Collaborative governance: The role of common institutional ownership in corporate environmental information disclosure

Abstract

Transparency regarding environmental practices is crucial not only for ethical corporate governance but also for empowering consumers and investors to make informed decisions. This study examines the impact of common institutional ownership (CInOwn) on the extent of environmental information disclosure (EnvInfoDis) by publicly traded Chinese companies from 2010 to 2020. Using robust empirical analysis, we demonstrate a positive relationship between CInOwn and EnvInfoDis, suggesting that firms with shared institutional investors are more likely to disclose environmental information, fostering public trust and aligning with government expectations. We further investigate the underlying mechanisms, revealing that CInOwn strengthens internal controls, promoting improved EnvInfoDis practices. Notably, the findings remain robust across various tests, including using different CInOwn thresholds. This study offers significant contributions to the ongoing debate on CInOwn implications, paving the way for policy and economic interventions to support Chinese firms in integrating EnvInfoDis practices, ultimately contributing to societal sustainability.

Keywords

common institutional ownership sharing investor collective governance environmental reporting environmental disclosure China

Introduction

Driven by mounting environmental challenges,¹ a wave of action is cresting across the globe. Governments are increasingly adopting carbon neutrality goals, while public awareness of eco-friendly practices surges. This potent tide is lapping at the doorsteps of corporations, prompting a growing number to embrace environmental information disclosure (EnvInfoDis). This move is fueled by a dual engine: the need to stay in step with evolving regulations and stakeholder expectations, and the growing public demand for businesses to prioritize environmental stewardship.²

Such transparency initiatives are not merely symbolic gestures; they are demonstrably changing corporate behavior. Studies have shown that EnvInfoDis programs can lead to reduced pollution levels, as they hold companies accountable and incentivize them to operate more sustainably.³ However, despite the compelling benefits, a significant roadblock exists in developing economies, particularly emerging nations.⁴ Overcoming these hurdles and ensuring broad participation in EnvInfoDis will be crucial to navigate the turbulent waters of our environmental crisis.

China, standing as the largest emerging developing economy, has witnessed impressive societal progress and economic growth fueled by market-oriented reforms over the past two decades.⁵ However, while this advancement is undeniable, concerns arise around the sector's impact on the environment. Documented ecosystem degradation highlights the significant environmental footprint of this growth engine.⁶ Recognizing these concerns, China actively seeks solutions, actively exploring strategies like corporate EnvInfoDis to foster better environmental management practices.⁷ Despite ongoing efforts, a key research gap remains. We lack a comprehensive understanding of the driving forces behind corporate EnvInfoDis within the Chinese context, particularly from the perspectives of governance and institutional monitoring, as well as investors. This research gap stems, in part, from the ongoing debate about the very factors propelling EnvInfoDis.⁸ To bridge this gap, this study delves into the role of corporate common institutional ownership and its influence on EnvInfoDis practices within the Chinese context.

The recent years have witnessed a surge in the phenomenon of “common institutional ownership” (CInOwn), where institutional investors hold stakes in multiple companies within the same sector.^9,10 This trend, distinct from traditional institutional investment, presents unique advantages and potential drawbacks for the market. CInOwn investors, acting as “industry hubs,” leverage their extensive experience and privileged information to gain an edge in the capital market.¹¹ They often specialize in specific sectors, acquiring stakes in multiple firms within that domain.¹² This concentrated ownership empowers them to exert significant influence on company management and strategic decisions.¹¹ The economic implications of CInOwn remain a subject of debate. Some studies highlight its potential for collective governance, fostering benefits across various domains. High access to information and resources facilitated by CInOwn is linked to improvements in innovation,¹³ external collaborations,¹⁴ financial accessibility,¹⁵ and corporate governance practices.¹⁶ However, concerns regarding reduced market competition and pricing distortions have also emerged. Research suggests that CInOwn can lead to increased cooperation among portfolio companies within a sector, potentially stifling competition and manipulating market prices.¹⁷

With the significant influence of common investors in firm decision-making, given their substantial shareholding in multiple firms and their collective governance role, a pivotal inquiry arises: How does CInOwn shape EnvInfoDis within the context of collective governance? Unraveling the mechanisms that underlie this connection is imperative for a nuanced evaluation of CInOwn's true impact on the market and its participants. CInOwn, in essence, acts as a double-edged sword. Although it has the potential to augment market efficiency and corporate governance, its implications for competition and transparency necessitate meticulous examination. Engaging in further research specifically into the EnvInfoDis dimension of CInOwn is crucial to comprehend this intricate phenomenon and uphold a robust and healthy market ecosystem.

The rise of CInOwn has prompted investigations into its potential influence on corporate decision and reporting practices.^17,18 This study specifically focuses on the context of Chinese publicly listed companies for several reasons. Firstly, these firms often possess robust institutional structures and solid economic performance, which equip them for proactive environmental management and information disclosure. Secondly, their reliance on a diverse investor base incentivizes them to utilize environmental information as a tool for stakeholder engagement. Finally, the abundance of quantitative analytical data readily available for Chinese-listed corporations makes them suitable subjects for this study.

This study empirically investigates the influence of CInOwn on EnvInfoDis, utilizing the dataset of Chinese A-shared listed companies spanning from 2010 to 2020. The study's findings reveal that institutional investors actively encourage firms to disclose environmental information. These results align with the findings of Ding,¹⁸ who observed that common investors show a greater inclination toward environmental performance. Furthermore, in contrast to Azar et al.,¹⁷ findings suggesting common investors’ involvement in fraudulent activities, our study does not support such claims. Additional mechanistic analysis indicates that the relationship between common investors and environmental disclosure is rooted in the enhancement of the firm's internal control. These findings lend support to Wang et al.,¹² notion that common investors contribute to governance. Importantly, these findings remain robust across alternative variable analyses, propensity score matching (PSM), and system generalized method of moments (sys-GMM).

This study contributes to the understanding of EnvInfoDis determinants in two key ways. Firstly, existing research on EnvInfoDis determinants has primarily focused on factors such as government regulations, firm characteristics, and media attention. While valuable, these studies often overlook the internal dynamics of a company, particularly the role of institutional investors in corporate governance. Our study addresses this gap by examining how CInOwn within a firm's governance structure influences the quality of EnvInfoDis. This broadens our understanding of how institutional investors, serving as board members or committee members, influence a company's environmental commitment and disclosure practices. It delves into questions such as: How does CInOwn contribute to shared environmental values and collective knowledge among institutional investors, fostering improved internal controls and transparency?

Secondly, previous studies on CInOwn have typically examined its direct impact on various green operations and activities, such as green innovation. However, these studies often lack a clear explanation for the underlying mechanism. Our study introduces the “governance effect” as a critical mechanism, proposing that CInOwn strengthens internal controls and nurtures a sustainability-oriented culture within the firm. This, in turn, enhances the quality of EnvInfoDis through mechanisms such as active monitoring of environmental practices and disclosure processes by institutional investors on boards and committees. Additionally, shared expertise and best practices among institutional investors contribute to improved environmental management and reporting systems. The collective influence of environmentally conscious institutional investors can also exert pressure on management, encouraging a prioritization of sustainability and transparency.

The remainder of this work is structured as follows: the second section provides a comprehensive literature review, while the third section focuses on hypothesis development. The fourth section details the methodology, emphasizing data collection and measurement. In the fifth section, we present the study's results and engage in a discussion. The sixth section concludes the work, offering insights into study implications, limitations, and future prospects.

Literature review

The paradox of common institutional owners and investor dilemma

In recent years, a discernible trend has emerged wherein a singular institutional investor acquires shares in multiple publicly traded companies operating within the same sector, a phenomenon referred to as CInOwn.¹⁹ This prevalent practice carries profound implications for fostering collaboration, aligning stakeholder interests, and augmenting corporate governance, thereby contributing to the cultivation of a more accountable and stable business environment. The intricate concepts of CInOwn and the institutional investor paradox pertain to the intricate nuances of corporate ownership structures and the consequential impact of institutional investors on both corporate governance and performance. For numerous institutional investors, the paramount objective remains the generation of profits. In this context, CInOwn proves instrumental in advancing this objective by optimizing the performance of their portfolio companies and capitalizing on sector-wide growth dynamics.²⁰ Furthermore, this practice endows investors with heightened influence over the companies in which they hold shares. This influence is wielded strategically to advocate for pivotal changes in corporate governance, strategic initiatives, or sustainability practices.^11,18

The ascendancy of CInOwn in capital markets has become a focal point of academic discourse, sparking deliberations from two distinct vantage points. The first viewpoint, denoted as “collective governance,” accentuates the favorable repercussions of CInOwn. Research indicates its propensity to foster heightened market share,²¹ an augmented number of patent applications,¹³ and improved performance in mergers and acquisitions.²² Furthermore, the presence of industry hubs is shown to magnify governance effects, facilitating efficient monitoring and the prevention of corporate misconduct.¹² Conversely, the second perspective, grounded in the “collusive fraud effect theory,” posits that CInOwn might disrupt market dynamics and deter engagement in corporate social responsibility among portfolio companies.¹⁹ This standpoint raises apprehensions about collusion and potential anticompetitive consequences.

In summation, the discourse surrounding the impact of CInOwn on microfirm behavior manifests as a dichotomy between proponents of collaborative governance and those emphasizing the conceivable risks associated with collusion and market disruption. These perspectives furnish valuable insights into the intricate dynamics characterizing institutional ownership within contemporary capital markets.

Environmental reporting strategies: a stakeholder and legitimacy theory dynamics

EnvInfoDis functions as a strategic instrument for communicating a company's commitment to environmental responsibility, encompassing its outputs, objectives, and operational procedures.²³ EnvInfoDis, with its dual emphasis on qualitative and quantitative data, endeavors to bolster a company's credibility and public image. This data serves to articulate and quantify the environmental impact of a company's operations.²⁴ In contrast to conventional financial reporting,²⁵ EnvInfoDis affords a more exhaustive portrayal of a company's environmental undertakings and their ramifications.²⁶ Consequently, EnvInfoDis assumes the role of the primary reservoir of information for the general public seeking insights into a company's environmental initiatives, fostering a channel of communication between businesses and society.²⁷ Furthermore, EnvInfoDis places significant emphasis on scrutinizing the influence of environmental regulations on a company's operations.²⁸ This scrutiny is deemed crucial in the regulatory framework governing corporate environmental practices.²⁹ Corporate EnvInfoDis has evolved into an indispensable component of a company's Corporate Social Responsibility (CSR) implementation strategy,³⁰ now entrenched in legal mandates in several countries.³¹

Within the framework of legitimacy theory, scholars have scrutinized corporate environmental and social disclosure policies.³² Legitimacy theory posits that a company's legitimacy is not solely contingent on adherence to legal or illegal norms, as societal expectations regarding business conduct may be either implicit or explicit.^33,34 In the context of shared institutional ownership, it is imperative to examine the role of social contracts. Implicit conditions within these contracts encompass unspoken societal expectations, while explicit terms involve legal obligations. The concept of a ‘legitimacy gap’ gains significance when evaluating the influence of CInOwn on their portfolio companies.³⁵ Any misalignment between the values of these owners and those of society can have ramifications for both the companies and the institutional owners themselves.⁴ To address this gap, organizations should identify areas under their control and recognize key individuals within CInOwn who possess the authority to confer legitimacy. Aligning with societal expectations is crucial for ensuring the sustainable and responsible management of investments.²⁶

On the contrary, stakeholder theory serves as a framework to navigate the evolving expectations of corporations by various interest groups, particularly in the context of CSR. Corporations are increasingly acknowledging their broader responsibilities in addressing environmental concerns, even those they may have contributed to.³⁶ In contrast to the institutional view, which perceives external standards imposed on corporations, stakeholder theory recognizes a company's ability to influence both society at large and specific stakeholders. Traditional stakeholders in corporate strategy include owners, clients, civic groups, and suppliers, each with distinct interests impacting strategic decisions. In CSR analysis, it is imperative to broaden the definition of stakeholders to encompass entities such as regulatory bodies, environmental advocacy groups, and organizations focused on social issues.³⁷ CInOwn as significant stakeholders wields influence in shaping corporate strategies and CSR initiatives.¹⁹

In summary, both stakeholder and legitimacy theories underscore the importance of CInOwn as key indicators of social strategic decisions within a business.³⁸ Stakeholder theory further enhances our understanding of how these CInOwn influence EnvInfoDis. Essentially, companies strategically organize their resources to establish governance structures and set priorities. The integration of stakeholder and legitimacy theory offers a comprehensive perspective that considers both CInOwn characteristics and organizational traits, thus reflecting the full spectrum of an organization's resources and capabilities. The interaction between institutional investors and corporate attributes jointly shapes the corporate disclosure strategy.³⁸ To harmonize the divergent conclusions surrounding the impact of institutional investors and corporate attributes on decision-making, as well as to disentangle the complex amalgamation of firm resources, as highlighted by Fainshmidt et al.³⁹ a more in-depth investigation is imperative to ascertain the robust findings pertaining to CInOwn rather than common investors. Considering the diverse influence of common investors and company characteristics on EnvInfoDis, our research meticulously navigates through the existing literature. We employ the configurational method to probe the intricate interplay of CInOwn effects, guided by two distinct theoretical frameworks.

Hypothesis development

The collective governance influence of common institutional owners

In recent years, EnvInfoDis has emerged as a critical element within the domain of corporate governance and sustainability, propelled by escalating environmental apprehensions. In response to heightened stakeholder expectations, corporations are under increasing pressure to exhibit transparency regarding their environmental practices and impacts. Central to stakeholder theory is the recognition that an organization is dependent on the support and consent of its stakeholders for the sustainability of its operations.⁴⁰ To secure this ongoing consent, the organization must align its operations with the expectations and needs of its stakeholders. EnvInfoDis, therefore, assumes the role of a communication mechanism between the organization and its stakeholders, serving to address their concerns and foster mutual understanding.⁴¹

Concurrently, within the broader spectrum of corporate governance, CInOwn emerges as a pivotal factor.¹² As institutional investors maintain substantial shares across multiple firms, their regular communication and interaction inadvertently induce a transformation in the collective knowledge framework utilized in decision-making.⁴² This transformative process cultivates an augmented reliance on a diverse range of company resources, thereby motivating businesses to strategically pursue long-term advantages.

CInOwn actively contributes to the enhancement of corporate governance through multifaceted mechanisms. Primarily, their substantial ownership concentration provides them with a more influential voice in corporate decision-making processes.⁴³ This elevated influence enables them to exert pressure on management, compelling the adoption of environmentally responsible practices and transparent disclosure of relevant information. The monitoring role by CInOwn acts as a potent incentive for companies to refine their external information disclosure (EnvInfoDis) practices, ensuring a positive rapport with these influential investors. Moreover, there is a growing trend among institutional investors to prioritize environmental considerations in their investment decisions. CInOwn, by aligning the interests of diverse institutional investors, cultivates a shared commitment to environmental sustainability.¹⁴ This alignment serves as a catalyst for firms to engage in more comprehensive and accurate EnvInfoDis, attracting and retaining investors who value environmental responsibility. Beyond aligning interests, CInOwn possesses the expertise to identify collaborative opportunities among various entities.¹⁵ Thus, it becomes evident that CInOwn play a pivotal role not only in maintaining trust between corporations and creditors but also in facilitating streamlined access to external resources. This substantial contribution acts as a driving force, motivating firms to embrace EnvInfoDis. The influence wielded by these investors emerges as a compelling force, shaping corporate strategies and fostering a commitment to transparent environmental reporting.

Given the intricate nature of the expertise required for effective EnvInfoDis,⁴⁴ the active involvement of CInOwn becomes a critical factor in optimizing environmental innovation.¹⁸ This optimized environmental performance, in turn, serves as a powerful motivator for corporations to willingly disclose environmental information, reflecting positively on their dedication to sustainability and transparency. In light of these discussions, this paper posits the following hypotheses:

H1:

Common institutional investor ownership positively influences a firm's EnvInfoDis practices.

H2:

Companies within the same industry that have shared ownership with the focal company are more likely to encourage and adopt proactive environmental disclosure practices.

H3:

The average count of unique institutional owners who hold positions in both the focal company and its industry peers is positively correlated with an increased likelihood of encouraging environmental disclosure practices among these companies.

H4:

The average cumulative instances of shared institutional ownership among the focal company and its industry peers, positively impact the propensity of these companies to promote and engage in environmental disclosure activities.

Data, sample selection, and variable measurement

Data sample and source

To rigorously investigate our research hypothesis, we employed a comprehensive dataset of Chinese-listed A-share companies spanning the period from 2010 to 2020. This dataset comprises 14,589 firm-year observations, devoid of any missing values or outliers after rigorous data cleansing procedures. The environment information disclosure (EnvInfoDis) data was extracted from sustainability reports on the China Research Data Service Platform (CNRDS) database, while the Common Institutional Investor (CInOwn) data was obtained from the China Stock Market & Accounting Research (CSMAR) database. To maintain consistent data characteristics, we excluded banking and finance firms from the dataset. Outliers were effectively mitigated by applying winsorization to the 1st and 99th percentiles. Furthermore, firms with special treatment status were removed to ensure the robustness and integrity of our dataset for empirical analysis. This meticulously refined dataset provides a solid foundation for our in-depth examination of research hypotheses. Table 1 provides the detailed explaination of sample distribution by year and industry.

Table 1.

Sample distribution by years and industry.

	Years
Industry name	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Total
Agriculture, forestry, husbandry, and fishery	24	27	24	26	28	27	29	29	27	31	34	306
Mining	38	42	43	45	48	50	53	55	59	63	64	560
Textile, manufacturing, and products of leather and fur, beverage	314	334	398	459	463	506	613	794	901	1,007	1,198	6,987
Production and supply of electric power, tap water	50	52	57	59	61	74	80	83	93	101	117	827
Construction	32	35	50	54	56	61	60	73	75	81	88	665
Wholesale and retail	78	91	110	114	108	104	112	120	125	131	152	1,245
Transportation	63	67	68	72	71	68	73	80	88	93	92	835
Hotel and accommodation	9	9	10	10	10	7	7	7	8	8	11	96
information and technology	51	54	52	53	52	51	67	81	99	101	107	768
Real estate	95	99	112	106	106	99	97	99	104	107	112	1,136
Commercial and leasing	16	19	14	15	17	17	27	29	32	37	39	262
Information technology	5	4	5	5	5	11	13	18	26	29	31	152
wood and furniture	6	7	15	16	19	20	19	26	28	31	33	220
Other services	6	7	0	0	0	0	0	0	0	0	0	13
Education	0	0	1	0	1	1	3	2	2	3	5	18
social services	1	1	1	1	1	0	5	5	7	9	11	42
communication	7	8	14	16	16	23	26	31	36	39	41	257
Conglomerates	36	34	14	15	15	13	14	15	12	14	18	200
Total by year	831	890	988	1,066	1,077	1,132	1,298	1,547	1,722	1,885	2,153	14,589

Variable measurement

Dependent variable

This study's dependent variable, EnvInfoDis, delves into the transparency of publicly listed companies regarding their environmental impact, particularly focusing on pollution-related data. Recognizing the heightened public and stakeholder interest in such information, we specifically examine disclosures around five critical aspects: sulfur dioxide (SO₂) emissions, chemical oxygen demand (COD), carbon dioxide (CO₂) emissions, wastewater discharges, and industrial solid waste production.

Following established scoring methods,⁴⁵ each aspect receives a grade: 0 for no disclosure, 1 for qualitative disclosure, and 2 for quantitative disclosure. EnvInfoDis for each company is then calculated by summing these individual scores and normalizing against the maximum achievable value.¹² This methodology enables a nuanced quantification of a company's EnvInfoDis practices, particularly concerning the specified pollution data. As the central dependent variable, EnvInfoDis forms the cornerstone of this investigation, allowing us to explore its determinants and its influence on relevant outcomes, ultimately contributing to a deeper understanding of corporate environmental transparency and its implications.

Independent variable

This study employs a multifaceted measure of CInOwn as its independent variable, capturing different aspects of this phenomenon's potential influence on corporate decisions. CInOwn is comprised of four distinct components and develop based on the study of,^16,46 each offering a unique lens through which to analyze its impact.

Firstly, CInOwn_Dummy: This binary variable acts as a basic switch, indicating whether the focal company and at least one industry peer share any CInOwn throughout the year. Its simplicity facilitates initial analyses of the presence or absence of this ownership structure. Secondly, CInOwn_Peer: This variable delves deeper, quantifying the extent of common ownership by counting the number of industry peers sharing institutional investors with the focal company. This offers insights into the peer network and potential information flow within the industry. Thirdly, CInOwn_N: focuses on the diversity of institutional ownership by measuring the average number of unique institutional investors holding both the focal company and its peers. This component sheds light on the potential concentration or dispersion of ownership control. Finally, CInOwn_Percentage quantifies the overall magnitude of common ownership by calculating the average percentage of ownership shared by institutional investors in the focal company across the year. This provides a direct measure of the potential economic influence exerted by common institutional owners.

Control variable

This study recognizes the potential influence of various firm-level characteristics on both CInOwn and EnvInfoDis. We incorporate firm and board-level control variables drawing from established research to account for these effects and ensure robust findings.^13,47

For firm-level financial variable, we acknowledge the role of company size in shaping strategic decisions and diversity. Therefore, we employ the natural logarithm of total sales (Firm_Size) as a control variable. Financial Performance: Recognizing the financial dimension of environmental initiatives, we incorporate key financial indicators. Return on assets (ROA), calculated as net profit divided by average total assets, reflects profitability. Cash flow, measured as operational cash flow divided by total assets, indicates a firm's liquidity. The total debt-to-asset (TDTA) ratio is computed by scaling total debts with total assets. Additionally, Tobin's Q index was employed in our analysis.

As, stakeholder engagement impacts corporate governance and environmental practices and decisions.^48,49 We include several board-level control variables. Independent Directors (IND): The percentage of independent directors on the board, calculated as the number of independent directors divided by the total number of directors, reflects independent oversight. Board Size (BSIZE): The total number of board members captures the board's overall composition and potential for diverse perspectives. CEO Duality: We employ a binary variable (CEO_Duality) set to “1” if the chairman and managing director roles are combined, and “0” if they are separate. This controls for potential power concentration at the executive level.

By incorporating these control variables, we aim to isolate the specific effect of CInOwn on EnvInfoDis while accounting for other relevant firm-level factors. This comprehensive approach enhances the credibility and accuracy of our analysis, leading to more robust and reliable conclusions. Table 2 provides the detailed definition and explanation of all variables used in this study.

Table 2.

Variables’ definitions.

Acronym	Variable's name	Definitions
Dependent variables
EnvInfoDis	Environment information disclosure	We specifically examine disclosures around five critical aspects: sulfur dioxide (SO₂) emissions, chemical oxygen demand (COD), carbon dioxide (CO₂) emissions, wastewater discharges, and industrial solid waste production. Following established scoring methods (Nguyen et al., 2021), each aspect receives a grade: 0 for no disclosure, 1 for qualitative disclosure, and 2 for quantitative disclosure. EnvInfoDis for each company is then calculated by summing these individual scores and normalizing against the maximum achievable value¹²
Independent variables
CInOwn_Dummy	Common institutional ownership	This binary variable acts as a basic switch, which equals 1 whether the focal company and at least one industry peer share any CInOwn throughout the year, otherwise 0
CInOwn_Peer	Peer common institutional owners	This variable quantifies the extent of common ownership by counting the number of industry peers sharing institutional investors with the focal company
CInOwn_N	No. of commonly held peers	This focuses on the diversity of institutional ownership by measuring the average number of unique institutional investors holding both the focal company and its peers
CInOwn_Percentage	Percentage of common institutional owners	This variable quantifies the overall magnitude of common ownership by calculating the average percentage of ownership shared by institutional investors in the focal company across the year
Control variables
Firm_Size	Firm size	The natural logarithm of the total sales of a given firm
CF	Cash flow	The natural logarithm of firm operating cash flows
Tobin's Q	Tobin's Q ratio	The ratio of total assets minus the book value of equities plus the market value of equities divided by total assets
TDTA	Debts to assets ratio	The ratio of total debt to total assets
ROA	Return on assets	Calculated as the ratio of net income to total assets
BSIZE	Board size	The natural log of a total number of directors serving on the board
IND	Board independence	The proportion of independent directors on the board, represented as a percentage
CEO_Duality	CEO-chair duality	The dummy variable takes 1 if the chairperson of the board is concurrently the CEO of the company, otherwise 0

Research model

This study analyzes the relationship between institutional ownership and EnvInfoDis using a fixed-effects ordinary least squares (OLS) estimation technique. This method accounts for potential time-invariant unobserved heterogeneity, meaning consistent but unobservable differences between entities across time. For robustness, fixed effects for both year and industry are incorporated into the models. Additionally, we built various regression models to explore and quantify how institutional ownership influences companies’ EnvInfoDis.

\begin{aligned} {EnvInfoDis}_{i, t} = α & + β_{1} CInOwn_{Dummy}_{i, t} + β_{2} Firm_{size}_{i, t} + β_{3} {CF}_{i, t} \\ + β_{4} Tobi n^{'} s Q_{i, t} + β_{5} {DTA}_{i, t} + β_{6} {ROA}_{i, t} + β_{7} {BSIZE}_{i, t} + β_{8} {IND}_{i, t} \\ + β_{9} CEO_{Duality}_{i, t} + {Year}_{tFE} + {industry}_{jFE} + ε_{i, t} \end{aligned}

(1)

\begin{aligned} {EnvInfoDis}_{i, t} = α + & β_{1} CInOwn_{Peer}_{i, t} + β_{2} Firm_{size}_{i, t} + β_{3} {CF}_{i, t} + β_{4} Tobi n^{'} s Q_{i, t} \\ + & β_{5} {DTA}_{i, t} + β_{6} {ROA}_{i, t} + β_{7} {BSIZE}_{i, t} + β_{8} {IND}_{i, t} + β_{9} CEO_{Duality}_{i, t} \\ + & {Year}_{tFE} + {industry}_{jFE} + ε_{i, t} \end{aligned}

(2)

\begin{aligned} {EnvInfoDis}_{i, t} = α + & β_{1} CInOwn_N_{i, t} + β_{2} Firm_{size}_{i, t} + β_{3} {CF}_{i, t} + β_{4} Tobi n^{'} s Q_{i, t} \\ + & β_{5} {DTA}_{i, t} + β_{6} {ROA}_{i, t} + β_{7} {BSIZE}_{i, t} + β_{8} {IND}_{i, t} + β_{9} CEO_{Duality}_{i, t} \\ + & {Year}_{tFE} + industry_{j} FE + ε_{i, t} \end{aligned}

(3)

\begin{aligned} {EnvInfoDis}_{i, t} = α + & β_{1} CInOwn_{Percentage}_{i, t} + β_{2} Firm_{size}_{i, t} + β_{3} {CF}_{i, t} + β_{4} Tobi n^{'} s Q_{i, t} \\ + & β_{5} {DTA}_{i, t} + β_{6} {ROA}_{i, t} + β_{7} {BSIZE}_{i, t} + β_{8} {IND}_{i, t} + β_{9} CEO_{Duality}_{i, t} + {Year}_{t} FE \\ + & industry_{j} FE + ε_{i, t} \end{aligned}

(4)

The dependent variable in all models, denoted as EnvInfoDis, serves as a metric for gauging the degree of EnvInfoDis by companies, with a specific focus on pollution-related data. In parallel, the independent variables, collectively referred to as CInOwn measures, encapsulate diverse facets of CInOwn, seeking to evaluate its influence on environmental disclosure. These variables include CInOwn_Dummy, signifying the presence or absence of common ownership; CInOwn_Peer, counting the number of industry peers sharing common ownership; CInOwn_N, measuring the average number of unique common institutional owners; and CInOwn_Percentage, quantifying the overall percentage of common ownership. Additionally, control variables are incorporated to account for firm-level factors that may impact environmental disclosure, such as firm size, financial performance, and corporate governance characteristics are defined in the section ‘Control variable’. The model is structured with subscripts (i and t), where “i” represents individual companies, and “t” represents specific years, facilitating a dynamic analysis of changes over time. The inclusion of an error term (ε) acknowledges any unexplained variation in the dependent variable not accounted for by the included independent variables.

Empirical results

Descriptive statistics and correlation matrix

Table 3 summarizes the descriptive statistics for all variables included in the study, including the number of firm-year observations, mean, standard deviation, and minimum and maximum values for each variable. Notably, the average EnvInfoDis index score of 0.147, with a median of 0, suggests that a majority of firms within the sample (over 50%) do not disclose information about their pollutant discharge. This finding highlights the potential lack of transparency surrounding environmental practices in the studied context. Additionally, the average CInOwn_Dummy score of 0.269 indicates that roughly 26.9% of the sampled firms exhibit CInOwn, adding another layer of complexity to the analysis. It is reassuring to observe that the descriptive statistics for the control variables fall within a reasonable range, suggesting that extreme outliers are unlikely to skew the study's findings.

Table 3.

Descriptive statistics.

	Mean	Std. dev.	Median	Min	Max	N
Dependent variable
EnvInfoDis	0.147	0.254	0.000	0	12	14,589
Independent variable
CInOwn_Dummy	0.269	0.443	0.000	0	1	14,589
CInOwn_Peer	0.851	2.593	0.000	0	33	14,589
CInOwn_N	0.295	0.514	0.000	0	4	14,589
CInOwn_percentage	7.209	15.575	0.000	0	99.38	14,589
Firm-level control variable
Firm_Size	21.374	1.394	21.235	15.571	27.813	14,589
CF	8.25	0.699	8.239	4.227	11.03	14,589
Tobin's Q	2.12	1.927	1.667	0.699	126.951	14,589
TDTA	0.4	0.236	0.382	0.008	8.612	14,589
ROA	0.045	0.096	0.044	−6.776	2.637	14,589
BSIZE	8.628	1.689	9.000	4	20	14,589
IND	0.373	0.054	0.333	0.182	.8	14,589
CEO_Duality	0.285	0.451	0.000	0	1	14,589

Moving on to Table 4, we encounter the Pearson correlation coefficient matrix, which sheds light on the relationships between the study's main variables. The highest coefficient observed is 0.948, a value that falls below the threshold for raising concerns about multicollinearity. This finding alleviates potential worries about spurious correlations influencing the regression analysis, allowing us to proceed with greater confidence in the validity of our results.

Table 4.

Pearson correlation matrix.

	EnvInfoDis	CInOwn_ Dummy	CInOwn_ Peer	CInOwn_N	CInOwn_ Percentage	Firm_ Size	CF	Tobin's Q	TDTA	ROA	BSIZE	IND	CEO_ Duality
EnvInfoDis	1
CInOwn_Dummy	.00438***	1
CInOwn_Peer	.00682***	.541***	1
CInOwn_N	.00780***	.948***	.557***	1
CInOwn_ Percentage	.00589***	.763***	.330***	.809***	1
Firm_Size	.193***	−.00724	.0118	.00268	−.0121	1
CF	.0604***	−.00330	−.00608	.00163	.0146	.197***	1
Tobin's Q	−.0256**	.00312	.00927	.00404	.0143	−.286***	−.0305***	1
TDTA	.0235**	.0278***	.0335***	.0293***	.0215**	.140***	−.0468***	.394***	1
ROA	.0105	−.0133	−.0319***	−.0115	−.00403	.0333***	.0477***	−.428***	−.893***	1
BSIZE	.128***	.0160	.0165*	.0195*	.0203*	.266***	.0754***	−.0983***	.0423***	.0182*	1
IND	−.0304***	.0150	.0252**	.0274***	.0245**	−.0763***	−.0181*	.0472***	−.0160	−.00517	−.509***	1
CEO_Duality	−.0779***	−.00562	−.0111	−.0147	−.0160	−.169***	−.0405***	.0440***	−.0302***	−.00664	−.181***	.117***	1

Note: *p < .05, **p < .01, ***p < .001.

Overall, the presented descriptive statistics and correlation analysis provide a clear image of the data landscape for this study. By understanding the distribution and relationships between key variables, we can move forward with a more informed and robust analysis of the research question at hand.

Baseline regression result

Table 5 presents the results of fixed-effects OLS regressions investigating the relationship between CInOwn and EnvInfoDis across different environmental disclosure policies (Models 1–4). Specifically, the coefficient for CInOwn_Dummy (direct ownership within a firm) is 0.085 (p < .01), indicating that an increase in this form of CInOwn leads to an 8.5 percentage point rise in EnvInfoDis. Similarly, CInOwn_Peer (shared ownership with peer firms) shows a positive association with EnvInfoDis at 0.009 (p < .01). This suggests that proximity within the ownership network also plays a role in influencing disclosure practices. Further supporting this notion, CInOwn_N (number of common institutional investors) exhibits a positive coefficient of 0.063 (p < .01), highlighting the cumulative effect of multiple shared owners. Finally, CInOwn_Percentage (percentage ownership held by common institutional investors) demonstrates a positive association with EnvInfoDis at 0.041 (p < .01), suggesting that the magnitude of shared ownership also contributes to increased transparency.

Table 5.

Baseline regression.

DV = EnvInfoDis	(1)	(2)	(3)	(4)
CInOwn_Dummy	.085***
	(.079)
CInOwn_Peer		.009***
		(.014)
CInOwn_N			.063***
			(.068)
CInOwn_Percentage				.041***
				(.002)
Firm_Size	.769***	.768***	.769***	.769***
	(.033)	(.033)	(.033)	(.033)
CF	.039***	.039***	.039***	.039***
	(.01)	(.01)	(.01)	(.01)
Tobin's Q	.107***	.107***	.107***	.107***
	(.02)	(.02)	(.02)	(.02)
TDTA	−.429***	−.421***	−.428***	−.426***
	(.149)	(.149)	(.149)	(.149)
ROA	−.356*	−.35*	−.356*	−.354*
	(.202)	(.202)	(.202)	(.203)
BSIZE	.132***	.134***	.132***	.133***
	(.026)	(.026)	(.026)	(.026)
IND	2.11***	2.154***	2.102***	2.119***
	(.751)	(.751)	(.751)	(.751)
CEO_Duality	−.211***	−.212***	−.211***	−.211***
	(.078)	(.078)	(.078)	(.078)
_cons	−15.355***	−15.331***	−15.342***	−15.334***
	(.795)	(.794)	(.795)	(.795)
Year-industry	Yes	Yes	Yes	Yes
Observations	14,589	14,589	14,589	14,589
R-squared	.131	.131	.131	.131

Note: Standard errors are in parentheses ***p < .01, **p < .05, *p < .1.

Taken together, these findings reveal a consistent and statistically significant positive relationship between CInOwn and EnvInfoDis. Moreover, the observed variations in coefficient values across different CInOwn measures suggest that both the degree and proximity of shared ownership influence environmental performance, with greater concentration and closer network ties leading to more pronounced enhancements in information disclosure.

Mechanism analysis

This study further explores the underlying mechanisms that drive the association between CInOwn and EnvInfoDis. Building upon prior research establishing the influence of CInOwn on internal governance (IC).¹² We propose and test the hypothesis that firm IC mediates the relationship between CInOwn and EnvInfoDis.

Drawing upon the framework proposed by Baron and Kenny,⁵⁰ we employ a three-step mediation model to analyze the data. Equation (5) examines the direct impact of CInOwn on EnvInfoDis, while equation (6) assesses the influence of CInOwn on IC. Finally, equation (7) incorporates both CInOwn and IC to evaluate the potential mediating effect of IC.

EnvInfoDis_{i t} = β_{0} + ψ CInOwn_{it} + \sum_{ϕ = 1}^{8} ϕ_{k} C o n t r o l s_{i t} + δ_{t} + γ_{j} + ε_{i t}

(5)

{IC}_{i t} = β_{0} + τ CInOw n_{it} + \sum_{ϕ = 1}^{8} ϕ_{k} C o n t r o l s_{i t} + δ_{t} + γ_{j} + ε_{i t}

(6)

{EnvInfoDis}_{i t} = β_{0} + σ CInOw n_{it} + υ FI C_{it} + \sum_{ϕ = 1}^{8} ϕ_{k} C o n t r o l s_{i t} + δ_{t} + γ_{j} + ε_{i t}

(7)

Our findings (summarized in Table 6) reveal that: CInOwn exerts a statistically significant and positive influence on EnvInfoDis (Model 5), suggesting that a higher presence of institutional investors encourages companies to disclose environmental information. Further, CInOwn positively and significantly impacts firm IC (Model 6), implying that institutional ownership contributes to improved internal control mechanisms. Moreover, when controlling for IC, CInOwn retains its positive and significant association with EnvInfoDis (Model 7), supporting the hypothesis of partial mediation.

These results collectively demonstrate that CInOwn influences EnvInfoDis through the intermediary mechanism of strengthening internal control. This underscores that the impact of institutional ownership on EnvInfoDis stems from their role in bolstering robust governance processes within firms.

Table 6.

Mechanism analysis.

	EnvInfoDis				IC				EnvInfoDis
	Model 5				Model 6				Model 7
CInOwn_Dummy	.085***				.017**				.037**
	(.079)				(.004)				(.004)
CInOwn_Peer		.009***				.097**				.033**
		(.014)				(.001)				(.001)
CInOwn_N			.063***				.013**				.045**
			(.068)				(.004)				(.004)
CInOwn_ Percentage				.041***				.018**				.023*
				(.002)				(.003)				(.002)

IC									.031***	.031***	.031***	.031***
									(.01)	(.01)	(.01)	(.01)
All control	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
_cons	−15.355***	−15.331***	−15.342***	−15.334***	5.591***	5.592***	5.593***	5.592***	−.255***	−.254***	−.252***	−.253***
	(.795)	(.794)	(.795)	(.795)	(.084)	(.084)	(.084)	(.084)	(.097)	(.097)	(.097)	(.097)
Year-industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	14,589	14,589	14,589	14,589	14,589	14,589	14,589	14,589	14,589	14,589	14,589	14,589
R-squared	.131	.131	.131	.131	.193	.192	.193	.193	.121	.121	.121	.121

Note: Standard errors are in parentheses ***p < .01, **p < .05, *p < .1.

Robustness analysis

One-year lag of dependent variable

This study acknowledges the potential for reverse causality between the presence of a controlling institutional investor (CInOwn) and EnvInfoDis. To mitigate this concern and bolster the reliability of the analysis, we utilize a strategic approach involving a one-period time lag. This technique acknowledges the possibility that firms actively engaged in robust environmental initiatives might inherently be more attractive to CInOwn, thereby potentially inflating the observed correlation.

As presented in Table 7, the results reveal significant positive relationships between EnvInfoDis and the coefficients of various CInOwn measures, including dummy variables, peer averages, number of investors, and percentage ownership. The regression coefficients (CInOwn_Dummy = 0.117, p < .01; CInOwn_Peer = 0.024, p < .01; CInOwn_N = 0.054, p < .01; CInOwn_Percentage = 0.031, p < .01) substantiate the empirical notion that the presence of a CInOwn fosters a synergistic effect on EnvInfoDis governance.

Table 7.

One-year lag of dependent variable.

	(1)	(2)	(3)	(4)
	L.EID	L.EID	L.EID	L.EID
CInOwn_Dummy	.117***
	(.094)
CInOwn_Peer		.024***
		(.015)
CInOwn_N			.054***
			(.08)
CInOwn_Percentage				.031***
				(.003)
Firm_Size	.894***	.893***	.893***	.893***
	(.046)	(.045)	(.046)	(.046)
CF	.034***	.034***	.034***	.034***
	(.012)	(.012)	(.012)	(.012)
Tobin's Q	.144***	.143***	.144***	.144***
	(.02)	(.02)	(.02)	(.02)
TDTA	−.714***	−.704***	−.71***	−.709***
	(.166)	(.165)	(.166)	(.166)
ROA	−.625***	−.619***	−.621***	−.62***
	(.209)	(.208)	(.209)	(.209)
BSIZE	.16***	.163***	.161***	.161***
	(.036)	(.036)	(.036)	(.036)
IND	1.883**	1.98**	1.89**	1.905**
	(.929)	(.928)	(.929)	(.932)
CEO_Duality	−.325***	−.328***	−.325***	−.325***
	(.08)	(.08)	(.08)	(.08)
_cons	−17.937***	−17.914***	−17.912***	−17.907***
	(1.129)	(1.127)	(1.128)	(1.128)
Year-industry	Yes	Yes	Yes	Yes
Observations	12,188	12,188	12,188	12,188
R-squared	.129	.129	.129	.129

Note: Standard errors are in parentheses ***p < .01, **p < .05, *p < .1.

This finding strengthens the understanding that proactive environmental management can serve as a valuable attractor for institutional investors, consequently influencing the disclosure of environmental information. This reinforces the positive feedback loop between corporate environmental responsibility and investor engagement, suggesting that robust EnvInfoDis practices can further attract CInOwn, potentially leading to even greater environmental commitment.

Propensity score matching

In light of potential functional misspecification concerns, this study leveraged the robust technique of PSM to strengthen study findings. PSM mitigates these concerns by identifying, for each treated and control group with comparable characteristics, effectively constructing a mirrored control group. This mirroring is achieved by incorporating the aforementioned control factors as matching variables. Notably, the treatment group encompasses enterprises with shared institutional ownership, while the control group comprises those lacking such ownership. A one-to-one closest neighbor matching approach, informed by established methodologies, was adopted.

The validity of the matching procedure was thoroughly assessed through pre- and postmatching propensity score density plots. As depicted in Figure 1, PSM effectively diminishes bias and ensures adherence to the common support assumption, bolstering confidence in the analysis.

Figure 1.

Density plot before and after matching.

Subsequent to the matching process, a rigorous regression analysis was conducted, with the results presented in Table 8. The coefficients associated with the shared institutional ownership variables (CInOwn_Dummy, CInOwn_Peer, CInOwn_N, and CInOwn_Percentage) reveal statistically significant positive relationships (β = CInOwn_Dummy = 0.081 (p < .01), CInOwn_Peer = 0.049 (p < .01), CInOwn_N = 0.115 (p < .01), and CInOwn_Percentage = 0.002 (p < .01)). These findings not only corroborate prior empirical evidence but also lend further credence to the proposed “collective governance assumption.”

Table 8.

Propensity score matching.

Panel A: Match sample
	Mean		t-test		V(T)/
Variable	Treated	Control	% bias	T	p > t	V(C)
Firm_Size	21.136	21.171	−2.500	−1.100	0.269	1.11*
CF	6.268	6.244	.7	0.290	0.769	1.000
Tobin's Q	2.096	2.115	−0.800	−0.460	0.646	1.13*
TDTA	0.405	0.411	−0.700	−1.130	0.257	0.960
ROA	0.036	0.033	.6	1.520	0.128	0.93*
BSIZE	8.545	8.540	.3	0.140	0.887	0.960
IND	0.375	0.376	−1.500	−0.650	0.518	0.960
Ceo_Duality	0.305	0.301	.8	0.340	0.731	.
Panel B: PSM regression
DV = EnvInfoDis			(1)	(2)	(3)	(4)
CInOwn_Dummy			.081***
			(.127)
CInOwn_Peer				.049***
				(.018)
CInOwn_N					.115***
					(.107)
CInOwn_Percentage						.002***
						(.003)
Firm_Size			.876***	.88***	.876***	.876***
			(.07)	(.07)	(.07)	(.07)
CF			.049***	.048***	.049***	.049***
			(.018)	(.018)	(.018)	(.018)
Tobin's Q			.128***	.129***	.129***	.129***
			(.029)	(.029)	(.029)	(.029)
TDTA			−.638*	−.658*	−.636*	−.63*
			(.364)	(.365)	(.364)	(.364)
ROA			−1.651**	−1.701***	−1.644**	−1.649**
			(.657)	(.658)	(.657)	(.658)
BSIZE			.183***	.182***	.183***	.183***
			(.055)	(.055)	(.055)	(.055)
IND			1.521	1.587	1.553	1.541
			(1.385)	(1.383)	(1.387)	(1.388)
CEO_Duality			−.386***	−.391***	−.388***	−.387***
			(.122)	(.122)	(.122)	(.122)
_cons			−17.777***	−17.819***	−17.771***	−17.808***
			(1.708)	(1.701)	(1.704)	(1.703)
Year-industry			Yes	Yes	Yes	Yes
Observations			5,222	5,222	5,222	5,222
R-squared			.137	.131	.136	.134

Note: Standard errors are in parentheses ***p < .01, **p < .05, *p < .1.

Alternative variable analysis: replacing CInOwn with top 3% rather than 5% measure

Within China's legal framework, shareholders holding over 3% of a company's shares gain the right to submit written interim proposals to the board of directors, provided 10 days’ advance notice before a general meeting. This raises the potential for significant influence on corporate decision-making, as documented by Bai et al.⁵¹

Recognizing this influential stake, our study recalibrated the key CInOwn proxies, lowering the threshold for shareholder ownership from 5% to 3%. Columns 1–4 of Table 9 display the resulting findings. Notably, even with the adjusted CInOwn proxies employing the 3% threshold, the coefficients remained significantly positive, indicating a robust association between shareholder ownership and EnvInfoDis. This relationship held true across all four CInOwn measures: CInOwn_Dummy_3 (0.053, p < .001), CInOwn_Peer_3 (0.015, p < .001), CInOwn_N_3 (0.017, p < .001), and CInOwn_Percentage_3 (0.019, p < .001). These findings reaffirm the strong link between shareholder ownership and EnvInfoDis, even after considering the potential influence of shareholders holding over 3% of the company's shares.

Table 9.

Replacing main variable with 3–5%.

DV = EnvInfoDis	(1)	(2)	(3)	(4)
CInOwn_Dummy_3	.053***
	(.013)
CInOwn_Peer_3		.015***
		(.015)
CInOwn_N_3			.017***
			(.094)
CInOwn_Percentage_3				.019***
				(.003)
Firm_Size	.367***	.367***	.368***	.367***
	(.064)	(.064)	(.064)	(.064)
CF	.002	.002	.002	.002
	(.01)	(.01)	(.01)	(.01)
Tobin's Q	.037*	.037*	.037*	.037*
	(.022)	(.022)	(.022)	(.022)
TDTA	.03	.026	.027	.028
	(.163)	(.163)	(.163)	(.163)
ROA	.061	.058	.058	.059
	(.213)	(.212)	(.213)	(.213)
BSIZE	−.053	−.054	−.053	−.053
	(.044)	(.044)	(.044)	(.044)
IND	−1.033	−1.057	−1.041	−1.038
	(1.052)	(1.053)	(1.052)	(1.052)
CEO_Duality	−.103	−.104	−.104	−.104
	(.107)	(.107)	(.107)	(.107)
_cons	−4.666***	−4.645***	−4.677***	−4.676***
	(1.792)	(1.792)	(1.792)	(1.792)
Year-industry	Yes	Yes	Yes	Yes
Observations	14,589	14,589	14,589	14,589
R-squared	.096	.096	.096	.096

Note: Standard errors are in parentheses ***p < .01, **p < .05, *p < .1.

Sys-GMM model

EnvInfoDis considered a form of communication between an organization and its stakeholders.⁴¹ Organizations with high levels of EnvInfoDis disclosure may attract more investors with common ownership, raising the possibility of a reciprocal cause-and-effect relationship between common ownership (CInOwn) and EnvInfoDis. To address this potential endogeneity, we employed a sys-GMM regression technique. The results, presented in columns 1–4 of Table 10, continue to show statistically significant positive coefficients for the CInOwn proxies. This reinforces our initial hypothesis that there is a positive connection between CInOwn and EnvInfoDis. The sys-GMM analysis enhances the robustness of our core findings while simultaneously addressing concerns related to endogeneity by modeling the potential bidirectional relationship between CInOwn and EnvInfoDis.

Table 10.

Sys-GMM model.

DV = EnvInfoDis	(1)	(2)	(3)	(4)
L. EnvInfoDis	.316***	.316***	.316***	.315***
	(.007)	(.007)	(.007)	(.007)
CInOwn_Dummy	.075***
	(.08)
CInOwn_Peer		.032***
		(.014)
CInOwn_N			.035***
			(.068)
CInOwn_Percentage				.031***
				(.002)
Firm_Size	.29***	.292***	.291***	.291***
	(.03)	(.03)	(.03)	(.03)
CF	.017	.016	.016	.016
	(.01)	(.01)	(.01)	(.01)
Tobin's Q	.025	.025	.024	.024
	(.018)	(.018)	(.018)	(.018)
TDTA	.076	.067	.072	.07
	(.14)	(.14)	(.14)	(.14)
ROA	.151	.142	.147	.144
	(.188)	(.188)	(.188)	(.188)
BSIZE	.151***	.15***	.151***	.15***
	(.025)	(.025)	(.025)	(.025)
IND	1.936***	1.876**	1.922***	1.911**
	(.742)	(.742)	(.743)	(.743)
CEO_Duality	−.149*	−.153**	−.148*	−.148*
	(.078)	(.078)	(.078)	(.078)
_cons	−7.506***	−7.511***	−7.52***	−7.517***
	(.704)	(.704)	(.704)	(.704)
Year-industry	Yes	Yes	Yes	Yes
Observations	12,188	12,188	12,188	12,188
Pseudo R²	.137	.137	.139	.138

Note: Standard errors are in parentheses ***p < .01, **p < .05, *p < .1.

Conclusion and discussion

Navigating the intricate landscape of corporate EnvInfoDis demands a nuanced understanding of the interconnectedness between stakeholders, investors, and inherent company characteristics. In light of escalating environmental concerns¹ and mounting pressure for transparency,⁴¹ this study wields the comprehensive CInOwn framework, merging quantitative and qualitative data^13,19 to unveil a richer, more robust tapestry of disclosure practices. Our central analysis unveils a compelling positive association between common CInOwn and EnvInfoDis, positing that shared institutional ownership acts as a potent driver of enhanced environmental reporting. This assertion finds strong empirical footing in our expansive dataset of Chinese firms listed on the Shanghai and Shenzhen stock exchanges (2010–2020). The identified relationship exhibits notable stability across diverse CInOwn proxies, further solidifying its credibility. Moreover, rigorous supplementary tests effectively address potential endogeneity concerns, lending further credence to our findings.

This study's contribution transcends the mere identification of a novel association. We weave our findings into the intricate tapestry of the ongoing debate surrounding the multifaceted consequences of shared institutional ownership, long recognized for its role in propelling corporate growth.^11,21 While the discourse regarding its influence on corporate governance remains polarized, oscillating between championing collaborative governance and voicing concerns over collusion, recent research exhibits a burgeoning consensus on its positive impact on corporate innovation.^13,16 Our study uniquely sheds light on its hitherto ambiguous influence on environmental reporting, illuminating its potential as a catalyst for enhanced transparency. Furthermore, we highlight the competitive advantage gleaned through “peer effects” within shared ownership networks. Importantly, we acknowledge the context-specific nature of shared ownership's impact, recognizing that its influence, whether perceived as collective governance or collusive opportunism, likely hinges on company-specific factors. It is on this premise that we advocate for the potent and potentially mitigating role of shared ownership in catalyzing responsible environmental reporting practices.

Study implication

The findings of this research carry significant implications for various stakeholders and offer avenues for constructive action. Advocacy for policy changes to promote CInOwn could be a key outcome, with recommendations such as tax breaks or adjustments to voting rights serving as potential incentives. Regulatory bodies can leverage these findings to enhance mandatory environmental disclosure requirements, particularly for companies with substantial CInOwn.

From a corporate standpoint, companies can use this research to refine their EnvInfoDis strategies. Tailoring these strategies to engage with common institutional investors may prove beneficial, potentially attracting more investment and bolstering their overall image. This might involve the provision of more detailed and transparent environmental data or aligning sustainability goals with those of major institutional investors.

Investors, too, stand to benefit from this study by recognizing the potential impact of CInOwn on environmental responsibility. The research encourages investors to actively engage with their portfolio companies on environmental issues and integrate environmental, social, and governance (ESG) factors into their investment decisions.

Study limitation and future prospect

As the study objectives are achieved, it is imperative to acknowledge certain limitations, signaling the need for further exploration in future prospects. Data limitations arise from the exclusive focus on Chinese companies from 2010 to 2020, restricting the generalizability of findings to other countries and time periods. Methodological limitations should be transparently addressed, including considerations of alternative models or approaches that might have been employed. Causality limitations emphasize the need to recognize correlation rather than causation and consider the influence of other factors on both variables.

For future propositions, expanding the scope by replicating the study with data from diverse countries and industries would enhance generalizability. Analyzing specific types of common institutional investors, such as pension funds or sovereign wealth funds, can provide insights into their varying impacts on environmental disclosure. Delving deeper into the mechanisms behind the observed relationship is crucial—do institutional investors actively pressure companies or do companies voluntarily disclose more information to attract common investors? Considering other environmental aspects, beyond basic disclosure, and analyzing the quality and accuracy of reported data would contribute to a more comprehensive understanding. Ultimately, addressing these limitations and exploring future propositions will fortify the credibility and applicability of the research.

Footnotes

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Umer Sahil Maqsood

Yishan Liu is a PhD student at School of Economics and Finance of the Xi'an Jiaotong University. Her research interests include common institutional ownership and ESG.

Hu Liu is a PhD student at School of Economics and Finance of the Xi'an Jiaotong University. His research interests include corporate finance.

Wei He is a PhD student at School of Law of the Xi'an Jiaotong University. Her research interests include corporate law.

Hanwen Deng is an undergraduate student at School of Management of Xi'an Jiaotong University City College. Her research interests include corporate management.

Umer Sahil Maqsood is currently pursuing his PhD in the School of Economics and Finance at Xi'an Jiaotong University, located in Xi'an, Shaanxi, China. His research focuses on various aspects of corporate endeavors such as environmental performance (ESG, green innovation & CSR), governance (CEO compensation & incentives), and digitalization (Artificial intelligence and Digital transformation). His work has been featured in esteemed journals such as Borsa Istanbul Review, Economics Letters, Finance Research Letters, Business Ethics, the Environment & Responsibility, Managerial and Decision Economics, and the Asia-Pacific Journal of Accounting & Economics, among others.

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