The Impact of Green Mergers and Acquisitions on the Market Power of Heavily Polluting Enterprises

Abstract

In the era of low-carbon economy, green mergers and acquisitions (green M&As) have emerged as a pivotal strategic pathway for heavily polluting enterprises to not only carve out a competitive edge in the market but also contribute significantly to the achievement of Sustainable Development Goal 12 (SDG 12)—Responsible Consumption and Production. Based on the data of China’s heavily polluting enterprises listed on the Shanghai and Shenzhen A-share markets from 2010 to 2022, this study applies the multi-temporal difference-in-differences method to analyze the impact of green M&As on the market power of heavily polluting enterprises. The findings suggest that the adoption of green M&As by heavily polluting enterprises in China can enhance market power, and this conclusion remains valid after a series of robustness tests. The mediation effect analysis shows that green M&As promote the market power of heavily polluting enterprises by increasing green total factor productivity, optimizing human capital structure and enhancing brand capital. Meanwhile, according to the heterogeneity study conducted, the implementation of green M&As by non-state-owned heavily polluting enterprises and heavily polluting enterprises within the growth period has a more pronounced effect on market power promotion. In addition, domestic green M&As have a stronger effect on the market power of heavily polluting enterprises. By bridging the theoretical gap between green M&As and the driving mechanisms of market power, this study not only enriches the academic literature but also provides actionable insights for heavily polluting enterprises seeking to enhance their market competitiveness while adhering to sustainable development principles.

1. Introduction

China declared to the world during the general debate of the seventy-fifth session of the United Nations General Assembly that “it strives to peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060.” According to the statistics released by the Ministry of Ecology and Environment (MOE) in China’s 2023 Ecological and Environmental Situation Bulletin, China’s total industrial solid waste generation reached 4.28 billion tons in 2023, causing serious negative impacts on the environment [1]. Against the backdrop of accelerated industrialization and the dramatic increase in energy consumption, the environmental pressure on China’s heavily polluting industries continues to intensify [2]. In this context, a primary objective is to ensure these enterprises adhere to relevant United Nations Sustainable Development Goals (SDGs) while simultaneously enhancing their core competitiveness. Consequently, China’s policies are gradually guiding heavily polluting enterprises to reduce their environmental burden and optimize their industrial structure through green transformation [3]. In August 2024, China issued the Opinions on Accelerating Comprehensive Green Transformation of Economic and Social Development, which emphasized that “promoting the green and low-carbon transformation of traditional industries” and “accelerating the cultivation of competitive green and low-carbon enterprises” signaled the full-scale advancement of an economic transformation centered on green transformation.

Traditional economic theory suggests that profit-maximizing enterprises tend to pursue higher economic growth by expanding their market power [4]. However, this traditional development model is increasingly revealing its drawbacks [5]. As environmental pressures intensify and policies become more stringent, enterprises must consider both environmental regulations and consumers’ sustainability expectations [6]. This compels most heavily polluting enterprises to re-examine their strategies. Consequently, these enterprises are evolving their market power from a solely economic orientation toward a multidimensional orientation incorporating environmental responsibility [7]. Under these constraints, how can heavily polluting enterprises maintain economic viability without compromising their market power? Green M&As may offer a viable path. “Green M&As” refers to mergers and acquisitions pursued to acquire green technologies, equipment, products, services, or clean energy assets, representing a strategic shift toward clean production and environmental industries [8]. Through green M&As, polluting enterprises can acquire environmental R&D talent and integrate clean technologies rapidly, transforming into high-efficiency, low-emission operations and thereby achieving green transformation [9]. However, some enterprises leverage green M&As merely as a “greenwashing” tactic driven by the need to alleviate public pressure and protect short-term interests, failing to achieve genuine transformation or secure long-term competitive advantages [10].

Therefore, this study addresses the following questions: How do green M&As affect the market power of heavily polluting enterprises? What underlying mechanisms drive this relationship? Existing studies provide limited systematic theoretical analysis or empirical evidence on these issues. This study aims to analyze this impact and its internal mechanisms through the lenses of green total factor productivity, human capital, and brand capital. Clarifying these dynamics will help reveal micro-level pathways for enhancing the core competitiveness of heavily polluting enterprises, fostering the development of competitive green enterprises, and addressing gaps in existing literature regarding the economic effects of green M&As and the determinants of enterprise market power.

2. Literature Review

2.1. Green Mergers and Acquisitions

Existing studies have mainly addressed the two economic consequences of green M&As from the perspective of legitimacy. Some of the studies believe that green M&As are a long-term strategy for heavily polluting enterprises to realize sustainable development and can bring good economic benefits to enterprises while improving their environmental governance performance, thus achieving a “win–win” effect in terms of environmental protection and profits [11]. In terms of innovation performance, green M&As promote green innovation in heavily polluting enterprises through external access to government subsidies [12], tax incentives [13], and commercial credit [14]. These are sufficient to indicate that green M&As represent strategic initiatives through which enterprises actively seek external partnerships and acquire critical resources to promote green innovation. Within the theoretical framework of the resource-based view, green technological innovations acquired through green M&As enable heavily polluting enterprises to strengthen their environmental governance capabilities [15], facilitate corporate green transformation [16], and ultimately enhance environmental performance by reducing unlawful pollution emissions [17], thereby contributing to sustainable development [18]. These studies are sufficient to illustrate the most significant environmental benefits associated with green M&As in the current context. Under the framework of the stakeholder theory, green M&As are effective in absorbing environmental protection investment for enterprises through the paths of strengthening the awareness of environmental responsibility of heavily polluting enterprises [19] and attracting the attention of utilization stakeholders [20]. This signal of gaining stakeholders’ trust may serve as a crucial approach for enterprises to overcome financing constraints. In addition, heavily polluting enterprises have been able to quickly break down international green trade barriers and expand their exports through green M&As [21]. Regarding social performance, signaling theory suggests that heavily polluting enterprises can actively communicate their commitment to environmental responsibilities through green M&As. This communication serves to establish their organizational legitimacy, thereby enhancing their risk-taking capacity [9]. Moreover, it can also improve the environmental awareness of business managers [22].

On the contrary, another part of the research argues that the implementation of green M&As by heavily polluting enterprises will bring some negative impacts on the development of the enterprises, and that the real purpose is not green transformation, but a tool to ease the pressure of the media on environmental protection and to “meet the needs of the media.” According to the instrumentalist hypothesis, some heavily polluting enterprises initiate green M&As in order to meet national and social expectations for environmental protection, with the motive of producing “watered-down” green innovations [13]. Green M&As under such motives will not bring economic consequences of profit growth for enterprises, but will produce a large number of low-quality innovative products, which will hinder the green transformation of heavily polluting enterprises [23]. In addition, some scholars believe that heavily polluting enterprises consume a lot of resources in the process of green M&As, which reduces the total factor productivity and R&D efficiency of enterprises [24].

The existing literature on the post-event effects of green M&As primarily examines their influence on green innovation and environmental pollution from the perspectives of stakeholder theory and resource-based theory. However, there is a lack of studies investigating the economic impacts of green M&As within the framework of industrial organization theory or under market competition scenarios. Furthermore, scholarly opinions remain divided on whether green M&As yield overall positive or negative outcomes.

2.2. Mergers and Acquisitions and Enterprise Market Power

Another type of literature related to this study focuses on the relationship between various mergers and acquisitions and the market power of enterprises. The research ideas mainly extend from traditional mergers and acquisitions to other special forms of mergers and acquisitions.

From the perspectives of industrial organization theory and international trade theory, mergers and acquisitions can enhance market power primarily by reducing the number of competitors [25]. Research focusing on the market power theory highlights the necessity of integrating mergers and acquisitions with efficiency improvements and governance optimization to sustainably enhance enterprises’ market power [26]. Based on the asset matching theory, a more comprehensive influence mechanism is derived, demonstrating that mergers and acquisitions directly enhance enterprise efficiency and influence the distribution of productivity across countries through optimized global asset allocation, thereby shaping market power. It was also pointed out that green investment and cross-border mergers and acquisitions coexist [27]. Different studies hold that M&As reduce market power due to the inconsistency between the objectives of the actual controller and the CEO [28]. The prospect theory framework offers a more precise explanation. Mergers and acquisitions may trigger loss aversion among target shareholders, which in turn compels the acquirer to offer a higher premium. This not only diminishes the acquirer’s short-term financial gains but also undermines its market power [29]. Some scholars have also analyzed the state of the European market and argued that mergers and acquisitions may enhance the market power of enterprises’ non-consolidated competitors [30]. In addition to traditional forms of mergers and acquisitions, scholars have further categorized M&A activities into various specialized types, and have empirically examined how different forms of mergers and acquisitions influence enterprise market power. Huang et al. (2024) have observed that cross-border mergers and acquisitions tend to occur more frequently among enterprises with higher market power. Moreover, these enterprises also exhibit relatively high levels of resilience [31]. Tang et al. (2022) argue that digital mergers and acquisitions accomplish the objectives of cost reduction, quality enhancement, and efficiency improvement through three key mechanisms: boosting total factor productivity within enterprises, refining the composition of human capital, and reinforcing service-oriented manufacturing [32]. These strategies collectively contribute to enhancing the market power of manufacturing enterprises [32]. The existing research on the relationship between mergers and acquisitions and market power has increasingly transitioned from theoretical model development to empirical analysis. While a majority of studies focus on financial, technological, and managerial mechanisms, limited attention has been given to dimensions such as productivity enhancement, human capital accumulation, and brand image development.

2.3. Research Gap

In conclusion, domestic and international scholars have conducted extensive research on the aftereffects of green M&As and the relationship between mergers and acquisitions and market power. However, the following problems persist: On one hand, current perspectives on the impact of green M&As on enterprise development are divided between positive effects and negative impacts, lacking a unified conclusion and requiring further in-depth investigation. On the other hand, previous studies have primarily examined the relationship between traditional M&As or other technology-based M&As and enterprise market power. Green M&As, however, incorporate characteristics of technology-based M&As while also integrating the concept of green development, differing significantly from other M&A types in terms of implementation motives and targets. Whether the relationship between green M&As and market power differs from that of other M&A types therefore requires further verification. Consequently, the marginal contribution of this study lies in three aspects: First, in terms of research perspective, this study selects green M&As as the analytical lens to investigate their impact on the market power of heavily polluting enterprises and the underlying mechanisms. This approach effectively bridges the gap between research on the economic consequences of green M&As and studies examining the factors influencing market power. Second, methodologically, the study focuses on the enterprise level. The market power of heavily polluting enterprises is quantified using the transcendental logarithmic function, while their green M&A behavior is identified via content analysis. Furthermore, the multiple mediating mechanisms through which green M&As influence market power are rigorously tested by applying a multi-time point DID model combined with a mediating effect model. Third, in terms of research content, the study finds that green M&As significantly enhance the market power of heavily polluting enterprises by improving green total factor productivity, optimizing human capital structure, and enhancing brand capital. Moreover, when subdividing by different M&A methods, the impact of green M&As on the market power of different types of heavily polluting enterprises exhibits heterogeneous characteristics.

3. Theoretical Analysis and Research Hypotheses

First, grounded in the legitimacy principle, green M&As provide heavily polluting enterprises with rapid access to green production materials and energy-saving technologies. This acquisition not only facilitates environmental compliance with stringent regulations and emission standards [33] but also signals alignment with governmental green transition policies [34]. Such alignment is crucial, as governments, acting as primary environmental regulators, typically prioritize compliant enterprises for favorable resource allocation [12]. Consequently, by mitigating compliance pressures and securing potential regulatory advantages, green M&As enhance these firms’ operational efficiency and competitive position, ultimately strengthening their market power. Second, signaling theory suggests that when heavily polluting enterprises undertake green M&As, they actively communicate environmental commitments to stakeholders [17]. This deliberate signal reduces political costs associated with negative media exposure and public scrutiny while enhancing environmental reputation [35]. Improved stakeholder trust subsequently facilitates access to financial institution support and external investments [36]. Critically, by lowering political risks and securing critical resources, this process directly strengthens market power. Finally, from a value-creation perspective, green M&As represent a long-term strategic investment in sustainable development [37]. By reducing resource consumption and implementing pollution controls, enterprises cultivate enduring competitive advantages [38]. Although initial pollution control investments may temporarily raise production costs, green M&As ultimately boost productivity and operational efficiency [39]. This reduces long-term input and pollution abatement costs, achieving environmental–economic synergies [40] while reinforcing market power. Based on the aforementioned analysis, this study puts forward Hypothesis 1.

Hypothesis 1.

Green M&As can enhance the market power of heavily polluting enterprises.

Enterprises can achieve greater control over technology, resources, and market access through mergers and acquisitions, thereby enhancing their productivity and innovation capabilities [41]. First, according to Porter’s Hypothesis, reconfiguring technology to enhance green productivity for heavily polluting enterprises is expected to be a time-consuming and labor-intensive process [42]. In the process of green M&As, the green and clean technology of the target enterprise will be swiftly integrated with the existing technology of the acquiring enterprise [43]. This facilitates the rapid realization of complementary green resource advantages between the two parties, thereby significantly enhancing the green total factor productivity of heavily polluting enterprises. Second, in contrast to the channels through which the cost effect and innovation compensation effect influence green total factor productivity [44], the enhancement in energy allocation efficiency and the internalization of external costs achieved via green M&As can partially reflect the use cost of energy factors, including negative environmental externalities [45]. This, in turn, compels heavily polluting enterprises to refine their production modes and rely on the efficient and intensive utilization of energy resources, thereby improving the green total factor productivity of enterprises [11]. This represents the green premium effect generated by green M&As within heavily polluting enterprises. Finally, with regard to capital factors, the integration of green technology following green M&As can lead to the formation of “green factories,” “green workshops,” and “green production lines” [46]. This can facilitate the optimization and upgrading of the production processes of heavily polluting enterprises while significantly reducing the production cycle [19]. Moreover, green M&As can facilitate the development of new green products by leveraging the existing traditional product production structure. This approach substantially diminishes the idiosyncrasies in asset utilization while enhancing capital efficiency, thereby contributing to the improvement in green total factor productivity for heavily polluting enterprises [47]. In the current competitive industrial market environment, heavily polluting enterprises, as key subjects of sustainable development, have garnered significant attention. Environmentally responsible practices are emerging as a “new battleground” for enterprises to pursue a “green” differentiated competitive advantage [48]. Therefore, enhancing green total factor productivity is expected to further augment the market power of heavily polluting enterprises. In conclusion, this study puts forward Hypothesis 2.

Hypothesis 2.

Green M&As strengthen the market power of heavily polluting enterprises by enhancing their green total factor productivity.

Green M&As can optimize the human capital structure of heavily polluting enterprises, facilitate knowledge spillover effects, and thereby positively influence the market power of such enterprises. Firstly, green M&As can facilitate knowledge sharing and experience accumulation within enterprises, thereby enabling the systematic development of a tiered structure for green R&D talents [46]. Although mergers and acquisitions often lead to efficiency losses due to difficulties in integrating human resources [49], green M&As can mitigate such negative impacts by strategically recruiting professionals with expertise in environmental protection technologies. The target enterprise possesses a substantial pool of green innovation talents specialized in the field of green technology, enabling the acquirer to leverage these human resources efficiently within a short timeframe [24]. The target enterprise and the acquiring enterprise facilitate technical exchange and collaboration among talents, while cultivating diversified talent pools through cross-departmental cooperation and internal knowledge sharing [50]. According to the resource-based view theory, enterprises integrate a variety of advantageous resources, with particular emphasis on high-quality human capital, which constitutes a critical source of their competitive advantages [51]. Secondly, green M&As introduce advanced environmental protection management concepts to heavily polluting enterprises [12], provide employees with training on environmental protection awareness and skills, and enhance their professional competence and capacity for green production. The training encompasses knowledge of environmental protection laws and regulations, the application of green technologies, and other relevant areas, thereby equipping employees with the necessary skills and knowledge to adapt to the development of the environmental protection industry. The incorporation of such intangible assets will dismantle the knowledge barriers for the acquiring entity and establish a dominant position in the development of green innovative products [52]. Finally, green M&As not only enhance the knowledge stock of the acquiring enterprise but also strengthen the knowledge complementarity between the acquirer and the target enterprise [53]. Furthermore, due to differences in employee technical levels, the acquirer may update its existing human resources to prevent over-reliance on existing knowledge [11]. Based on the knowledge-based theory, scarce knowledge resources constitute a critical component of the strategic assets for heavily polluting enterprises. Mastering the core knowledge resource of high-quality human capital can enhance the overall operational efficiency and market power of heavily polluting enterprises. In conclusion, this study puts forward Hypothesis 3.

Hypothesis 3.

Green M&As can enhance the market power of heavily polluting enterprises by optimizing the structure of human capital.

As an intangible asset, brand capital enables heavily polluting enterprises and their products to establish a distinctive image, thereby enhancing their core competitiveness through increased market share [54]. Green M&As, conversely, enhance the acquirer’s portfolio by increasing the number of low-carbon and sustainability-focused brands, diminishing brand overlap and thereby promoting brand diversification [55]. Firstly, green M&As enable heavily polluting enterprises to complement their respective sustainable resource advantages more effectively [47]. This allows the acquirer to gain a broader range of non-overlapping brands post-merger, thereby facilitating dominance across diverse green product lines, expanding market coverage, and maximizing brand diversity within the low-carbon sector [42]. Secondly, as a strategic market positioning tool, green M&As allow the acquirer to flexibly introduce green differentiated brands or reposition existing brands to align with sustainability goals. This enhances consumer acceptance and recognition in competitive markets [56]. The premium associated with environmental protection brands resulting from green M&As represents a typical synergy effect [57]. This synergy enhances bargaining power by increasing recognition among both consumers and investors, ultimately translating into market power. Additionally, based on the limited attention theory, when confronted with numerous heavily polluting enterprises, stakeholders are more inclined to focus on those that possess an environmental protection brand image and high sustainable brand value [58], thereby increasing their trust in such enterprises. Finally, following the green M&As of heavily polluting enterprises, the correlation between the original brands is significantly diminished, thereby enhancing the flexibility of the acquirer in management. During this process, the acquirer typically gains access to sustainable complementary resources, which facilitates reducing marketing organizational friction during the green M&A integration phase and diminishes the reliance on redundant resources from the heavily polluting enterprises that have been eliminated through green M&As [59]. In accordance with the resource-based view theory, an enterprise’s competitive advantage stems from unique, valuable, and non-imitable resources and capabilities. As an intangible asset, brand capital’s irreplaceability and difficulty of imitation substantially bolster the market power of heavily polluting enterprises. To sum up, this study puts forward Hypothesis 4.

Hypothesis 4.

Green M&As strengthen the market power of heavily polluting enterprises by enhancing brand capital.

Consistent with the proposed hypothesis, Figure 1 illustrates the conceptual model framework of this study.

4. Materials and Methods

4.1. Data Sources

This study selected A-share listed enterprises in heavy-pollution industries from 2010 to 2022 as research samples and drew on existing literature [60] to process the data according to the following criteria: listed enterprises are the primary acquirers; M&A samples with failed transactions are excluded; M&A samples involving business types such as asset stripping, asset replacement, debt restructuring, and share repurchase were excluded, retaining only equity acquisitions; asset acquisition transactions, such as land and property purchases, were excluded; samples with acquisition amounts of less than RMB 1 million were excluded; samples with an equity acquisition ratio of less than 30% were excluded; M&A samples that already held more than 30% of the target enterprise’s equity were excluded; multiple M&A cases by the same enterprise in the same year with the same acquisition target were merged; for multiple M&A cases by the same enterprise in the same year with different acquisition targets, only the sample with the highest acquisition proportion was retained; samples of merging enterprises belonging to the ST or *ST category were excluded; and samples with missing data for more than two consecutive years were eliminated, while the linear interpolation method was used to fill in data with only one year of missing information. After screening, a total of 6487 observations from 499 heavily polluting enterprises were obtained. To eliminate the influence of extreme values, all continuous variables were winsorized at the 1% level. M&A transaction data were sourced from the CSMAR database and cross-checked in detail with the Wind database. The main data for other variables came from the CSMAR and CNRDS databases, supplemented manually using annual reports from the Shanghai Stock Exchange, Shenzhen Stock Exchange, and individual enterprises.

4.2. Variable Selection

4.2.1. Explained Variable

Market power (MKP). This refers to the markup calculated by De Loecke and Warzynski (2012) [61] to measure an enterprise’s market power. Its advantage is that it does not rely on the data of enterprise capital cost, which is difficult to obtain, and it relaxes the assumption of constant returns to scale in the production function. It is assumed that the production function of the enterprise is:

$${\mathrm{Q}}_{\mathrm{i}\mathrm{t}}={\mathrm{F}}_{\mathrm{i}}\left({\mathrm{K}}_{\mathrm{i}\mathrm{t}},{\mathrm{L}}_{\mathrm{i}\mathrm{t}},{\mathrm{M}}_{\mathrm{i}\mathrm{t}},{\omega }_{\mathrm{i}\mathrm{t}}\right)$$

(1)

where ${\mathrm{Q}}_{\mathrm{i}\mathrm{t}}$ represents the output of a heavily polluting enterprise i in period t, and ${\mathrm{K}}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{L}}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{M}}_{\mathrm{i}\mathrm{t}}$, and ${\omega }_{\mathrm{i}\mathrm{t}}$ represent capital input, labor input, intermediate product input, and enterprise productivity, respectively. The production function of each enterprise is continuous and twice differentiable for all elements. Consider the following enterprise cost minimization problem:

$$\begin{array}{cc}\underset{\left\{{\mathrm{K}}_{\mathrm{i}\mathrm{t}},{\mathrm{L}}_{\mathrm{i}\mathrm{t}},{\mathrm{M}}_{\mathrm{i}\mathrm{t}}\right\}}{\min }& {\mathrm{r}}_{\mathrm{i}\mathrm{t}}{\mathrm{K}}_{\mathrm{i}\mathrm{t}}+{\mathrm{W}}_{\mathrm{i}\mathrm{t}}{\mathrm{L}}_{\mathrm{i}\mathrm{t}}+{\mathrm{P}}_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}{\mathrm{M}}_{\mathrm{i}\mathrm{t}}\\ \mathrm{s}.\mathrm{t}.& {\mathrm{F}}_{\mathrm{i}}\left({\mathrm{K}}_{\mathrm{i}\mathrm{t}},{\mathrm{L}}_{\mathrm{i}\mathrm{t}},{\mathrm{M}}_{\mathrm{i}\mathrm{t}},{\omega }_{\mathrm{i}\mathrm{t}}\right)={\mathrm{Q}}_{\mathrm{i}\mathrm{t}}\end{array}$$

(2)

where ${\mathrm{r}}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{W}}_{\mathrm{i}\mathrm{t}}$, and ${\mathrm{P}}_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}$ represent the capital interest rate, wage rate, and intermediate input price of enterprise I, respectively. According to the first-order condition, this study can obtain:

$${\displaystyle \frac{\partial {\mathrm{F}}_{\mathrm{i}}}{\partial {\mathrm{M}}_{\mathrm{i}\mathrm{t}}}}{\displaystyle \frac{{\mathrm{M}}_{\mathrm{i}\mathrm{t}}}{{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}}}={\displaystyle \frac{{\mathrm{P}}_{\mathrm{i}\mathrm{t}}}{\mathrm{M}{\mathrm{C}}_{\mathrm{i}\mathrm{t}}}}{\displaystyle \frac{{\mathrm{P}}_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}{\mathrm{M}}_{\mathrm{i}\mathrm{t}}}{{\mathrm{P}}_{\mathrm{i}\mathrm{t}}{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}}}$$

(3)

where ${\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ represents the price level of the enterprise’s final product. ${\displaystyle \frac{{\mathrm{P}}_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}{\mathrm{M}}_{\mathrm{i}\mathrm{t}}}{{\mathrm{P}}_{\mathrm{i}\mathrm{t}}{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}}}\equiv {\alpha }_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}$ represents the share of intermediate input expenditure in the total income of enterprises, which can be directly calculated from the data. ${\displaystyle \frac{\partial {\mathrm{F}}_{\mathrm{i}}}{\partial {\mathrm{M}}_{\mathrm{i}\mathrm{t}}}}{\displaystyle \frac{{\mathrm{M}}_{\mathrm{i}\mathrm{t}}}{{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}}}\equiv {\theta }_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}$ denotes the output elasticity of intermediate inputs, which needs to be calculated by estimating the production function parameters. This study specifically assumes the production function in the form of translog and draws on the practice of Ackerberg et al. (2015) [62] to estimate the parameters of the production function. Finally, the expression of enterprise markup is:

$${\mathrm{u}}_{\mathrm{i}\mathrm{t}}={\displaystyle \frac{{\theta }_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}}{{\alpha }_{\mathrm{i}\mathrm{t}}^{\mathrm{M}}}}$$

(4)

In the specific calculation process, the log value of operating income is taken as the total income of the enterprise, the log value of net fixed assets is taken as capital, and the log value of the number of employees is taken as labor. The log value of the total intermediate input is used as the intermediate input. Among them, the total intermediate inputs are calculated by the following accounting identity: Total intermediate inputs = operating costs + sales expenses + administrative expenses + financial expenses − depreciation and amortization − cash paid to and for employees.

4.2.2. Explanatory Variable

Green mergers and acquisitions (GMAs). This was constructed based on prior research [15,60]. Specifically, this variable is obtained by multiplying the enterprise grouping dummy variable (Treat_i, coded as 1 for enterprises in the treatment group and 0 for those in the control group) with the event impact time dummy variable (Post_t, coded as 1 for the year when a green M&A is completed and all subsequent years, and 0 otherwise). This interaction term is designed to examine the change in market power among heavily polluting enterprises in the treatment group following a green M&A, compared to heavily polluting enterprises in the control group that did not undergo a green M&A. If heavily polluting enterprises engage in multiple green M&As across different years, this study designates the year in which the enterprise completes its first green M&A as the treatment year. It is important to note that when multiple green M&As occur within the same year, given their temporal proximity and the annual observation framework of this study, they are treated collectively as a single transaction, or equivalently, as a single shock, within the empirical model [32]. The process of identifying green M&A events was based on the background status of the primary acquirer, the business information of both enterprises, and the core details of the merger and acquisition. First, this study manually collected and systematically organized announcements, annual reports, and other relevant information regarding M&A activities of heavily polluting listed enterprises. Secondly, by focusing on the background, objectives, impacts, and enterprise development strategies associated with these merger and acquisition activities, as well as the core businesses of both the acquired and target companies, this study conducted a content analysis using text analysis methods [18]. It identified key terms such as “green,” “clean technology,” “bioenergy,” “renewable energy,” “sustainable development,” “environmental protection,” “solar energy,” and “air quality,” among others, to extract meaningful and relevant information. Finally, based on the presence of these keywords in the M&A information, this study determined whether the transaction could be classified as a green M&A event. The simplified process of green M&As is shown in Figure 2.

4.2.3. Mediating Variables

Green total factor productivity (Gtfp). Referring to the practice of Sun and Fei (2021), this study adopted the super efficiency SBM model, including unexpected output for measurement [63]. The expected output of the enterprise was measured by operating income; for the selection of undesired output, the pollutant discharge fee of enterprises was selected as the undesired output to take into account the free-rider situation of heavily polluting enterprises in environmental governance to measure enterprise Gtfp more accurately.

Human capital structure (Hum). Referring to the practice of Tang et al. (2022) [32], the ratio of employees with master’s degree and doctorate degrees employed by heavily polluting enterprises was used to represent the human capital structure of heavily polluting enterprises.

Brand capital (Bc). Referring to the practice of Belo et al. (2019), brand capital was measured by advertising expenditure [64]. The advertising expenditure of enterprises plays an important role in developing products in new fields and enhancing brand value [65]. Therefore, it is reasonable to use advertising expenditure to measure brand capital. This study used the perpetual inventory method to calculate the brand capital stock of enterprises:

$$\mathrm{B}{\mathrm{c}}_{\mathrm{i},\mathrm{t}}=\left(1-{\tau }_{\mathrm{B}\mathrm{c}}\right)\mathrm{B}{\mathrm{c}}_{\mathrm{i},\mathrm{t}-1}+\mathrm{A}{\mathrm{D}}_{\mathrm{i},\mathrm{t}}$$

(5)

where Bc represents the stock of enterprise brand capital; τ_Bc represents the depreciation rate of brand capital, referring to the method of Hasan and Taylor (2023) [66]; AD represents the advertising expenditure of the enterprise in the current year; and Bc_i,0 represents the initial value of the advertising expenditure of the enterprise.

4.2.4. Control Variables

Drawing on existing research, the following control variables were selected [12,32]: enterprise size (Size), measured by the natural logarithm of annual total assets; asset–liability ratio (Lev), measured by the ratio of total liabilities to total assets at the end of the year; TobinQ, measured by the ratio of the sum of liquid stock market value, debt book value, and non-tradable stock assets to total assets at the end of the year; number of directors (Board), measured by the natural logarithm of the number of directors; if the chairman and the general manager are the same person, the value is 1—otherwise, the value is 0; shareholding ratio of the largest shareholder (Top 1), measured by the ratio of the number of shares held by the largest shareholder to the total number of shares; and listing years (Listage), measured by the difference between the current year and the listing year.

Table 1. Measurement and origin of variables.

Variable	Implication	Measurement Mode
MKP	Market power	Calculated by the translog function
GMA	Green mergers and acquisitions	Content analysis method
Gtfp	Green total factor productivity	Super-efficiency SBM model
Hum	Human capital structure	Number of employees of Shuobo/total number of employees
Bc	Brand capital	Perpetual inventory method
Size	Size of enterprise	The logarithm of total assets is taken
Lev	Asset–liability ratio	Total liabilities/total assets
TobinQ	Tobin’s Q value	Market value/total assets
Board	Size of directors	The logarithm of the number of directors is taken
Dual	Dual-career path	The chairman and the general manager are the same person 1, otherwise 0
Top 1	Shareholding proportion of the largest shareholder	Number of shares held by the largest shareholder/total number of shares
Listage	Years on the market	The logarithm of the difference between the current year and the listing year is taken

lists the measurement methods of the dependent, explanatory, mediating, and control variables in detail.

4.3. Dodel Design

4.3.1. Benchmark Regression Model

To verify Hypothesis 1 presented above and examine the impact of green M&As on the market power of heavily polluting enterprises, this study constructed the following model:

$$\mathrm{M}\mathrm{K}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}={\beta }_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}}+{\beta }_1\mathrm{G}\mathrm{M}{\mathrm{A}}_{\mathrm{i}\mathrm{t}}+{\displaystyle \sum _{\mathrm{j}}{\beta }_{\mathrm{j}}\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}{\mathrm{l}}_{\mathrm{j}\mathrm{i}\mathrm{t}}}+{\mathrm{Y}}_{\mathrm{t}}+{\mathrm{F}}_{\mathrm{i}}+{\epsilon }_{\mathrm{i}\mathrm{t}}$$

(6)

where MKP is the dependent variable enterprise market power; GMA is the explanatory variable green M&As; Control is the group of control variables; j is the number of control variables; i and t represent the enterprise and year, respectively; ${\beta }_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}}$, ${\beta }_1$, and ${\beta }_{\mathrm{j}}$ represent coefficients to be estimated; Y_t, and F_i represent year fixed effects and enterprise fixed effects, respectively; and ${\epsilon }_{\mathrm{i}\mathrm{t}}$ represents the random error term.

4.3.2. Mediating Effect Model

According to the previous theoretical analysis on the influence mechanism, green M&As mainly promote the market power of heavily polluting enterprises through three channels: improving enterprises’ green total factor productivity, optimizing human capital structure, and enhancing brand capital. Based on this, this part will test the above potential influence mechanism and hypotheses. Refer to Jiang’s two-step method of the mediating effect [67]. This study mainly tested whether green M&As can significantly affect each mediating variable, and the mediating effect test model is shown in Equation (7):

$$\mathrm{G}\mathrm{t}\mathrm{f}{\mathrm{p}}_{\mathrm{i}\mathrm{t}}/\mathrm{H}\mathrm{u}{\mathrm{m}}_{\mathrm{i}\mathrm{t}}/\mathrm{B}{\mathrm{c}}_{\mathrm{i}\mathrm{t}}={\varphi }_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}}+{\varphi }_1\mathrm{G}\mathrm{M}{\mathrm{A}}_{\mathrm{i}\mathrm{t}}+{\displaystyle \sum _{\mathrm{j}}{\varphi }_{\mathrm{j}}\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}{\mathrm{l}}_{\mathrm{j}\mathrm{i}\mathrm{t}}+}{\mathrm{Y}}_{\mathrm{t}}+{\mathrm{F}}_{\mathrm{i}}+{\epsilon }_{\mathrm{i}\mathrm{t}}$$

(7)

Gtfp, Hum, and Bc represent the mediating variables green total factor productivity, human capital structure, and brand capital, respectively, and the other symbols are consistent with Equation (6).

5. Empirical Analysis

5.1. Descriptive Statistics Result

The descriptive statistical results for the main variables are presented in

Table 2. Descriptive statistics of the variables.

Variable	Observations	Mean	Std. Dev.	Min	Max
MKP	6487	1.235	0.427	0.723	3.186
GMA	6487	0.186	0.271	0	1
Gtfp	6487	1.011	0.038	0.664	1.392
Hum	6487	0.187	0.288	0	0.261
Bc	6487	16.655	2.766	7.755	21.149
Size	6487	22.565	1.380	19.910	26.376
Lev	6487	0.445	0.212	0.045	0.967
TobinQ	6487	1.821	1.164	0.197	7.236
Board	6487	2.175	0.194	1.609	2.708
Dual	6487	0.195	0.396	0	1
Top 1	6487	0.356	0.151	0.095	0.764
Listage	6487	2.452	0.706	0	3.332

. The average market power of heavily polluting enterprises is 1.235, suggesting that, at this stage, the overall market power of such enterprises in China remains relatively low. Most heavily polluting enterprises have yet to reach the average level, indicating a need for further improvement. The minimum value of market power is 0.723, the maximum is 3.186, and the standard deviation is 0.427, which highlights significant variation in market power among these enterprises. The mean value of green M&As is 0.186, implying that approximately 18.6% of the sample enterprises have engaged in green M&A activities. The descriptive statistics for other control variables fall within a reasonable range and are largely consistent with existing research findings [13,32,34].

5.2. The Baseline Regression Result

To examine the impact of green M&As on the market power of heavily polluting enterprises, this study conducted regression analysis based on the formulation of Model (6). The results are presented in

Table 3. Model regression results.

Variable	(1) MKP	(2) MKP	(3) MKP
GMA	0.204 *** (12.01)	0.092 *** (5.45)	0.177 *** (7.967)
Size		0.006 (0.99)	0.100 *** (3.950)
Lev		−0.091 *** (−3.89)	−0.024 *** (−3.332)
TobinQ		−0.007 ** (−2.21)	−0.014 *** (−3.930)
Board		−0.073 *** (−2.88)	−0.025 (−0.992)
Dual		0.003 (0.31)	−0.002 (−0.178)
Top 1		0.001 *** (2.81)	0.002 *** (3.701)
Listage		0.156 *** (18.91)	0.025 ** (2.027)
Constant	1.219 *** (74.25)	0.877 *** (6.40)	1.091 *** (4.518)
Ind-fixed effect	YES	YES	YES
Time-fixed effect	YES	YES	YES
R2	0.002	0.018	0.782
N	6487	6487	6487

Note: ** and *** indicate statistical significance at the 5% and 1% levels, respectively.

. The regression results from Columns (1) to (3) indicate that, as enterprise-level control variables are progressively added and fixed effects at all levels are controlled, the regression coefficients of green M&As remain significantly positive at the 1% level. This suggests that the implementation of green M&As by heavily polluting enterprises can substantially enhance their market power, thereby supporting Hypothesis 1 of this study. It is evident that green M&As, as a “pro-environment” initiative undertaken by heavily polluting enterprises, can rapidly demonstrate their legitimacy. This enables the establishment of a constructive relationship with the government and secures resource support from governmental entities. It is consistent with the research conclusion of Yang et al. (2023) [46]. Simultaneously, it garners favor among other stakeholders, leading to increased external investment opportunities and laying a robust foundation for enhancing the market power of heavily polluting enterprises. This further substantiates the research argument presented by Huang et al. (2023) [14]. Furthermore, the benchmark regression results confirm that the implementation of green M&As by heavily polluting enterprises is not aimed at “strategic ingratiation” but rather focuses on leveraging the complementary sustainable resource advantages of both merging entities. This approach aims to break through path dependency on the original extensive production model, thereby expanding the market share of heavily polluting enterprises and strengthening their long-term position within the industry. These findings suppose Hypothesis 1.

5.3. Robustness Test Result

5.3.1. Empirical Testing with Matched Samples

To address the issue of sample selection bias, the control variables in the model were employed as matching criteria, and 1:1 nearest neighbor matching with replacement was implemented. The test results are presented in Column (1) of

Table 4. Regression results for the matched samples.

Variable	(1) 1:1 PSM	(2) 1:3 PSM	(3) CEM	(4) EBM
GMA	0.139 *** (3.872)	0.172 *** (4.171)	0.259 *** (4.069)	0.093 *** (5.764)
Constant	0.948 ** (2.110)	1.351 *** (2.312)	5.725 * (1.713)	1.386 *** (4.561)
Control variables	YES	YES	YES	YES
Ind-fixed effect	YES	YES	YES	YES
Time-fixed effect	YES	YES	YES	YES
R2	0.892	0.821	0.984	0.816
N	1038	2112	409	6487

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

. After matching, the regression coefficient of the explanatory variable, green M&As, on the market power of heavily polluting enterprises is significantly positive at the 1% significance level. This finding aligns with the direction of the benchmark regression results, thereby providing preliminary evidence for the robustness of the regression results. In addition, when the sample size of the control group significantly exceeds that of the treatment group, one-to-many matching can effectively reduce sampling variance. In addition to the propensity score matching method, this study employs the coarsened exact matching (CEM) and entropy balancing matching (EBM) methods to re-estimate the impact of green M&As on the market power of heavily polluting enterprises. The CEM method reduces imbalance by minimizing estimation errors and population variance, thereby decreasing model dependence. The EBM method assigns weights to sample observations, ensuring that the sample moments of control variables between the treatment and control groups achieve balance in the weighted samples, thus maximizing precise matching [68]. The regression results of these two methods are presented in Columns (3) and (4) of Table 4. The regression coefficient for green M&As on the market power of heavily polluting enterprises is significantly positive at the 1% significance level, further confirming the robustness of the benchmark regression results.

5.3.2. Other Robustness Tests

In addition to the robustness test conducted after matching samples, this study performed a series of additional robustness tests.

The first test involved incorporating interactive fixed effects for city, industry, and year, respectively. Compared with the traditional panel fixed-effects model, the interactive fixed-effects model is capable of comprehensively accounting for multi-dimensional shocks present in the real economy as well as the heterogeneity of these shock effects experienced by different enterprises. The regression results are presented in Column (1) of

Table 5. Regression results for the matched samples.

Variable	(1) Interaction Fixed Effects	(2) Replace the Explained Variable	(3) Replace the Explanatory Variable	(4) Eliminate Policy Interference	(5) Replace the Estimation Method	(6) Add Control Variables	(7) Exclude Multiple Green M&As
GMA	0.081 *** (4.862)	0.103 *** (15.149)	0.012 *** (5.618)	0.059 *** (4.179)	0.073 *** (6.164)	0.085 *** (5.089)	0.053 ** (2.269)
Constant	1.632 *** (10.202)	−0.967 *** (−13.247)	1.347 *** (7.521)	1.735 ** (11.162)	1.031 *** (10.216)	1.324 *** (7.409)	1.372 *** (5.520)
Green credit				0.110 *** (14.618)
Low carbon				0.078 *** (12.035)
Control variables	YES	YES	YES	YES	YES	YES	YES
Ind-fixed effect	YES	YES	YES	YES	YES	YES	YES
Time-fixed effect	YES	YES	YES	YES	YES	YES	YES
R2	0.786	0.597	0.781	0.784	0.716	0.782	0.818
N	6487	6487	6487	6487	5613	6487	3172

Note: ** and *** indicate statistical significance at the 5% and 1% levels, respectively.

and are consistent with the aforementioned benchmark regression results.

The second approach involved replacing the measurement method of the explained variable. Drawing on the methodology proposed by Spierdijka and Zaourasa (2018) [69], the Lerner index [(operating revenue − operating cost − sales expenses)/(operating income)] was utilized as an alternative measure for enterprise market power. The regression results are presented in Column (2) of Table 5 and remain consistent with the prior benchmark regression analysis.

The third approach involved modifying the measurement methodology of the explanatory variable. Relying solely on content identification to assess green M&As may lead to an overemphasis on keyword-based detection, which carries the risk of misclassification and could undermine causal inference. To address this issue, this study adopted the method employed by Sun et al. [35]. Specifically, a merger or acquisition was classified as “green” if the target company had obtained at least one green patent within the three-year period preceding the transaction. The data on corporate green patents were sourced from the Green Patent Research Database of the National Intellectual Property Information Center of China. The results are presented in Column (3) of Table 5 and further confirm the robustness of the experimental findings.

The fourth approach involved re-evaluating the results after excluding the influence of other policies. Given that the green credit policy and low-carbon pilot policy implemented by the state during the sample period may have had a certain impact on the market power of heavily polluting enterprises, it was necessary to control for these potential confounding factors. To this end, two dummy variables—“green credit” and “low-carbon pilot city”—were introduced into the regression model. For enterprises in industries restricted by the green credit policy, the dummy variable was set to 1 for the current year and all subsequent years, and it was set to 0 for all other cases. Similarly, if an enterprise’s location was designated as a pilot city under the low-carbon pilot policy, the corresponding dummy variable was set to 1 starting from the year the policy was implemented and for all subsequent years; otherwise, it was set to 0. The regression results are presented in Column (4) of Table 5, further validating the conclusions derived from the benchmark regression analysis.

The fifth approach used an alternative estimation method. Given that the rationality of propensity score matching (PSM) hinges solely on the accuracy of the treatment equation or outcome equation specification, to address this limitation, the study also incorporated an inverse probability weighted regression adjustment (IPWRA) approach to estimate the causal effect of green M&As on the market power of heavily polluting enterprises. This method integrates propensity score weighting with regression adjustment, assigning weights to observations based on the estimated propensity scores of observable characteristics. By doing so, it effectively mitigates sample selection bias arising from the non-random participation of heavily polluting enterprises in green M&A activities, thereby reducing the risk of parameter mis-specification and associated estimation errors. The test results presented in Column (5) of Table 5 are largely consistent with the benchmark regression results, thereby further validating the robustness of the aforementioned conclusions [70].

The sixth approach involved the continued addition of control variables. In this study, a range of financial indicators were included as control variables in the benchmark regression model. To more comprehensively account for potential confounding factors at different levels, this study further incorporated R&D subsidies, management turnover, and digital mergers and acquisitions as control variables, representing the dimensions of enterprise R&D activities, executive management, and transactional behavior, respectively. The results are presented in Column (6) of Table 5. After controlling for these additional variables, green M&As continue to exhibit a significant impact on the market power of heavily polluting enterprises, thereby reaffirming the robustness of the benchmark regression findings.

The seventh approach excluded samples that had engaged in multiple green M&A transactions during the research period. In the benchmark estimation, while the approach of designating the year of an enterprise’s first green M&A as the treatment year helps avoid overlapping treatment effects that may arise when heavily polluting firms undergo multiple green M&As across different years, it could also somewhat compromise the accuracy of the average treatment effect estimation. To address this concern, this study follows the methodology of Tang et al. [32], focusing only on heavily polluting enterprises that experienced a single green M&A. These enterprises constitute the treatment group used in the robustness test, with results presented in Column (7) of Table 5. The regression coefficient associated with green M&As remains significantly positive, thereby reinforcing the robustness of the study’s central findings.

In addition to the aforementioned series of robustness tests, this study further distinguished itself from previous robustness verification methods, such as altering variable measurement approaches, replacing regression models, conducting sample matching, and excluding alternative influencing factors, by focusing on the intrinsic characteristics of the samples. Specifically, this study modified the control group by selecting heavily polluting enterprises that had undergone non-green M&As as the new control group, matching the samples accordingly. Furthermore, this study conducted the regression analysis after excluding heavily polluting enterprises located in municipalities directly under the Central Government. The regression coefficients for green M&As remain significantly positive across all these specifications.

5.3.3. Endogeneity Test

The impact of green M&As on the market power of heavily polluting enterprises may be endogenous. First, endogeneity could arise due to omitted variables in the model specification. Second, there might exist reverse causality between green M&As and the market power of heavily polluting enterprises. Green M&A decisions may be influenced by the forward-looking strategic orientations of enterprises, thereby introducing potential non-randomness between the treatment group and the control group. For instance, heavily polluting enterprises with substantial market power might possess greater resources to identify and acquire high-quality green targets. Therefore, this study employed the two-stage least squares method to address the issue of endogeneity. Following the approach of Shi et al. [22], the number of dialects surrounding heavily polluting enterprises and the spherical distance from the local government were selected as instrumental variables. On one hand, green M&As involve extensive communication processes, where communication barriers often serve as a key factor contributing to the failure of such mergers and reorganizations. Regional dialects can help reduce these barriers, thereby increasing the likelihood of successful green M&As. Hence, dialects are significantly associated with the outcomes of green M&As. On the other hand, dialects have developed over a long historical period, and the physical distance between heavily polluting enterprises and local government offices is unrelated to market power.

Column (1) of

Table 6. Endogeneity test.

Variables	The First Stage	The Second Stage
	GMA	MKP
Dialect	0.541 *** (4.546)	0.050 ** (2.228)
Constant	−0.170 (−1.298)	1.294 *** (6.861)
Control variables	YES	YES
Ind-fixed effect	YES	YES
Time-fixed effect	YES	YES
N	6487	6487
R2	0.880	0.820
F	131.84 ***	43.87 ***
Unidentifiable test		1610.69 ***
Weak instrumental variable test		2064.91 ***

Note: ** and *** indicate statistical significance at the 5% and 1% levels, respectively.

indicates that the instrumental variable exerts a statistically significant effect on the endogenous variables in the first stage. The results presented in Column (2) confirm that the instrumental variable successfully passes both the unidentifiability test and the weak instrument test. A two-step estimation procedure is employed. The impact of green M&As on the market power of heavily polluting enterprises remains significant, which is largely consistent with the findings from the benchmark regression analysis.

5.3.4. Parallel Trend Test

The precondition for the consistency of the estimated results in the DID model is that the experimental group and the control group satisfy the parallel trend assumption prior to the implementation of the policy. Consequently, following the steps of the event study methodology, relative year information both before and after a green M&A was incorporated into the regression analysis, thereby constructing a dynamic DID model:

$$\mathrm{M}\mathrm{K}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}={\beta }_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}}+{\displaystyle \sum _{\mathrm{n}\ne -1}{\beta }_{\mathrm{n}}\mathrm{G}\mathrm{M}{\mathrm{A}}_{\mathrm{i},\mathrm{t}+\mathrm{n}}+}{\displaystyle \sum _{\mathrm{j}}{\beta }_{\mathrm{j}}\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}{\mathrm{l}}_{\mathrm{j}\mathrm{i}\mathrm{t}}}+{\mathrm{Y}}_{\mathrm{t}}+{\mathrm{P}}_{\mathrm{i}}+{\mathrm{F}}_{\mathrm{i}}+{\epsilon }_{\mathrm{i}\mathrm{t}}$$

(8)

In Equation (8), n represents the year in which the green M&A is completed. If n = −1, this indicates the year prior to the completion of a green M&A. Therefore, GMA_i,t+n equals 1 in year t for heavily polluting enterprise i when it conducts a green M&A relative to year n, and otherwise equals 0. The meanings of the remaining symbols are consistent with Equation (6). It is important to note that for enterprises engaging in multiple green M&As, this study adopted the situation in the year preceding the completion of the first green M&A as the comparative baseline. The results of the parallel trend test are presented in Figure 3. In the years preceding green M&As, there is no statistically significant difference in market power between the experimental group and the control group, suggesting that a common trend exists between the two groups prior to green M&As, which satisfies the assumption of parallel trends. Following the implementation of green M&As, the market power of the experimental group is significantly greater than that of the control group, indicating that green M&As by heavily polluting enterprises can substantially enhance market power. The dynamic effect becomes increasingly pronounced over time and eventually stabilizes.

5.3.5. Counterfactual Tests

In addition to the more intuitive parallel trend test, this study drew upon the methodology of Xu et al. [15] and further controlled for potential enterprise-level confounding factors that may influence market forces through counterfactual analysis. This approach enhances the statistical validity of the randomness in the timing of green M&A implementations. Specifically, this study set the implementation timing of green M&As as occurring one to two years earlier or being delayed by one to two years, followed by sequential regression analyses. If fluctuations in the implementation timing of green M&As do not consistently and significantly influence the market power of heavily polluting enterprises, it suggests that the observed increase in their market power can be attributed to the execution of such green M&A activities. As indicated by the results presented in

Table 7. Counterfactual tests.

Variable	(1) Two Years in Advance	(2) One Year in Advance	(3) One Year Delay	(4) Two Years in Delay
GMA	0.032 (1.575)	0.014 (0.871)	0.059 *** (3.395)	0.027 (1.521)
Constant	1.078 *** (5.121)	1.324 *** (7.408)	1.322 *** (7.068)	1.295 *** (6.522)
Control variables	YES	YES	YES	YES
Ind-fixed effect	YES	YES	YES	YES
Time-fixed effect	YES	YES	YES	YES
R2	0.791	0.781	0.801	0.819
N	6487	6487	6487	6487

Note: *** indicates statistical significance at the 1% level.

, the regression coefficients associated with green M&As are not statistically significant when the implementation period is set one year in advance, two years in advance, or two years after the reference period. However, when the implementation is delayed by one year, the corresponding regression coefficient becomes significantly positive. This approach effectively controls for potential internal and external confounding factors, thereby confirming the robustness of the baseline regression results. The continued statistical significance observed in the one-year lagged model suggests that it takes time, following the execution of green M&As, for heavily polluting firms to fully integrate the technologies, equipment, and resources of both the acquirer and the target firm before the intended benefits can manifest.

5.3.6. Placebo Test

In the benchmark regression process, potential interference may arise from unobservable omitted variables, which can introduce bias into the results. To assess the robustness of our findings, this study conducted a placebo test as follows. Since there were 80 heavily polluting enterprises undergoing green M&As in the sample, to ensure comparability, an equal number of enterprises were randomly selected to form a pseudo-treatment group, while the remaining enterprises constituted a pseudo-control group. Following the same DID methodology as our benchmark analysis, 500 simulated regressions were performed to generate a distribution of placebo coefficients under the null hypothesis of no real treatment effect. Subsequently, the kernel density distribution of these coefficients was plotted. As depicted in Figure 4, the regression coefficient of the pseudo green M&As effect is centered around the value of 0 and exhibits a normal distribution. This is notably distinct from the vertical dotted line representing the statistically significant coefficient estimate from the benchmark regression. Such a discrepancy suggests that the significant benchmark regression outcome is unlikely to be driven by unobservable omitted variables, thereby further substantiating the robustness of the conclusion that green M&As enhance the market power of heavily polluting enterprises.

5.3.7. Multi-Time DID Heterogeneous Treatment Effects

In the application of the multi-time difference-in-differences method for research, given the varying impacts of the same treatment on different heavily polluting enterprises, discrepancies may arise in the Two-Way Fixed Effects (TWFE) statistical results. Consequently, the group-period weighted average treatment effect and relative event time-stacked data were employed to estimate the heterogeneous robustness of the treatment effect, thereby mitigating estimation bias induced by treatment effect heterogeneity to some extent. As depicted in Figure 5 and Figure 6, the estimated results obtained under the two heterogeneous robust estimation methods are largely consistent with the parallel trend test. This further substantiates the robustness of the research findings and indicates that green M&As can enhance the market power of heavily polluting enterprises.

5.4. Mediating Effect Test

5.4.1. Mediating Effect Test of Green TFP

According to prior theoretical analysis, green M&As can facilitate clean technology transfer and provide guidance for green innovation within a short timeframe. This can enhance the resource utilization efficiency of heavily polluting enterprises as well as the production capacity of environmentally friendly products, thereby stimulating improvements in enterprises’ green total factor productivity and subsequently driving an increase in market power. Column (1) in

Table 8. Regression results of the mediating effect.

Variable	(1) Gtfp	(2) Hum	(3) Bc
GMA	0.101 *** (5.879)	0.093 *** (6.103)	0.585 *** (4.795)
Constant	0.911 *** (4.409)	−0.668 *** (4.175)	1.544 (1.405)
Control variables	YES	YES	YES
Ind-fixed effect	YES	YES	YES
Time-fixed effect	YES	YES	YES
R2	0.646	0.154	0.712
N	6487	6487	6487

Note: *** indicates statistical significance at the 1% level.

presents the regression analysis results with the green total factor productivity of heavily polluting enterprises as the dependent variable. The findings reveal that the regression coefficient of green M&As is significantly positive at the 1% level, suggesting that green M&As play a crucial role in enhancing enterprise green total factor productivity. This outcome corroborates Hypothesis 2 presented earlier. This research perspective aligns closely with the conclusions drawn by earlier scholars [11,43].

5.4.2. Mediating Effect Test of Human Capital

According to prior theoretical analysis, green M&As can optimize the human capital structure of heavily polluting enterprises. As presented in Column (2) of Table 8, the regression coefficient of green M&As on the human capital structure is significantly positive at the 1% significance level. These findings indicate that green M&As conducted by heavily polluting enterprises generate knowledge spillover effects, promote the development of a green talent hierarchy within these enterprises, optimize their human capital structure, and consequently enhance market power. This result aligns with Hypothesis 3 presented above. Furthermore, it is also in agreement with certain research findings that examine the relationship between technology mergers and acquisitions and human capital [71].

5.4.3. Mediating Effect Test of Brand Capital

The theoretical analysis presented in this study indicates that green M&As can effectively facilitate brand building for heavily polluting enterprises, enhance the diversity of their sustainable brands, and accumulate brand capital within the green domain. The development of such brand capital enables these enterprises to gradually acquire unique and valuable resources that are difficult to imitate or substitute, thereby strengthening their market power. As presented in Column (3) of Table 8, the regression coefficient of green M&As on the brand capital of heavily polluting enterprises is significantly positive at the 1% level. This finding robustly demonstrates that the “brand capital” mechanism is valid. Specifically, the initiation of green M&As enables heavily polluting enterprises to accumulate brand capital, thereby enhancing public trust and ultimately strengthening the market power of these enterprises. This result provides strong support for Hypothesis 4.

5.5. Heterogeneity Test

5.5.1. Heterogeneity of Merger and Acquisition Methods

Due to the significant disparities in policy environment, business model, language, and culture between the parties involved in cross-border green M&A transactions, the synergistic effects and technology spillover effects of green M&As on the market power of heavily polluting enterprises may differ from those observed in domestic green M&A activities. The regression results are presented in Columns (1) and (2) of

Table 9. Heterogeneity test results: green M&A approach.

Variable	(1) Domestic	(2) Cross-Border
GMA	0.096 *** (5.065)	0.048 (1.395)
Constant	1.281 *** (6.792)	1.262 *** (3.655)
Control variables	YES	YES
Ind-fixed effect	YES	YES
Time-fixed effect	YES	YES
R2	0.779	0.789
N	1223	5264

Note: *** indicates statistical significance at the 1% level.

. Specifically, the regression coefficient for domestic green M&As on the market power of heavily polluting enterprises is significantly positive at the 1% level, whereas the coefficient for cross-border green M&As is not statistically significant. This suggests that the influence of domestic green M&As on the market power of heavily polluting enterprises is stronger compared to that of cross-border green M&As. It is evident that, although numerous high-quality overseas enterprises with a core focus on green development can offer more complementary and sustainable resources to China’s heavily polluting enterprises, domestic green M&As can rapidly achieve synergistic effects due to the advantage of lower integration and reorganization costs. Moreover, domestic green M&As demonstrate a more pronounced impact on influencing the market power of heavily polluting enterprises.

5.5.2. Enterprise Heterogeneity: State-Owned Enterprises vs. Non-State-Owned Enterprises

Owing to the disparities in institutional advantages, resource allocation, and other aspects among enterprises with different ownership structures, the implementation of green M&As may exert varying impacts on the market power of heavily polluting enterprises. In this context, by considering the nature of enterprise ownership, this study investigates the impact of green M&As on the market power of both state-owned and non-state-owned heavily polluting enterprises. The regression results are presented in Columns (1) and (2) of

Table 10. Heterogeneity test results: enterprise type.

Variable	Heterogeneity of Property Rights		Heterogeneity of Life Cycle
	(1) Non-State-Owned	(2) State-Owned	(3) Growing	(4) Mature	(5) Declining
GMA	0.096 *** (5.065)	0.048 (1.395)	0.038 (1.248)	0.104 *** (3.243)	0.054 * (1.680)
Constant	1.281 *** (6.792)	1.262 *** (3.655)	0.883 ** (2.550)	0.754 ** (2.175)	0.043 (1.580)
Control variables	YES	YES	YES	YES	YES
Ind-fixed effect	YES	YES	YES	YES	YES
Time-fixed effect	YES	YES	YES	YES	YES
R2	0.779	0.789	0.808	0.820	0.838
N	1223	5264	1935	2195	2357

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

. It is evident that the regression coefficient of green M&As on the market power of non-state-owned enterprises is significantly positive at the 1% level, whereas the regression coefficient for state-owned enterprises is not significant. This suggests that the impact of green M&As on enhancing the market power of non-state-owned heavily polluting enterprises is stronger compared to that of state-owned heavily polluting enterprises. Owing to the intrinsic political connections, state-owned enterprises enjoy higher levels of legitimacy, encounter less pressure from environmental regulations and media scrutiny, and thus exhibit weaker motivation for implementing green M&As compared to non-state-owned enterprises. Conversely, non-state-owned enterprises lack such inherent advantages and are in urgent need of establishing favorable relationships with stakeholders through green M&As to enhance their market power. Consequently, green M&As tend to have a more sustained and positive impact on the market power of non-state-owned enterprises, particularly those in heavily polluting industries.

5.5.3. Enterprise Heterogeneity: Growing, Mature, and Declining Enterprises

The enterprise life cycle theory indicates that enterprises at different stages of their life cycle exhibit significant differences in terms of production and operation, organizational characteristics, and capital allocation capabilities [72]. Consequently, for enterprises at varying life cycle stages, the impact of green M&As on the market power of heavily polluting enterprises may differ. In this context, the samples are categorized based on the life cycle stages of heavily polluting enterprises to investigate the impact of green M&As on the market power of growing, mature, and declining heavily polluting enterprises, respectively. The regression results are presented in Columns (3)–(5) of Table 10. The regression coefficient of green M&As on the market power of mature, heavily polluting enterprises is significantly positive at the 1% level. In contrast, the regression coefficient for declining heavily polluting enterprises is only significantly positive at the 10% level, indicating a reduced significance of the impact effect compared to mature enterprises. Notably, the regression coefficient for the market power of growing heavily polluting enterprises is not statistically significant. It is evident that mature enterprises typically possess stable market shares and substantial capital accumulation, which enhances the efficiency of mergers and acquisitions. This enables them to swiftly leverage new resources and technologies post-green M&A, effectively transforming their business models to align with environmental regulations and market expectations, and thereby significantly augmenting their market power. Although green M&As can infuse new vitality into these enterprises, such as rebuilding market trust through the provision of more environmentally friendly technologies or brands, the majority of them are zombie enterprises, resulting in a relatively weakened overall market presence. While growing enterprises possess innovation capabilities and market expansion potential, they are typically small in scale and lack sufficient resources and experience to address the cost challenges inherent in the process of green M&As.

6. Discussion and Conclusions

6.1. Discussion

Firstly, this study reveals that green M&As, which represent a form of technology M&A incorporating the principles of sustainable development, continues to exhibit the potential to enhance market power. While Jiang (2021) and Stiebale et al. (2022) established M&As’ market power effects [30,73], prior research has not examined how sustainability-integrated green M&As influence heavily polluting enterprises. By integrating legitimacy and stakeholder theory within the operational context of these enterprises and using green M&A events as a research focal point, this study provides robust empirical evidence that green M&As enable heavily polluting enterprises to reconcile competitiveness with sustainable development. In addition to empirical research, actual operational cases involving heavily polluting enterprises can also serve as strong evidence supporting the correlation between green M&As and market power. Hanlan Environment (600323) has successfully completed the privatization of Yuefeng Environmental Protection, the largest waste incineration company in Guangdong Province. Following this environmentally strategic merger and acquisition, Hanlan Environment’s daily waste processing capacity has increased to 97,600 tons, positioning it as the leading waste incineration enterprise in the A-share market and among the top three nationwide. The integration of both parties’ supply chains is expected to reduce per-ton waste treatment costs, leveraging economies of scale to lower financing expenses. It is projected that annual financial expenditures will be reduced by HKD 221 million. This enhancement in cost efficiency strengthens Hanlan Environment’s bargaining power and reinforces its competitive position in the market [74].

Secondly, existing literature primarily focuses on the influencing mechanisms of green M&As on enterprise performance, which include green technological innovation, production costs, environmental awareness, social responsibility, and financial constraints [9,18,22]. However, compared to traditional technological innovation, the green concept embedded in green M&As is more likely to enhance enterprise performance by strengthening the sustainable brand image of heavily polluting enterprises and accumulating distinctive brand capital. Furthermore, human capital, which is closely tied to the operations of heavily polluting enterprises, represents another critical mechanism that should not be overlooked. Simultaneously, adopting green total factor productivity as an influencing mechanism of green M&As and enterprise performance can comprehensively reflect an enterprise’s investment in green technology and its subsequent environmental protection outcomes. This study reveals that green M&As can boost the market power of heavily polluting enterprises via three key mechanisms: enhancing green total factor productivity, optimizing human capital, and strengthening brand capital. These findings broaden current understanding of how green M&As influence enterprise performance. These three mechanisms can also explain why heavily polluting enterprises are more likely to enhance their market power through green M&As. From the perspective of green total factor productivity, the green total factor productivity of non-polluting enterprises is generally higher than that of heavily polluting enterprises, leaving limited room for technological upgrading, and resulting in relatively modest cost-saving benefits and weaker improvements in market bargaining power through green M&As [75]. From the perspective of human capital, non-polluting enterprises typically possess a more adaptable human resource structure; therefore, modifying this structure via green M&As may even lead to increased management costs due to potential functional redundancies [76]. From the perspective of brand capital, heavily polluting enterprises face greater urgency in improving their public image and enterprise reputation. In contrast, the enhancement of brand value through green M&As among other enterprises tends to reflect only marginal gains in terms of differentiated competitive advantages [77].

Third, similar to most prior studies, this study also categorizes the sample of heavily polluting enterprises into state-owned enterprises and non-state-owned enterprises based on their property rights attributes. Furthermore, it investigates the intricate relationship between green M&As and heavily polluting enterprises with different property rights [18,35]. In reality, the relationship between green M&As and the market power of heavily polluting enterprises suggests that various forms of green M&As, as well as differences in the life cycle stages of these enterprises, warrant further exploration. Consequently, comparative analyses grounded in different types of green M&As and variations in enterprise life cycles hold greater practical and guiding significance.

6.2. Conclusions

This study selects the financial and operational data of heavily polluting listed enterprises from 2010 to 2022, and empirically investigates the impact and underlying mechanisms of green M&As on the market power of heavily polluting enterprises by employing a multi-time DID model and a mediating effect model. The findings indicate that:

Firstly, green M&As can substantially enhance the market power of heavily polluting enterprises. This conclusion remains robust after a series of rigorous robustness tests. Secondly, green M&As exert their influence through mediating variables such as enterprise green total factor productivity, human capital structure, and brand capital, thereby promoting the market power of heavily polluting enterprises. Lastly, heterogeneity analysis reveals that domestic green M&As have a more pronounced effect on enhancing the market power of heavily polluting enterprises compared to cross-border green M&As. Moreover, green M&As are found to be more effective in boosting the market power of non-state-owned heavily polluting enterprises than state-owned ones. Additionally, the impact of green M&As is more significant for mature heavily polluting enterprises compared to growing or declining ones.

6.3. Recommendations

This study puts forward the following policy recommendations:

Based on the benchmark regression analysis presented in this study, the government should establish targeted fiscal incentives, such as tax deductions specifically tied to the transaction value of green M&As and subsidies for post-merger environmental technology upgrades, to lower the financial barriers identified and encourage heavily polluting enterprises to pursue green M&As as a viable pathway to enhance their environmental standards. Secondly, the governments should design incentive programs specifically for heavily polluting enterprises achieving above-average market power post-green M&A, leveraging this study’s evidence of 17.7% market power enhancement. Simultaneously, publish anonymized case studies highlight this dual benefit—significant environmental compliance alongside measurable market power gains—to demonstrate strategic value to peer firms. Finally, it is crucial to optimize the regulatory environment surrounding green M&As in order to ensure transparency and fairness throughout the acquisition process. This will help prevent market monopolies and unfair competition while avoiding situations where “too much regulation is as bad as too little.”

According to the three transmission pathways through which green M&As influence the market power of heavily polluting enterprises, it is essential for the government to concentrate on the following aspects during the implementation process of green M&As involving these enterprises. First, it is essential to promote the innovation and transfer of green technology. The government should establish a dedicated fund for research and development in green technology, facilitate the international transfer of such technologies, and assist heavily polluting enterprises that engage in green M&As by providing them with access to advanced environmental protection technologies. This support aims to enhance their green productivity. Second, there is a need to strengthen the training of talent in the field of environmental protection and improve relevant skills. The government, in collaboration with enterprises, should establish professional training institutions focused on environmental protection. Measures such as scholarships and subsidies should be implemented to elevate the quality of human capital. This initiative will help cultivate more technical personnel and management professionals specialized in environmental protection, thereby providing necessary talent support for the effective implementation of green M&As among heavily polluting enterprises. Finally, given that green M&As significantly enhance brand capital by over 50% and subsequently market power for heavily polluting enterprises, policymakers should prioritize measures facilitating successful green M&As. This includes targeted financial mechanisms and technical assistance specifically designed to help these enterprises integrate environmental and social responsibility into their core brand strategy through green M&A implementation. Concurrently, regulatory frameworks and market supervision should be optimized to ensure the transparency and credibility of the resulting brand capital gains, fostering fair competition.

According to the findings of the heterogeneity test, it is recommended that the government prioritize strategies for green M&As and formulate supportive policies tailored to the specific conditions of enterprises. Firstly, based on our finding that domestic green M&As significantly enhance market power for heavily polluting firms while cross-border deals show limited effects, policy should prioritize facilitating domestic green M&A activity. Streamlining administrative processes and providing targeted support for domestic green M&A initiatives can accelerate their implementation. Concurrently, refining industry standards and certification systems is crucial to guiding firms, particularly within their domestic supply networks, to strengthen green collaboration with suppliers. This integrated approach promotes supply chain greening and effectively bolsters the market power of participating enterprises. Secondly, when considering enterprises with varying property rights, particular attention should be paid to state-owned heavily polluting enterprises. It is essential to provide increased support for their green projects in order to stimulate enthusiasm for green M&A endeavors. Strengthening cooperation with relevant enterprises in green technology innovation will also serve to enhance their market competitiveness. Finally, concerning both emerging and declining heavily polluting enterprises, it is crucial for the government to offer greater innovation incentives aimed at guiding these entities towards sustainable business model innovation. This strategic direction will promote simultaneous improvements in both market power and pollution control capabilities in these enterprises while narrowing the performance gap compared to more mature enterprises.

6.4. Limitations and Future Directions

Although this study conducted a series of analyses on the relationship between green M&As and the market power of heavily polluting enterprises, it still has certain limitations, which also represent potential directions for future research. In terms of data and methodology, this study utilizes the data of A-share heavily polluting enterprises listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2022. Future research should consider extending the data timeframe and integrating the effects of recent policy shocks, such as the expansion of the carbon market post-2023 and the European Union’s Carbon Border Adjustment Mechanism (CBAM). Furthermore, a dual machine learning approach should be employed to enhance the robustness of the empirical findings. In identifying green M&A events, it is recommended to integrate complementary methods such as field research, grounded theory development, and questionnaire surveys in order to improve the accuracy of measuring green M&As. Additionally, third-party certification should be utilized to validate the accuracy of green M&A identification. In terms of research content, this study centers on heavily polluting enterprises. However, the carbon intensity across sub-industries such as steel, chemicals, and thermal power varies significantly. Green M&As in high-carbon industries may rely more heavily on government subsidies, whereas low-carbon, technology-intensive industries may depend more on market mechanisms. The current classification system has not been refined to the four-digit industry code level, making it challenging to uncover the differentiated impacts of green M&A strategies under varying emission reduction pressures. Future research should further refine the categorization of heavily polluting enterprises and provide a more in-depth discussion of the empirical findings. In a research sense, the existing data provide empirical support for transnational green M&As. However, these studies fail to adequately address the complex influence of “carbon barriers” in cross-border M&As on the market power of heavily polluting enterprises. Additionally, there is a lack of comparative analysis with other major industrialized countries, such as India and Brazil, which restricts the generalizability of the conclusions. Future research should integrate green M&As with the competitiveness enhancement pathways for heavily polluting enterprises in other developing countries. Meanwhile, it is also essential to explore additional factors contributing to the strengthened market power of heavily polluting enterprises, particularly from the perspective of overcoming carbon barriers and achieving carbon reduction outcomes through green M&As.