The Impact of Mergers and Acquisitions on Firm Environmental Performance: Empirical Evidence from China

Abstract

In this study, we examine the impact of mergers and acquisitions (M&As) on firm environmental performance, aiming to address the gap in research and guide firms, investors, and policymakers toward more environmentally conscious decision-making in M&A. Using panel data from Chinese A-share listed firms (2008–2022), we estimate a two-way fixed effect model. The Propensity Score Matching and the instrumental variable method address potential endogeneity concerns, and robustness checks validate the findings. We found that M&As have a significantly positive effect on firm environmental performance, with heterogeneous impacts across regions, industries, and M&A types. The environmental benefits are most pronounced in heavily polluting industries and hybrid M&A deals. Eastern China shows more modest improvements. The results of mechanism tests revealed that M&As enhance environmental performance primarily by boosting total factor productivity and fostering innovation. This study offers a novel perspective by linking M&A activities to environmental sustainability, enriching the literature on both M&As and corporate environmental performance. We show that even conventional M&A deals (not sustainability-focused) can improve environmental performance through operational synergies. Expanding beyond polluting industries, we reveal how sector characteristics shape M&A’s environmental impacts. We identify practical mechanisms through which standard M&A activities can advance sustainability goals, helping firms balance economic and environmental objectives. It provides empirical evidence from China, an emerging market with distinct institutional and regulatory contexts. The findings offer guidance for firms engaging in M&A to strategically improve sustainability performance. Policymakers can leverage these insights to design incentives for M&A in pollution-intensive industries, aligning economic growth with environmental goals. By demonstrating that M&As can enhance environmental outcomes, this study supports the potential for market-driven mechanisms to contribute to broader societal sustainability objectives, such as reduced industrial pollution and greener production practices.

1. Introduction

With the escalation of global environmental challenges, environmental governance has evolved beyond national or regional boundaries, emerging as a pressing global priority. International agreements such as the Paris Agreement and the UN Framework Convention on Climate Change have established clear objectives and frameworks for collective action. Meanwhile, governments worldwide have implemented tailored policies—ranging from emission controls to clean energy incentives and green finance—to address local environmental concerns. In China, stringent regulations such as the Environmental Protection Tax Law and the Cleaner Production Promotion Law have raised corporate environmental standards, compelling businesses to transition from resource-intensive practices to green, low-carbon models. The “dual carbon” goals (carbon peak and neutrality) have further intensified policy demands, amplifying challenges for corporate sustainability.

In recent years, mergers and acquisitions (M&A) have become a dominant strategy for corporate growth, market expansion, and competitive advantage. The effects of M&As actually extend beyond economic efficiency to influence environmental performance [1,2,3,4,5,6,7]. Nevertheless, while the financial and operational impacts of M&As are widely studied, their effect on environmental performance remains underexplored [3]. Tightening global environmental regulations and rising stakeholder expectations impose stricter compliance demands, challenging firms to bolster their environmental management post-M&A.

The existing research has predominantly examined the economic effects of M&As, with limited focus on their environmental impact. A review study on M&As [3] suggests that their value creation potential in terms of social responsibility and the environment is currently underexplored. The research question of our study is how M&As affect firm environmental performance, aiming to address the gap in research and guide firms, investors, and policymakers toward more environmentally conscious decision-making in corporate mergers and acquisitions.

Specifically, we construct a theoretical framework, integrating synergy theory and resource-based view (RBV). Based on data of Chinese A-share listed firms from 2008 to 2022, our empirical results confirm that not only green M&As but also traditional M&As exhibit a significantly positive impact on environmental performance. Consistent with the literature [4,8,9], green M&A refers to mergers, acquisitions, or other forms of business combinations that are driven by sustainability goals, environmental considerations, or the transition to a low-carbon economy. For instance, a traditional energy company acquires a renewable energy firm to diversify into solar/wind power.

We also show the heterogeneous effects across regions, industries, and M&A types. In Eastern China, post-M&A improvements are modest. Conversely, in Central and Western China, M&As lead to greater environmental gains. Furthermore, the environmental benefits of M&As are most pronounced in heavily polluting industries. Additionally, compared with the other types of M&As, hybrid M&As contribute more significantly to improving environmental performance. We also demonstrate that M&As primarily advance environmental performance by driving up total factor productivity (TFP) and accelerating innovation. We confirm the robustness of these results through rigorous checks, and address potential endogeneity concerns using Propensity Score Matching (PSM) and the instrumental variable (IV) method.

Our study extends the theoretical framework of M&A research while also offering practical guidance for firms navigating sustainability-driven markets. Firms should target acquisitions with strong environmental capabilities to boost both ecological performance and competitiveness. Post-merger, rigorous monitoring and compliance are critical. In addition, the research findings have important policy implications. Policymakers should implement incentive schemes to encourage M&As, particularly green M&As, guiding firms to prioritize environmental protection during the merger process. Importantly, our multi-industry analysis reveals substantial variation in environmental outcomes, suggesting that one-size-fits-all approaches to sustainable M&As are likely to prove ineffective. It is crucial to formulate differentiated regulations on M&As and the environment tailored to firms with varying characteristics, thereby effectively promoting corporate restructuring and green transformation while ensuring environmental compliance throughout M&A activities.

The remainder of this paper is organized as follows. In Section 2, we review the related literature and compare our study with prior work. In Section 3, we introduce the theoretical framework. In Section 4, we describe the data and outline the estimation model. In Section 5, we present the empirical findings, with a discussion in the following section. Lastly, Section 7 concludes this paper.

2. Literature Review

Our study relates to the rich literature on mergers and acquisitions in general. M&As have long been a cornerstone of corporate strategy, enabling firms to achieve growth, diversify risks, and enhance competitive advantages. The dominant rationale for M&As is that combining firms creates value through operational, financial, or managerial synergies [10,11]. Operational synergies include cost reductions (e.g., economies of scale) and revenue enhancements (e.g., cross-selling). Empirical evidence demonstrates mixed performance post M&A. Some researchers have found improved operating performance following mergers compared with others [12]. Nevertheless, many deals fail to improve profitability [13,14]. For cross-border M&As, cultural and institutional differences increase integration risks [15].

The authors of a number of studies also explore the reasons for successful M&As [16,17,18,19,20,21]. For instance, misalignment in corporate culture is a leading cause of failure [22]. Ineffective institutional environments may inhibit value-creating deals [23,24,25,26].

As global concerns about climate change, resource depletion, and pollution intensify, understanding the factors that drive environmental performance has become crucial for businesses, policymakers, and researchers. Another strand of related literature examines the determinants of their environmental performance, including firms’ personal characteristics such as size, resources, ownership structure, corporate governance, etc. Larger firms tend to be characterized by more significant environmental performance due to better financial and technological resources [27]. Small- and medium-sized enterprises often lag due to budget constraints but can improve through innovation [28]. The “slack resources” hypothesis suggests that profitable firms with better financial performance invest more in sustainability [29]. Moreover, family-owned firms may prioritize long-term sustainability over short-term profits [30]. Board composition is also important. For instance, a higher proportion of outside board directors is associated with more favorable environmental performance [31].

External institutional pressures also drive firms to enhance their environmental performance [32,33,34]. Command-and-control regulations (e.g., emissions limits) directly improve environmental performance. Market-based instruments (e.g., carbon taxes, cap-and-trade) incentivize voluntary reductions. In addition to the government, other stakeholders are also influential as well [35]. Firms are responsive to value-chain and internal stakeholder pressures. In addition, consumers’ demand for sustainable products drives environmental performance [35]. Negative publicity (e.g., oil spills) forces firms to improve [36]. Many plant- and parent-company-specific factors, such as the competitive position of the parent company and the organizational structure of the plant, affect firms’ responses to external pressures [32].

As firms face growing pressure to adopt sustainable business practices, the relationship between M&As and environmental performance is gaining increasing attention. While M&As are traditionally studied based on their financial and operational impacts, there is limited research on their environmental consequences. The authors of a handful of studies exploring this research question have predominantly examined green M&As and concentrate on pollution-intensive industries only [1,2,4,5,6,7]. For instance, Ref. [1] demonstrated in their study that green M&As can promote green innovation in the first and second post-acquisition years; however, this effect disappears in the third year. Both [2] and [6] found evidence in their studies that green M&As can improve environmental performance. In the study of [4], the results show that green M&As have a significantly positive impact on the environmental information disclosure quality of acquiring firms.

Differing from the above studies, our investigation breaks new ground through our systematic analysis of conventional M&As across diverse sectors in China’s rapidly evolving regulatory landscape. Three key innovations distinguish our work from prior studies. First, we demonstrate that even transactions not explicitly targeting sustainability can generate meaningful environmental improvements through operational synergies and resource optimization—challenging the prevailing dichotomy between “green” and “ordinary” M&As. Secondly, we move beyond the narrow focus on heavy polluters to incorporate light manufacturing, high-tech, and service sectors, revealing how industry characteristics fundamentally shape the environmental outcomes of M&As. Thirdly, our findings provide a more nuanced understanding of the mechanisms through which routine business combinations can contribute to sustainability goals, offering practitioners evidence-based strategies for enhancing environmental performance while pursuing traditional M&A objectives. These contributions not only expand the theoretical boundaries of M&A research but also provide actionable insights for firms operating in increasingly sustainability-conscious markets. The study’s emphasis on China’s context further provides unique insights into how emerging economies can leverage M&As as part of their broader environmental governance strategies.

3. Theory and Hypotheses Development

M&As can serve as a powerful catalyst for environmental governance by unlocking synergies that drive sustainable transformation. In the present study, we developed a framework that integrates synergy theory [37,38] and the RBV [39,40] to explain how M&As facilitate environmental improvements. The synergy theory posits that M&As enhance both economic and environmental performance through resource integration, process optimization, and collaborative efficiency. This theoretical framework is further augmented by the RBV, which elucidates how M&As enable firms to rapidly acquire critical environmental resources—such as green technologies and sustainable management practices—that would otherwise require substantial time and investment to develop internally. By acquiring targets with complementary environmental capabilities, firms can circumvent the slow, costly process of organic resource accumulation, thereby accelerating their sustainability transitions.

In the following paragraphs, we will provide a more detailed analysis of three key pathways: operational efficiencies, technological innovation, and reputational enhancement.

Operational synergies enable environmental improvements through shared management systems (e.g., ISO 14001 [41] adoption), consolidated green procurement, and best-practice diffusion, leading to streamlined processes and reduced waste. For example, merging manufacturing facilities can cut energy use and emissions through economies of scale, while shared supply chains minimize redundant logistics.

Technological synergies enable firms to leapfrog sustainability challenges by acquiring cutting-edge green capabilities [42,43,44]. The combination of complementary capabilities—such as pairing one firm’s strong basic research with another’s commercialization expertise—creates powerful synergies that accelerate the innovation cycle from laboratory to market. For instance, a tech giant acquires a startup with AI-driven carbon accounting tools. The combined entity can then deploy innovations such as smart energy systems or circular product designs faster than standalone firms. Industries with high R&D intensity, such as renewable energy or electric vehicles, benefit most from this pathway, while traditional sectors may require longer adaptation periods.

Reputational synergies emerge when merged firms leverage their combined brand equity to enforce stricter environmental standards. Merger entities with pre-existing sustainability commitments achieve higher post-M&A valuations by signaling credibility to stakeholders [45]. A consumer-facing company, for instance, might adopt a target’s sustainability certifications to meet rising stakeholder expectations, thereby incentivizing greener practices across its supply chain.

Regulatory pressures and stakeholder activism further shape this process. In tightly regulated markets, M&A-driven environmental gains are amplified, and investor and NGO scrutiny can accelerate post-deal sustainability commitments. Ultimately, this framework positions M&As not just as tools for competitive advantage but as a strategic lever for systemic environmental progress, with outcomes varying by industry, synergy type, and governance context.

Integrating these perspectives, synergy theory and RBV directly elucidate how M&As enhance firm environmental performance through resource integration and optimization. Together, these theories demonstrate that M&As serve not only as a critical mechanism for acquiring scarce resources and improving environmental management, but also as a strategic pathway toward green development and competitive environmental advantage.

Based on the discussion above, we propose the following hypothesis:

H1: 

M&As can significantly enhance firm environmental performance.

The impact of M&As on firm environmental performance varies significantly across regions due to differences in economic conditions, industrial structures, and regulatory frameworks. In developed regions, firms typically operate with advanced technological capabilities and stringent environmental regulations, leaving limited room for marginal improvements post-M&A. Their industries—often dominated by high-tech and service sectors—already exhibit lower pollution levels, while stakeholder pressures further incentivize sustained environmental responsibility. In contrast, firms in less developed regions face distinct challenges and opportunities. Resource constraints, weaker environmental policies, and a higher concentration of pollution-intensive industries (e.g., traditional manufacturing) create greater potential for M&A-driven environmental gains. Post-acquisition, these firms often benefit substantially from technological transfers, financial resources, and operational upgrades, leading to significant emission reductions. Thus, while M&As in advanced economies yield incremental refinements, their impact in developing regions can be transformative.

The preceding analysis led us to formulate the following hypothesis:

H2: 

The effect of M&As on environmental performance exhibits regional heterogeneity.

The different types of M&As may also have differential effects on firm environmental performance. Hybrid M&As generate multidimensional synergies through cross-industry integration [46]. First, hybrid M&As facilitate the transfer of non-overlapping environmental technologies between unrelated sectors (e.g., a manufacturing firm acquiring a clean-tech startup). This factor creates novel solutions for pollution control (e.g., adapting renewable energy tech from one industry to another) and circular production models (e.g., applying waste-to-resource innovations across sectors). Secondly, hybrid M&As enable diversified resource integration. By combining complementary assets from different industries, firms achieve balanced environmental investment portfolios (mitigating sector-specific risks) and shared best practices in green management (e.g., merging ISO 14001 systems with digital monitoring tech). Lastly, hybrid M&As aid firms in taking advantage of regulatory arbitrage opportunities. Hybrid deals enable strategic compliance positioning by leveraging weaker environmental standards in one sector to fund upgrades in another and pooling sustainability certifications across business units.

Based on the above discussion, we formulated the following hypothesis:

H3: 

Hybrid M&As demonstrate a more significant improvement in firm environmental performance compared to other types of M&As.

The transfer and integration of intangible environmental capabilities through M&A vary significantly across industries due to differences in regulatory pressures, technological complexity, and market dynamics. M&As are expected to exert a particularly strong effect on improving environmental performance in heavily polluting industries. Firms operating in sectors like cement, steel, and chemicals face intense regulatory scrutiny and higher compliance costs due to their substantial environmental footprints, thus possessing greater potential for enhancing their environmental performance [47,48]. M&As enable them to rapidly acquire and implement advanced pollution control technologies that would otherwise require prohibitively expensive independent R&D efforts. The scale effects of M&A transactions further amplify these benefits—combined operations can enable firms to better afford large-scale environmental upgrades such as wastewater treatment plants and share best practices across facilities, while consolidated purchasing power lowers the cost of cleaner inputs. The regulatory dimension is particularly critical for heavily polluting industries. M&As aid them in navigating increasingly stringent environmental policies by optimizing compliance strategies across merged entities, and reducing the risk of penalties. Beyond compliance, market pressures are driving this trend—investors and consumers now penalize polluters more severely, making M&As attractive tools for reputational rehabilitation through acquiring environmentally certified firms or green technology startups. The above stands in contrast to less polluting industries, wherein the environmental benefits of M&A are often marginal due to lower baseline emissions, weaker regulatory pressures, and the predominance of intangible assets that are harder to upgrade.

Based on the preceding analysis, we formulated the following hypothesis:

H4: 

M&As exert a stronger promoting effect on environmental performance for heavily polluting industries.

4. Data and Methods

The data used in this study include information on M&A transactions, environmental performance, and financial information of A-share listed companies on the Shanghai and Shenzhen stock exchanges in China. We obtain this dataset from the China Stock Market & Accounting Research (CSMAR) database. Similar to Standard & Poor’s Compustat and Thomson databases in China, the CSMAR database is an economic and financial research platform developed by Shenzhen Xishima Data Technology Co., Ltd., specifically for academic studies. Widely recognized for providing high-quality and standardized data, the CSMAR provides detailed data on M&As, environmental performance, and financial information of Chinese listed companies. Many peer-reviewed journals, including top-tier examples such as The Journal of Finance, recognize CSMAR, ensuring reproducibility and reliability [49,50,51].

We restricted our analysis to acquiring firms and excluded related-party transactions and transactions involving asset divestitures, debt restructuring, asset swaps, equity transfers, and share repurchases. We also excluded ST firms and financial institutions, and discarded incomplete records with missing data. ST Firms are publicly listed companies in China that have been placed under Special Treatment (ST) by stock exchanges (Shanghai or Shenzhen) due to financial or operational abnormalities. This label serves as a warning to investors about higher risks. To address the potential bias from extreme values, we winsorized all continuous variables at the 1st and 99th percentiles. The final dataset comprised 36,409 observations for 4584 firms between 2008 and 2022.

We employed a two-way fixed effects regression model, a panel data estimation technique that controls for both time-invariant unit-specific heterogeneity and common time shocks by including fixed effects for individual units and time periods. In the following section, to resolve the endogeneity problem, we applied both PSM and the instrumental variable method.

The regression model is specified below.

$${EP}_{it}={\alpha }_0+{\alpha }_1{\mathrm{M}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{r}}_{it}+{\sum }_k{\alpha }_k{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{kit}+{Year}_t+{Firm}_i+{\epsilon }_{it}$$

(1)

The dependent variable ${EP}_{it}$ represents the environmental performance of firm i in year. The measurement of environmental performance encompasses four key dimensions: environmental regulation and certification, environmental information disclosure, environmental management, and environmental performance and governance. In each dimension, there are related questions (indicators). For instance, the first dimension about environmental regulation and certification disclosure includes questions like whether the firm is listed as a key pollution monitoring unit, and whether the firm complies with pollutant emission standards. Example questions in the last dimension are emission reduction in waste gas, wastewater, dust, and smoke, and utilization and disposal of solid waste. For each question in the first three dimensions, if the answer is yes, a firm obtains a score of 1, otherwise 0. For the last dimension, scores are assigned based on the disclosure level of each indicator (0 for none, 1 for qualitative information, and 2 for quantitative information). The variable EP is equal to the natural logarithm of the sum of scores for all the indicators. For instance, if the answers to the total of 15 questions in the first 3 dimensions are all “yes”, and quantitative information is provided for all 4 questions in the last dimension, then the EP score of a company is 23 (1 × 15 + 2 × 4). Details of questions of each dimension are provided in

Table A1. Details of environmental performance measurement.

Dimension	Details
Environmental regulation and certification disclosure	Whether the firm is listed as a key pollution monitoring unit
	Whether the firm complies with pollutant emission standards
	Whether the records of environmental violations are disclosed
	Whether handling of environmental accidents is disclosed
	Whether the number of environmental complaint cases is disclosed
	Whether international certifications (ISO14001, ISO9001 [41,60]) are adopted
Environmental information disclosure	Whether annual reports disclose environment-related information
	Whether social responsibility reports disclose environment-related information
	Whether separate environmental reports are provided
Environmental management disclosure	Whether an environmental management system is established
	Whether special environmental actions are taken
	Whether environmental education and training are provided
	Whether an emergency response mechanism is established
	Whether the firm has received environmental awards and honors
	Whether the “Three Simultaneities” system is implemented
Environmental performance and governance	Emission reduction in waste gas, wastewater, dust, and smoke
	Utilization and disposal of solid waste
	Noise, light, and radiation control
	Implementation of clean production

.

The key explanatory variable ${Merger}_{it}$ is a dummy variable equal to 1 if firm i has completed at least one M&A transaction by year t, and 0 otherwise. Following examples in the literature [4,6], we included the following control variables: Size (the natural logarithm of total assets), Lev (total liabilities divided by total assets), ROA (return on assets), ROE (return on equity), Cashflow (operating cash flow divided by total assets), and Growth (revenue growth rate). We include the year fixed effect (${Year}_t$) to control for unobserved time-specific factors, and the firm-fixed effect (${Firm}_i$), to control for time-invariant unobserved firm characteristics. ${\epsilon }_{it}$ is the error term. The definitions of all of the variables are summarized in

Table 1. Definitions of Variables.

Types	Variables	Definition
Dependent Variable	environmental performance EPit	The natural logarithm of the sum of scores for all the indicators of environmental performance.
Independent Variable	Mergerit	equal to 1 if firm i has completed any M&A transaction by year t, and 0 otherwise
Control Variables	Size	the natural logarithm of total assets
	Levit	total liabilities divided by total assets
	ROAit	return on assets
	ROEit	return on equity
	Cashflowit	operating cash flow divided by total assets
	Growthit	operating income/operating income last year − 1
Fixed effects	${Year}_t$	year fixed effect
	${Firm}_i$	firm fixed effect
The error term	${\epsilon }_{it}$	the error term

.

The descriptive statistics are presented in

Table 2. Descriptive Statistics of Variables.

Variables	Observation	Mean	SD	Min	Max
EP	36,409	1.402	0.871	0.000	3.296
Merger	36,409	0.585	0.493	0.000	1.000
Size	36,409	22.162	1.318	19.406	26.430
Lev	36,409	0.422	0.208	0.027	0.925
ROA	36,409	0.044	0.066	−0.375	0.254
ROE	36,409	0.069	0.133	−0.962	0.420
Cashflow	36,409	0.048	0.071	−0.224	0.283
Growth	36,409	0.175	0.400	−0.654	3.808

. Environmental performance shows substantial variation across firms, with a mean of 1.402 and a standard deviation of 0.871, ranging from 0 to 3.296. Regarding M&A activities, the mean value of 0.585 for ${Merger}_{it}$ suggests that 58.5% of firms in the sample have completed M&A transactions. For control variables, the mean value of the natural logarithm of total assets is 22.162 with moderate variation; in comparison, the leverage ratio shows considerable dispersion, ranging from 0.027 to 0.925. Profitability also demonstrates significant variation, with the maximum ROA (ROE) being 0.254 (0.420) and the minimum ROA (ROE) being −0.375 (−0.962). The financial conditions also vary substantially, with the cash flow ratio ranging from −0.224 to 0.283 and the growth rate ranging from −0.654 to 3.808. The correlation analysis results are presented in

Table 3. Correlation Matrix of Variables.

	EP	Merger	Size	Lev	ROA	ROE	Cashflow	Growth
EP	1.000
Merger	0.095	1.000
Size	0.389	0.198	1.000
Lev	0.091	0.150	0.500	1.000
ROA	0.047	−0.084	−0.040	−0.389	1.000
ROE	0.060	−0.034	0.077	−0.212	0.895	1.000
Cashflow	0.131	0.029	0.061	−0.154	0.372	0.297	1.000
Growth	−0.023	−0.015	0.042	0.026	0.243	0.255	0.023	1.000

. Correlation coefficients are mostly below 0.5, indicating minimal multicollinearity. The only exceptions are ROA and ROE, which are highly correlated. A strong correlation between ROA and ROE suggests that leverage (debt) does not significantly impact returns, which could imply low financial leverage or poor use of debt. To alleviate the concern of multicollinearity, we include either ROE or ROA and rerun the regression. The results remain robust, which are available upon request.

5. Empirical Results

5.1. Results Testing the Proposed Hypotheses

The regression results of Equation (1) are presented in

Table 4. Testing H1: Baseline Regression.

Variables	Fixed Effects Included	Control Variables Included	The Full Model
Merger	0.575 ***	0.232 ***	0.059 ***
	(0.011)	(0.011)	(0.011)
Size		0.421 ***	0.130 ***
		(0.005)	(0.007)
Lev		−0.313 ***	−0.008
		(0.031)	(0.030)
ROA		−0.364 ***	−0.019
		(0.136)	(0.128)
ROE		−0.085	0.117 **
		(0.060)	(0.056)
Cashflow		0.232 ***	0.092 *
		(0.052)	(0.050)
Growth		−0.062 ***	−0.038 ***
		(0.008)	(0.008)
Year fixed effect	No	No	Yes
Firm fixed effect	No	No	Yes
N	36,462	36,409	36,409

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

. Column (1) presents the results without control variables or fixed effects, providing preliminary evidence of a positive association between M&As and firm environmental performance. In Column (2), after incorporating control variables, the coefficient of ${Merger}_{it}$ remains positive and significant. The results of the full model are presented in Column (3). The positive and statistically significant coefficient for ${Merger}_{it}$ supports Hypothesis 1—M&As lead to improved environmental performance.

To test Hypotheses 2–4, we added an interaction term between $Z_i$ and ${Merger}_{it}$ in the baseline equation, as shown in Equation (2). The variable $Z_i$ captures heterogeneous effects, with its operational definition contingent upon the specific hypothesis under examination, as detailed in the subsequent discussions. ${\tau }_{it}$ is the error term. The other variables are defined as in Equation (1).

$${EP}_{it}={\beta }_0+{\beta }_1{\mathrm{M}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{r}}_{it}+{\beta }_2{\mathrm{M}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{r}}_{it}\times {\mathrm{Z}}_i+{\sum }_k{\beta }_k{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{kit}+{Year}_t+{Firm}_i+{\tau }_{it}$$

(2)

The regression analysis reported in

Table 5. Testing H2: Regional Heterogeneity.

Variables	The Baseline Model	The Regional Heterogeneity
Merger	0.059 ***	0.094 ***
	(0.011)	(0.019)
East × Merger		−0.048 **
		(0.022)
Size	0.130 ***	0.130 ***
	(0.007)	(0.007)
Lev	−0.008	−0.007
	(0.030)	(0.030)
ROA	−0.019	−0.022
	(0.128)	(0.128)
ROE	0.117 **	0.118 **
	(0.056)	(0.056)
Cashflow	0.092 *	0.093 *
	(0.050)	(0.050)
Growth	−0.038 ***	−0.038 ***
	(0.008)	(0.008)
Year fixed effect	Yes	Yes
Firm fixed effect	Yes	Yes
N	36,409	36,409

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

provides compelling evidence supporting Hypothesis 2 regarding regional heterogeneity in the promotion effects of M&As on environmental performance. We report the result of the baseline model in Column (1) for comparison purposes. Given the noteworthy differences between China’s eastern coastal provinces and less developed interior regions, ${\mathrm{Z}}_{it}$ is defined to be a dummy variable indicating the eastern region. The negative and statistically significant coefficient for the interaction term reported in Column (2) indicates that the boosting effects of M&As on environmental performance are substantially weaker in the economically advanced area compared to the central and western regions in China. The findings shed light on how geographic differences moderate the relationship between M&As and environmental outcomes.

The regression results presented in

Table 6. Testing H3: Merger Type.

Variables	Hybrid	Horizontal	Vertical
Merger	0.053 ***	0.061 ***	0.062 ***
	(0.011)	(0.011)	(0.011)
Hybrid × Merger	0.024 **
	(0.012)
Horizontal × Merger		−0.012
		(0.016)
Vertical × Merger			−0.006
			(0.011)
Size	0.130 ***	0.130 ***	0.130 ***
	(0.007)	(0.007)	(0.007)
Lev	−0.009	−0.008	−0.008
	(0.030)	(0.030)	(0.030)
ROA	−0.020	−0.020	−0.021
	(0.128)	(0.128)	(0.128)
ROE	0.117 **	0.117 **	0.117 **
	(0.056)	(0.056)	(0.056)
Cashflow	0.092 *	0.092 *	0.092 *
	(0.050)	(0.040)	(0.050)
Growth	−0.038 ***	−0.038 ***	−0.038 ***
	(0.008)	(0.008)	(0.008)
Year fixed effect	Yes	Yes	Yes
Firm fixed effect	Yes	Yes	Yes
N	36,409	36,409	36,409

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

provide robust evidence supporting Hypothesis 3. In this case, $Z_{it}$ is a dummy variable indicating each of the three types of M&As—horizontal, vertical, and hybrid. While all types show a positive association with environmental performance, as indicated by the consistently positive and significant coefficient of ${Merger}_{it}$ across all columns, the size of the enhancing effects differs substantially across types. Only the coefficient of the interaction term for hybrid M&As demonstrates a statistically significant and positive coefficient, suggesting a greater accelerating effect of this type on environmental performance, validating our hypothesis that hybrid M&As lead to superior environmental outcomes compared to other M&A types.

To test Hypothesis 4, we define $Z_{it}$ as a dummy variable indicating a heavily polluting industry. The classification is based on the Guidelines for Industry Classification of Listed Companies (revised in 2012) by the China Securities Regulatory Commission (CSRC). As shown in

Table 7. Testing H4: Industry Type.

Variables	Baseline	Polluting Industry
Merger	0.059 ***	0.045 ***
	(0.011)	(0.012)
Polluting × Merger		0.065 ***
		(0.019)
Size	0.130 ***	0.130 ***
	(0.007)	(0.007)
Lev	−0.008	−0.006
	(0.030)	(0.030)
ROA	−0.019	−0.026
	(0.128)	(0.128)
ROE	0.117 **	0.117 **
	(0.056)	(0.056)
Cashflow	0.092 *	0.090 *
	(0.050)	(0.050)
Growth	−0.038 ***	−0.038 ***
	(0.008)	(0.008)
Year fixed effect	Yes	Yes
Firm fixed effect	Yes	Yes
N	36,409	36,409

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

, environmental performance differs significantly between these two types of industries. In particular, the interaction term demonstrates a significantly positive coefficient, implying that the environmental performance improvement from M&As is more pronounced for heavily polluting industries.

5.2. Endogeneity

It is possible that some common factors may drive both M&A decisions and environmental performance. Firms may prioritize targets that can enhance their environmental performance. Therefore, in the baseline regression, endogeneity may arise due to omitted unobserved variables and reverse causality. To address the issue of endogeneity, we employed the PSM and the IV method. Following the method of [52], a post-matching standardized bias below 20% suggests good matching. Our results demonstrate that all post-matching standardized biases fall below 10%, confirming that PSM effectively reduces systematic differences between the treatment group and control group. We re-estimate Equation (1) using the matched sample. The regression results are reported in Column (1) of

Table 8. The PSM and IV Results.

Variables	PSM	IV
Merger	0.041 **	0.091 ***
	(3.12)	(0.020)
Size	0.140 ***	0.132 ***
	(17.83)	(0.008)
Lev	−0.015	−0.068 **
	(−0.45)	(0.033)
ROA	−0.066	−0.121
	(−0.46)	(0.139)
ROE	0.126 *	0.143 **
	(2.01)	(0.061)
Cashflow	0.113 *	0.064
	(2.05)	(0.054)
Growth	−0.039 ***	−0.045 ***
	(−4.67)	(0.008)
Year fixed effect	Yes	Yes
Firm fixed effect	Yes	Yes
N	30,302	31,034

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

. And then we employed the IV method, selecting the lagged merger variable as the instrumental variable. Based on the Cragg–Donald Wald F statistic, there is no problem of weak instrumental variables. The regression results are reported in Column (2) of Table 8. As is shown, the coefficients for Merger remain significantly positive in both specifications. This suggests that, after accounting for endogeneity, M&As continue to exert a positive influence on environmental performance, consistent with our earlier findings.

5.3. Robustness Tests

To further validate our results, we conducted robustness tests using alternative measurements for the dependent variable. The construction method of these measurements is similar to that used in the baseline regression, which is the sum of scores of various indicators. The difference is that now we focus on a different set of indicators. Specifically, following the method of [53], we use two different measurements: one is based on pollutant reduction (waste gas, wastewater, and dust emissions), solid waste utilization and disposal, noise and light pollution control and implementation of clean production; and the other is based on environmental philosophy, management systems, education and training, specialized actions, emergency mechanisms, the “three simultaneous” system, honors and awards, and ISO 14001 certification.

The regression results are presented in

Table 9. The Robustness Check.

Variables	EP2	EP3
Merger	0.046 ***	0.026 ***
	(0.011)	(0.009)
Size	0.085 ***	0.113 ***
	(0.007)	(0.006)
Lev	−0.045	−0.051 **
	(0.030)	(0.025)
ROA	0.147	0.198 *
	(0.126)	(0.105)
ROE	0.010	0.061
	(0.055)	(0.046)
Cashflow	0.055	0.038
	(0.049)	(0.041)
Growth	−0.0121	−0.032 ***
	(0.008)	(0.006)
Year fixed effect	Yes	Yes
Firm fixed effect	Yes	Yes
N	38,077	38,077

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

. As is demonstrated, the coefficients of ${Merger}_{it}$ in both columns are still significantly positive, aligning with our initial findings.

5.4. The Mechanism Tests

In the following paragraph, we explore the mechanisms through which M&A enhances environmental performance. First, M&As can strengthen environmental performance by increasing TFP. Empirical evidence suggests that successful M&As—particularly those focused on technology and strategic fit—contribute meaningfully to TFP growth [54]. TFP growth leads to better environmental performance [48]. Higher TFP enables firms to produce more output with fewer inputs, reducing resource waste and energy consumption per unit of production, directly lowering emissions (e.g., CO₂, waste byproducts). In addition, more productive firms can spread the fixed costs of environmental compliance (e.g., pollution control systems) over greater output, making green investments more cost-effective. Overall, TFP growth becomes a powerful driver of environmental progress, ultimately supporting the decoupling of industrial activity from ecological degradation.

Secondly, M&As serve as a powerful driver of environmental performance by fundamentally reshaping firms’ knowledge bases and innovation capabilities [43,44]. By combining complementary technological assets and R&D resources, merged entities can achieve significant breakthroughs that would be difficult for standalone firms to accomplish. Innovation plays a vital role in improving a firm’s environmental performance. Several studies have found that innovation and technology play a promoting role [55,56,57,58].

Based on the above analysis, we investigate the following two possible mechanisms: M&As increasing environmental performance by improving TFP or by promoting innovation. Following the three-step approach of [59], we specify the additional regression equations as follows:

$$M_{it}={\gamma }_0+{\gamma }_1{\mathrm{M}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{r}}_{it}+{\sum }_k{\gamma }_k{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{kit}+{Year}_t+{Firm}_i+{\theta }_{it}$$

(3)

$${EP}_{it}={\delta }_0+{\delta }_1{\mathrm{M}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{r}}_{it}+{\delta }_2{\mathrm{M}}_{it}+{\sum }_k{\delta }_k{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{kit}+{Year}_t+{Firm}_i+{\mu }_{it}$$

(4)

In Equations (3) and (4), the variable $M_{it}$ serves as the mechanism, with its operational definition detailed in the discussion further below. ${\theta }_{it}$ and ${\mu }_{it}$ are error terms. The definitions of the remaining variables are identical to those in Equation (1).

The regression results are presented in

Table 10. The Mechanism Test: TFP.

Variables	(1)	(2)	(3)
	TFP	EP	EP
Merger	0.003 ***	0.059 ***	0.038 ***
	(0.011)	(0.011)	(0.012)
TFP			0.137 *
			(0.078)
Size	0.062 ***	0.130 ***	0.120 ***
	(0.011)	(0.007)	(0.009)
Lev	0.042 ***	−0.008	−0.038
	(0.002)	(0.030)	(0.031)
ROA	0.120 ***	−0.019	0.004
	(0.010)	(0.128)	(0.134)
ROE	0.016 ***	0.117 **	0.095
	(0.004)	(0.056)	(0.058)
Cashflow	0.075 ***	0.092 *	0.080
	(0.004)	(0.050)	(0.053)
Growth	0.025 ***	−0.038 ***	−0.045 ***
	(0.001)	(0.008)	(0.008)
Year fixed effect	Yes	Yes	Yes
Firm fixed effect	Yes	Yes	Yes
N	34,182	36,409	32,602

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

when the mechanism variable $M_{it}$ is TFP. We estimated TFP using both the Olley–Pakes and Levinsohn–Petrin methods and took the average of the two estimates. The information presented in Column (1) denotes the regression results of Equation (3), wherein the dependent variable is TFP, and the explanatory variable is ${Merger}_{it}$. The results demonstrate that the coefficient of ${\mathrm{m}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{r}}_{it}$ is positive and significant at the 1% level, suggesting that M&As can elevate TFP. The regression results of Equation (1) are repeated in Column (2). The information reported in Column (3) denotes the results of Equation (4), wherein the dependent variable is environmental performance, and the explanatory variables include both ${Merger}_{it}$ and TFP. As clearly demonstrated, the coefficient of both ${Merger}_{it}$ and TFP are significant and positive. These results combined suggest that M&As advance environmental performance by boosting TFP.

With the mechanism variable being innovation, Column (1)–(3) in

Table 11. The Mechanism Test: Innovation.

Variables	(1)	(2)	(3)
	Innovation	EP	EP
Merger	0.051 *	0.059 ***	0.065 ***
	(2.04)	(5.42)	(3.86)
Innovation			0.017 **
			(2.82)
Size	0.456 ***	0.130 ***	0.129 ***
	(23.57)	(18.35)	(9.39)
Lev	0.097	−0.008	−0.034
	(1.22)	(−0.28)	(−0.63)
ROA	0.110	−0.019	−0.164
	(0.32)	(−0.15)	(−0.69)
ROE	−0.252	0.117 *	0.078
	(−1.51)	(2.08)	(0.68)
Cashflow	0.335 *	0.092	0.207 *
	(2.54)	(1.86)	(2.28)
Growth	−0.051 *	−0.038 ***	−0.046 **
	(−2.43)	(−5.05)	(−3.23)
Year fixed effect	Yes	Yes	Yes
Firm fixed effect	Yes	Yes	Yes
N	15,959	36,409	15,004

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; Robust standard errors in the brackets.

report the regression results for Equation (1), (3), and (4), respectively. Innovation is measured by a firm’s average number of patents in each year. Both the coefficients for ${Merger}_{it}$ in Column (1) and the coefficients for ${Merger}_{it}$ and Innovation_it in Column (3) are significant. This finding suggests that innovation is another channel through which M&As contribute to improved environmental performance.

6. Discussion

The regression results shown in Section 5 confirm all four hypotheses. The empirical results indicate that M&As have a statistically significant and positive impact on firms’ environmental performance, supporting Hypothesis 1. In addition, the regression results offer robust empirical support for Hypothesis 2, revealing significant regional heterogeneity in the environmental benefits of M&A activities. Notably, the positive impact of M&As on environmental performance is markedly attenuated in China’s economically developed regions when compared to their central and western counterparts. Furthermore, our findings validate that hybrid M&A transactions generate significantly stronger environmental improvements than other M&A types, confirming our theoretical expectations as stated in Hypothesis 3. Finally, our analysis demonstrates that the environmental performance enhancements derived from M&As are particularly salient in heavily polluting sectors, thereby providing strong evidence in support of Hypothesis 4.

We broaden M&A scholarship and equip firms with actionable strategies for sustainability-focused markets. Our study contributes to the literature in several ways. First, unlike the literature that focuses on green M&As, we find that even “non-green” M&As can improve sustainability through operational, technical, and reputational synergies, blurring the line between traditional and eco-friendly deals. We reveal how routine M&As, which primarily focus on financial synergies, market expansion, or cost efficiencies, can also advance sustainability, helping firms balance environmental and business goals. Additionally, we expand beyond heavy industries to include light manufacturing, high-tech, and service sectors, showing how industry traits shape the environmental impacts of M&As. Lastly, focusing on China, we offer emerging economies new insights on using M&As for environmental governance.

7. Conclusions

Building upon synergy theory and the resource-based view, we examined the impact of M&As on firm environmental performance using a sample of listed Chinese companies from 2008 to 2022. The regression results demonstrate that M&As exert a significantly positive effect on environmental performance. We additionally investigated how regional heterogeneity, industry, and M&A types lead to differential effects. The results of our robustness tests consistently corroborate the baseline results. We also resolve the endogeneity problem using the PSM and the IV method. The mechanism test results reveal that TFP improvement and innovation acceleration serve as the main mechanisms through which M&As upgrade environmental performance.

Our research results have important policy and managerial implications. The government should provide policy incentives for M&As. Our multi-industry analysis highlights varied outcomes, warning against uniform sustainability strategies. Environmental regulations and M&A policies must therefore account for heterogeneity, with tailored approaches for different industries and regions. Firms can strategically target acquisitions that enhance environmental capabilities, particularly those offering advanced green technologies, eco-efficient equipment, and proven environmental management systems. By integrating these assets, companies can simultaneously improve environmental performance and strengthen market competitiveness. Post-merger, firms must implement rigorous environmental performance monitoring and compliance systems.

Some limitations of the present study warrant acknowledgment. First, our reliance on data of Chinese listed firms, despite covering multiple industries and regions within China, may limit the findings’ applicability to other countries. Divergent economic conditions, cultural contexts, and environmental regulations—particularly for cross-border M&As—may yield different outcomes in other markets. Secondly, due to data availability, our measurement of some variables was relatively simple. For instance, we measure M&As with a binary variable. Ideally, we aim to measure M&A transactions by deal size, type, and nature. In addition, innovation is measured only by patent counts. It would be interesting to differentiate between types or quality of patents. One possible direction could be for the authors of future studies to conduct cross-national comparative analyses and develop more comprehensive measurements of these variables.

From a corporate practice perspective, we recommend the following future research directions. First, examining how to systematically integrate ESG factors throughout the M&A process—from due diligence and valuation to post-merger integration—to mitigate environmental risks while unlocking green growth opportunities. Second, developing industry-specific M&A frameworks (e.g., differentiated approaches for heavy polluters vs. clean-tech firms) to provide practical guidance for enhancing environmental performance. Third, investigating real-world cases of environmental standard “harmonization” in cross-border deals (e.g., whether EU acquirers elevate emerging market targets’ eco-standards), offering multinationals actionable insights for global environmental governance. Additionally, research should evaluate the efficacy of digital tools (e.g., blockchain-based carbon tracking, AI-powered environmental risk assessment) in improving M&A-related environmental management. Finally, longitudinal studies tracking post-M&A environmental commitment compliance are critical to prevent greenwashing and provide investors/regulators with reliable benchmarks. These evidence-based insights will empower firms to design M&A strategies that simultaneously create business value and advance sustainability goals—transforming transactions into catalysts for both profit and planetary benefit.