The Peer Effects of Green Management Innovation in China’s Listed Companies

Abstract

Green management innovation meets the requirements of sustainable development and is conducive to achieving an organic unity of economic, social, and ecological benefits for enterprises. Based on the peer effects theory, this study constructs a fixed-effects model to empirically analyze the existence, action mechanism, moderation, and heterogeneity of peer effects on green management innovation behavior using panel data from listed Chinese companies from 2012 to 2022. The results indicate that peer effects significantly positively impact green management innovation, and the robustness test verifies the results in various ways. Furthermore, the mechanisms of peer effects from the perspectives of information learning and competition have been explored in this study. Specifically, peer effects are more likely to be promoted when enterprises possess a greater capacity for information acquisition, higher environmental uncertainty, and stronger environmental regulations. Notably, the peer effect of green management innovation is more significant for enterprises that face higher financing constraints, are in non-heavily polluting industries, and are supported by industrial policies. This study not only helps clarify the potential real motivation of corporate green management innovation but also provides strong empirical evidence for developing relevant policies and regulations.

1. Introduction

In recent years, ecological and environmental issues, such as resource scarcity and climate warming, have attracted widespread attention, and green development has become both a common goal pursued by countries and an important aspect of global governance. As the main entities of production and business activities, enterprises exert significant pressure on the environment and are one of the primary causes of environmental degradation. Therefore, enterprises should actively carry out their social responsibilities and explore the coordinated and green development of economics and the environment [1]. Green management innovation represents the green culture of a corporation, with the core pursuit of achieving green development. It should advocate for enterprises to focus not only on economic development but also on conserving resources and protecting the environment. This aligns with the requirements of social sustainable development and is conducive to achieving an organic unity of economic, social, and ecological benefits. Through green management innovation, enterprises can aim toward the following: striking a balance between production, operation, and environmental protection; alleviating the contradiction between economic growth and environmental pollution so as to form a unique competitive advantage to improve the economic benefits and environmental effects of companies; promoting green transformation [2]; and achieving sustainable economic growth [3]. Consistent with green innovation activities, green management innovation is characterized by its complexity, long cycle length, and high investment, and it also possesses “dual externalities” generated by “spillover effects” and “external environmental costs” [4,5]. Therefore, companies confront a series of challenges and obstacles in executing green management innovation activities, which leads to their relatively low willingness to take part in and be efficient at green management innovation. In this context, the role of mutual guidance, promotion, and learning may be the key to driving enterprises’ green management innovation and achieving sustainable development goals.

Research on social interaction theory suggests that individual behavior decisions within a group are affected by the behaviors of others, which is known as the peer effect. As independent individuals, enterprises will refer to and imitate the behavior of peer firms when making investment decisions, undergoing mergers and acquisitions, and engaging in other related financial and non-financial behaviors [6]. However, existing studies mainly analyze the motivating factors of green management innovation from two aspects, namely, external regulation and internal management characteristics, and they rarely focus on the relation between the peer effect and green management innovation. In fact, enterprises do not exist in isolation within an industry, and similar enterprises will emerge in every industry. At this point, it is not yet clear whether independent individual enterprises are influenced by other enterprises, especially whether individual corporate green management innovation is affected by the green management innovation behavior of peer enterprises. Therefore, this paper explores whether other companies influence Chinese companies’ green management innovation decisions in the same industry from the perspective of peer effects and discusses the existence and mechanism of peer effects on green management innovation, thereby delving into the determining factors of green management innovation.

This paper examines green management innovation decisions from the aspect of peer effects using Chinese, A-share, non-financial listed companies from 2012 to 2022 as the research sample. We constructed a fixed-effects model and found that peer effects significantly exist in green management innovation within the same industry. After the endogeneity and robustness tests, the results are still significant; there are peer effects on green management innovation behavior. In addition, a company’s market position and industry competition intensity are included in the regression equation to examine the mechanisms of peer effects. The results show that peer effects affect green management innovation behavior through the mechanisms of information learning and competition. This study also establishes a moderation model and uses group tests to investigate how the peer effects of green management innovation vary under different conditions. The results indicate that companies with higher information acquisition capability, stronger external environmental uncertainty, and environmental regulations can also significantly promote the peer effects of green management innovation behavior. Furthermore, the peer effect of green management innovation is particularly pronounced in organizations with higher financing constraints, non-polluting industries, and companies supported by industrial policies.

This research makes three notable contributions. First, it broadens the research on enterprises’ green management innovation decision-making behavior regarding industry peer effects. At present, the relevant studies on green management innovation are based on the assumption of independent decision behavior while neglecting the potential influence between companies. This paper tests the existence and mechanisms of industry group effects in green management innovation to reveal the real decision-making motivations of green management innovation. Second, this study supplements the understanding of peer effects from the perspective of green management innovation. Previous research has found that a peer effect exists in general investment decisions, financing decisions, and corporate governance. Still, few studies have focused on whether green innovation has peer effects, especially in green management innovation. By extending the research on peer effects to the unique behavior in green management innovation, this study provides an incremental contribution to the research on peer effects. Third, in practice, this study can provide an important reference for improving the level of corporate green management innovation and formulating government policies. Through the interactive communication of green innovation decision-making behavior between companies in the same industry and the enhancement of the environmental governance policies of the Chinese government, it is of great significance for companies to achieve green transformation.

The remainder of this study is organized as follows: Section 2 presents the literature review and provides the research hypotheses. Section 3 presents the model design, variable selection, and data resources. Section 4 explains the results of the impact of peer effects on green management innovation. Section 5 includes the research conclusions and corresponding recommendations. The research framework based on the above analysis is shown in Figure 1.

2. Literature Review and Hypothesis

2.1. Literature Review

2.1.1. Peer Effect

The peer effect, also known as the peer group effect, refers to enterprises within the same group or social reference group that are faced with a similar environment, thus possessing the conditions and motivation for competitive or imitative learning. This leads the firms to consciously focus on and imitate the behavior of peer firms for the purpose of reducing costs and avoiding risks associated with independent decision behavior [7]. The peer effect is predicated on the assumption that rational people conform to the crowd, emphasizing the inherent logic of learning and imitation and the bidirectional influence between individuals. The literature has previously shown that the peer effect widely exists in all aspects of enterprises, such as corporate financialization [8], corporate investment [9], mergers and acquisitions [10], IPO [11], information disclosure [12,13], capital structure [6,14], risk-taking [15], digital transformation [16,17], top executive compensation [18], and charitable giving [19], as well as other related financial and non-financial behavior decisions that are affected by peer firms’ related behaviors.

The peer effect can be categorized into two types: industrial peer effects and regional peer effects. The research suggests that differences in the same industry can form peer effects because firms face similar internal and external aspects, such as enterprise structure, operation management, business operations, and target groups. Additionally, they face similar resource environments, market spaces, and industry risks. On this basis, enterprises will face more intense competition from the aspect of dynamic competition. They will tend to follow excellent companies within the same industry so as to reduce their decision-making risk and maintain their competitive advantage. At the same time, from the perspective of learning, peer enterprises are accompanied by the phenomenon of mutual learning. Peer enterprises share information and experience through different channels, such as network channels and meeting channels [20]. The relevant literature on regional peer effects found that when affected by a region’s unique level of economic development, marketization process, and policy environment, an enterprise will choose peer firms close to its geographical location as its reference object to alleviate the problems of resource lock-in, uneconomical decision making, and low market and policy legitimacy [21]. Additionally, corporate social responsibility performance [22] and innovation behavior [23] have significant regional peer effects.

Specifically, no one has studied green management innovation peer effects, and only a few studies in the literature have focused on peer effects from the perspective of green technology innovation. These studies found that green technology innovation has industrial peer effects [24,25,26] and regional peer effects [27].

2.1.2. The Impact of Green Management Innovation

Green management innovation means the application of environmental protection management innovation ideas, technologies, and methods during the green innovation process. It refers to improving resource utilization efficiency and environmental protection efforts in all stages, including research and development, manufacturing, and sales, to achieve sustainable development goals [28]. The factors influencing green management innovation mainly come from the external environment and internal perspectives. Like other economic activities, green management innovation cannot be separated from the policy environment and has dual externalities. Compared with traditional innovation, the driving force of development is no longer limited to technological innovation and market demand. Enterprises face higher cost pressure, resulting in insufficient motivation for green management innovation, which also needs active intervention and guidance from government policies. Therefore, environmental regulation and government subsidies, as important measures and means of government implementation, have become key external factors affecting enterprises’ green management innovation. Sun et al. [29] stated that environmental regulation will facilitate companies’ green management innovation via environmental subsidies, tax relief, and so on. Han et al. [30] indicated that the policy support of environmental regulation can not only enhance the power of enterprise green management innovation but also reduce resistance to enterprise green management innovation. On the one hand, as the controller of rare resources, the government provides more scarce resources for enterprises through environmental regulation policies, which helps enterprises obtain resources related to green management innovation. On the other hand, environmental regulation mitigates obstacles such as capital scarcity and technological backwardness in the process of enterprise green management innovation through incentive measures such as emission reduction subsidies and technical standard demonstration, thereby promoting corporate green management innovation. Other external factors affecting green management innovation include external knowledge supply [28] and green finance [31]. The internal factors in green management innovation mainly involve the impact of management characteristics. Xi et al. [32] found that the dual cognition of senior executives significantly positively impacts green management innovation, thereby improving the sustainable development of companies. This paper also points out that executives with opportunistic cognition are better at coordinating internal and external resources to implement green strategies and actively implement green management innovation activities.

In summary, the peer effect is widely consistent in various aspects of enterprises. Still, the existing literature on green management innovation in enterprises mainly analyzes driving factors in green management innovation from two perspectives: external regulation and internal management characteristics. However, these studies inherently assume that enterprises make decisions independently and will not be affected by the decisions and behaviors of other industry peer companies. In the process of innovation activities, enterprises face challenges such as long cycles and large investments. The “dual externalities” aspect also restricts enterprises’ green management innovation decisions. Therefore, in the face of similar market conditions and development prospects, the green management innovation of peer firms in the same industry may have an impact on their decision behavior; however, there is limited research analyzing green management innovation from the perspective of peer effects.

2.2. Research Hypotheses

2.2.1. Peer Effects of Green Management Innovation

Green management innovation is an important method and approach for firms to attain sustainable development. Through green management innovation, firms can build up their competitiveness, obtain government support, and improve their brand image. Based on social interaction theory, to avoid the uncertainty of decision-making benefits and increased costs caused by the limitation of resources and individuals, companies will choose to imitate the behaviors of companies with similar characteristics during the decision-making process, resulting in the peer effect [7].

In the complicated and volatile economic environment, the cost of an enterprise making decisions independently will be relatively high. In order to reduce decision-making costs, the enterprise is likely to gather pertinent industrial information from the green management activities of other firms and make decisions by imitating them [33]. Compared with technological innovation, green management innovation is characterized by high interactivity, and external innovation sources are more important for management innovation. The process of collecting high-quality, relevant information to make decisions is time-consuming and laborious, especially since management innovation has the characteristics of decentralization and gradualness, and the cost of making decisions alone is higher. Green management innovation is subject to more pressure from stakeholders and social attention, and it is more susceptible to the influence of green management innovation in peer firms within the same industry. The target firms are more inclined to imitate industry enterprises that have already implemented green management innovation and achieved good performance in order to effectively reduce costs and avoid decision-making risks.

According to dynamic competition theory, peer enterprises have improved their industry competitiveness by implementing green management innovation, whereas the competitiveness of individual companies without green management innovation has declined. Due to the competitive pressure of the industry, individual enterprises will initiate green management innovation to seek a breakthrough. One study [32] shows that green management innovation can obtain governments’ support and preferential policies to improve enterprises’ industry market competitiveness and achieve the leading position. Driven by the pressure of industry competition, individual enterprises are highly sensitive to the dynamic behavior of peer companies. To preserve their competitive stance within the industry, they will also actively participate in green management innovation activities and, ultimately, expand their scale with green management innovation. Based on the above analysis, this paper proposes Hypothesis 1:

Hypothesis 1 (H1).

There are peer effects on green management innovation.

2.2.2. Information Learning Mechanism of Green Management Innovation Peer Effects

According to information theory, enterprises rely on searching for information to make decisions. There is a lot of information noise in industries and markets, which is caused by the complexity of information redundancy; this information noise will increase the cost of searching for information [34]. However, the green innovation of enterprises relies on a professional knowledge system; most Chinese enterprises lack this cognition and resources, meaning the effect on green innovation is not satisfactory [35]. Problems such as high investment risk and insufficient policy support are challenges that green management innovation needs to face in practice. In order to save on the cost of information identification, enterprises tend to receive information on green management innovation behavior from companies in the same industry [36]. Therefore, when facing a complex information environment, enterprises tend to follow and learn the behaviors of similar enterprises. The important information associated with decision making within industry peer enterprises can reduce the costs of information searching and help deal with future uncertainty [37,38]. Enterprises with high levels of green management innovation are mostly industry leaders with information advantages and more discourse rights. They can convey a basis of potential resource information and decision making to other enterprises. From this, peer enterprises can reduce the decision-making cost to the greatest extent and enhance their ability to explore information and resources [39], thereby improving their own level of green management innovation and more easily forming industry peer effects. In summary, this study puts forward Hypothesis 2:

Hypothesis 2 (H2).

Peer effects will affect green management innovation through the information learning mechanism.

2.2.3. Competitive Mechanism of Green Management Innovation Peer Effects

According to competition theory, to sustain a relatively competitive situation, enterprises usually focus on the behavior of peer companies; they will respond positively to their behavior to reduce the pressure from competitors [40]. In a competitive environment, innovation has emerged as the key to the core competitiveness of firms. Green management innovation is a type of innovation that organizations undertake to adjust to shifts in the external environment, which supports enterprises in finding market opportunities and investment opportunities and achieving sustainable development through the “win–win” of cost reduction and income increase. When firms notice the green management innovation behavior of industry peer firms and managers perceive competitive pressure, these enterprises will respond quickly by imitating their behavior to obtain a competitive advantage. To sum up, the level of competition in the industry is a potential generation mechanism of the peer effects of firms’ green management innovation. Drawing on this analysis, the following hypothesis is proposed in this paper:

Hypothesis 3 (H3).

Peer effects will affect green management innovation through the competitive mechanism.

3. Research Design

3.1. Data Source and Sample

This paper used listed Chinese A-share companies from 2012 to 2022 as research samples. To ensure the reliability of this study, these research samples were processed as follows: (1) removing financial industry samples; (2) excluding data from ST, PT, and delisted companies; (3) excluding samples with incomplete data; (4) keeping listed companies that have data for at least 5 years; (5) winsorizing all continuous variables at the 1% and 99% percentiles to mitigate the influence of outliers. After screening, there were 4487 listed companies and 34,026 annual company sample observations. The specific industry criteria are in accordance with the “guidance on industry classification of listed companies issued by the CSRC in 2012”. Given the substantial number of manufacturing companies, these were subdivided using two-digit codes, and the rest of the industries were given a one-digit code. The relevant financial data come from the WIND, CCER, and CSMAR databases. In addition, we utilized Stata software (version 17.0) for data analysis and processing.

3.2. Measures

3.2.1. Green Management Innovation

Companies can accomplish green management innovation goals by implementing environmental management methods and systems [41]. This article refers to the approaches of Li et al. [42] and Zhao et al. [43] and measures five indicators of green management innovation design. Regarding the availability of data, based on the disclosure of environmental supervision and the certification of listed companies in the CSMAR environmental database—including whether they have obtained ISO14001 certification [44], ISO9001 certification [45], an environmental management system, environmental education and training, and environmental special actions (listed in the management disclosure table of listed companies)—a comprehensive score was obtained by adding these factors up as a proxy indicator for a firm’s green management innovation (CGM).

3.2.2. Green Management Innovation of Peer Firms

The theory of dynamic competition indicates that peer companies have consistent competitive and interactive interactions and similar environmental risks, markets, and institutional aspects, and this facilitates the creation of foundational conditions for generating peer behavior. Therefore, this article defines peer enterprises as those in the same industry and draws on the measurement methods of Leary et al. [6] and Wang [46] in using the average value of the current green management innovation of other enterprises in the same industry as a proxy variable for peer enterprise green management innovation (PGM). This measurement method assists in mitigating endogeneity concerns within the model to a certain degree, underscores the cross-sectional interactive relationships among firms within the same industry, and more precisely assesses the peer effects of firms’ green management innovation. The specific formula is shown in Equation (1), where i, j, and t represent the firm, industry, and year, respectively. N represents the number of firms in the industry to which i belongs. The equation signifies the average degree of green management innovation of peer firms for enterprise i in industry j (excluding enterprise i) in the t-th year.

$${PGM}_{i,j,t}={\displaystyle \frac{1}{N-1}}\left({\sum }_{i=1}^N{CGM}_{i,j,t} - {CGM}_{i,j,t}\right)$$

(1)

3.2.3. Control Variables

The basic characteristics of enterprises will influence green management innovation. This study refers to the approaches of Xi et al. [32] and Wang [46], selecting enterprise size (Size), financial leverage (Lev), corporate profitability (Roa), cash flow (Cashflow), the size of the board of directors (Board), dual role (Dual), largest shareholder shareholding (Top1), company value (TobinQ), and corporate age (ListAge). Furthermore, the model incorporates firm and year fixed effects and accounts for clustering at the firm level when adjusting the standard errors. The definitions of the pertinent variables are presented in

Table 1. Definition of major variables.

Variable Type	Variable Name	Variable Symbol	Measurement of Variable
Interpreted variable	Green management innovation of object firms	CGM	Natural logarithm of (five indicators of green management innovation for the object firms add up +1)
Explanatory variable	Green management innovation of peer firms	PGM	Natural logarithm of (the average sum of five indicators of green management innovation of other firms in the same industry and year as the object firms +1)
Control variables	Firm size	Size	Natural logarithm of (the total assets +1)
	Asset–liability ratio	Lev	Total liability/Total assets
	Return on total assets	Roa	Net profit/Total assets
	Cash flow	Cashflow	Net cash flow from operating activities/Total assets
	Size of the board of directors	Board	Natural logarithm of the board member count
	Dual role	Dual	The board chairman and general manager equal 1; otherwise, 0
	Largest shareholder shareholding	Top1	The proportion of the largest shareholder
	Company value	TobinQ	(Market value of circulating shares + number of non-circulating shares × net asset value per share + book value of liabilities)/period
	Corporate age	ListAge	Natural logarithm of firm listing time
	Firm dummy variable	Firm FE	Firm fixed effects
	Year dummy variable	Year FE	Year fixed effects

.

3.3. Model Specification

To test research hypothesis H1, the model in this study is formulated as follows:

$${CGM}_{i,t}={\alpha }_0+{\alpha }_1{PGM}_{i,t}+{\alpha }_2{Controls}_{i,t}+Firm FE+Year FE+{\epsilon }_{i,t}$$

(2)

In model (2), ${CGM}_{i,t}$ represents the green management innovation of focus firm i in year t. ${PGM}_{i,t}$ is the average green management innovation of other firms in the same industry as firm i in year t. ${Controls}_{i,t}$ represents the control variables, and model (2) also controls for firm and year fixed effects. Based on the previous discussion, if there is an industry peer effect on corporate green management innovation, it is reasonable to expect that the coefficient of ${\alpha }_1$ in model (2) will be significantly positive, indicating that the green management innovation behavior of the focus firm will refer to the green management innovation decisions of other firms in the same industry.

To test research hypothesis H2, which posits that the peer effects of green management innovation are affected by an information learning mechanism, we construct the following models from the perspective of industry leaders and followers, drawing on the research of Leary and Roberts [6] and Lu et al. [47]:

$${CGM}_{i,t}={\alpha }_0+{\alpha }_1{PGM\_leader}_{i,t}+{\alpha }_2{Controls}_{i,t}+Firm FE+Year FE+{\epsilon }_{i,t}$$

(3)

$${CGM}_{i,t}={\alpha }_0+{\alpha }_1{PGM\_follower}_{i,t}+{\alpha }_2{Controls}_{i,t}+Firm FE+Year FE+{\epsilon }_{i,t}$$

(4)

This article follows the approach of Peress [48] and selects the formula ((operating revenue-operating costs-selling expenses-management expenses)/Operating revenue) to measure a company’s market position. We divided the sample companies that have the same year and industry into three groups based on their market positions, with the top 30% being industry leaders and the bottom 30% being industry followers. Compared to companies with lower market positions, companies with higher market positions usually have stronger information advantages and have been in a dominant leadership position for a long time, making them easier targets for followers to learn from and imitate. Specifically, in model (3), a sample of followers is used to test whether individual companies imitate industry leaders. The calculation of ${PGM\_leader}_{i,t}$ is based on the average level of green management innovation of industry leaders as peer firms. In model (4), the sample of leaders is used to test whether individual firms imitate industry followers, and ${PGM\_follower}_{i,t}$ is recalculated based on the average level of green management innovation of industry leaders as peer firms. In addition, some control variables are included in models (3) and (4), denoted as ${Controls}_{i,t}$. The precise definitions of these variables are presented in Table 1. Furthermore, the fixed effects of firms and years were further controlled to consider the impact of other factors on the outcomes.

To test research hypothesis H3, which assumes that the competitive mechanism drives peer effects on green management innovation, we chose the Herfindahl–Hirschman Index (HHI) to measure the intensity of market competition, following the practice of Lu et al. [47]. Due to the inverse relationship between market concentration and internal competition, the HHI can be used as a reverse indicator to measure the level of market competition, meaning that the smaller the value of the HHI, the higher the level of market competition.

This paper references the approach of Wu et al. [49], defining the dummy variable DummyHHI to indicate the degree of market competition. When the HHI is less than 0.1 in this industry, it indicates that the market competition is relatively fierce. DUMMYHHI takes a value of 1; otherwise, it is 0. The regression model is as follows:

$$\begin{gathered} {CGM}_{i,t}={\alpha }_0+{\alpha }_1{PGM}_{i,t}+{\alpha }_2{PGM}_{i,t}\times {DUMMYHHI}_{i,t}+{\alpha }_3{DUMMYHHI}_{i,t} \\ +{\alpha }_4{Controls}_{i,t}+Firm FE+Year FE+{\epsilon }_{i,t} \end{gathered}$$

(5)

Suppose the interaction regression coefficient ${\alpha }_2$ is significantly greater than zero. In that case, this also shows that the “peer effect” of green management innovation in listed companies with higher market competition is more significant, indirectly proving the existence of competitive mechanisms affecting channels.

4. Empirical Results and Analysis

4.1. Descriptive Statistical Analysis

Table 2. Descriptive statistical results.

Variable	Obs	Mean	Std.Dev.	Min	Median	Max
CGM	34,026	0.564	0.574	0.000	0.693	1.792
PGM	34,026	0.712	0.202	0.201	0.736	1.124
Size	34,026	22.218	1.299	19.922	22.022	26.276
Lev	34,026	0.413	0.205	0.053	0.403	0.896
ROA	34,026	0.042	0.066	−0.232	0.040	0.224
Cashflow	34,026	0.048	0.068	−0.152	0.047	0.242
TobinQ	34,026	2.012	1.287	0.841	1.596	8.514
ListAge	34,026	2.027	0.958	0.000	2.197	3.367
Board	34,026	2.114	0.196	1.609	2.197	2.639
Dual	34,026	0.301	0.459	0.000	0.000	1.000
Top1	34,026	0.339	0.148	0.084	0.317	0.742

presents the descriptive statistical results of the primary variables in this article. The mean value of the object firm’s CGM is 0.564, with a minimum value of 0, a maximum value of 1.792, and a standard deviation of 0.574. This shows that the green management innovation of Chinese enterprises is relatively low and that there are significant differences in the level of green management innovation among enterprises. The maximum value of PGM among peer enterprises is 1.124, the minimum value is 0.201, and the standard deviation is 0.202, indicating that, compared to the focus enterprise, the differences in green management innovation levels among other enterprises in the same industry are relatively small. From the perspective of the mean and standard deviation, the distribution characteristics of the results for other variables fundamentally align with previous research and demonstrate good comparability.

4.2. Basic Regression Results

Table 3. Basic regression results of peer effects on green management innovation.

Variable	(1)	(2)
	CGM
PGM	0.276 ***	0.267 ***
	(6.269)	(6.140)
Size		0.084 ***
		(8.516)
Lev		−0.007
		(−0.207)
ROA		0.051
		(0.877)
Cashflow		−0.000
		(−0.006)
TobinQ		0.007 **
		(2.018)
ListAge		0.004
		(0.383)
Board		−0.054 *
		(−1.948)
Dual		−0.004
		(−0.435)
Top1		0.072
		(1.162)
_cons	0.367 ***	−1.429 ***
	(11.769)	(−6.522)
Firm FE	Yes	Yes
Year FE	Yes	Yes
F	39.302	13.553
adj_R2	0.557	0.560
N	33,597	33,597

Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. T-statistics are reported in parentheses. Robust standard errors are adjusted for clustering at the firm level (the same notation is used for the following tables).

represents the basic regression results on whether peer effects exist in corporate green management innovation. In the baseline regression, the main focus is on the explanatory and interpreted variables, which are CGM and PGM, respectively. Column (1) only controls for firm fixed effects and year fixed effects, and column (2) further includes control variables. According to the results shown in Table 3, the coefficient of PGM in column (1) is 0.276 without other control variables, indicating a significant positive correlation at the 1% level. After adding control variables for regression, the coefficient of PGM in column (2) is 0.267, which is significant at the 1% level, revealing that there is a significant peer effect on green management innovation; the green management innovation of the object enterprise is positively correlated with the level of green management innovation exhibited by peer enterprises. Based on the above, there are peer effects on green management innovation; that is, the green management innovations implemented by other enterprises within the same industry will positively influence an object enterprise. Hypothesis 1 has been verified.

4.3. Endogeneity Test

4.3.1. Explanatory Variable with One-Period Lag

The basic regression tests in the previous section controlled for firm and year fixed effects, which avoided endogeneity issues caused by omitted variables. In this section, we chose to lag the explanatory variable by one period by considering the time lag characteristics of innovation and decision responses and alleviating the endogeneity problem of causal inversion. In

Table 4. Regression results with a one-period lag.

Variable	(1)	(2)
	CGM
PGM (One-Period Lag)	0.171 ***	0.173 ***
	(3.872)	(3.956)
Size		0.083 ***
		(7.808)
Lev		0.024
		(0.665)
ROA		0.055
		(0.919)
Cashflow		0.038
		(0.787)
TobinQ		0.016 ***
		(4.381)
ListAge		−0.035 ***
		(−2.723)
Board		−0.048
		(−1.615)
Dual		0.005
		(0.517)
Top1		0.053
		(0.812)
_cons	0.450 ***	−1.284 ***
	(14.895)	(−5.440)
Firm FE	Yes	Yes
Year FE	Yes	Yes
F	14.994	10.080
adj_R2	0.565	0.567
N	28,587	28,587

Note: *** denotes significance at the 1% level.

, column (1) only controls for firm and year fixed effects, while column (2) includes control variables. The results show that the estimated coefficient for peer firms’ green management innovation levels is positive and significant at the 1% level in both columns. This indicates that when the green management innovation level of peer firms increases in the previous year, the green management innovation degree of the object enterprise also improves in the following year.

4.3.2. Propensity Score Matching Test

To alleviate the potential regression error caused by endogeneity issues, we used the propensity score matching (PSM) method for testing. The specific approach is as follows: divide the mean of peer firms’ green management innovation into two groups, with 0 for those below the mean and 1 for those above the mean. By utilizing the control variables in model (2) as matching variables, 1:1 matching was performed using the nearest-neighbor method. A total of 432 unsuccessful matching samples were excluded and subjected to stationarity testing. After conducting a stationarity test, regression analysis was performed to obtain the results.

Table 5. Propensity score matching test results.

Variable	(1)	(2)
	CGM
PGM	0.274 ***	0.266 ***
	(6.244)	(6.114)
Size		0.084 ***
		(8.542)
Lev		−0.009
		(−0.267)
ROA		0.049
		(0.853)
Cashflow		0.002
		(0.036)
TobinQ		0.007 **
		(1.987)
ListAge		0.005
		(0.408)
Board		−0.054 *
		(−1.940)
Dual		−0.004
		(−0.430)
Top1		0.072
		(1.162)
_cons	0.368 ***	−1.433 ***
	(11.801)	(−6.542)
Firm FE	Yes	Yes
Year FE	Yes	Yes
F	38.983	13.536
adj_R2	0.557	0.560
N	33,594	33,594

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

shows the test results of the samples after PSM, which are still significantly positive, supporting the previous conclusion.

4.3.3. Instrumental Variable Method

Since firms within the same industry typically confront the same macroeconomic policies, economic environment, and institutional background, these similar common factors may drive firms to exhibit similar behaviors rather than the interactions between them. To alleviate this endogeneity problem, this article draws on the research of Leary and Roberts [6]. We used the variable of idiosyncratic stock returns (PeerShock) as an instrumental variable for corporate green management innovation. On the one hand, the green management innovation of enterprises is an investment decision of the company, which will ultimately be reflected in the company’s stock price. Therefore, the average idiosyncratic returns of peer enterprises are related to the green management innovation decisions of peer enterprises; that is, they meet the requirements related to endogenous variables. On the other hand, idiosyncratic stock returns represent the residual portion of stock returns after excluding risk and industry factors, which are only related to the company’s own characteristic factors and do not include those that can affect the overall market and industry. For a company, the average idiosyncratic stock return of peer companies is only related to the green management innovation of peer companies; it is not related to the green management innovation decision-making behavior of the target company. Therefore, it satisfies the two conditions of instrumental variables. The process of constructing the idiosyncratic stock returns of peer enterprises is as follows:

$$r_{i,j,t}={\alpha }_{i,j,t}+{\beta }_{i,j,t}^M\left({rm}_t-r{f}_t\right)+{\beta }_{i,j,t}^{IND}\left({\overline{r}}_{-i,j,t}-r{f}_t\right)+{\eta }_{i,j,t}$$

(6)

$${\widehat{r}}_{i,j,t}{=\widehat{\alpha }}_{i,j,t}+{\widehat{\beta }}_{i,j,t}^M\left({rm}_t-r{f}_t\right)+{\widehat{\beta }}_{i,j,t}^{IND}\left({\overline{r}}_{-i,j,t}-r{f}_t\right)+{\eta }_{i,j,t}$$

(7)

$${\widehat{\eta }}_{i,j,t}=r_{i,j,t}-{\widehat{r}}_{i,j,t}$$

(8)

where $r_{i,j,t}$ refers to the total return for firm i in industry j over month t, $\left({rm}_t-r{f}_t\right)$ for excess market return, and (${\overline{r}}_{-i,j,t}-r{f}_t$) is the excess return on unequally weighted industry portfolios, excluding firm i’s return. Regression is performed on model (6) using data from the previous 60 months, and regression with at least 24 months of data is retained to obtain the estimated values of each regression coefficient. Then, the expected return (${\widehat{r}}_{i,j,t}$) is calculated using the estimated values of the regression coefficients in model (7). According to model (8), the expected return is subtracted from the stock return ($r_{i,j,t}$) to obtain the monthly specific return (${\widehat{\eta }}_{i,j,t}$) of the enterprise. Finally, the monthly idiosyncratic returns are compounded to obtain the annual stock idiosyncratic returns of the enterprise for that year. After removing the data of the target firm, the average annual stock idiosyncratic return of other companies in the same industry can be calculated to obtain the average idiosyncratic stock return of the peer firms.

Based on the selection of effective instrumental variables, we used the 2SLS method to regress model (2). The regression results are shown in column (1) of

Table 6. Instrumental variable method results.

Variable	(1)	(2)
	First-Stage Regression	Second-Stage Regression
	PGM	CGM
PeerShock	−0.084 ***
	(−29.554)
PGM		0.994 ***
		(6.944)
Size	0.035 ***	0.068 ***
	(11.232)	(7.031)
Lev	−0.057 ***	0.016
	(−5.136)	(0.446)
ROA	−0.019	0.055
	(−1.097)	(0.960)
Cashflow	−0.015	−0.001
	(−1.285)	(−0.024)
TobinQ	−0.003 ***	0.006 *
	(−3.065)	(1.895)
ListAge	0.103 ***	−0.034 *
	(40.833)	(−1.789)
Board	−0.023 ***	−0.043
	(−2.605)	(−1.529)
Dual	0.001	−0.004
	(0.213)	(−0.391)
Top1	−0.080 ***	0.111 *
	(−4.047)	(1.784)
_cons		−1.549 ***
		(−7.667)
Firm FE	Yes	Yes
Year FE	Yes	Yes
adj_R2	0.359	0.034
N	33,597	33,597
Unidentifiable test	512.966 ***
Weak instrumental variable test	873.460 <16.38>

Note: The value 16.38 is the critical value for the Stock–Yogo weak instrument test at the 10% level. ***, * denote significance at the 1%, 10% levels, respectively.

. In the first-stage regression, the average of peer firms’ green management innovation (PGM) is the interpreted variable, and the instrumental variable is the mean idiosyncratic stock return of peer firms. The coefficient is significant at the 1% level, indicating that peer firms’ average idiosyncratic stock return significantly affects their green management innovation. Combined with the weak instrumental variable test results in Table 6, this shows that the selected instrumental variables are essentially effective.

In the second-stage regression, the interpreted variable is the green management innovation (CGM) of peer firms, as presented in column (2). The coefficient of the core explanatory variable PGM to CGM is 0.994 and is significant at the 1% level. Based on the weak instrumental variable test, the Cragg Donald Wald F statistic is greater than the critical value of 10% bias under the Stock Logo weak instrumental variable test, showing that there is no weak instrumental variable problem. In the unidentifiable test, the Kleiberen Paap rk LM test rejected the null hypothesis of the insufficient identification of instrumental variables at the 1% significance level, meaning that the instrumental variables constructed in this study are identifiable. Therefore, the results of instrumental variables solve the endogeneity issue in this article and also support research hypothesis H1.

4.4. Robustness Testing

4.4.1. Changing the Regression Model

To prevent regression significance from depending on specific distributions or the characteristics of the explanatory variable—namely, the green management innovation data of the target firm—we used the Tobit model for robustness testing.

Table 7. Results of Tobit model.

Variable	(1)	(2)
	CGM
PGM	0.523 ***	0.529 ***
	(21.825)	(22.437)
Size		0.097 ***
		(20.447)
Lev		−0.044 **
		(−1.980)
ROA		0.048
		(1.012)
Cashflow		0.054
		(1.326)
TobinQ		0.009 ***
		(3.570)
ListAge		−0.038 ***
		(−7.311)
Board		0.001
		(0.034)
Dual		−0.001
		(−0.144)
Top1		0.074 **
		(2.433)
_cons	0.144 ***	−1.920 ***
	(7.858)	(−19.062)
Firm FE	Yes	Yes
Year FE	Yes	Yes
N	34,026	34,026
Log likelihood	−20,406.012	−20,145.766
Chi2	1989.632	2568.124

Note: ***, ** denote significance at the 1%, 5% levels, respectively.

presents the estimation results of the Tobit model. Column (1) only controls for the fixed effects of firm and year, and column (2) includes control variables. The final results show that the regression is significantly positive at the 1% significance level, indicating that the regression results of the Tobit model support the previous conclusion that a firm’s green management innovation level is influenced by its peer firms in the industry.

4.4.2. Changing the Sample Period

We considered that China implemented the “Environmental Protection Tax Law of the People’s Republic of China” on 1 January 2018. To eliminate the influence of fluctuations in environmental protection taxes on the peer effects of firms’ green management innovation, we narrowed the sample period and selected samples before 2018 for regression testing in the model. As shown in

Table 8. Regression test results before 2018.

Variable	(1)	(2)
	CGM
PGM	0.260 ***	0.259 ***
	(4.722)	(4.706)
Size		0.024
		(1.641)
Lev		−0.054
		(−1.092)
ROA		−0.141
		(−1.424)
Cashflow		0.043
		(0.684)
TobinQ		−0.015 ***
		(−3.278)
ListAge		0.048 **
		(2.418)
Board		−0.033
		(−0.815)
Dual		−0.012
		(−0.842)
Top1		0.073
		(0.905)
_cons	0.313 ***	−0.205
	(9.093)	(−0.645)
Firm FE	Yes	Yes
Year FE	Yes	Yes
F	22.302	5.304
adj_R2	0.610	0.611
N	14,171	14,171

Note: ***, ** denote significance at the 1%, 5% levels, respectively.

, regardless of whether control variables are included, the regression coefficients are significantly positive at the 1% level, supporting the previous conclusion.

4.5. Mechanism Analysis

Table 9. Mechanism analysis results.

Variable	(1)	(2)	(3)	(4)	(5)
	Information Learning Mechanism				Competitive Mechanism
	CGM
PGM	0.159 ***	0.162 ***	−0.004	−0.009	0.211 ***
	(3.331)	(3.442)	(−0.084)	(−0.186)	(4.596)
PGM × DUMMYHHI					0.083 ** (2.056)
DUMMYHHI					−0.030
					(−1.067)
Size		0.061 ***		0.114 ***	0.086 ***
		(3.677)		(5.730)	(8.597)
Lev		0.049		−0.029	−0.006
		(0.962)		(−0.405)	(−0.174)
ROA		0.168 **		0.074	0.067
		(2.398)		(0.467)	(1.167)
Cashflow		0.094		−0.041	−0.005
		(1.342)		(−0.455)	(−0.117)
TobinQ		−0.000		0.004	0.006
		(−0.031)		(0.682)	(1.642)
ListAge		−0.089 ***		0.030	0.002
		(−2.923)		(1.479)	(0.199)
Board		−0.013		−0.035	−0.046 *
		(−0.308)		(−0.595)	(−1.650)
Dual		0.015		−0.001	−0.003
		(0.976)		(−0.058)	(−0.344)
Top1		−0.128		0.076	0.075
		(−1.166)		(0.583)	(1.199)
_cons	0.423 ***	−0.671 *	0.568 ***	−1.973 ***	−1.457 ***
	(15.745)	(−1.828)	(21.677)	(−4.308)	(−6.609)
Firm FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
F	11.097	4.792	0.007	4.505	11.743
adj_R2	0.584	0.587	0.585	0.589	0.553
N	9810	9810	9615	9615	33,597

Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Columns (1) and (2) are industry leaders’ reactions to followers, and columns (3) and (4) are industry followers’ reactions to leaders.

shows the regression results of the information learning mechanism based on the enterprise market position. The results in columns (1) and (2) show that the green management innovation of high-market-position industry peer firms reflects the green management innovation of low-market-position firms. Column (1) only controls for the fixed effects of the firm and year, and column (2) further adds control variables. The regression coefficients are all positive and significant at the 1% level. The results in the last two columns, (3) and (4), show that the green management innovation of high-market-position firms is not affected by the green management innovation of low-market-position peer enterprises within the industry. Regardless of whether control variables are included, the regression coefficients remain insignificant. The results indicate that, in the same industry, the higher the green management innovation of high-market-position firms, the higher the green management innovation level of low-market-position firms. This suggests that companies with a lower market position tend to imitate the green management innovation practices of those with a higher market position, showing the existence of an information learning mechanism; this aligns with the expected outcomes.

Table 9 also presents the impact of the competitive mechanism on green management innovation with industry peer effects. In column (5), the coefficient of the interaction term PGM × DUMMYHHI is 0.083 at the 5% level; it is significantly positive, indicating that the more intense the market competition, the more significant the industry peer effects of green management innovation in enterprises. This indirectly proves that one of the reasons for the peer effects of green management innovation in the same industry is based on the competitive mechanism.

4.6. Moderating Effect Analysis

4.6.1. Moderating Effect of Information Acquisition

Common institutional ownership establishes a social network of interconnected companies in the same industry through collaborative governance, which helps to strengthen information dissemination, experience sharing, and behavioral learning among connected companies [50]. Institutional investors can transmit experience, information, and knowledge among multiple holding companies in the same industry through formal and informal social connections [51], promoting imitative learning behavior among companies. Green management innovation is, to some extent, affected by the degree of information disclosure and a company’s own ability to obtain information among industry peer firms. Common institutional investors can increase the channels for enterprises to obtain information through information sharing; they can also influence the investment decisions and management of holding enterprises through the board of directors [52], encouraging them to learn from high-level green management innovation enterprises, thereby promoting the peer effects of enterprise green management innovation.

Common institutional ownership refers to institutional investors holding 5% or more of the shares in one company while holding no less than 5% of other company shares in the same industry. This article refers to the approaches of He and Huang [53] and Du and Liu [54] to select the number of common institutional investors holding shares in the same listed company; we added one and took the logarithm to measure the degree of linkage (COZ) of common institutional ownership. On the basis of model (1), an interaction term (PGM × COZ) has been added to measure the impact of common institutional ownership on the peer effects of green management innovation in enterprises. The regression results are shown in column (1) of

Table 10. Moderating effect results.

Variable	(1)	(2)	(3)
	Information Acquisition	Environmental Uncertainty	Environmental Regulation
	CGM
PGM	0.254 ***	0.232 ***	0.221 ***
	(5.807)	(5.108)	(4.670)
COZ	−0.100
	(−1.445)
PGM × COZ	0.215 **
	(2.378)
EU		−0.023 **
		(−2.341)
PGM × EU		0.048 ***
		(3.046)
ERI			−0.015 **
			(−1.985)
PGM × ERI			0.026 **
			(2.573)
Size	0.082 ***	0.084 ***	0.083 ***
	(8.108)	(8.345)	(8.277)
Lev	0.001	−0.003	−0.002
	(0.024)	(−0.077)	(−0.060)
ROA	0.054	0.058	0.064
	(0.948)	(1.018)	(1.121)
Cashflow	0.002	0.003	0.003
	(0.041)	(0.063)	(0.059)
TobinQ	0.006 *	0.007 *	0.007 **
	(1.763)	(1.835)	(1.961)
ListAge	0.005	0.000	0.002
	(0.409)	(0.011)	(0.205)
Board	−0.053 *	−0.054 **	−0.054 **
	(−1.918)	(−1.965)	(−1.961)
Dual	−0.005	−0.005	−0.005
	(−0.500)	(−0.493)	(−0.490)
Top1	0.073	0.071	0.072
	(1.177)	(1.137)	(1.162)
_cons	−1.382 ***	−1.404 ***	−1.385 ***
	(−6.219)	(−6.336)	(−6.240)
Firm FE	Yes	Yes	Firm FE
Year FE	Yes	Yes	Year FE
F	10.701	11.596	11.073
adj_R2	0.560	0.560	0.552
N	33,597	33,597	33,597

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

; the regression coefficient of the interaction term between green management innovation and common institutional ownership in the peer firms is 0.215, which is significant at the 5% level. The results indicate that common institutional ownership positively moderates the peer effects of corporate green management innovation.

4.6.2. Moderating Effect of Environmental Uncertainty

Green management innovation requires significant economic risks. When environmental uncertainty increases, enterprises will face increased operational risks [55], short-term cash flow pressures [56], and survival pressures. In order to avoid risks and reduce costs, enterprises will prefer, instead, to imitate other enterprises’ green management innovation behaviors. On the other hand, high environmental uncertainty can lead to a lot of noise in market information [57,58]. At this time, the target company believes that the behavior decisions of peer companies will convey useful information, such as development opportunities and industry prospects [59]. Therefore, it will actively learn from the decisions of peer firms to reduce the uncertainty and costs associated with trial and error.

This paper follows the approaches of Ghosh and Olsen [60] and Shen et al. [61] to calculate unadjusted environmental uncertainty by using the ratio of the standard deviation of sales revenue, excluding normal growth, over the past five years to the mean sales revenue over the same period. Then, the unadjusted environmental uncertainty is categorized by the median of the industry’s environmental uncertainty to represent the environmental uncertainty (EU). Based on model (1), the interaction term between peer firms’ green management innovation and environmental uncertainty (PGM × EU) is added to measure the impact of environmental uncertainty on the peer effects of corporate green management innovation. The regression results are represented in column (2) of Table 10, which shows that the regression coefficient of the interaction term between peer firms’ green management innovation and environmental uncertainty is positive and significant at the 1% level. The results show that the higher the environmental uncertainty, the more pronounced the peer effects on green management innovation.

4.6.3. Moderating Effect of Environmental Regulation

As an important driving force for corporate green innovation, environmental regulations guide firms to meet their environmental responsibilities through mandatory guidelines. The “Porter Hypothesis” shows that mandatory and strict environmental regulations can induce firms to increase their innovation investment and attempt to compensate for their environmental compliance costs through innovation compensation [62,63]. The government’s policies on environmental protection, energy utilization, and other aspects are constantly adjusting and changing, which introduces particular uncertainties and risks to the green management innovation of enterprises. Enterprises need to constantly adapt to changes in the policy environment and develop corresponding strategies and plans, which poses certain challenges to their business management and increases uncertainty in market competition. Peng et al. [64] found that under strict environmental regulations, external environmental pressures enhance companies’ motivation and enthusiasm for green innovation, facilitating the spread of green knowledge and technology among companies. Therefore, under the pressure of environmental regulations, companies will imitate other enterprises’ green management innovation decision-making behavior.

This study draws on the research of Liu et al. [65], using the proportion of the amount invested in waste gas and wastewater pollution control in a year to the industry output value of that year in the location of the listed company to measure the environmental regulation intensity (ERI). Based on model (1), an interaction term between peer firms’ green management innovation and environmental regulation intensity (PGM × ERI) is added to measure the impact of environmental regulation on the peer effects on green management innovation. The empirical results are shown in column (3) of Table 10, where the regression coefficient is significantly positive at the 5% level, indicating that environmental regulation has a positive moderating effect on the peer effects of green management innovation.

4.7. Heterogeneity Analysis

4.7.1. Grouping Study Based on Financing Constraints

In the process of promoting green management innovation, companies must allocate substantial funds to key areas, including technological upgrades, equipment enhancements, and employee training. These costs will put certain pressures on enterprises. Moreover, enterprises with high financing constraints have weaker risk resistance when facing fierce market competition. In this context, when companies face high financing constraints, they are more likely to follow the behavior trends of most enterprises to achieve cost savings and risk avoidance. Therefore, companies with higher financing constraints are more likely to follow other companies’ green management innovation decision-making behavior.

We refer to the approaches of Hadlock and Pierce [66] and Ju et al. [67] to construct an SA variable to measure the financing constraint pressure faced by enterprises. When the financing constraints of the companies are greater than the industry average in the current year, they are classified as high financing constraints; otherwise, they are classified as low financing constraints. As shown in columns (1) and (2) of

Table 11. Heterogeneity regression results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	High Financing Constraints	Low Financing Constraints	Heavily Polluting Industries	Non-Heavily Polluting Industries	Industrial Policy Support	Without Industrial Policy Support
	CGM
PGM	0.291 ***	0.154 **	−0.011	0.210 ***	0.190 ***	0.101
	(4.735)	(2.428)	(−0.096)	(4.301)	(2.645)	(1.643)
Size	0.076 ***	0.094 ***	0.070 ***	0.088 ***	0.089 ***	0.073 ***
	(4.968)	(6.413)	(3.054)	(7.547)	(6.058)	(4.763)
Lev	−0.016	−0.016	−0.000	−0.014	0.000	−0.022
	(−0.334)	(−0.308)	(−0.005)	(−0.371)	(0.006)	(−0.467)
ROA	−0.039	0.169 **	0.005	0.093	0.014	0.092
	(−0.462)	(2.097)	(0.033)	(1.467)	(0.182)	(1.136)
Cashflow	0.069	−0.034	−0.084	0.034	0.075	0.032
	(1.052)	(−0.556)	(−0.854)	(0.674)	(1.159)	(0.516)
TobinQ	0.001	0.007	−0.010	0.008 **	0.009 **	−0.001
	(0.264)	(1.360)	(−1.027)	(2.137)	(1.977)	(−0.287)
ListAge	−0.015	0.016	0.052 *	−0.007	0.006	0.006
	(−0.711)	(1.021)	(1.887)	(−0.520)	(0.354)	(0.322)
Board	0.024	−0.123 ***	−0.170 **	−0.020	−0.061	0.012
	(0.569)	(−3.345)	(−2.490)	(−0.663)	(−1.587)	(0.306)
Dual	0.016	−0.018	−0.025	0.004	−0.003	−0.003
	(1.125)	(−1.269)	(−1.024)	(0.336)	(−0.249)	(−0.177)
Top1	0.088	0.051	0.011	0.133 **	0.025	0.214 **
	(0.942)	(0.551)	(0.075)	(1.991)	(0.273)	(2.390)
_cons	−1.400 ***	−1.413 ***	−0.555	−1.577 ***	−1.420 ***	−1.350 ***
	(−4.171)	(−4.309)	(−1.110)	(−6.136)	(−4.356)	(−4.031)
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
F	5.689	7.436	2.778	9.378	6.006	4.324
adj_R2	0.539	0.592	0.534	0.551	0.550	0.565
N	16,919	16,532	7376	26,194	19,926	12,876

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

, the regression coefficients of green management innovation in the peer firms are positive in both samples. However, when the companies have serious financing constraints, the regression coefficient is larger, and the significance level is higher. The bdiff inter-group coefficient test showed a significant difference at the 1% level, indicating that the peer effects of green management innovation are more significant in enterprises with severe financing constraints. The above results show that when firms face significant financing constraints, they choose to imitate the green management innovation of peer firms for cost-saving and risk avoidance.

4.7.2. Grouping Study Based on Industry Classification

Compared to other industries, enterprises in heavily polluting industries are subject to relatively strong policy constraints, have higher policy sensitivity, and also bear more social and environmental responsibilities, thus facing stronger external pressure [68]. Based on this, the research sample was divided into two groups, heavily polluting industries and non-heavily polluting industries, for comparative testing. This article refers to the definitions of heavily polluting industries by Pan et al. [69] and Wang et al. [46]. We combined these with the industry classification standards issued by the China Securities Regulatory Commission in 2012 to specifically select the codes for heavily polluting industries, which are B06, B07, B08, B09, C17, C19, C22, C25, C26, C28, C29, C30, C31, C32, and D44; any others are non-heavily polluting industries. The regression results are presented in columns (3) and (4) of Table 11. There are no significant peer effects of green management innovation in heavily polluting industries, while non-heavily polluting industries show positive significance at the 1% level. This suggests that non-heavily polluting industries will learn from the decision-making behavior of other enterprises when implementing green management innovation. However, heavily polluting industries face strong environmental and industry regulations, making it difficult to find imitators and implementation paths, thus resulting in lesser peer effects.

4.7.3. Grouping Study Based on Industrial Policy

Industrial policy involves the government’s focus on fostering the economy through various interventions and guidance, which can intuitively bring resource increases to an enterprise and promote industrial coordination [70]. The enterprises supported by industrial policies often have higher development potential and greater market demand [71]. Enterprises in these industries will face greater opportunities and more intense market competition. In this case, policy support will play a role in guiding corporate green innovation decision making [72]. Enterprises will, thus, actively implement green management innovation behavior to cope with fierce market competition. Therefore, enterprises in industries supported by policies are more likely to generate peer effects.

This article refers to the approach of Guo et al. [73] and is based on the development plan document “The Five Year Plan” released by the province where the enterprise is located. When industries are mentioned in the plan, they are supported by industrial policy; otherwise, they are without industrial policy support. As shown in columns (5) and (6) of Table 11, the regression coefficient of green management innovation is significantly positive in the industrial policy support group, and the other group is not significant. The results indicate that when enterprises exist within industries supported by industrial policies, they tend to imitate the green management innovation behavior of other enterprises in the same industry; that is, the peer effect of green management innovation is more significant.

5. Conclusions and Implications

5.1. Conclusions and Discussion

This study uses data from China’s listed companies from 2012 to 2022 to test industry peer effects that may exist in green management innovation. Moreover, the mechanisms of the peer effects are examined in this work from the viewpoints of information learning and competition. In addition, this study verified the heterogeneity of peer effects on green management innovation within the same industry according to the following aspects: information acquisition ability, environmental uncertainty, environmental regulation, and different levels of financing constraints, as well as whether a firm belongs to heavily polluting industries and whether it is supported by provincial industrial policies. The research findings of this article are based on China’s institutional background, providing empirical evidence for China’s institutional environment and lessons for other countries with similar development backgrounds. The study results are as follows.

First, the benchmark regression results show that the object companies tend to imitate green management innovation within the same industry, where firms have already achieved good results; that is, the green management innovation level of object firms increases with the improvement of the green management innovation of peer firms.

Second, the mechanisms of peer effects on green management innovation are analyzed from the perspectives of information learning and competition. From the perspective of information learning, enterprises with a high industry status usually have more information advantages. In order to reduce decision-making costs, enterprises with a low industry status improve their green management innovation level by obtaining relevant information from high-industry-status enterprises and imitating them to make decisions. From the perspective of the competitive mechanism, enterprises will pay attention to others within the industry that have already implemented green management innovation and achieved success. Object enterprises will imitate the green innovation behaviors of peer enterprises in the same industry to obtain a competitive advantage. The results show that the more fierce the market competition, the stronger the industry peer effects on green management innovation.

Third, according to the moderating effect in this article, it can be observed that common institutional investors can expand certain channels to obtain information and increase communication, thereby promoting the peer effects of green management innovation. When environmental uncertainty increases, to avoid risks and reduce costs, enterprises tend to imitate the green management innovation of peer enterprises in the same industry. In response to environmental regulatory pressures, enterprises will choose to imitate the green management innovation decision-making behavior of other enterprises to cope with fierce market competition and promote the dissemination of green knowledge and technology among enterprises.

Lastly, the heterogeneity analysis results show that when enterprises face high financing constraints, they are more inclined to emulate the decision-making behavior regarding peer firms’ green management innovation to save costs and avoid risks. Compared with non-polluting industries, heavily polluting industries face stronger environmental and industry regulations and have higher policy sensitivity. Here, it is difficult to follow objects and paths when implementing green management innovation; thus, this aspect is unable to have peer effects. The enterprises supported by industrial policies also have higher development potential and greater market demand. Therefore, enterprises in industries with policy support are more likely to generate peer effects.

This study provides compelling evidence for the decision-making processes in green management innovation and the mechanisms of related peer effects, enriching the research on green management innovation in enterprises. In addition, it offers crucial managerial insights into Chinese enterprises’ green innovation decision-making and inter-industry linkage development, as well as the refinement of government policies.

5.2. Inspiration and Suggestions

From the empirical findings, this article proposes the following practical suggestions: (1) Green management innovation among Chinese listed companies remains underdeveloped. When formulating their own green management innovation decisions, enterprises should not only take into account their business environment but also the inter-relation of peer behaviors to improve the rationality and effectiveness of decision making. (2) Enterprises can establish interconnected social networks to improve the tracking quality of green management innovation of peer firms, build a positive innovation linkage relationship, and make full use of the learning mechanism of peer effects to improve green management innovation levels. Industry leaders can build a green management innovation knowledge platform to help target companies understand the green management innovation of their peers in a more timely and authentic manner in order to share innovation risks and achieve innovation synergy. (3) The more fierce the market competition, the more susceptible an enterprise’s green management innovation behavior is to the influence of peer enterprises. Thus, when formulating pertinent decisions, enterprises should pay attention to the behavior of competitors in order to keep a competitive balance or restrict competition. Regulators should not just focus on the positive imitative effects of imitating competitors’ behavior when formulating policies but also on the fierce competition it may bring, creating a good competitive environment among companies and promoting the harmonious development of green innovation. (4) Enterprises should establish an industry green innovation ecosystem to amplify the synergistic effect of green management innovation among enterprises. Especially when confronted with high environmental uncertainty, firms should strengthen communication and exchange, reduce information searching difficulty and costs, and better enhance the coordinated development of green management innovation among industries. (5) Government departments can enhance the innovation induction effect of environmental regulation. By coordinating stakeholders in the market and industry, they can strengthen the cognitive, normative, and regulatory pressures faced by enterprises, thereby increasing the legitimacy risk of individual enterprises and enhancing their willingness to follow green management innovation. (6) This paper also shows that the industry peer effects of green management innovation are more significant toward companies that have high financing constraints, are within non-polluting industries, and are supported by industrial policies. Therefore, regulatory authorities should formulate relevant policies that vary from enterprise to enterprise to avoid a “one-size-fits-all” approach.

5.3. Research Limitations and Future Prospects

This study examines the peer effects of green management innovation among companies in the same industry using data from listed Chinese companies. However, this study is subject to the following limitations. First, this study emphasizes the dynamics of peer effects on green management innovation among enterprises within the same industry. Existing research on peer effects has found that future studies can analyze whether regional peer effects affect corporate green management innovation and whether the research mechanism is the same as the industry peer effect. Second, the data utilized in this study are grounded in the Chinese institutional setting, which can, to some extent, reflect the behavior of green innovation in developing countries. Yet, the green management approach in developed countries will likely differ from that in China. In the future, researchers can apply listed companies’ data from multiple countries to further study the motives of green management innovation decision-making behaviors in different countries.