}\text{d}}_{\text{i}}+{{\epsilon }}_{\text{i},\text{t}})$$

In the above equation, measures whether the green M&As of the enterprise take place in Year t, which shall be given the value of 1 if yes and the value of 0 if no. ${Tax}_{i,t-1}$is the main core explanatory variable, representing the environmental protection tax paid by listed companies in year t-1 ,and a series of control variables are denoted by $\sum control$.${Year}_{t} and {Ind}_{i}$ indicate industry and year effects respectively. In addition, considering that M&As are a non-continuous event and there are only a few firms in some of the industries in the sample, Referring to Jiang Hong's (2022)practice[32], this paper chooses a level 1 industry dummy variable for control of industry effects. ${\epsilon }_{i,t}$is a randomized perturbation term. If ${b}_{1}$is greater than zero significantly, it means that the environmental protection tax positively promotes corporate green M&As.

The Probit model answers the question of whether enterprises engage in green M&As and does not address how much enterprises invest in green M&As.This paper further explores the relationship between the factors involved and the size of corporate green M&As. The size of green M&As is the ratio of total green M&A transactions to total M&A transactions, and the data is truncated, resulting in the zero censoring problem. Therefore, this paper chooses to use the Tobit model to further estimate the impact of environmental protection tax on the size of corporate green M&As, and the Tobit model is as follows:

${y}^{\ast }$ denotes the fraction of total green M&A transactions greater than 0 (If ${y}^{\ast }$>0, then ${\text{y}}^{\ast }={\text{y}}^{\ast }$. If ${y}^{\ast }$<0, $\text{t}\text{h}\text{e}\text{n} {y}^{\ast }=0$), The core explanatory and control variables are the same as in Eq. (1) and the industry and year effects are kept under control.

4.1. Baseline regression results

Table 2 reports the results of the benchmark regression on the impact of environmental protection tax on corporate green M&As. Columns (1) and (2) show the regression results of the Probit model. The results reveal that with the inclusion of control variables, the core explanatory variable, environmental protection tax, is estimated to be significantly positive at the 5% level; this result indicates that the higher the environmental protection tax, the more willing the enterprises to carry out green M&A activities. Columns (3) and (4) further examine the impact of environmental protection tax on the size of corporate green M&As using the Tobit model. The results show that the coefficients of the core explanatory variables are significantly positive at the 5% level, which proves the more enterprises are affected by the environmental protection tax, the greater the scale of corporate green M&As. The above results indicate that the environmental protection tax has an incentive effect on corporate green M&As. To adapt to strict environmental regulations and standards, enterprises tend to improve production efficiency, change production methods through green M&As, and move towards green development.

Table 2

Basic regression results.
	(1)	(2)	(3)	(4)
	Probit		Tobit
	Green	Green	Green_ratio	Green_ratio
Tax	0.0121	0.0268**	0.0094	0.0223**
	(1.0910)	(2.1841)	(1.0013)	(2.2408)
Scale		0.0995		0.0735
		(1.3235)		(1.2376)
Age		-0.0292**		-0.0272**
		(-2.0639)		(-2.3511)
Growth		0.8832**		0.7395**
		(2.4073)		(2.5061)
lnperGDP		0.1082		0.0363
		(0.6861)		(0.2820)
Merge_type		0.1061**		0.0976**
		(2.0083)		(2.3352)
Merge_ratio		0.0052		0.0045
		(1.5717)		(1.6375)
MFEE_ratio		-0.9541		-1.4666
		(-0.4087)		(-0.7793)
Op_capacity		-0.2459		-0.2443
		(-1.0767)		(-1.2951)
SOE		-0.3826*		-0.2951*
		(-1.7578)		(-1.6562)
Lr_ratio		0.4384		0.2552
		(1.0355)		(1.2222)
Ind_ratio		-1.7535		-1.3092
		(-1.4059)		(-1.2991)
Network		-0.0642***		-0.0463**
		(-2.6789)		(-2.3676)
_cons	-0.9228***	-3.5255	-0.6949**	-2.0387
	(-2.9392)	(-1.3945)	(-2.5491)	(-0.9987)
Time-Fixed Effects	YES	YES	YES	YES
Industry-Fixed Effects	YES	YES	YES	YES
N	328	328	328	328
Wald chi2	20.6434	52.3178	26.0905	54.3354
Note: statistics in parentheses. p < 0.1, p < 0.05, **p < 0.01.same below.

4.2. Robustness analysis

4.2.1. Endogeneity Test

Considering that omitted variables may cause endogenous,the paper employs an instrumental variable approach to address possible endogeneity. Drawing on relevant studies on environmental regulation, this paper selects the air circulation coefficient (Air_circulation) of the prefecture-level city where the enterprise is located as the instrumental variable [33][34]. In terms of relevance, the air circulation coefficient, which results from wind speed and mixing height, directly affects pollutant concentrations. Exogenously, the air circulation coefficient is determined by regional meteorological conditions. It will not affect the green M&As of enterprises. Therefore, the air circulation coefficient is an appropriate instrumental variable. In this paper, Ivprobit's method of maximum likelihood estimation (MLE) and the two-step method (Two-Step) are used for regression analysis, respectively. The results are shown in Table 3: the MLE coefficients are significantly positive at the 10% level. The regression coefficients of the air circulation coefficient variables in the first step regression of the two-step method are extremely positive at the 1% level and the F-value is greater than 10, indicating that the instrumental variables are correlated with the endogenous variables. In the second-step regression, the signs of the main variables are consistent with the benchmark regression, indicating that the benchmark regression conclusion is reliable.

Table 3

Endogeneity Test.
	(1)	(2)
	MLE	Twostep
		Phase 1	Phase 2
	Green	Tax	Green
Tax	0.0209*		0.0211*
	(1.7051)		(1.6476)
Air_circulation		1.8971***
		(64.3181)
Scale	0.1015	0.7263***	0.1025
	(1.4319)	(9.1704)	(1.3547)
Age	-0.0274**	0.0432***	-0.0277*
	(-2.0390)	(2.6787)	(-1.9402)
Growth	0.8587**	-0.0348	0.8670**
	(2.3346)	(-0.0740)	(2.3321)
lnperGDP	0.1096	-0.1125	0.1107
	(0.7064)	(-0.5993)	(0.6966)
Merge_type	0.1020*	0.0305	0.1030*
	(1.8751)	(0.5824)	(1.9397)
Merge_ratio	0.0051	-0.0031	0.0051
	(1.5392)	(-0.7507)	(1.5217)
MFEE_ratio	-1.1186	-2.4207	-1.1293
	(-0.5420)	(-1.1288)	(-0.4785)
Op_capacity	-0.2426	-0.0564	-0.2450
	(-0.9992)	(-0.2328)	(-1.0708)
SOE	-0.3778*	-0.6943***	-0.3815*
	(-1.9487)	(-2.9470)	(-1.7435)
Lr_ratio	0.4267*	-0.3446	0.4308
	(1.7059)	(-1.6263)	(1.0260)
Ind_ratio	-1.6968	1.7859	-1.7131
	(-1.3178)	(1.1950)	(-1.3623)
Network	-0.0623***	0.0160	-0.0629***
	(-2.6615)	(0.5594)	(-2.6037)
_cons	-3.5444	-14.9708***	-3.5785
	(-1.4416)	(-5.1385)	(-1.4045)
Time-Fixed Effects	YES	YES	YES
Industry-Fixed Effects	YES	YES	YES
Step 1-F		337.26
N	328		328
Wald chi2	55.3100		50.5421

4.2.2. Alternative Key Variables

In this paper, the tax deviation (Tax_cz) is selected to replace the indicator of environmental protection tax paid by enterprises: it is expressed as the difference between the total amount of environmental protection tax paid by enterprises and the average total amount of environmental protection tax of the industry in which the enterprises are; the new measure is utilized to regress again on the benchmark regression, and the regression results are as shown in Columns (1) of Table 4: the estimated coefficients of the replacement variables are significantly positive at the 5% level. The regression results further demonstrate the promotional effect of environmental protection tax on corporate green M&As

4.2.3. Control the Impact of Other Policy Effects for the Same Period

Other related policies in the same period can interfere with the impact of environmental protection tax on corporate green M&As. First, Carbon Emissions Trading Pilot Cities (Carbon). With the pilot implementation of China's carbon emissions trading policy in 2013, companies can sell their emission rights on the trading market for profit by improving green productivity and other ways to reduce emissions. As a result, heavily-polluting enterprises in carbon emissions trading pilot regions may implement green M&As. Second, the establishment of Air Pollution Transmission Corridor Cities (Pollution). In 2017, 26 cities in the Beijing-Tianjin-Hebei and neighboring regions started air pollution prevention and control efforts, putting the heavy-polluting companies at the risk of elimination and closure. The strict control of this air pollution prevention program has prompted companies to achieve rapid emission reductions through green M&As. Third, the establishment of pilot zones for green financial reform (Finance). In 2017, the State Council announced the establishment of five provinces (autonomous regions), including Zhejiang and Xinjiang, as pilot zones for green financial reform and innovation, to support green development with green finance, and to offer “green channels” for green projects of enterprises. This policy fills the funding gap in corporate green transformation and will impact corporate green M&As. To exclude the impact of the above green-related policies on corporate green M&As, this paper adds policy dummy variables to the model and regresses them again. The results are shown in columns (2) to (4) of Table 4: the coefficients of the core explanatory variables, environmental protection tax, are all significantly positive at the 5% level. The above illustrates the robustness of the benchmark regression results.

Table 4

Robustness analysis.
	(1)	(2)	(3)	(4)
	Alternative Key Variables	Carbon Emissions Trading Pilot Cities	Air Pollution Transmission Corridor Cities	Green Financial Reform
	Green	Green	Green	Green
Tax_cz	0.0244**
	(1.9700)
Tax		0.0268**	0.0277**	0.0266**
		(2.1824)	(2.2293)	(2.1671)
Carbon		-0.0300
		(-0.1730)
Pollution			-0.1015
			(-0.4711)
Finance				-0.1389
				(-0.7411)
Scale	0.0915	0.0995	0.1038	0.1003
	(1.2067)	(1.3235)	(1.3716)	(1.3324)
Age	-0.0273*	-0.0292**	-0.0294**	-0.0298**
	(-1.9567)	(-2.0677)	(-2.0776)	(-2.1017)
Growth	0.8509**	0.8868**	0.8735**	0.9276**
	(2.3232)	(2.4118)	(2.3771)	(2.4942)
lnperGDP	0.1157	0.1151	0.1093	0.1195
	(0.7336)	(0.7074)	(0.6927)	(0.7531)
Merge_type	0.1072**	0.1060**	0.1069**	0.1055**
	(2.0246)	(2.0068)	(2.0229)	(1.9896)
Merge_ratio	0.0051	0.0052	0.0052	0.0053
	(1.5311)	(1.5469)	(1.5490)	(1.5837)
MFEE_ratio	-1.0525	-0.9376	-0.9428	-0.7908
	(-0.4494)	(-0.4018)	(-0.4044)	(-0.3393)
Op_capacity	-0.2562	-0.2453	-0.2578	-0.2075
	(-1.1234)	(-1.0744)	(-1.1214)	(-0.8875)
SOE	-0.3696*	-0.3852*	-0.3849*	-0.3759*
	(-1.7037)	(-1.7649)	(-1.7678)	(-1.7196)
Lr_ratio	0.4475	0.4359	0.4407	0.4174
	(1.0410)	(1.0273)	(1.0358)	(0.9910)
Ind_ratio	-1.7485	-1.7425	-1.7985	-1.6328
	(-1.4028)	(-1.3954)	(-1.4375)	(-1.2999)
Network	-0.0636***	-0.0642***	-0.0657***	-0.0631***
	(-2.6552)	(-2.6786)	(-2.7162)	(-2.6230)
_cons	-3.4643	-3.5919	-3.5676	-3.7343
	(-1.3698)	(-1.4047)	(-1.4108)	(-1.4659)
Time-Fixed Effects	YES	YES	YES	YES
Industry-Fixed Effects	YES	YES	YES	YES
N	328	328	328	328
Wald chi2	52.0567	52.3229	52.4714	52.7143

4.3. Heterogeneity Analysis

4.3.1. Heterogeneity of inherent characteristics of polluting enterprises: pollution level

The incentive effect of environmental protection tax on corporate green M&As may also differ due to different district environmental protection tax rates in different places. Taking into account factors such as regional environmental status and development goals, regions have successively introduced specific local tax rates for taxable pollutants within the prescribed tax range. Enterprises in regions with high tax rates have higher transaction costs for green production and greater incentives to engage in green M&As than those in regions with low tax rates. Therefore, this paper refers to the practice of Huang Ji-Qiang[22], divides the samples into two groups of high and low environmental protection tax standard based on the median of the specific tax standard of each place and then regress them separately. The regression results, as shown in columns (1) and (2) of Table 5, indicate that the coefficient of environmental protection tax is significant in regions with high tax rates, and not significant in the group with low tax rates. This result indicates that the environmental protection tax has a more evident policy effect on corporate green M&As in regions with high tax rates.

4.3.2. Regional heterogeneity

Due to China’s vast territory, there are variations in resource availability and economic development levels across different regions. Consequently, enterprises in different parts of the country make diverse investment decisions. In comparison to the economically prosperous eastern region, businesses in the central and western regions heavily rely on high investment and high pollution production models. These regions also lack a strong foundation in innovative technological capabilities, making it imperative for them to pursue green M&As as a means to obtain clean energy and green production technologies. To investigate this further, the present study divides the sample into three distinct regions: the eastern region with higher levels of economic development, and the relatively underdeveloped central and western regions. This classification aligns with the "Seventh Five-Year Plan" adopted at the Fourth Session of the Sixth National People's Congress Regression analysis results, as depicted in columns (3) and (4) of Table 5, reveal that the environmental protection tax coefficient holds significant importance in the central and western regions, while insignificance of the coefficient is observed in the eastern region. These findings indicate a greater inclination among enterprises in the central and western regions towards acquiring green resources and technologies through green M&As.

4.3.3. Heterogeneity in the nature of enterprises

SOEs and non-SOEs differ in terms of their ties to the government, and will react differently in the face of environmental regulation. State-owned enterprises have a natural connection with government departments. The government will help SOEs in environmental management and reduce the risks in the process of green M&As, thus the promotional effect of environmental protection tax on green M&As may be more obvious in the sample of SOEs. Therefore, this paper divides the sample into the state-owned enterprise group and the non-state-owned enterprise group according to the nature of the enterprise and then conducts regression analysis separately. The regression results are shown in columns (5) and (6) of Table 5: the estimation result of the state-owned enterprise group is 0.0363 and is significantly positive at the 5% level, while the estimated coefficient of the non-state-owned enterprise group is not significant. The results here indicate that in response to the environmental protection tax, the state-owned heavy-polluting enterprises are more proactive in green M&As than the enterprises of other natures.

Table 5

Heterogeneity Analysis.
	(1)	(2)	(3)	(4)	(5)	(6)
	High tax rates	Low tax rates	Eastern region	Central and Western region	Non-SOE	SOE
	Green	Green	Green	Green	Green	Green
Tax	0.0327*	0.0425	0.0094	.0641***	0.0282	0.0363**
	(1.8043)	(1.5448)	(0.5440)	(3.0309)	(1.2146)	(2.0802)
Scale	0.0096	0.0955	0.0791	0.2528*	0.1471	0.1812
	(0.0763)	(0.7048)	(0.7280)	(1.8871)	(1.1995)	(1.5995)
Age	-0.0318	-0.0094	-0.0354*	-0.0390	-0.0501*	-0.0192
	(-1.5384)	(-0.3423)	(-1.8222)	(-1.4797)	(-1.7316)	(-0.8778)
Growth	-0.1776	2.1096**	0.9427**	0.8463	1.1137**	0.9896
	(-0.2988)	(2.3205)	(2.1491)	(0.9660)	(2.1427)	(1.2496)
lnperGDP	0.2751	-0.0177	0.3370	0.0388	0.4692*	-0.4392*
	(1.2151)	(-0.0553)	(1.4536)	(0.1211)	(1.6688)	(-1.7400)
Merge_type	0.1373*	-0.0226	0.1162*	0.3373*	0.1570*	0.1138
	(1.9358)	(-0.1659)	(1.8344)	(1.9555)	(1.7518)	(1.4258)
Merge_ratio	0.0076	0.0044	0.0062	0.0073	0.0045	0.0078
	(1.5222)	(0.7467)	(1.3488)	(1.3158)	(0.8172)	(1.5151)
MFEE_ratio	-3.6685	-1.3937	1.8970	-4.0540	1.7649	-3.4873
	(-1.0840)	(-0.2700)	(0.6291)	(-0.8551)	(0.4689)	(-0.8355)
Op_capacity	-0.9163	-0.0648	-0.0006	-0.4395	-0.0309	-0.5711
	(-1.6296)	(-0.1747)	(-0.0018)	(-1.1043)	(-0.0952)	(-1.3058)
SOE	-0.5190*	-0.3072	-0.5165	-0.5218
	(-1.7089)	(-0.6871)	(-1.6386)	(-1.2752)
Lr_ratio	1.3964	0.0156	0.4307	0.6826	0.2525	2.3796
	(1.3333)	(0.0225)	(0.7035)	(0.9135)	(0.4991)	(1.6144)
Ind_ratio	-2.7584	0.4542	1.6459	-6.0057***	-0.4502	-6.0052***
	(-1.6308)	(0.1726)	(0.9589)	(-2.6062)	(-0.2508)	(-2.6950)
Network	-0.0315	-0.1115*	-0.0385	-0.0813*	-0.0668	-0.0967***
	(-0.9659)	(-1.8230)	(-1.1663)	(-1.7485)	(-1.5797)	(-2.7151)
_cons	-2.7081	-3.0647	-8.0198**	-5.2668	-14.6779	1.0069
	(-0.6842)	(-0.6702)	(-2.0456)	(-1.0765)	(-0.0216)	(0.2761)
Time-Fixed Effects	YES	YES	YES	YES	YES	YES
Industry-Fixed Effects	YES	YES	YES	YES	YES	YES
N	170	158	192	136	163	165
Wald chi2	33.2009	10.3316	33.8635	34.8022	10.2789	42.1744

5.1. The effect of green M&As on green innovation

It has been shown that green M&As (M&A) can lead to green innovation, but it has also been found that enterprises making low investment in their own innovation and having insufficient short-term innovation capacity are more likely to pursue M&As, which creates a substitution effect on green innovation[35]. So, do green M&As affect the green innovation of main merging enterprises as a whole? Are green M&As a substitute or a complement for green innovation? Through the enterprise green patent application data in CNRDS database, therefore, this paper selects the number of enterprise green patent applications (Green_sum) as the metric indicator of the enterprise green innovation level, and distinguishes the enterprise invention-type patents (Green_v) from utility model patents (Green_m), and constructs the regression model (3) to test the impact of enterprise green M&As on the green innovation of the enterprise, and the coefficient reflects the effect of impact of green M&As on the green innovation of the enterprise.

$${Green\_sum}_{i,t} = {b}_{0}{+{b}_{1}{Green}_{i,t}+b}_{2}{\sum control+Year}_{t}+{Ind}_{i}+{\epsilon }_{i,t}$$

${Green\_sum}_{i,t}$ denotes the number of green patent applications filed by enterprise i in year t; denotes a series of control variables, Year_t and denote industry and year effects, respectively. The regression results are shown in Table 6: enterprises' green innovation output is significantly increased after initiating green M&As, and the green invention patents of the main merging enterprises are significantly enhanced. This shows that green M&As have a complementary effect on the green innovation of the main merging enterprise, and the enterprises can make up for the lack of their own basic resources in green innovation technology through green M&As.

Table 6

The effect of green M&As on green innovation.
	(1)	(2)	(3)
	Green_sum	Green_v	Green_m
Green	4.0129**	2.1615***	1.9520*
	(2.5072)	(3.0127)	(1.7921)
Scale	7.6139***	3.5983***	4.0982***
	(6.3756)	(6.0772)	(5.8437)
Age	-0.6361***	-0.2828**	-0.3228**
	(-2.7281)	(-2.4196)	(-2.3833)
Growth	0.8100	0.3674	0.7126
	(0.1991)	(0.1974)	(0.2663)
lnperGDP	3.6039	2.4558*	1.2589
	(1.3406)	(1.8375)	(0.7999)
Merge_type	0.2727	0.2007	0.0624
	(0.5474)	(0.9017)	(0.1828)
Merge_ratio	0.0335	0.0136	0.0192
	(1.0793)	(0.9732)	(0.9137)
MFEE_ratio	39.7084	16.5559	21.2370
	(1.2789)	(1.0774)	(1.1614)
Op_capacity	3.5451	1.8139	1.5210
	(1.0644)	(1.1306)	(0.7503)
SOE	-1.9596	0.1287	-1.0788
	(-0.5551)	(0.0737)	(-0.5191)
Lr_ratio	1.3144	0.4947	0.5420
	(0.3625)	(0.2798)	(0.2500)
Ind_ratio	7.6119	-2.0461	7.1591
	(0.5599)	(-0.3296)	(0.7973)
Network	-0.2180	-0.0390	-0.1544
	(-0.6746)	(-0.2565)	(-0.7617)
_cons	-2.1e + 02***	-1.1e + 02***	-1.1e + 02***
	(-5.0510)	(-5.2016)	(-4.3657)
Time-Fixed Effects	YES	YES	YES
Industry-Fixed Effects	YES	YES	YES
N	328	328	328
Wald chi2	80.8850	69.9253	71.8119

5.2. The moderating role of the marketization process

The governance effects of policies cannot be separated from the market, and the marketization process is an important factor influencing the decision-making behaviour of enterprises. It has been found that enterprises in regions with different degrees of marketization have different levels of strong responses to environmental policy[36]. Therefore, this paper adopts the marketization index system compiled by Fan Gang to measure the degree of market development in each region (market). The larger the index, the higher the marketization process in the region. On the basis of the empirical model (1), the marketization development level variable is interacted with the environmental protection tax variable respectively, expanding into model (4); based on the cross term coefficient ${b}_{1}$, the moderating effect of the marketization process is determined.

$$Pr\{{Green}_{i,t}=1\} = \varPhi ({{b}_{0}+{{b}_{1}{tax}_{i,t-1}+b}_{2}tax}_{i,t-1}\ast {X}_{i,t-1}+{b}_{3}{X}_{i,t-1}+{b}_{3}{\sum control+Year}_{t}+{Ind}_{i}+{\epsilon }_{i,t})$$

The results of the regression of the moderating effect of adding the marketization process are shown in column (1) of Table 7: the coefficient of the interaction term between the marketization process and the environmental protection tax is negative and reaches the significance level of 1%. One possible explanation is that variations in the level of marketization development influence the selection of performance indicators for evaluating enterprise managers. In regions with a higher degree of marketization, managers prioritize profit management and tend to disregard the importance of social responsibility, such as environmental protection, energy conservation, and emission reduction. Conversely, in regions with a lower degree of marketization, non-financial indicators are given more weight in the evaluation of managers[37]. As a result, with a high degree of marketization, corporate managers divert due environmental investments to other purposes and engage in green investment activities negatively for the sake of performance or personal interests.

5.3. The moderating role of corporate legitimacy awareness

The effectiveness of the enforcement of environmental regulations is under the assumption that the polluter complies with the environmental regulations[38]. It is difficult for environmental regulations to bind enterprises in their emissions when they have a low corporate legitimacy awareness. This paper uses the annual number of corporate violations to measure their legitimacy awareness (law); the higher the number of corporate violations, the lower the corporate legitimacy awareness. In reference to model (4), the number of corporate violations is interacted with the environmental protection tax variable, and the moderating effect of corporate legitimacy awareness is determined based on the coefficient of the cross term. The regression results are shown in column (2) of Table 7: the coefficient of the interaction term is -0.0395, which is significantly negative at the 10% level. These findings suggest that the impact of environmental protection tax on corporate green M&As diminishes when there is a low corporate legitimacy awareness. This can be attributed to violations reflecting an enterprise's noncompliance with laws and regulations, which highlights internal corporate governance issues[39]. In the face of external institutional pressure, industry competition, or business challenges, corporate executives may prioritize short-term gains and take short-sighted actions that undermine the incentive effect of environmental protection tax on green M&As.

Table 7

Results of the Moderating Effect Test.
	(1)	(2)
	Marketization process	Corporate legitimacy awareness
	Green	Green
Tax	0.1860***	0.0326**
	(2.9885)	(2.5595)
Tax×market	-0.0167***
	(-2.6099)
market	0.1109
	(1.3372)
Tax×law		-0.0395*
		(-1.7499)
law		0.2102
		(1.2663)
lnscale	0.1141	0.0989
	(1.5012)	(1.3051)
Age	-0.0308**	-0.0268*
	(-2.1452)	(-1.8797)
Growth	1.0169***	0.8903**
	(2.6971)	(2.3988)
lnperGDP	0.1599	0.1181
	(0.9130)	(0.7458)
Merge_type	0.0976*	0.1111**
	(1.7835)	(2.0817)
Merge_ratio	0.0059*	0.0053
	(1.7403)	(1.5762)
MFEE_ratio	-0.2927	-0.8237
	(-0.1213)	(-0.3439)
Op_capacity	-0.1758	-0.2519
	(-0.7585)	(-1.0970)
SOE	-0.3883*	-0.3382
	(-1.7253)	(-1.5088)
Lr_ratio	0.4390	0.4493
	(1.0149)	(1.0589)
Ind_ratio	-1.7032	-1.8586
	(-1.3601)	(-1.4822)
Network	-0.0673***	-0.0631***
	(-2.7630)	(-2.6136)
_cons	-5.7554**	-3.7411
	(-2.1317)	(-1.4599)
Time-Fixed Effects	YES	YES
Industry-Fixed Effects	YES	YES
N	328	328
Wald chi2	57.9415	54.9659

In the future, promoting green development will become a critical initiative for the Chinese economy to achieve high-quality growth. The new development concept is guiding governments at all levels to intensify their efforts in environmental management and increasing pressure on all types of polluting enterprises to launch green transformation. Consequently, heavy-polluting enterprises are rapidly seeking green resources through M&As to adapt to the increasingly stringent environmental regulations. This study focuses on green M&A events in industries with high pollution levels, analyzing the impact of environmental protection tax on corporate green M&A activities. The findings indicate that enterprises are more inclined to engage in green M&As when they are required to pay a higher environmental protection tax. This effect is particularly pronounced in regions with more stringent environmental protection tax standards, in central and western regions with limited innovation resources, and among state-owned enterprises. Furthermore, the study reveals that the implementation of green M&As leads to increased green innovation output and enhances the green innovation capabilities of enterprises. However, it’s also found that the level of marketization in the region where the enterprise is located and the corporate legitimacy awareness hinder the promotion of environmental protection tax's impact on green M&As. These factors also affect the governance function of environmental protection tax. These empirical findings provide valuable insights on the effectiveness of implementing environmental protection tax and offer guidance for enterprises seeking green transformation.

Firstly, improving the environmental protection tax system and continuously promoting the green development of enterprises. Compared with command-based environmental regulation, the market-based environmental regulation of environmental protection tax is an invisible legal regulation in nature, able to incentivize enterprises to reduce pollution through the price mechanism. This plays a crucial role in promoting the green development of enterprises. In the future, tax authorities should continue to enhance the environmental protection tax system and its supporting measures, unify collection standards, and ensure precise pollution control according to law. Furthermore, increasing tax breaks and concessions will provide further incentives for local enterprises to undertake green transformation and upgrading.

Secondly, raising the environmental awareness of enterprises and promoting their green investment. Corporate contempt for regulations is detrimental to the proper governance effect of environmental policies and puts the enterprise in a predicament of accepting penalties. All parties need to work together to raise the corporate legitimacy awareness and to facilitate the implementation of environmental policy requirements. For instance, the government can give legal training to enterprises to enhance their awareness of compliance with the law; environmental protection-related departments can jointly impose penalties and strengthen communication and cooperation with the State-owned Assets Supervision and Administration Commission and the Securities and Futures Commission to enable enterprises to improve the environmental information disclosure standards. With the help of public opinion, the positive and negative behaviors of enterprises in environmental law enforcement can be reported and spread through the media.

Thirdly, it is crucial to foster a supportive environment for green M&As among enterprises. Such strategic moves can expedite the acquisition of resources necessary for enhancing the strength of green development within organizations. To facilitate this process, local governments should focus on improving the efficiency of decision-making regarding green M&As by establishing an accessible information platform. Additionally, they can encourage market institutions to participate in the green M&As of listed companies by broadening the available financing channels and implementing standardized guidelines. Simplifying the approval procedures and reducing the time required for M&A approvals will further enhance efficiency and incentivize the enterprises, particularly those capable of driving innovation, to actively pursue green M&A opportunities.

Lastly, marketization development plays a crucial role in enhancing enterprises' green development capabilities. A healthy and competitive market environment provides the necessary incentives for enterprises to improve their corporate efficiency and focus on sustainable practices. Local governments should actively foster a fair and orderly market environment that propels green development among enterprises. Under the guidance of the principles of the new development concept, it is essential to encourage enterprises to prioritize social responsibilities and obligations alongside economic benefits. This approach will encourage enterprises to incorporate green indicators into their development goals through the price mechanism, thereby reinforcing the significance of green development in their overall corporate strategies and facilitating the promotion of sustainable growth.

Funding: This research was funded by the Humanities and Social Science Youth Foundation Project of Ministry of Education (No.19YJC790143), General Project of Social Science Planning of Gansu Province (No.20YB0707) .

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Conflicts of Interest: The authors declare no conflict of interest.

Data Availability Statement: Most of the data used in this paper are available in the China Statistical Yearbook and China Stock Market & Accounting Research Database. Other variable data such as green M&A is available on request from the corresponding author.

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Heavy-polluting industries: According to the Guidelines for Industry Classification of Listed Companies revised by the China Securities Regulatory Commission in 2012, the codes for the heavy-polluting industries finally selected were as follows: B06, B07, B09, C17, C22, C25, C26, C28, C29, C30, C31, C32 and D44.
Carbon Emissions Trading Pilot Cities including༚Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong and Shenzhen should carry out pilot carbon emissions trading.
Air pollution transmission corridor cities: Beijing, Shijiazhuang, Tianjin, Tangshan, Baoding, Langfang, Cangzhou, Hengshui, Handan, Xingtai, Jinan, Zibo, Liaocheng, Dezhou, Binzhou, Jining, Heze, Zhengzhou, Xinxiang, Hebi, Anyang, Jiaozuo, Puyang, Kaifeng, Taiyuan, Yangquan, Changzhi and Jincheng.
Green Financial Reform And Innovation Piots Areas: Zhejiang, Jiangxi, Guangdong, Guizhou and Xinjiang

No competing interests reported.

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The impact of environmental protection tax on green M&As

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Abstract

1. Introduction

2. Literature review and research hypotheses

2.1. Factors affecting corporate green investments

2.2. Environmental Governance Effectiveness of Environmental Protection Tax

2.3. Environmental protection tax and corporate green M&As

3. Data, variables and econometric modelling

3.1. Sample selection and data sources

3.2. Variables

3.2.1. Dependent Variable: Green M&A

3.2.2. Independent Variable: Environmental Protection Tax

3.2.3. Independent Variable: Environmental Protection Tax

3.3. Econometric Modelling

4. Main results and discussion

4.1. Baseline regression results

4.2. Robustness analysis

4.2.1. Endogeneity Test

4.2.2. Alternative Key Variables

4.2.3. Control the Impact of Other Policy Effects for the Same Period

4.3. Heterogeneity Analysis

4.3.1. Heterogeneity of inherent characteristics of polluting enterprises: pollution level

4.3.2. Regional heterogeneity

4.3.3. Heterogeneity in the nature of enterprises

5. Further Analysis

5.1. The effect of green M&As on green innovation

5.2. The moderating role of the marketization process

5.3. The moderating role of corporate legitimacy awareness

6. Conclusions

Declarations

References

Footnotes

Additional Declarations

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