Research on the Effect of Common Institutional Ownership on Corporate Environmental Responsibility Disclosure: A Performance Feedback Perspective

Abstract

The rise of common institutional ownership has a profound impact on corporate environmental policies, and the business environment in which the enterprises operate can significantly affect the decisions of institutional investors. This study evaluates the effect of common institutional ownership on corporate environmental responsibility disclosure (CERD) practices in Chinese manufacturing firms from the performance feedback perspective. Utilizing a sample period spanning from 2008 to 2021, the study indicates several key findings. Firstly, the presence of common institutional ownership is demonstrated to enhance the level of CERD in these firms, especially soft information on environmental responsibility. Secondly, this positive effect is amplified when positive performance expectation gaps exist. Mechanism tests reveal that under the dual pressures of common institutional investor exit threats and a negative expected performance gap, firms tend to lower their level of CERD. Conversely, synergistic effects effectively promote this disclosure. Furthermore, analysis of the impact pathway demonstrates that under such conditions, common institutional ownership exerts pressure to reduce both monetary and non-monetary private benefits accruing to management, thereby leading to optimized CERD. In addition, heterogeneity analysis indicates a more significant effect of common institutional ownership on CERD enhancement in private enterprises compared to their state-owned counterparts, particularly when positive performance expectation gaps are present.

1. Introduction

As the world normalizes from the COVID-19 pandemic turbulence and economic activity resumes, environmental concerns have returned to the forefront. According to a report by the International Energy Agency (IEA), global energy-related carbon dioxide (CO₂) emissions are estimated to have grown by 0.8% in 2024, following an increase of 1.1% in 2023. This growth has pushed emissions to a new record high of 37.8 billion metric tons. China accounts for one-third of global emissions. This coincides with China’s economic rise, with a Gross Domestic Product (GDP) of 134.91 trillion yuan in 2024 and securing its place as the second-largest economy globally. Moreover, China’s rapid industrialization over recent decades is evident in the manufacturing sector’s contribution to its GDP, reaching 24.9% in 2024 and maintaining its status as the world’s largest manufacturing power for 15 consecutive years. However, it is important for China to first address the conflict between industrial expansion and environmental issues before it can effectively respond to that between economic growth and environmental sustainability [1]. The resolution of these issues is of great significance for both the sustainable development of China and global ecological and economic progress.

Enterprises, which serve as major social actors and economic drivers, also bear the significant responsibility of promoting sustainable environmental development [2]. In China, how to encourage businesses to fulfill their environmental responsibilities is a focal point of concern across various sectors of society. Corporate Environmental Responsibility Disclosure (CERD) serves as a significant means for companies to demonstrate their environmental responsibility. Existing research hypothesizes that institutional investors, as key stakeholders in corporations, possess superior information and resource advantages compared to other investor types [3,4]. Therefore, their stance on CERD significantly affects the CERD-related policies.

Past analyses into the effect of institutional investors on CERD have primarily approached individual investments as isolated and mutually exclusive events. However, the evolution of China’s capital markets has led to a rise in the formation of coalitions among institutional investors, contributing to shared equity stakes [5,6,7]. Through this common ownership structure, institutional investors wield amplified power and possess an increased incentive to reduce competitive pressures from other institutional investors, thereby aiming to optimize portfolio value [8]. Data indicates a surge in companies listed on the Shanghai and Shenzhen stock exchanges with shared ownership by institutional investors, increasing from 186 in 2008 to 621 in 2021. The academic literature presents two conflicting views on the economic effects of common institutional ownership. On one hand, common institutional investors are seen as a positive force due to their superior ability to gather and process information [3]. This leads to more effective corporate monitoring [9,10]. Furthermore, their strong ties across industries can generate significant synergistic effects for firms [11]. On the other hand, a contrasting perspective suggests that common institutional investors may collude with management to pursue excess profits. By internalizing the externalities of competition, they could potentially trigger industry monopolization [12,13]. Therefore, it is necessary to fully discuss the role of common ownership by institutional investors in CERD so as to justify such behavior. Different views on the economic effects of common institutional investors demonstrate that their impact on CERD is not static. This leads to a crucial question: under what conditions do their positive effects dominate, and under what conditions do the negative effects prevail?

A firm’s operating environment can significantly affect institutional investors’ decision-making processes. Prospect theory and performance feedback theory hypothesize that decision-makers utilize reference points to evaluate the efficacy of corporate decisions and operational outcomes, gauging the disparity between actual and expected performance. Thereafter, they adapt to future decision-making attitudes and behaviors accordingly [14]. While recent years have witnessed a surge in research according to performance feedback theory, these studies primarily concentrate on the behavior of internal corporate decision-makers, particularly in areas such as international expansion [15], innovation efficiency [16], and technological choices [17]. However, the governance effects exerted by stakeholders, especially influential institutional investors, under varying expected performance levels remain relatively unexplored in contemporary literature. To address this gap, this paper integrates performance feedback theory to analyze the effect of common institutional ownership on CERD across different expected performance levels, thus expanding the research scope of governance mechanisms employed by common institutional ownership and cultivating the healthy development of joint shareholding among institutional investors.

This paper offers several key contributions to the understanding of CERD. First, it establishes a framework for analyzing the governance effects of common ownership by institutional investors on CERD. While existing research has primarily focused on the effect of individual institutional investors on CERD, this paper addresses the overlooked collective influence of common ownership, a phenomenon that has become increasingly prevalent. The paper hypothesizes that the concerted actions of common owners are likely to exert a more significant effect on CERD. Second, the paper evaluates the evolutionary trajectory of the governance effects of common ownership on CERD through the perspective of performance feedback theory. Existing research on common institutional investors often adopts a singular perspective, focusing solely on either the governance effect or the collusion effect. This approach overlooks the dynamic choices common institutional investors make. In practice, their behavior involves a contingent decision-making mechanism, representing an optimal choice after balancing multiple factors. Consequently, their effects manifest differently depending on the context of the performance gap they anticipate. Finally, the paper evaluates the impact mechanisms of common ownership by institutional investors on CERD, drawing upon insights from corporate governance and performance feedback theory.

This study theoretically contributes to the existing body of knowledge on common institutional ownership in the Chinese market. It integrates performance feedback theory into the analysis of governance effects exerted by common ownership structures on CERD. By broadening the scope of prior research, which primarily focused on internal decision-making under varying performance expectation gaps, this study incorporates the role of key stakeholders, specifically institutional investors. Therefore, it explains the effect of common institutional ownership on corporate environmental policies. From a practical standpoint, the findings offer valuable insights for both investors and regulators to identify the effect of common institutional ownership on CERD under diverse performance expectations. This enhanced understanding can improve decision-making efficacy, guide the behavior of institutional investors, and incentivize capital market engagement in green sustainable development initiatives. Finally, the study offers a theoretical framework for promoting green growth and sustainable development practices.

The remaining sections of this paper are as follows: Section 2: Literature Review and Hypothesis; Section 3: Research Design; Section 4: Empirical Analysis; and Section 5: Conclusions and Implications.

2. Literature Review and Hypothesis

2.1. Common Institutional Ownership and Corporate Environmental Responsibility Information Disclosure

The effect of institutional investors on CERD remains a contentious topic. Information transmission theory hypothesizes that CERD acts as a crucial signaling mechanism, enabling institutional investors to assess a company’s environmental performance. This, in turn, helps mitigate the implicit and explicit costs incurred by environmental issues and thus lowers investment-related risks [18]. Consequently, institutional investors may pay particular attention to companies’ environmental information due to their interests. Furthermore, their supervisory role can further enhance CERD. However, some scholars have observed that institutional investors in China may prioritize personal interests compared to Western capital markets. When their interests diverge from those of small and medium-sized shareholders, they may conspire with the management. The company will make less effort to fulfill its environmental responsibilities [19,20]. However, this series of studies is commonly based upon the consideration of institutional investors as independent individuals.

Existing research has shown that the behavior of institutional investors is not independent; instead, they may collaborate to impose influence on the policies of companies in which they have common ownership [21]. Even if each investor holds only a small percentage of shares, they can function as effective independent holding entities to fulfill corporate governance [22]. With the ever-developing capital markets in China, common ownership by institutional investors has become increasingly prevalent.

Common ownership by institutional investors refers to the simultaneous holding of shares in the same company by two or more institutional investors in order to maximize the overall value of their portfolios [3]. The academic community views the impact of common ownership by institutional investors on micro-level corporate behavior with ambivalence. On the one hand, some scholars believe that common ownership by institutional investors plays a positive role in corporate behavior. Supervision and synergy represent the two main effects. Regarding the supervision effect, coalitions formed by institutional investors can enhance their information advantage [22,23,24], thereby increasing transparency between investors and managers and reducing costs incurred by information search and processing for investors [3], and enhancing their corporate supervision efficiency. For example, it may help mitigate the risks of management earnings manipulation [25,26] and improve investment efficiency [27]. Additionally, common institutional ownership also possesses the advantage of cross-industry experience, which grants them greater voice [28] to enhance their monitoring effectiveness. This is evidenced by an increased likelihood of them voting against management-submitted proposals at shareholder meetings [3] and their ability to improve the quality of financial reporting [29]. For the synergistic effect, common institutional investors play a strong role as industry hubs, facilitating tactical alliances to enhance collaboration among firms. This helps align incentives across companies within the same industry [30] and reduces detrimental competition among portfolio firms, which can lead to benefits such as lower financing costs and improved M&A performance [31].

On the other hand, there are some different views. Since common institutional investors seek to maximize portfolio value, institutional investors with shares in competing companies may exert pressure on managers to internalize the externalities of competitive behavior, so as to reduce competition in the industry and increase market share [30] and gain excess profits [6]. Therefore, common ownership by institutional investors may intensify collusion between investors and managers, distort market mechanisms, and give rise to market monopolization. In line with theoretical expectations, many studies have obtained empirical evidence from various sectors, such as airline ticket pricing monopolies [8] and product markets [32]. CERD, as a crucial strategic behavior for businesses, not only serves as a positive response to social responsibility or stakeholder needs for legitimacy but also helps foster a positive corporate image, reputation, and brand, so as to enhance corporate value [33]. However, according to the “Porter Hypothesis”, environmental responsibility fulfillment may encroach upon profit margins and thus pose significant risks and uncertainties [34,35]. This raises the question: does common ownership by institutional investors influence CERD through a “positive effect” or a “negative effect”?

Past research, such as the evaluation report on environmental responsibility information disclosure of listed companies in China, indicates a rising trend in Chinese listed companies disclosing environmental responsibility information. However, scholarly observations point to a significant peer effect influencing this disclosure [36], alongside instances of “greenwashing” [37,38].

First, common institutional investors possess the advantage of cross-industry experience and greater voice [28]. This increases their likelihood of casting a dissenting vote on management’s proposals at shareholder meetings [3], thereby improving the quality of financial reporting [29]. Additionally, firms within the same industry typically share similar business environments, operational practices, and financial reporting models, which significantly lowers the monitoring costs for common institutional investors to oversee environmental responsibility information [26]. Motivated by the objective of maximizing their investment portfolios, common institutional investors are more incentivized to leverage their monitoring advantage to urge firms to enhance their environmental responsibility information disclosure.

Second, the correlation effect from common ownership by institutional investors establishes informal information-sharing channels among companies [31], which accordingly amplifies the spread of CERD. Therefore, for companies with multiple shared institutional investors, CERD is perceived less as a prohibitive expense and more as a strategy for economic gain and reputation enhancement. Therefore, considering the institutional investors’ motive to maximize returns and their capacity to offer greater information resources, common institutional ownership is likely to encourage CERD.

However, the inherent uncertainty of environmental responsibility information makes hard environmental disclosures more costly and more likely to expose corporate shortcomings. Consequently, acting in their own self-interest, common institutional investors may collude with firms to prioritize the disclosure of soft environmental information. This strategy, which is less costly and easier to emulate, is pursued to achieve a disclosure approach that best serves their interests. Additionally, common institutional ownership’s information advantage amplifies herding behavior.

Based on the preceding discussions, Hypothesis 1a and Hypothesis 1b are offered:

H1a. 

Common institutional ownership will enhance the level of CERD.

H1b. 

Compared to hard information on corporate environmental responsibility, common institutional ownership will have a more significant effect on the enhancement of the disclosure of soft information on environmental responsibility.

2.2. Strategic Behavior of Common Institutional Ownership at Different Expected Performance Levels

Drawing upon behavioral economics and performance feedback theory, we hypothesize that a firm’s performance directly affects stakeholder behavior [14]. When actual performance surpasses expectations, several theoretical perspectives offer valuable insights. Prospect theory suggests that managers, exhibiting risk aversion, may favor conservative strategies to preserve a strong cash position [39,40]. Slack resource theory hypothesizes that financially robust firms possess greater resources and risk tolerance, enabling more flexibility [41,42,43]. Moreover, exceeding performance expectations alleviates external pressures, potentially encouraging bolder actions. Accordingly, institutional investors seeking to maximize returns may leverage their informational advantages to advocate for deploying idle resources towards actions that amplify economic gains, thereby enhancing CERD. Therefore, in a positive performance expectation scenario, institutional investors often act as “collaborators”, urging management to pursue higher levels of CERD to further capitalize on existing profitability. However, considering the prevalence of “greenwashing” in Chinese corporate environmental disclosures, managers may opt for disclosing readily imitated, low-cost, and less substantive information under reduced external supervisory pressure.

When firms experience a negative performance-expectation disparity, meaning their actual performance declines below expected performance, several theoretical mechanisms suggest potential strategic responses. Prospect theory hypothesizes that managers facing such circumstances may exhibit increased risk preference, leading to the adoption of aggressive behaviors [39,40]. These behaviors may include increased expenditure on R&D [44], expansion into new markets [45], rapid international expansion [15], or even bribery [46]. These behaviors may, by promoting environmental information disclosure [47], alleviate operational performance downturn. Secondly, the company’s failure to meet performance expectations intensifies external supervision. Common institutional investors, with significant informational and professional advantages, exhibit greater sensitivity to operational challenges. To protect long-term stable returns, these investors may actively intervene to adjust and rectify short-sighted managerial behaviors. While rational corporate finance theory emphasizes a forward-looking approach to asset investment decision-making [48], prospect theory suggests that under the constraints of “bounded rationality”, managers may exhibit excessive fixation on past performance expectations, leading to suboptimal investment decisions directed towards the past [49]. Therefore, common institutional investors, acting as vigilant stewards of their investments, are likely to intervene to prevent further value erosion by deterring such short-sighted managerial actions.

Based on the preceding discussions, Hypothesis 2a and Hypothesis 2b are offered:

H2a. 

In positive performance expectation gaps, common institutional ownership will have a more obvious effect on the enhancement of CERD. However, in negative performance expectation gaps, there will be a weaker enhancement effect.

H2b. 

In positive performance expectation gaps, common institutional ownership’ impact on the enhancement of the disclosure of soft information on environmental responsibility will be more significant compared to hard information on corporate environmental responsibility. However, in negative performance expectation gaps, there will be a weaker enhancement effect.

3. Research Design

3.1. Data Collection and Sample Size

This research evaluates the effect of common institutional ownership on CERD through the perspective of performance feedback, utilizing a sample of A-share companies listed on the Shanghai and Shenzhen stock exchanges between 2008 and 2021. According to the data processing standards of relevant studies, several datasets are excluded: (1) companies in the financial and insurance sectors; (2) companies labeled with “ST”; (3) samples with significant data gaps, deriving an initial observations of 18,399 firm-year observations over this 14-year period. CERD data was extracted from annual and CSR reports utilizing text mining methods, while quarterly common institutional ownership data was manually collected from the CSMAR database. Corporate governance characteristics and foundational company data were sourced from both the CSMAR and WIND databases. In addition, to minimize the effect of outliers on empirical results, tail trimming is conducted on continuous variables at the 1st and 99th percentiles.

3.2. Variable Measurement

3.2.1. Dependent Variable

This study evaluates CERD as the dependent variable. Following the content analysis methodology proposed by Clarkson [50], a CERD index is constructed through the extraction of relevant data from corporate annual reports and social responsibility reports. The establishment of the CERD index scoring table follows the following steps: Firstly, the CERD indicator system must be established. As the CERD in China is primarily government-driven, this study combines the indicator system established by Clarkson (2008) [50] with the disclosure requirements outlined in the “Guidelines for Environmental Information Disclosure of Listed Companies” issued by China’s Ministry of Ecology and Environment in 2010. Additionally, the CERD indicator system is further developed based on the steps taken by Jiang et al. [51] and Zeng et al. [47]. This system encompasses seven major categories, including environmental protection investment, environmental costs, environmental revenue, environmental liabilities, environmental performance, environmental status, and voluntary environmental actions, as well as twenty-nine sub-categories. Secondly, indicators are further classified. Given that disclosure of soft information is prone to “greenwashing” and imitation, this study categorizes CERD indicators into “hard disclosure” and “soft disclosure”. “Hard disclosure” comprises specific numerical disclosures, characterized by their resistance to imitation and ease of verification compared to their soft counterparts, which primarily consist of descriptive textual disclosures. Following the evaluation framework of Wiseman (1982) [52], this research assesses the CERD index by assigning scores based on the level of disclosure. Specifically, quantitative disclosures received a score of 2, general qualitative disclosures a score of 1, and non-disclosure a score of 0. The index comprises 29 equally weighted indicators, yielding a maximum score of 45. A subset of 16 indicators was identified as ‘hard disclosure’, contributing to a maximum possible score of 32. A detailed presentation of the specific indicator contents is offered in

Table 1. CERD Indicator System.

Disclosed Item	Indicator	Score	Disclosed Item	Indicator	Score
Environmental Investment	Total Environmental Investment	0/2	Environmental Status	Completion of “Three Simultaneities”	0/1
	Pollution Fee/Environmental Tax	0/2		Emergency Environmental Incidents	0/1
Environmental Costs	Energy Consumption per Ten Thousand Yuan of Production	0/2		Environmental Violations	0/1
	Total Standard Coal Consumption	0/2		ISO14001 Certification [53]	0/1
Environmental Revenue	Environmental Awards	0/1/2		ISO9001 Certification	0/1
Environmental Liabilities	Wastewater Discharge	0/2		Implementation of Clean Production	0/1
	CO2 Emissions	0/2		Disclosure of Social Responsibility Report	0/1
	SO2 Emissions	0/2		Disclosure of Environmental Responsibility Report	0/1
	COD Emissions	0/2	Voluntary Environmental Actions	Corporate Environmental Philosophy	0/1
	Dust and Particulate Emissions	0/2		Corporate Environmental Goals	0/1
	Industrial Solid Waste	0/2		Establishment of Environmental Management Systems	0/1
Environmental Performance	Reduction in Overall Energy Consumption	0/1/2		Environmental Training and Education	0/1
	Reduction in Wastewater Discharge	0/1/2		Environmental Public Welfare Activities	0/1
	Reduction in Air Emissions	0/1/2
	Reduction in Dust and Particulate Emissions	0/1/2
	Utilization Rate of Industrial Solid Waste	0/2
Total Score for “Hard Disclosure”		32	Total Score for Disclosure		45

. (Examples as shown in Appendix A 

Table A1. Examples of CERD Indicator System.

CERD Indicator	Example	Score
Hard Disclosure	“In 2021, the company’s self-generated electricity, including that from waste heat power generation and photovoltaic power generation, reached nearly 400 million kilowatt-hours, which is equivalent to a reduction of more than 360,000 tons of carbon dioxide emissions.”	2
	“The environmental protection investment for this year is 518,369.621 thousand yuan, of which the annual environmental protection investment of key pollutant-discharging enterprises is 459,145.278 thousand yuan.”	2
	“Sulfur dioxide: 37,328 tons per year”	2
Soft Disclosure	“The company advocates and implements the concept of ecological environmental protection, strengthens energy conservation and environmental protection efforts, implements the ISO14001 environmental management system, and has obtained the certification.”	1
	“During the project construction period, the construction of pollution prevention and control facilities shall be carried out in strict accordance with the requirements of the project’s “Three Simultaneities” principle, and these facilities shall be put into production and use simultaneously with the main project.”	1
	“The company fulfills its mission of ‘creating sustainable value for society’ and focuses on its responsibility performance initiatives carried out in four key areas: ‘responsible governance, addressing climate change, supporting global logistics, and demonstrating corporate care’.”	1

).

3.2.2. Independent Variables

Following the methodology of He and Huang (2017) [30], this study utilizes the prevalence of shared institutional investors in a firm as a proxy variable to measure the extent of common institutional ownership. Specifically, the measurement method is as follows: at the quarterly level, the number of common institutional investors holding shares in the firm is calculated; then the mean is taken at the annual level; and finally, 1 is added and the natural logarithm is applied. Common institutional investors are defined as institutional investors who hold at least 5% of the shares in two or more firms within the same industry.

3.2.3. Mechanism Test Variable

This paper selects exit threat and the market power of common institutional investors to further examine the supervisory and synergistic effects of common institutional investors on corporate environmental responsibility disclosure. The specific measurement methods are detailed in Section 5, “Mechanism Test”.

3.2.4. Grouping Variables

This study evaluates the relationship between corporate governance practices related to CERD and the performance discrepancies observed among common institutional ownership. Drawing upon the theoretical frameworks established by Cyert and March (1963) [14] and Greve (2003) [40], this research quantifies performance expectations, categorizing them into two categories: historical performance expectations and social performance expectations. The specific methodology employed for this analysis is outlined in the following model (1).

$$A_{i,t}={\alpha }_{1}H{A}_{i,t}+(1-{\alpha }_1)S{A}_{i,t}$$

(1)

where HA_i,t represents the historical performance expectation for company i in year t, calculated as the average return on assets (ROA) for years t-1 and t-2. SA_i,t denotes the social performance expectation for company i, determined by averaging the ROA of companies in the same industry, excluding company i itself. ${\alpha }_1$ expresses the weight that ranges from 0 to 1. Each increase of 0.1 assigns a weight. take ${\alpha }_1$ s the logarithm of the maximum probability. Following Greve (2003) [40], who reported a result with as 0.8, and acknowledging the differences between the Chinese market and the Japanese market studied by Greve, this study adopts the common practice among domestic scholars by setting ${\alpha }_1$ at 0.5 [47]. To ensure the robustness of the empirical results and account for potential randomness introduced by setting ${\alpha }_1$ at 0.5, this paper also reports empirical results with ${\alpha }_1$ of 0.3 and 0.7. The performance expectation gap, (P_i,t − A_i,t), (P_i,t − A_i,t) represents the discrepancy between actual performance P_i,t and performance expectation A_i,t, where A_i,t specifically refers to the actual return on assets (ROA). A positive performance expectation gap (Dgap > 0) indicates that actual performance exceeds expectations ((P_i,t − A_i,t) > 0), with all values greater than 0, while a negative performance expectation gap (Dgap < 0) signifies a negative performance expectation gap, i.e., (Pi,t − Ai,t) ˂ 0, with all values less than 0. The specific diagram is shown in Figure 1 and Figure 2.

3.2.5. Control Variables

According to existing literature on corporate governance and CERD, this study incorporates several control variables in the empirical regression analysis framework. These variables consist of: Age: Age of the company. Size: Size of the company. Growth: Growth capability of the company. Cashflow: Cash holdings of the company. Lev: Financial leverage of the company. ATO: Asset turnover ratio of the company. First: Concentration of ownership in the company. Balance: Equity balance in the company. Board: Size of the board of directors. Dual: Duality of the CEO and chairman roles. Indep: Independence of the board of directors. Specific details regarding these control variables are presented in

Table 2. Definitions of Variables.

Variable Type	Variable	Measurement
Dependent Variable	CERD	Score of environmental responsibility disclosure of listed companies
	CERHD	Score of environmental responsibility hard disclosure of listed companies
Independent Variable	Common Institutional Ownership (CIO)	The number of common institutional investors for a firm in the current year is calculated as the annual average of the quarterly figures, then log-transformed by adding 1.
Mechanism Test Variable	Exit Threat (NET)	The product of the competition level among common institutional investors and stock liquidity
	The Market Power of Common Institutional Investors (numcon)	The number of same-industry firms connected by a company through all its common institutional investors
	The Market Power of Common Institutional Investors (avecon)	The average number of same-industry firms connected by a company through a single common institutional investor
Grouping Variable	Positive Performance Expectation Gap	Dgap > 0
	Negative Performance Expectation Gap	Dgap < 0
Control Variable	Age	Years since the first IPO of the listed company
	Size	Ln (Total assets)
	Growth	The growth rate of enterprise operating income
	Cashflow	The ratio of cash and cash equivalents to total assets at the beginning of the year
	Lev	The ratio of total liabilities to total assets
	ATO	The ratio of operating income to total assets
	First	The proportion of shares held by the largest shareholder
	Balance	The ratio of the proportion of shares held by the largest shareholder to the proportion of shares held by the second largest shareholder
	Board	Ln (the number of board members)
	Dual	Dummy variable that takes the value of “1” if the manager concurrently serves as chairman of the board, and “0” otherwise.
	Indep	The ratio of independent directors to the total number of board members

.

3.3. Model Specification

To validate hypotheses H1a through H2b and analyze the governance behaviors of common institutional ownership in CERD across varying performance levels, drawing upon theoretical frameworks such as performance feedback theory, this study employed two models. Following the methodological approaches of Lemmon et al. (2008) [54] and Malmendier et al. (2011) [55], fixed-effects regression models were utilized to account for potential industry and year effects. The model specifications are presented as follows:

$${CERD}_{it}={\gamma }_0+{\gamma }_1{CIO}_{it}+{\gamma }_2{Control}_{it}+\sum Industry+\sum Year+\sum Firm+{\mu }_{it}$$

(2)

$${CERHD}_{it}={\gamma }_0+{\gamma }_1{CIO}_{it}+{\gamma }_2{Control}_{it}+\sum Industry+\sum Year+\sum Firm+{\mu }_{it}$$

(3)

Econometric model (2) assesses the behavioral patterns of typical institutional investors intervening in CERD, aiming to validate hypotheses H1a and H2a. Conversely, model (3) evaluates the behavioral patterns of common institutional ownership participating in CERHD, with the objective of validating hypotheses H1b and H2b. Specifically, the variable “Control” comprises control variables in the models, whose specific symbols and definitions are comprehensively detailed in Table 2.

4. Empirical Research and Analysis

4.1. Descriptive Statistics

Table 3. Summary Statistics.

VarName	Obs	Mean	SD	Min	Median	Max	Correlation Coefficient (CERD)	Correlation Coefficient (CERHD)
CERD	18,399	8.8579	6.008	3.00	7.00	28.00
CERHD	18,399	4.5762	4.284	1.00	3.00	18.00
CIO	18,399	0.0579	0.184	0.00	0.00	0.69	0.180 ***	0.163 ***
NET	18,399	0.0375	0.199	0.00	0.00	5.41	0.043 ***	0.035 ***
numcon	18,399	0.2995	1.511	0.00	0.00	22.50	0.124 ***	0.110 ***
avecon	18,399	0.2857	1.461	0.00	0.00	22.50	0.114 ***	0.101 ***
Age	18,399	1.9807	0.905	0.00	2.08	3.30	0.133 ***	0.120 ***
Size	18,399	22.0101	1.161	19.92	21.86	25.52	0.120 ***	0.108 ***
Growth	17,318	0.1676	0.353	−0.47	0.11	2.11	−0.020 ***	−0.018 **
Cashflow	18,399	0.0487	0.065	−0.14	0.05	0.23	0.020 ***	0.018 **
Lev	18,399	0.3920	0.195	0.05	0.38	0.89	0.064 ***	0.059 ***
ATO	17,319	0.6687	0.380	0.12	0.59	2.34	0.065 ***	0.066 ***
First	18,399	33.5148	13.992	8.98	31.40	71.24	0.055 ***	0.050 ***
Balance	18,398	0.3740	0.285	0.01	0.30	1.00	−0.053 ***	−0.044 ***
Board	18,399	2.1140	0.187	1.61	2.20	2.56	0.076 ***	0.065 ***
Dual	18,399	0.3170	0.465	0.00	0.00	1.00	−0.071 ***	−0.066 ***
Indep	18,399	37.5631	5.335	33.33	33.33	57.14	−0.046 ***	−0.042 ***

*** p < 0.01, ** p < 0.05, * p < 0.1.

presents descriptive statistics for the variables analyzed in this study. The average CERD score is 8.8579, with a range of 0 to 36, while the mean CERHD score is 4.5762, spanning from 0 to 25. The distribution of CERD and CERHD scores exhibited a significant positive skewness, indicating that the majority of participants in this sample scored at the lower end, while a small number of high scores inflated the mean. These figures suggest a relatively low level of environmental responsibility information disclosure among Chinese listed companies, particularly concerning hard disclosure, and highlight significant variability in both CERD and CERHD across firms. The mean CIO value stands at 0.0579, with a median of 0 and a maximum of 0.69, indicating a lack of common institutional ownership for most companies and a maximum of approximately three per company. The mean NET value stands at 0.0375, with a median of 0 and a maximum of 5.41, This suggests that most firms face a relatively low level of exit threat from common institutional investors. The average numcon (number of connected firms) and avecon (average connections) are both 0.2995, with a maximum value of 22.5 and a median of 0. This indicates that only a small portion of firms gain significant market power through common institutional investors. The remaining control variables align with findings from prior research and are not discussed further.

4.2. Correlation Test

In addition, the correlation test between key explanatory and dependent variables and VIF test are also carried out (Due to space constraints, we only present the correlation analysis for the key variables in Table 3). Table 3 shows that the correlation coefficients of CIO, NET, numcon, and avecon with both CERD and CERHD are all significantly positive, providing preliminary support for Hypothesis 1. In addition, the results of variance inflation factors (VIFs) further testify the original judgment of no multicollinearity since all the VIFs are below the reference value of 5.

4.3. Multiple Regression Analysis

4.3.1. Common Institutional Ownership and Corporate Environmental Responsibility Disclosure

Table 4. Regression Results of Common Institutional Ownership on CERD.

	m1	m2	m3	m4	m5	m6
VARIABLES	CERD	CERD	CERD (Standardized Coefficients)	CERHD	CERHD	CERHD (Standardized Coefficients)
CIO	1.258 ***	1.363 ***	0.042 **	0.669 **	0.729 **	0.031 **
	(2.95)	(3.13)	(3.13)	(2.07)	(2.21)	(2.21)
Age		0.052	0,08		0.074	0.016
		(0.50)	(0.50)		(0.91)	(0.91)
Size		−0.057	−0.011		−0.054	−0.015
		(−0.79)	(−0.79)		(−0.91)	(−0.91)
Growth		0.043	0.003		0.031	0.003
		(0.47)	(0.47)		(0.44)	(0.44)
Cashflow		0.757	0.008		0.454	0.007
		(1.35)	(1.35)		(1.03)	(1.03)
Lev		−0.198	−0.006		−0.044	−0.002
		(−0.61)	(−0.61)		(−0.17)	(−0.17)
ATO		−0.181	−0.011		−0.139	−0.012
		(−1.00)	(−1.00)		(−0.97)	(−0.97)
First		0.005	0.012		0.006	0.020
		(0.88)	(0.88)		(1.28)	(1.28)
Balance		−0.268	−0.013		−0.111	−0.007
		(−1.09)	(−1.09)		(−0.57)	(−0.57)
Board		−0.136	−0.004		−0.343	−0.015
		(−0.41)	(−0.41)		(−1.28)	(−1.28)
Dual		0.017	0.001		0.026	0.003
		(0.16)	(0.16)		(0.31)	(0.31)
Indep		−0.018	−0.016		−0.015 *	−0.019 *
		(−1.58)	(−1.58)		(−1.74)	(−1.74)
Industry	Control	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control	Control
Firm	Control	Control	Control	Control	Control	Control
Constant	−1079.549 ***	−1079.775 ***		−793.755 ***	−794.420 ***
	(−32.56)	(−32.37)		(−30.85)	(−30.65)
R-squared	0.200	0.205	0.205	0.180	0.184	0.184
n	18,399	17,317	17,317	18,399	17,317	17,317

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents regression results analyzing the relationship between common institutional ownership and CERD. Model 1 (m1) and Model 2 (m2) assess the effect of common institutional ownership on the overall level of CERD, while Model 4 (m4) and Model 5 (m5) focus on the effect of common institutional ownership on CERHD, to test Hypotheses 1a and 1b. Furthermore, Models 3 (m3) and 6 (m6) report the results of the standardized coefficients. The analysis indicates a statistically significant positive association between common institutional ownership and both overall CERD and CERHD, thus supporting Hypotheses 1a and 1b. Specifically, after including control variables, the coefficients of CIO on CERD and CERHD are 1.363 and 0.729, respectively. Both coefficients are statistically significant at the 1% and 5% levels, respectively. The standardized coefficients of CIO are 0.042 and 0.031, suggesting that a one standard deviation increase in shared institutional ownership leads to a 0.042 standard deviation increase in CERD and a 0.031 standard deviation increase in CERHD. This suggests a propensity for companies with common institutional shareholding to disclose more soft environmental information, potentially indicating a degree of “greenwashing”. This is largely due to the significant information advantages and network effects that common institutional investors bring, which amplify imitative behaviors regarding CERD. Moreover, compared to hard information on environmental responsibility—which is more easily scrutinized and costly to produce—soft information is easier to embellish and less expensive. Moreover, considering the widely acknowledged economic value of CERD, common institutional ownership, driven by portfolio maximization, are unlikely to intervene excessively in this strategic corporate behavior unless it threatens their fundamental interests.

4.3.2. Expected Performance Gaps, Common Institutional Ownership and CIO

Table 5. Regression Results under Different Expected Performance Gaps.

	Dgap > 0	Dgap > 0	Dgap < 0	Dgap < 0
	m1	m2	m3	m4
VARIABLES	CERD	CERHD	CERD	CERHD
CIO	1.531 ***	0.852 *	0.964	0.439
	(2.66)	(1.91)	(1.57)	(0.96)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−1169.346 ***	−852.745 ***	−985.596 ***	−724.106 ***
	(−27.06)	(−25.37)	(−21.74)	(−20.83)
R-squared	0.229	0.207	0.181	0.162
n	8453	8453	8864	8864

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents regression analyses analyzing the effect of shared institutional investors on CERD under varying expected performance gaps. m1 and m2 specifically analyze the relationship between shared institutional investors and CERD when the expected performance gap is positive, thereby testing Hypothesis 2a. Conversely, models m3 and m4 explore the effect of shared institutional investors on CERD when the expected performance gap is negative, thus evaluating Hypothesis 2b.

A study of m1 and m2 in Table 5 indicates a significant positive correlation between expected performance gaps and the propensity of common institutional ownership to promote both CERD and CERHD, with a more significant effect observed for CERD. This implies that companies exhibiting performance advantages experience reduced external oversight, capitalizing on the informational benefits derived from common ownership by institutional investors to disseminate a greater volume of environmental responsibility information, particularly easily replicated soft information, thereby enhancing their competitive edge. This phenomenon can be attributed to two primary factors. Firstly, common institutional ownership, motivated by portfolio interest maximization, allocates greater resources towards underperforming companies while concurrently reducing oversight of high-performing entities. Therefore, companies with operational performance advantages not only leverage the informational benefits associated with common institutional ownership but also attract less attention, which consequently increases the incentives for “greenwashing”. This explains the increased disclosure of environmental information, particularly soft information, observed under positive expected performance gaps. Secondly, companies demonstrating exceptional performance possess greater surplus resources, cultivating opportunities for rent-seeking behavior and increasing the likelihood of collusion between common institutional ownership and management. Accordingly, this elevates the probability of companies amplifying their performance advantages through enhanced disclosure of soft information pertaining to environmental responsibility that is prone to being misinterpreted as “greenwashing”.

Analysis of m3 and m4 in Table 5 demonstrates a lack of significant correlation between common institutional ownership and either CERD or CERHD when expected performance declines below expectations. This suggests that increased external oversight during periods of underperformance discourages firms from employing CERD as a remedial strategy. The rationale behind this lies in the negative effect of an excessive focus on past performance and the implementation of aggressive measures on long-term organizational growth. In addition, underperforming firms attract the attention of common institutional investors, resulting in increased external oversight. This enhanced supervision restricts firms from persisting with the volatile strategic behavior of environmental responsibility information disclosure, particularly concerning easily embellished soft environmental information aimed at achieving short-term performance objectives set by management. Therefore, common institutional investors are more inclined to assume a “supervisors” role under these circumstances.

Additionally, this paper presents a marginal effects plot (as shown in Figure 3) to visually illustrate how the influence of common institutional ownership on corporate environmental responsibility disclosure varies across different performance expectation gaps (Dgap) scenarios.

The results indicate that as the Dgap increases from negative to positive values, the positive effect of common institutional ownership (CIO) on corporate environmental responsibility disclosure (CERD) also strengthens. This finding supports the paper’s hypothesis that the positive effect of common institutional ownership on CERD is more pronounced when a firm’s performance is strong (i.e., when Dgap is positive).

4.4. Robustness Test

4.4.1. Endogeneity Test

The primary regression findings suggest a positive relationship between common ownership by institutional investors and both CERD and CERHD, with the effect being particularly significant when positive expected performance gaps exist. However, potential endogeneity concerns warrant consideration. Firstly, a reverse causality issue may arise as firms exhibiting higher CERD levels and positive expected performance gaps could attract greater attention from institutional investors. Secondly, omitted variable bias may occur due to unobserved factors influencing the behavioral decision-making of both large-scale external investors and the firms themselves. To reduce these endogeneity issues and ensure the robustness and accuracy of the hypotheses and conclusions, this study employs instrumental variable tests and propensity score matching methods.

PSM-OLS Test

Institutional investor stock investments are characterized by non-randomness and may be determined by the firm-specific characteristics. Therefore, this study employs propensity score matching (PSM) to control for the effects of sample selection bias. The listed companies with common institutional investors were taken as the treatment group, and the series of control variables mentioned earlier (Age, Size, Growth, Cashflow, Lev, ATO, First, Balance, Board, Dual, Indep) were used as matching variables. Then, one-to-one nearest neighbor matching was employed to find control groups with similar features to the treatment group.

Figure 4 shows that after applying one-to-one nearest neighbor matching, the propensity score distributions of the treated and control groups largely overlap across the main range. This indicates that the matching procedure effectively reduced covariate imbalance and enhanced sample comparability. Although some differences remain in the lower propensity score range, the overall results provide a reliable basis for subsequent causal effect estimation.

The efficacy of the PSM procedure hinges on achieving covariate balance in the matched sample. To verify the success of this matching objective, a balance test was performed, with the results detailed in

Table 6. Balance Test Results of PSM.

Variable	Unmatched	Mean		%Bias	%Reduct |Bias|	t-Test		V (T)/ V (C)
	Matched	Treated	Control			t	p > |t|
Age	U	2.394	2.072	42.0	98.3	16.31	0.000	0.94
	M	2.394	2.389	0.7		0.22	0.829	1.11 *
Size	U	22.337	22.036	24.4	97.1	10.05	0.000	1.24 *
	M	22.337	22.416	−0.7		−0.20	0.844	1.03
Growth	U	0.200	0.363	−2.5	71.1	−0.73	0.465	0.01 *
	M	0.200	0.153	0.7		2.16	0.031	1.91 *
Cashflow	U	0.048	0.049	−1.0	6.2	−0.39	0.698	1.01
	M	0.048	0.047	0.9		0.26	0.793	0.90 *
Lev	U	0.427	0.399	13.9	87.2	5.56	0.000	1.06
	M	0.427	0.431	−1.8		−0.52	0.603	1.02
ATO	U	0.698	0.674	4.9	89.5	2.17	0.030	1.68 *
	M	0.698	0.701	−0.5		−0.15	0.884	1.41 *
First	U	33.776	33.251	3.7	96.8	1.46	0.144	1.02
	M	33.776	34.793	−0.1		−0.03	0.973	0.99
Balance	U	0.342	0.374	−10.9	93.1	−4.36	0.000	1.07
	M	0.342	0.344	−0.8		−0.22	0.825	1.07
Board	U	2.144	2.114	15.5	85.9	6.24	0.000	1.13 *
	M	2.144	2.140	2.2		0.63	0.526	1.11 *
Dual	U	0.232	0.313	−18.0	90.5	−6.84	0.000	.
	M	0.232	0.225	1.7		0.53	0.598	.
Indep	U	37.476	37.573	−1.7	−18.3	−0.69	0.493	1.06
	M	37.476	37.555	1.4		−0.40	0.688	0.95

. After one-to-one nearest neighbor matching, most standardized mean differences fell below 10%, and the t-tests indicated no significant differences across the majority of covariates. These results confirm that the matched sample achieves satisfactory balance. This successful balance mitigates concerns regarding selection bias and strengthens the parallel trends assumption, thereby supporting the study’s causal claims that the change in ownership structure directly influenced firm disclosure behavior.

To provide a more intuitive representation of the results, Figure 5 presents a covariate balance plot, which illustrates the effectiveness of the PSM procedure in creating a comparable control group. The plot shows the standardized mean differences (SMD) for all covariates between the treated and control groups before and after matching. After the matching procedure, represented by the crosses (x), the plot demonstrates a dramatic improvement in covariate balance. The SMDs for all covariates are now clustered tightly around the zero line, indicating that the treated and matched control groups are statistically indistinguishable on all observable characteristics. This finding provides strong evidence that our PSM procedure successfully mitigated the observable selection bias.

Table 7. Test Results of PSM-OLS.

	m1	m2		m1	m2
VARIABLES	CERD	CERHD	VARIABLES	CERD	CERHD
CIO	4.5544 ***	2.8781 ***	Balance	0.7060	0.7068 *
	(12.9920)	(11.5230)		(1.3953)	(1.9607)
Age	1.1583***	0.7430 ***	Board	0.3333	0.0568
	(6.1037)	(5.4955)		(0.4487)	(0.1073)
Size	0.4146 ***	0.2707 ***	Dual1	−0.2179	−0.1497
	(3.6042)	(3.3025)		(−0.7913)	(−0.7629)
Growth	−0.3410	−0.1897	Indep	−0.0642 ***	−0.0463 ***
	(−1.0795)	(−0.8428)		(−2.6571)	(−2.6915)
Cashflow	−3.2378 *	−1.7858	Industry	Control	Control
	(−1.7162)	(−1.3285)	Year	Control	Control
Lev	−0.1244	0.2382	Firm	Control	Control
	(−0.1773)	(0.4766)	Constant	−5.3399 *	−3.9478 *
ATO	0.5453	0.3032		(−1.8106)	(−1.8787)
	(1.6434)	(1.2825)	n	3147	3147
First	0.0455 ***	0.0298 ***	adj. R2	0.155	0.146
	(4.2050)	(3.8708)

Robust t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the results of the PSM-OLS estimation. (due to space limitations, only the test results with CERD as the outcome variable are reported). The coefficient on CIO is positive and statistically significant at the 1% level in both m1 and m2. This significance was even stronger when CERD was used as the dependent variable. These results indicate that after correcting for sample selection bias, our earlier findings remain robust.

To test the robustness of our core findings against potential unobserved confounding variables, this paper conducted a sensitivity analysis using the Rosenbaum bounds method.

Table 8. Test Results of Rosenbaum Bounds.

Gamma	sig+	sig−	t-hat+	t-hat−	CI+	CI−
1	0	0	2.5	2.5	2	2
1.1	0	0	2.5	3	2	2
1.2	<0.0001	0	2	3.5	3.5	1.5
1.3	<0.0001	0	1.5	4	4	1
1.4	<0.0001	0	1	4	4	0.9999
1.5	<0.0001	0	1	4.5	4.5	0.5
1.6	0.0017	0	0.5	4.5	4.5	0
1.7	0.0292	0	0.5	5	5	0
1.8	0.1786	0	0	5	5	−0.5
1.9	0.4992	0	0	5.5	5.5	−0.5
2	0.8077	0	0	5.5	5.5	−0.5

presents the results of this analysis.

The results show that in the baseline scenario (Γ = 1), the treatment effect is highly significant (p < 0.0001). As the value of Γ increases, the p-value of the treatment effect gradually rises, and its significance weakens. When Γ increases to 1.8, the p-value of the treatment effect rises to 0.1786, which is above the 5% significance level.

This indicates that if an unobserved confounding variable were to exist, it would need to make the odds of two individuals—who are perfectly matched on all observed covariates—being assigned to the treatment group 1.8 times more likely for our main conclusion to become non-robust. Given that a Γ value of 1.8 represents a relatively strong hidden bias, and this threshold is higher than those commonly seen in many empirical studies, we conclude that our core findings are robust to most plausible unobserved confounders. This result provides strong support for our main conclusions and enhances their credibility.

PSM-DID Test

This paper employs a multiple-period Difference-in-Differences (DID) model to estimate the differences in environmental responsibility disclosures of firms before and after changes in their ownership structure (from no common institutional investors to having common institutional investors). The specific model is as follows:

$${CERD}_{it}={\gamma }_0+{\gamma }_1{After}_{it}\times {Treat}_{it}+{\gamma }_2{Control}_{it}+\sum Industry+\sum Year+\sum Firm+{\mu }_{it }$$

(4)

The sample in which the ownership structure changes from no common institutional investors to having common institutional investors is treated as the treatment group, with ‘Treat’ taking the value of 1, while the sample that consistently lacks common institutional investors is treated as the control group, with ‘Treat’ taking the value of 0. ‘After’ is a dummy variable that takes the value of 1 for the years after a firm gains common institutional ownership in the treatment group and 0 for the years before. For firms in the control group, ‘After’ is uniformly set to 0. Additionally, since the differences between the treatment and control groups prior to the common institutional ownership linkage may lead to selection bias, potentially reducing the validity of the model estimation, this paper first uses propensity score matching (PSM) for one-to-one nearest neighbor matching and then performs the analysis. Moreover, individual fixed effects are considered in the model. Nevertheless, the methodology employed in this study is most applicable to firms that share similar characteristics with the treatment group (Chinese A-share manufacturing companies).

Table 9. Test Results of PSM-DID.

	(1)	(2)
VARIABLES	CERD	CERHD
After × Treat	0.7459 *	0.2946
	(1.7622)	(0.8956)
Control variable	Control	Control
Industry	Control	Control
Year	Control	Control
Firm	Control	Control
Constant	16.8665 ***	10.7495 ***
	(3.8737)	(3.1741)
n	3147	3147
adj. R2	0.260	0.235

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the results of the PSM-DID analysis. The results show that when the dependent variable is CERD, the coefficient of the interaction term After × Treat is significantly positive. This suggests that when a listed company changes from having no common institutional ownership to having common institutional ownership, it discloses more environmental responsibility information that is prone to “greenwashing” which is consistent with the earlier findings.

Instrumental Variables

To address potential issues related to endogeneity, this study follows Gao et al. (2019) [9]. Two-stage regression is conducted with a company listed on the Shanghai and Shenzhen 300 Index or the CSI 500 Index as instrumental variables. On the one hand, a firm’s inclusion in or removal from a stock index, along with changes in its constituent status, is related to the level of common institutional ownership, thus satisfying the relevance requirement for instrumental variables. On the other hand, inclusion in a stock index is not determined by a firm’s environmental responsibility disclosure performance, as stock exchanges base their selection criteria on factors other than such disclosures. Therefore, the exogeneity requirement for instrumental variables is also met. Nevertheless, a potential endogeneity problem persists. This issue may arise if a firm’s past corporate governance decisions or financial performance—both of which are related to the dependent variable—also systematically influences its probability of being included in the index. Should the test results yield findings consistent with the PSM-DID analysis, it would provide an additional layer of confidence in the robustness of the preceding conclusions.

The results of this analysis are presented in

Table 10. Regression Results of Instrumental Variables.

	m1	m2	m3	m4
	First	Two	First	Two
VARIABLES	CIO	CERD	CIO	CERHD
IV	0.093 ***		0.093 ***
	(28.22)		(28.22)
CIO		38.893 ***		23.627 ***
		(23.25)		(21.53)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−13.328 ***	−615.262 ***	−13.328 ***	−508.111 ***
	(−14.55)	(−13.05)	(−14.55)	(−16.43)
F	33.62		33.62
n	17,317	17,317	17,317	17,317

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. Analyzing m1 and m3 indicates a statistically significant positive association between inclusion in the aforementioned indices and the degree of common ownership by institutional investors, suggesting that firms in these indices tend to exhibit higher levels of institutional common ownership. Further analysis of m2 and m4 across the panel indicates that as institutional common ownership increases, so too does the level of CERD, with a significant effect on soft disclosure, corroborating the earlier findings.

The Independent Variables Lagged by One Period

The method of lagging the independent variables by one period effectively weakens the potential reverse impact of the dependent variable on the explanatory variables, thereby reducing the endogeneity problem caused by contemporaneous causal relationships. In this paper, the independent variables and control variables are lagged by one period for regression, and the specific results are shown in

Table 11. Regression Results of The Independent Variables Lagged by One Period.

	m1	m2
VARIABLES	CERD	CERHD
CIO	0.871 **	0.490 *
	(2.21)	(1.65)
Control variable	Control	Control
Industry	Control	Control
Year	Control	Control
Firm	Control	Control
Constant	−1252.410 ***	−965.354 ***
	(−11.56)	(−11.34)
R-squared	0.206	0.186
n	18,230	18,230

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. The regression results indicate that even after lagging the core independent variables, CIO, by one period, the higher the degree of common institutional ownership, the higher the level of corporate environmental responsibility information disclosure, especially in terms of soft disclosures. This finding is consistent with the previous conclusions.

4.4.2. Testing for the Alternative Dependent Variables

Utilizing common institutional ownership as a proxy for the dependent variable in the primary regression framework, this study conducted robustness checks, with the results displayed in

Table 12. Results of Testing for Alternative Dependent Variables.

	m1	m2	m3	m4	m5	m6
	All	All	Dgap > 0	Dgap > 0	Dgap < 0	Dgap < 0
VARIABLES	CERD	CERHD	CERD	CERHD	CERD	CERHD
CIO1	0.638 ***	0.320 *	0.743 **	0.419 *	0.289	0.030
	(2.66)	(1.76)	(2.45)	(1.81)	(0.78)	(0.11)
Control variable	Control	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control	Control
year	Control	Control	Control	Control	Control	Control
Constant	−1084.472 ***	−797.344 ***	−1174.986 ***	−855.703 ***	−990.029 ***	−727.133 ***
	(−32.43)	(−30.70)	(−27.17)	(−25.40)	(−21.81)	(−20.92)
R-squared	0.204	0.184	0.229	0.207	0.180	0.162
n	17,317	17,317	8453	8453	8864	8864

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. The analysis indicated a more significant and positive effect of common institutional ownership on CERD compared to CERHD after the substitution of the dependent variable. In addition, this effect was consistently observed under positive expected performance gaps. Conversely, aligning with the primary regression findings, no significant correlation has been observed under negative expected performance gaps.

4.4.3. Testing for Alternative Grouping Variable

Results for Different Values of ${\alpha }_1$

Following the prevalent methodology in Chinese literature, this study initially defines expected performance with a weight coefficient ${\alpha }_1$ ranging from 1 to 0.5. However, acknowledging the potential subjectivity in this approach, the study further explores scenarios where the weight coefficient for the expected performance gap is set to ${\alpha }_1$ = 0.3 and ${\alpha }_1$ = 0.7, respectively. The regression results, presented in

Table 13. Results of Testing for Alternative Grouping Variable.

	m1	m2	m1	m2	m1	m2	m1	m2
	Dgap1 > 0	Dgap1 > 0	Dgap1 < 0	Dgap1 < 0	Dgap2 > 0	Dgap2 > 0	Dgap2 < 0	Dgap2 < 0
VARIABLES	CERD	CERHD	CERD	CERHD	CERD	CERHD	CERD	CERHD
CIO	2.013 ***	1.133 **	0.644	0.275	1.437 **	0.818 *	1.198	0.530
	(3.30)	(2.40)	(1.06)	(0.61)	(2.56)	(1.87)	(1.97)	(1.16)
Control variable	Control	Control	Control	Control	Control	Control	Control	Control
IND	Control	Control	Control	Control	Control	Control	Control	Control
year	Control	Control	Control	Control	Control	Control	Control	Control
Constant	−1160.802 ***	−851.605 ***	−990.409 ***	−726.232 ***	−1148.424 ***	−841.911 ***	−1015.222 ***	−740.752 ***
	(−26.03)	(−24.86)	(−21.50)	(−20.52)	(−26.68)	(−25.01)	(−23.54)	(−22.37)
R-squared	0.222	0.203	0.180	0.160	0.226	0.204	0.191	0.171
n	8535	8535	8782	8782	8196	8196	9121	9121

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, demonstrate that regardless of whether ${\alpha }_1$ is set to 0.3 or 0.7, the findings remain consistent with the primary regression analysis.

Results for Alternative Historical Performance Expectation Measures

In our primary analysis, historical expected performance (HA_i,t) is defined as the average of a firm’s return on assets (ROA) over the past two years.

To ensure the robustness of our findings, this study re-examined the results using an alternative measure of historical expected performance. Specifically, this study used the average of the firm’s ROA over the past three years as the historical performance expectation. As shown in

Table 14. Regression Results for Historical Performance Expectation Using a Three-Year Moving Average.

	Dgap3 > 0	Dgap3 > 0	Dgap3 < 0	Dgap3 < 0
	m1	m2	m3	m4
VARIABLES	CERD	CERHD	CERD	CERHD
CIO	1.996 ***	1.018 **	0.837	0.416
	(3.14)	(2.05)	(1.42)	(0.94)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−1052.275 ***	−767.861 ***	−1057.289 ***	−773.663 ***
	(−21.98)	(−20.66)	(−23.28)	(−21.95)
R-squared	0.197	0.174	0.180	0.159
n	7654	7654	9663	9663

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, the results remain consistent with those of our main analysis, suggesting our conclusions are robust to the choice of the rolling-year benchmark.

5. Mechanism Test

5.1. Supervision Effect

Due to the industry-linking characteristics of common institutional investors, they possess more information transmission channels within the same industry. When the cost of voice for common institutional investors is high or obstructed, they may resort to exit threats to engage in strategic games with the company. In response, management and controlling shareholders, in order to protect their own interests, may feel compelled to accept supervision under this “threat”. Furthermore, in situations where there is a negative expected performance gap, external supervision is likely to strengthen further. Under the dual pressures, will the company choose to improve the level of corporate environmental responsibility information disclosure? To address this question, this paper uses the exit threat of common institutional ownership to measure their supervisory effect. Dou et al. (2018) [56] suggest that the effectiveness of an exit threat by shareholders is primarily influenced by the degree of shareholder competition and the liquidity of the stock. Thus, the exit threat (NET) of common institutional investors is measured by the product of the competition level among common institutional investors and stock liquidity. Stock liquidity is measured by the daily average turnover rate of the stock, and the competition level of co-investing institutional investors (CIC) is calculated using the following model.

$$\sum {CIC}_{it}={\sum }_{k=1}^N{\left({\displaystyle \frac{{CIS}_{kit}}{{TCIS}_{it}}}\right)}^2$$

(5)

CIC is the degree of competition among common institutional investors of the listed company, CIS is the shareholding of a single common institutional investor, and TCIS is the sum of all common institutional investors’ shareholdings. A larger CIC value indicates a higher degree of competition among common institutional investors in the listed company.

The exit threat of common institutional investors (NET) was incorporated into the baseline regression model for testing, with the regression results shown in

Table 15. Results of Testing for Supervision Effect.

	m1	m2	m1	m2	m1	m2
	All	All	Dgap > 0	Dgap > 0	Dgap < 0	Dgap < 0
VARIABLES	CERD	CERHD	CERD	CERHD	CERD	CERHD
NET	−0.243	−0.236	0.003	−0.085	−0.423 *	−0.368 *
	(−1.35)	(−1.65)	(0.01)	(−0.38)	(−1.79)	(−1.83)
Control variable	Control	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control	Control
Firm	Control	Control	Control	Control	Control	Control
Constant	−1098.338 ***	−804.866 ***	−1199.266 ***	−869.957 ***	−994.375 ***	−728.719 ***
	(−32.57)	(−30.82)	(−27.16)	(−25.32)	(−21.91)	(−20.98)
R-squared	0.203	0.184	0.227	0.206	0.181	0.162
n	17,317	17,317	8453	8453	8864	8864

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. The results indicate that only under the negative expected performance scenario, the coefficients of NET for CERD and CERHD are significantly negative. Specifically, the coefficients for NET on CERD and CERHD are −0.423 and −0.368, respectively, both significant at the 10% level. This suggests that, under the dual pressures of the exit threat from common institutional investors and a performance crisis, companies exhibit defensive responses by lowering their level of environmental responsibility information disclosure. This could be due to two reasons: On the one hand, Based on short-termism and survival orientation. Based on legitimacy theory, due to the high uncertainty associated with environmental responsibility information, companies may avoid exposing more vulnerabilities during fragile periods to prevent further legitimacy crises. Moreover, based on resource dependence theory, under the dual pressures, companies may allocate their limited financial and managerial resources towards addressing the immediate survival threats. On the other hand, the corporate governance mechanism fails. Common institutional investors are typically seen as a significant force in corporate governance. Their threat of exit signifies that this crucial external monitoring mechanism is failing, which provides management with the opportunity for rent-seeking—that is, lowering disclosure transparency to conceal potential operational issues or environmental violations. Furthermore, this dual pressure can intensify the agency conflict between the firm’s owners (shareholders) and their agents (management). In pursuit of their own short-term interests, such as bonuses or job security, management may choose to reduce environmental information disclosure, sacrificing the company’s long-term value, including its reputation and sustainability.

5.2. Synergistic Effect

The synergistic effect of common institutional ownership depends on its influence within the same industry. If a common institutional owner has greater influence among its peers, it is more capable of coordinating against unfavorable competition and incomplete contractual conflicts among the listed companies within its portfolio. This also better facilitates resource sharing and effective collaboration, creating more favorable conditions for firms to disclose environmental responsibility information.

This paper constructs two indicators to measure the market power of common institutional investors. The first indicator is the number of same-industry firms connected by a company through all its common institutional investors (numcon). The second indicator is the average number of same-industry firms connected by a company through a single common institutional investor (avecon).

This paper incorporates these two indicators into the baseline model, with the regression results presented in

Table 16. Results of Testing for Synergistic Effect.

PanelA all
	m1	m2	m3	m4
VARIABLES	CERD	CERHD	CERD	CERHD
numcon	0.127 ***	0.075 ***
	(3.63)	(2.90)
avecon			0.125 ***	0.073 ***
			(3.44)	(2.71)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−1075.773 ***	−791.080 ***	−1077.529 ***	−792.288 ***
	(−31.72)	(−29.99)	(−31.78)	(−30.06)
R-squared	0.205	0.184	0.205	0.184
n	17,317	17,317	17,317	17,317
PanelB Dgap > 0
	m1	m2	m3	m4
VARIABLES	CERD	CERHD	CERD	CERHD
numcon	0.096 **	0.054 *
	(2.46)	(1.89)
avecon			0.089 **	0.048 *
			(2.22)	(1.65)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−1173.143 ***	−854.632 ***	−1176.662 ***	−857.134 ***
	(−26.43)	(−24.73)	(−26.51)	(−24.79)
R-squared	0.228	0.207	0.228	0.207
n	8453	8453	8453	8453
PanelC Dgap < 0
	m1	m2	m3	m4
VARIABLES	CERD	CERHD	CERD	CERHD
numcon	0.207 ***	0.130 **
	(2.78)	(2.37)
avecon			0.211 ***	0.134 **
			(2.84)	(2.37)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−976.193 ***	−716.959 ***	−976.717 ***	−717.140 ***
	(−21.49)	(−20.52)	(−21.50)	(−20.53)
R-squared	0.182	0.163	0.182	0.163
n	8864	8864	8864	8864

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. Panels A, B, and C present the regression results for the full sample, the positive expected performance gap subsample, and the negative expected performance gap subsample, respectively.

The results show that, regardless of the context, numcon (number of connected firms) and avecon (average connections) have a statistically significant and positive relationship with both CERD and CERHD. This suggests that a greater number of firms connected through common institutional investors leads to deeper information sharing, knowledge spillovers, and experience exchange within the same industry, thereby significantly reducing the costs of environmental responsibility information disclosure.

5.3. Impact Pathway

The extant literature suggests that the influence by common institutional ownership on corporate conduct is primarily reflected through their engagement in corporate governance mechanisms, informational advantages, and the mitigation of financing constraints. Recognizing CERD as a conduit for information dissemination and acknowledging the correlation between expected performance gaps and corporate performance, this study evaluates the impact mechanism of common institutional ownership on CERD under varying performance gaps, specifically through the perspective of corporate governance. According to agency theory, which postulates that the separation of ownership and management rights can lead to a dual-layered agency problem—including tunneling effects by controlling shareholders and “internal control” by management—this study seeks to explain how common institutional ownership, operating in different performance gap contexts, modulate CERD strategies by exerting influence on both the agency issues from controlling shareholders and those originating from top management. The specific impact pathway is shown in Figure 6.

This study evaluates the issues of agency conflicts arising from controlling shareholders, specifically analyzing both overt and covert mechanisms of expropriation. Following the framework established by Wang et al. (2020) [57], we quantify overt expropriation through the ratio of other receivables to total assets, represented by the variable Holder_Occupy. Conversely, covert expropriation is operationalized as the ratio of related-party transactions to operating income, denoted as Holder_Invade.

This study evaluates managerial agency issues, including both monetary and non-monetary forms of private benefits enjoyed by executives. Following the methodology of Quan et al. (2010) [58], monetary private benefits are determined by calculating the discrepancy between actual executive compensation and predicted normal earnings. The estimation of expected normal earnings is derived from Model 5, which employs a regression analysis of industry- and year-specific executive earnings to determine normal compensation levels based on the estimated coefficients. The specification of Model 5 is as follows:

$$\begin{array}{cc}{LnExePay}_{it}={\beta }_0\hfill & +{\beta }_1{Size}_{i,t}+{\beta }_2{ROA}_{i,t}+{\beta }_3{ROA}_{i,t-1}+{\beta }_4{Areawage}_{i,t}\hfill \\ & +{\beta }_5{Central}_{i,t}+{\beta }_6{Weat}_{i,t}+\sum {Industry}_{i,t}\hfill \\ & +\sum {Year}_{i,t}+{\epsilon }_{i,t}\hfill \end{array}$$

(6)

where Size is the scale of the enterprise, measured by the natural logarithm of total assets; ROA is enterprise performance; Areawage is the average wage in the respective region; Central is a dummy variable indicating the location of enterprise in the central region, and West indicates its location in the western region. Executive monetary private benefits are denoted by Exc_Mon.

Executive non-monetary private benefits are likewise determined by analyzing the discrepancy between observed and expected levels of on-the-job consumption. The benchmark for typical executive on-the-job consumption is established through regression analysis, as demonstrated in Model 6, which accounts for industry and temporal differences. This model facilitates the estimation of regression coefficients, thereby enabling the computation of expected levels of on-the-job consumption for executives. The specifics of Model 6 are as follows:

$$\begin{array}{cc}{\displaystyle \frac{{Perks}_{i,t}}{{Asset}_{i,t-1}}}={\beta }_0\hfill & +{\beta }_1{\displaystyle \frac{1}{{Asset}_{i,t-1}}}+{\beta }_2{\displaystyle \frac{{∆sale}_{i,t}}{{Asset}_{i,t-1}}}+{\beta }_3{\displaystyle \frac{{PPE}_{i,t}}{{Asset}_{i,t-1}}}\hfill \\ & +{\beta }_4{\displaystyle \frac{{Inventory}_{i,t}}{{Asset}_{i,t-1}}}+{\beta }_5{lnEmployee}_{i,t}+{\epsilon }_{i,t}\hfill \end{array}$$

(7)

where Perks is executive on-the-job consumption, calculated by subtracting expenses that are clearly not part of on-the-job consumption, such as management fees, wages, benefits, and intangible asset amortization. Asset is total assets of the enterprise, ∆sale is the change in revenue, PPE is the net value of fixed assets, and LnEmployee is the natural logarithm of the total number of employees of the enterprise. Non-monetary private benefits of executives are denoted by Exc_NonMon.

Table 17. Results of the Test on the Mediating Effect of Monetary Private Benefits to Executives.

	All	All	All	Dgap > 0	Dgap > 0	Dgap > 0
	m1	m2	m3	m4	m5	m6
VARIABLES	Exc_Mon	CERD	CERHD	Exc_Mon	CERD	CERHD
CIO	−0.011 ***			−0.014 ***
	(−4.82)			(−4.68)
Exc_Mon		−9.716 ***	−5.473 ***		−8.100 ***	−4.399 **
		(−4.95)	(−3.65)		(−2.98)	(−2.10)
Control variable	Control	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control	Control
Firm	Control	Control	Control	Control	Control	Control
Constant	6.177 ***	−1041.274 ***	−772.194 ***	7.099 ***	−1147.234 ***	−840.844 ***
	(35.60)	(−29.59)	(−28.17)	(28.60)	(−24.66)	(−23.16)
R-squared	0.205	0.207	0.185	0.242	0.230	0.208
n	17,111	17,111	17,111	8359	8359	8359

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

and

Table 18. Results of the Test on the Mediating Effect of Non-Monetary Private Benefits to Executives.

	All	All	All	Dgap > 0	Dgap > 0	Dgap > 0
	m1	m2	m3	m1	m2	m3
VARIABLES	Exc_NonMon	CERD	CERHD	Exc_NonMon	CERD	CERHD
CIO	−0.009 ***			−0.012 ***
	(−4.48)			(−4.78)
Exc_NonMon		−8.940 ***	−4.601 **		−10.301 **	−6.012 *
		(−3.24)	(−2.18)		(−2.54)	(−1.90)
Control variable	Control	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control	Control
Firm	Control	Control	Control	Control	Control	Control
Constant	−0.201	−1103.414 ***	−807.162 ***	0.090	−1203.629 ***	−871.333 ***
	(−1.34)	(−32.45)	(−30.59)	(0.42)	(−27.15)	(−25.23)
R-squared	0.012	0.205	0.184	0.023	0.230	0.208
n	17,111	17,111	17,111	8359	8359	8359

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

(results regarding explicit and implicit expropriation by controlling shareholders are omitted due to their insignificance and for space limitations) indicate a significant relationship between institutional investor presence and enhanced CERD. This impact is mainly achieved by reducing both monetary and non-monetary executive perks, particularly influencing softer forms of disclosure. In addition, the mediating effect of institutional investors on CERD is amplified under conditions of positive firm performance.

6. Heterogeneity Test

Institutional economics hypothesizes a complementary relationship between governmental regulations and corporate governance mechanisms. When agency problems become particularly significant, government intervention can serve a crucial supplementary role. The preceding analysis highlights the limited supervisory pressure exerted by common institutional ownership. While they may encourage firms to disclose a certain degree of environmental responsibility information, their influence on the disclosure of substantive information remains rather constrained. In addition, positive performance expectation gaps tend to weaken external supervisory pressure, exacerbating this phenomenon. Therefore, robust and effective external supervisory mechanisms are essential for curbing short-term corporate behavior. In China, state-owned enterprises face greater governmental oversight compared to their private counterparts. Therefore, government intervention may effectively reduce short-sighted behavior in corporations. To this end, this paper delves deeper into the differential impact of common institutional ownership on CERD between state-owned and private enterprises under varying performance expectation gaps.

The regression analyses of common institutional ownership’s influence on CERD and high-quality CERD and CERHD in state-owned and private enterprises, considering both the full sample and positive performance expectation gaps, are presented in

Table 19. Regression Results of Common Institutional Ownership on CERD in State-Owned Enterprises.

	All	All	Dgap > 0	Dgap > 0
	m1	m2	m1	m2
VARIABLES	CERD	CERHD	CERD	CERHD
CIO	1.450 **	0.908 *	1.373	0.816
	(2.20)	(1.82)	(1.60)	(1.29)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−1080.251 ***	−777.512 ***	−1196.776 ***	−897.424 ***
	(−14.50)	(−13.49)	(−10.68)	(−10.29)
R-squared	0.183	0.161	0.223	0.209
n	5021	5021	2318	2318

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

and

Table 20. Regression Results of Common Institutional Ownership on CERD in Private Enterprises.

	All	All	Dgap > 0	Dgap > 0
	(1)	(2)	(1)	(2)
VARIABLES	CERD	CERHD	CERD	CERHD
CIO	1.244 **	0.556	1.738 **	0.896
	(2.27)	(1.33)	(2.25)	(1.47)
Control variable	Control	Control	Control	Control
Industry	Control	Control	Control	Control
Year	Control	Control	Control	Control
Firm	Control	Control	Control	Control
Constant	−1081.937 ***	−795.523 ***	−1142.771 ***	−819.293 ***
	(−27.22)	(−25.71)	(−22.10)	(−20.57)
R-squared	0.206	0.187	0.223	0.209
n	12,296	12,296	6135	6135

Robust standard errors clustered at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, respectively. The findings indicate that common institutional ownership exhibits a consistent positive effect on both CERD and CERHD in state-owned enterprises across the entire sample. Specifically, even under positive performance expectation gaps and reduced external supervisory pressure, state-owned enterprises do not engage in short-sighted actions, such as increasing environmental information disclosure or resorting to soft disclosure, due to the robust governmental oversight, emphasizing the effectiveness of state supervision. Conversely, in private enterprises, common institutional ownership demonstrates a promoting effect solely on CERD across the entire sample, with no significant effect on CERHD. This indirectly suggests that private enterprises leverage the informational advantage offered by common institutional ownership to disclose easily imitable soft information, leading to more pronounced “greenwashing” behavior, particularly when external supervisory pressure reduces under positive performance expectation gaps.

7. Conclusions and Implications

7.1. Conclusions

Amid increasing environmental concerns, corporations are increasingly recognizing the importance of environmental responsibility and demonstrating their commitment through enhanced environmental information disclosure. Simultaneously, the evolution of capital markets has cultivated collaboration among institutional investors, leading to shared ownership in companies and amplified resource advantages. This rise of common ownership by institutional investors is poised to exert a significant effect on CERD. Considering China’s critical role in the global ecological environment, this study employs data from A-share listed companies on the Shanghai and Shenzhen stock exchanges between 2008 and 2021 to empirically appraise the governance effects and behavioral choices of institutional investors concerning CERD under varying performance expectations. This delves deeper into the pathways through which governance behavioral differences are influenced. Additionally, this study, based on the Chinese institutional background, considers the differences in government supervision to further explore the differences in behavioral choices of common institutional ownership regarding CERD under different property rights. The results indicate that common institutional ownership does not involve simply acting as “supervisors” or “conspirators” in CERD. Based on their motive for portfolio maximization, common institutional ownership exhibits diverse and complex governance effects on CERD under different performance expectation gaps. Specifically, (1) companies tend to disclose more environmental responsibility information due to the informational advantages and synergies associated with common ownership, with a more significant effect on soft CERD levels. In these instances, common institutional ownership assumes the roles of “collaborators” and “observers”. (2) In scenarios with positive performance expectation gaps, where external supervisory pressure is weaker, the positive effect of common ownership on corporate environmental responsibility information, particularly soft disclosure, becomes more evident, with investors acting as “conspirators”. Conversely, (3) negative performance expectation gaps and increased external supervisory pressure prompt common institutional ownership to prevent companies from increasing CERD as a short-sighted measure to counteract short-term operational disadvantages, thus acting as “supervisors”. (4) Mechanism tests indicate that under the dual pressure of shared institutional investor exit threats and unmet performance targets, firms adopt defensive measures, leading to a decrease in their corporate environmental responsibility information disclosure. In contrast, the synergistic effect of shared institutional investors effectively enhances the level of this disclosure. Further analysis into the impact pathways of common ownership on CERD from a corporate governance perspective indicates that it compels managers to minimize self-interested behavior, both monetary and non-monetary, and allocate resources towards CERD. However, this effect is more significant in soft environmental responsibility information disclosure, where common institutional ownership plays a limited “supervisor” role. (5) Finally, considering the nature of corporate ownership, the study finds that due to weaker government supervision, the positive effect of common ownership on CERD is more pronounced in private enterprises, with no significant effect on CERHD. This effect is further amplified in the presence of positive performance expectation gaps.

This study transcends the conventional dichotomy of common institutional ownership as either simple “monitors” or “conspirators”. Instead, it reveals their dynamic and context-dependent role in corporate environmental responsibility disclosure (CERD). This research demonstrates that the behavior of common institutional investors is profoundly shaped by external forces, particularly the expected performance gap and government regulation. The findings offer fresh empirical support for both agency theory and stakeholder theory, underscoring how institutional investors employ their information advantages and network synergies to influence corporate environmental reporting in different situations. A key contribution is the distinction made between soft and hard disclosure, which provides a novel analytical framework for better understanding the motives and outcomes of institutional investor governance.

7.2. Implications

This study reveals the complex behavioral patterns of common institutional ownership across different contexts, which is crucial for regulators to formulate effective environmental disclosure policies. First, the distinction between soft and hard disclosure necessitates a stratified approach to regulation. Policymakers should introduce stricter, more enforceable mandatory disclosure standards, particularly for hard environmental data such as carbon emissions and pollutant discharge figures, to ensure information is both truthful and credible. Second, the varied impact of the supervisory environment requires a differentiated regulatory strategy. The research shows that in private enterprises with weaker government oversight, the positive effect of common institutional ownership on environmental disclosure is more pronounced but primarily limited to soft information. This suggests that in less stringent regulatory domains, firms are more inclined to use common investors as “conspirators” to gain market recognition rather than making substantive environmental improvements. Consequently, regulators should strengthen their oversight of hard environmental disclosures by private firms, and utilize a combination of incentives and penalties to guide corporate behavior. Beyond merely tying tax benefits to environmental performance or imposing stricter fines for non-compliance, regulators could invest in new technologies such as Natural Language Processing (NLP) and machine learning. These tools could be used to systematically analyze and flag ambiguous, contradictory, or vague environmental statements in corporate reports, thereby making the supervision of ‘soft’ disclosures more efficient. Furthermore, a tiered system of penalties could be implemented, with more severe fines and public censure for firms that engage in systematic misrepresentation or insufficient environmental disclosure. This would effectively increase the cost of ‘greenwashing’, incentivizing companies to provide more truthful information. Third, regulators could introduce policies that promote long-term, responsible stewardship to counteract the short-term focus of some common institutional investors. Therefore, regulators should strengthen their oversight of common institutional investors and guide them toward more responsible behavior. For example, mandating that institutional investors publicly disclose their proxy voting records, especially on environment and governance-related proposals, would enhance decision-making transparency and deter collusion with management. Additionally, policymakers could explore offering tax incentives or other regulatory benefits to those who demonstrate a long-term commitment to their portfolio companies, as evidenced by a minimum holding period or active engagement on environmental responsibility issues.

This study also provides insights into how common institutional investors can more effectively fulfill their governance responsibilities. First, they must recognize their dual role. Common institutional investors should understand that their actions can cast them as both “monitors” and “conspirators” depending on the context. When facing performance pressure, investors should avoid colluding with management to sacrifice long-term environmental responsibility for short-term gains. Instead, they should leverage their robust networks and information advantages to guide firms in integrating environmental responsibility into their core strategy, thus maximizing the long-term value of their portfolios. Second, they must strengthen their environmental due diligence. Institutional investors should deepen their evaluation of a firm’s environmental risks and opportunities during investment decisions. Relying solely on a company’s soft disclosure is insufficient; investors should be more proactive in demanding verifiable hard environmental data and factor in how management’s behavior shifts under different performance pressures.

7.3. Limitations and Future Research

This study is based on the Chinese context, so its conclusions may have limited generalizability to markets with different institutional backgrounds. Future research could apply this analytical framework to other emerging or developed markets to validate and extend the understanding of the governance effects of common institutional ownership in various regulatory and cultural settings. Meanwhile, while this study employs more nuanced proxies—such as the exit threat and market power of common institutional investors—to capture the multifaceted nature of common institutional ownership, these measures may not fully account for all supervisory and synergistic effects. This is primarily due to inherent limitations in data availability and the challenge of using proxies to perfectly represent complex governance mechanisms. Future research could explore more granular data or alternative econometric methods to further refine these concepts. Additionally, future studies could further examine how the portfolio strategies of common institutional ownership influence their behavioral choices regarding CERD, thereby offering a more comprehensive understanding of their multi-faceted role in corporate governance.