Public Water Concern, Managerial Green Cognition, and Corporate Water Responsibility: Evidence from High-Water-Consuming Enterprises in China

Abstract

To address water sustainability challenges, this study investigates how public water concern influences corporate water responsibility (CWR) and how managerial green cognition moderates this relationship. Drawing on institutional theory and cognitive theory, we analyze a panel of 1292 publicly listed high-water-consuming firms in China from 2015 to 2024. The results show that public water concern significantly improves CWR by increasing legitimacy pressure, while its effect through government water governance attention is not statistically significant. Furthermore, managerial green cognition—including both economic and moral dimensions—positively moderates this relationship. Heterogeneity analysis reveals that the moderating effect is stronger in firms with more female directors, older executives, and internationally experienced teams. These findings contribute to refining institutional theory in the context of environmental responsibility and highlight the critical role of executive cognition and demographic structure in corporate sustainability behavior.

1. Introduction

Freshwater scarcity is becoming one of the most pressing sustainability challenges of the 21st century. According to the United Nations World Water Development Report (2024), global water demand is projected to exceed supply by 40% by 2030 [1]. In many regions, declining water availability and increasing competition over resources are already disrupting industrial production and social stability. However, the impacts of water scarcity vary significantly across industries, with high-water-consuming sectors—such as energy, chemicals, textiles, food processing, and mining—facing disproportionately higher operational, financial, and reputational risks [2,3,4,5]. For instance, in the Yellow River Delta, virtual water trade analysis reveals a structural mismatch between industrial water consumption and ecological capacity [6]. Thus, at the global level, differences in water use intensity and flexibility across industries emphasize the urgency of sector-specific sustainability strategies [7].

Given these heightened risks, firms—particularly in water-intensive sectors—must play a more proactive role in addressing water-related challenges. As major users of freshwater and stakeholders in local water systems, they are expected to engage in corporate water responsibility (CWR)—a focused dimension of environmental responsibility that includes efficient water use, risk disclosure, stakeholder engagement, and sustainable resource management [8,9,10]. However, enterprises are not very enthusiastic about upholding their responsibilities regarding water resources. Investigations by Zeng, Zhou, etc., regarding corporate water management revealed that the mean and peak scores for corporate water responsibility among high-water-consuming enterprises listed on China’s A-share market from 2010 to 2019 were merely 6.5 points and 18 points, respectively, out of a possible 27 points [11,12]. To force these enterprises to perform CWR, regulatory interventions such as China’s River Chief Policy [13] and water tax reforms [14] have strengthened formal oversight, with increasing attention now directed toward informal institutional pressures—including public concern, media scrutiny, and investor expectations—because excessive regulatory pressure may backfire [15,16]. However, the focus on non-governmental institutions is predominantly directed towards media, shareholders, and creditors [17,18,19,20]. There is insufficient empirical evidence regarding whether the prevailing public water concern is adequate to impose its requisite institutional power.

While the importance of CWR is increasingly being acknowledged, the mechanisms driving firm-level differences in water behavior remain underexplored. Yet, under comparable external pressure, firms exhibit divergent responses: some adopt substantive water practices, while others remain symbolic or passive [21]. For example, Shenhua Group was awarded for its outstanding performance in the CDP’s enterprise water safety survey, and Want Want Group attained the ‘Gold Level’ certification for international sustainable water management from the International Water Management Alliance. Nevertheless, according to the investigations by Zeng, Zhou, etc., most firms have not adequately prioritized water management. This variation may point to the role of managerial cognition in interpreting and acting upon eternal expectations, because, according to the management cognition theory, differences in individual behaviors reflect the freedom of individual choices. Meanwhile, managerial cognition concerning environmental issues exhibits duality. Some may see environmental management as strategic, while others may see it as a duty [22]. However, limited research has investigated the influence of this duality on the performance of corporate environmental responsibility [23,24,25].

Based on these perspectives, we propose the following research questions:

(1)

Does public water concern promote greater corporate water responsibility in high-water-consuming industries? If so, how does this effect occur?

(2)

How does managerial green cognition—particularly its economic and moral components—moderate the relationship between public water concern and CWR?

In investigating these questions, this study selected a sample of 1292 publicly listed high-water-consuming businesses from the Shanghai and Shenzhen A-shares from 2015 to 2024 to empirically examine the influence of public water concern on corporate water responsibility. Simultaneously, we examined the moderating influence of managerial green cognition on the relationship between public water concern and corporate water responsibility and assessed the heterogeneous impact of this moderating effect based on upper echelon theory. This study found that public water concern promotes businesses to achieve a greater water responsibility performance, and the green cognition of managers positively moderates the relationship between public water concern and corporate water responsibility. The moderating effect of managerial green cognition exhibits age, gender, and international experience heterogeneity among different top management teams.

In response to the research questions, this study makes the following key contributions. First, it extends institutional theory by examining how public water concern, as a form of informal institutional pressure, influences corporate water responsibility (CWR)—a domain that has received limited empirical attention in environmental governance scholarship. This study identifies the following two distinct mechanisms through which public concern operates: a normative legitimacy pathway, which directly compels firms to adopt water-responsible behaviors, and a regulative legitimacy pathway, which is expected to function via enhanced government oversight. However, empirical results reveal that while normative pressure is significant, the pathway through governmental attention is not, highlighting a structural disconnection between formal and informal institutions. This reflects broader issues of policy inertia and fragmented governance, especially salient in China’s environmental regulatory system.

Second, the study introduces and empirically validates a dual-dimensional model of managerial green cognition, differentiating between green economic cognition (strategic and instrumental reasoning) and green moral cognition (ethical and normative reasoning). This model clarifies how cognitive interpretation among top managers moderates the effect of public water concern on CWR, thereby offering a more nuanced understanding of how firms internalize informal societal expectations. In doing so, the study advances upper echelons theory by moving beyond demographic proxies and focusing on the content and structure of executive cognition as key determinants of environmental strategy formulation.

Third, drawing on upper echelon theory, the study investigates how top management team attributes—such as gender composition, age distribution, and international experience—influence the strength of managerial green cognition as a moderator. This heterogeneity analysis uncovers the cognitive diversity underlying firms’ environmental responses and opens the “black box” linking executive team structure to environmental strategic outcomes. These findings contribute to an enriched behavioral theory of firms in sustainability contexts.

The subsequent sections of this study are structured as follows. The research hypotheses are formulated following theoretical analysis. The methodologies employed in this research and the resultant findings are articulated comprehensively. The research findings are summarized and debated. The research implications are summarized, and further research directions are suggested.

2. Theoretical Analysis and Research Hypotheses

This section elucidates the concept of corporate water responsibility. Secondly, this article predicts that public water concern can promote corporate water responsibility. This article then presents the moderating effect of managerial green cognition to improve the efficacy of public water concern.

2.1. What Is Corporate Water Responsibility?

Corporate water responsibility is a subset of corporate environmental responsibility and, more broadly, falls under corporate social responsibility [26,27]. The performance of corporate water responsibility necessitates that firms undertake appropriate measures to minimize water use and mitigate water contamination [10,24,27]. Nevertheless, experts now exhibit varying perspectives about the steps that companies ought to take to attain these outcomes.

From the standpoint of corporate water management, businesses must guarantee that their effects on water resources, regarding both quantity and quality, are controllable. Scholars have recommended the creation of environmental protection institutions and systems within enterprises, improving water risk awareness among managers, advancing technological innovations for water conservation and pollution reduction, augmenting investment in environmental protection facilities, and so on [28,29,30]. From the standpoint of corporate water stewardship, businesses must evaluate the sustainability of water resources from a systemic viewpoint at the watershed level. Consequently, enterprises must undertake greater responsibilities, such as managing water resources within the supply chain, fostering awareness of water protection among watershed residents, and safeguarding the water-related human rights of those residents, among other obligations [9,31,32].

William Sarni emphasized that neglecting the liquidity of water resources throughout the industrial value chain does not allow firms to effectively mitigate water risks [33]. Vos and Hinojosa contended that enterprises rectifying their own issues does not constitute a viable solution for optimal water resource management within a watershed; rather, such actions may intensify water supply and demand conflicts in other areas by industrial relocation and value chain repositioning [34]. Consequently, considering the enduring assurance of water sustainability and the preservation of their intrinsic commercial value, experts assert that firms must concurrently undertake initiatives in the following three domains: management, involvement, and innovation [33]. Management actions encompass corporate green management systems, the establishment of strategic objectives for water resources, and employee education. Participation actions highlight the necessity for enterprises to transcend organizational boundaries and accept corresponding social and moral responsibilities for water resource issues they create to prioritize the comprehensive sustainability of watershed water resources. This includes enhancing the watershed community’s capacity to consistently access sufficient and clean water resources, serving as a model in collective movements for water protection, and advancing the optimal overall water protection standards within the enterprise value chain. Innovation actions pertain to the necessity for firms’ to enhance technical innovation and invest in environmental protection, thereby achieving water conservation and pollution reduction through novel technologies, processes, models, and products.

This study asserts that while the disparity between water supply and demand encountered by enterprises is a localized environmental concern, enterprises, as primary stakeholders in this issue, possess limited effectiveness in addressing it by solely concentrating on their individual water use [35]. If the water resources conserved and safeguarded by firms are not appropriately allocated and utilized within the river basin, enterprises will continue to experience the repercussions of the disparity between water supply and demand in their production and business activities. Then, the behavioral benefit of corporate water protection diminishes in significance. Consequently, firms must collaborate with all stakeholders within the river basin, encompassing both surface and subterranean water systems, to efficiently address water challenges. Thus, corporate water responsibility refers to achieving water usage and water pollution reduction through management, participation, and innovation, thereby alleviating the water problems caused by production activities and their associated social impacts.

2.2. Public Water Concern and Corporate Water Responsibility

Organizational sociology emphasizes that firms are embedded within a complex institutional environment, which shapes corporate behavior primarily through the pursuit and maintenance of legitimacy—defined as the perception that an organization’s actions are appropriate within a socially constructed system of norms, values, and beliefs [36,37]. As Scott argues, legitimacy is not a tangible asset, but a foundational condition for organizational survival, and its loss often results in diminished access to external resources, reputational damage, and ultimately organizational failure [38]. Firms, therefore, actively seek to align with the legitimacy expectations posed by their institutional environment, particularly in domains such as environmental and social responsibility [39,40].

According to institutional theory, legitimacy operates through three distinct pillars—regulative, normative, and cognitive. Each reflects different sources of institutional pressure: regulative legitimacy stems from government rules and enforcement; normative legitimacy is rooted in the social and moral expectations of the public; and cognitive legitimacy emerges from widely shared understandings and expectations within industries or professional fields [37,38].

In the context of this study, public concern over water resources represents a form of informal institution, closely tied to normative legitimacy. As awareness grows regarding the risks of water scarcity, pollution, and ecological degradation, the public establishes informal behavioral standards that emphasize corporate responsibility in water management. These standards are based on collective moral expectations aimed at preserving essential ecological goods, such as safe and sufficient water supply. Firms that fail to meet these public expectations risk social criticism, reputational loss, and exclusion from legitimate market participation. Consequently, to gain and maintain normative legitimacy, companies are compelled to adopt substantive water management practices.

Beyond this direct normative influence, institutional change theory also highlights the dynamic interdependence between informal and formal institutions. While informal institutions such as public discourse and social norms can independently shape firm behavior, they also possess the capacity to transform formal institutions through bottom-up pressure [41,42,43,44]. In situations where formal rules are ineffective, outdated, or selectively enforced, informal institutions may act as catalysts for policy change, regulatory strengthening, and administrative reform. This bottom-up pathway reflects a spiral institutional evolution from “informal institutions → adaptive informal institutions → formal institutional adaptation” [44].

Applied to water governance, this dynamic suggests that intensified public concern over water issues can prompt government agencies to adjust their regulatory focus and capacity. Public scrutiny can expose lax enforcement or government–business collusion, thereby placing reputational pressure on local governments to restore public trust through stricter water oversight. Furthermore, formal channels such as China’s People’s Congress system and political consultation mechanisms allow public concern to be institutionalized into legislative agendas and regulatory amendments [45]. These mechanisms enhance regulative legitimacy pressure on firms by increasing the legal and compliance risks associated with water irresponsibility. Firms that fail to respond may face administrative penalties, environmental lawsuits, and reduced political support.

In sum, public concern over water issues exerts institutional influence through two complementary pathways. The first pathway reflects the normative legitimacy mechanism, where public expectations directly shape corporate behavior through informal moral constraints. The second pathway follows a regulative legitimacy logic, whereby public concern indirectly drives governmental regulatory response, thereby increasing formal compliance pressure on enterprises. Distinguishing these two paths enriches our understanding of how informal societal forces interact with formal institutional mechanisms to influence corporate sustainability strategies in water-intensive sectors.

Based on the analysis above, this study posits the following:

Hypothesis 1a (H1a).  

Public water concern increases normative legitimacy pressure on enterprises, thereby directly promoting their performance in corporate water responsibility.

Hypothesis 1b (H1b).  

Public water concern increases governmental attention to water governance, thereby indirectly promoting corporate water responsibility by enhancing regulative legitimacy pressure.

2.3. The Moderating Effect of Managerial Green Cognition

While firms may be subject to similar institutional environments, their strategic responses often vary. This divergence reflects the role of internal cognitive mechanisms in shaping how external pressures are interpreted and acted upon. According to the cognitive school of strategic management, managers do not passively receive environmental signals—they selectively allocate attention and interpret environmental information based on their cognitive schemas, which, in turn, influence organizational actions [46,47,48]. In the environmental management literature, this interpretive process is referred to as managerial green cognition—defined as the extent to which top executives perceive, prioritize, and internalize environmental challenges and responsibilities [49,50]. Managerial green cognition influences the degree to which firms respond to informal institutional signals, such as public water concern, through the following two primary mechanisms: attention orientation and behavioral interpretation.

First, managerial attention is a scarce organizational resource. Environmental issues, particularly those raised by indirect stakeholders like the public, often lack immediate financial salience and may be overlooked in the absence of cognitive sensitivity. Managers with strong green cognition are more attuned to environmental risks, more likely to perceive public concern as a credible informal pressure, and better able to recognize its implications for firm legitimacy and performance [51,52,53]. Therefore, they enable an organization to notice, interpret, and respond to subtle environmental cues more effectively.

Second, green cognition also influences how managers interpret the appropriate responses to these pressures, showing the behavioral casual logic of managers. It shapes whether executives regard environmental issues as peripheral compliance burdens or as central to their firm’s value system and strategic orientation. Managers with higher green cognition are more likely to align with sustainability values, internalize public expectations, and view environmental responsibility as a necessary and legitimate action [54,55,56,57].

While green cognition functions as a unified cognitive structure, research has shown that it encompasses the following two distinct dimensions with divergent motivational bases: green economic cognition and green moral cognition [22,24,58,59]. This study adopts this typology to explore how different internal logics influence the interpretation of external institutional pressures.

Green economic cognition reflects managers’ awareness of the economic consequences and strategic opportunities associated with environmental issues. Managers with high economic cognition recognize that ignoring public environmental concern may lead to increased transaction costs, reputational damage, and even regulatory sanctions. Conversely, addressing such concerns may generate competitive advantages, including operational efficiency, product differentiation, and enhanced market reputation [23,25,60,61,62]. In the context of water resources, public concern may seem economically irrelevant to firms that perceive the public as a non-direct stakeholder. However, managers with strong green economic cognition understand that such concerns can escalate into tangible costs. They are, thus, more likely to interpret public pressure as a risk management issue and respond with proactive water responsibility initiatives.

Green moral cognition, by contrast, refers to managers’ ethical awareness of the social and human consequences of environmental degradation. Managers with high moral cognition regard ecological challenges not merely as operational risks, but as moral imperatives rooted in public well-being, social justice, and corporate citizenship [24,63,64,65,66,67]. In water governance, this includes recognizing the impact of pollution on public health and the role of water scarcity in exacerbating social inequality. Such managers are more inclined to interpret public concern as a legitimate ethical expectation and to respond based on principles rather than profit. They also view environmental responsibility as a collective social duty—one that goes beyond legal compliance or strategic calculation [68].

In sum, managerial green cognition moderates the translation of legitimacy pressures of public water concern into environmental behavior by enhancing both attention to informal signals and willingness to act. Disaggregating this cognition into economic and moral dimensions clarifies whether managerial motivation stems from strategic reasoning or ethical commitment. This dual-pathway approach provides a more comprehensive explanation for variations in firms’ responses to public water concern.

Based on the analysis above, this study posits the following:

Hypothesis 2 (H2).  

The stronger the green cognition of managers, the stronger the positive effect of public water concern on corporate water responsibility.

Hypothesis 2a (H2a).  

The stronger the green economic cognition of managers, the stronger the positive effect of public water concern on corporate water responsibility.

Hypothesis 2b (H2b).  

The stronger the green moral cognition of managers, the stronger the positive effect of public water concern on corporate water responsibility.

According to the assumptions above, the theoretical model of this study is presented in Figure 1.

3. Research Design

3.1. Research Purpose

This study aims to explore how public water concern influences corporate water responsibility (CWR) in water-intensive industries, and whether such effects are mediated by legitimacy pressure and government regulatory scrutiny. Furthermore, it investigates the moderating role of managerial green cognition—specifically green economic and green moral cognition—in shaping firms’ responses to public water concerns. This framework provides a theoretical foundation for testing both mediating and moderating effects, thereby offering a multidimensional understanding of the institutional and cognitive drivers of CWR.

3.2. Sampling

This study focuses on Chinese A-share listed companies in high-water-consuming industries. According to the “13th Five-Year Plan for the Construction of Water-saving Society” issued by the National Development and Reform Commission, the Ministry of Water Resources, and the Ministry of Housing and Urban-Rural Development, high-water-consuming industries mainly include steel, petroleum, chemical, power, coal, paper, textile, food, etc. Based on the “Guidelines for the Classification of Listed Companies by Industry (2012)” issued by the China Securities Regulatory Commission (CSRC), the relevant industry classifications include B06 (Coal Mining and Washing Industry), B08 (Black Metal Ore Mining and Processing Industry), B09 (Nonferrous Metal Ore Mining and Processing Industry), C13 (Agricultural Food Processing Industry), C14 (Food Manufacturing Industry), C15 (Wine, Beverage, and Tea Manufacturing Industry), C17 (Textile Industry), C18 (Textile Clothing and Apparel Industry), C19 (Leather, Fur, Feathers, and Textile Products and Footwear Industry), C22 (Paper and Paper Products Industry), C25 (Petroleum, Coal, and Other Fuels Processing Industry), C26 (Chemical Raw Materials and Chemical Products Manufacturing Industry), C27 (Pharmaceutical Manufacturing Industry), C28 (Chemical Fiber Manufacturing Industry), C29 (Rubber and Plastic Products Industry), C30 (Non-Metallic Mineral Products Industry), C31 (Black Metal Smelting and Rolling Processing Industry), C32 (Nonferrous Metal Smelting and Rolling Processing Industry), C33 (Metal Products Industry), D44 (Electricity, Heat, and Power Production and Supply Industry), and D46 (Water Production and Supply Industry).

The initial sample includes all A-share listed firms from 2015 to 2024. We apply the following screening criteria:

(1)

Firms must belong to one of the identified water-consuming sectors based on the CSRC industry codes. Whether a firm is a high-water-consuming one is essentially determined by its business, and the industry to which a firm belongs is a concentrated manifestation of its business.

(2)

Firms with ST/*ST status or delisted during the observation period are excluded to ensure financial stability, which may affect the performance of environmental responsibilities by companies.

(3)

Firms with missing data for key variables (e.g., financials, ESG disclosures, and CEO statements) are removed.

After filtering, the final unbalanced panel comprises 1292 companies over 10 years, yielding 8494 firm-year observations.

3.3. Variable Measurements

3.3.1. Independent Variable: Public Water Concern

To capture informal institutional pressure arising from societal awareness of water-related issues, this study adopts a dual approach to measuring public water concern. The first approach uses count-based behavioral indicators, while the second applies a structural demographic index constructed via the entropy weight method.

Consistent with prior research [69,70,71], this study employs the Baidu Search Index as the primary measure of public water concern. There are three main reasons for this choice. First, from the market coverage perspective, Baidu is the dominant search engine in China, accounting for over 60% of the national market share. It serves as a key information gateway for the public and, thus, provides a large-scale, spontaneous reflection of public attention to social and environmental issues. Second, from the data quality and representativeness perspective, compared to petition data published by environmental protection departments, the Baidu Search Index, through appropriate keyword selection, can reflect both the intensity and diversity of public interest in water issues across regions. As an organic form of public attention, it is less subject to political manipulation or procedural barriers, while petition data focus narrowly on wastewater discharge and are influenced by administrative factors such as local governance capacity. Third, the limitations of alternative sources must be considered, although media-based indicators are also commonly used to assess public concern; however, our primary databases (CSMAR and CNRDS) contain limited coverage of water-related reporting, particularly at the regional level. Therefore, relying solely on media frequency may not adequately capture the geographic heterogeneity in public concern. Accordingly, we collect Baidu Index data for the keywords “water resources” and “water pollution” across 256 prefecture-level cities (excluding Tibet) from 2015 to 2024. The indices are aggregated annually and log-transformed after adding 1 to correct for skewness. This measure is denoted as RWrisk_1.

To ensure robustness, we follow the study conducted by Yuan and Xie to construct a secondary measure of public water concern (RWrisk_2) using the entropy weight method [72]. This index combines demographic variables—per capita income, education level, population density, and age structure—that are theoretically linked to citizens’ environmental awareness and capacity for civic engagement.

In sum, the measurement of the Baidu Search Index is used in baseline regression to initially test the relationship of public water concern, and the measurement of the composite index is used for a robustness check.

3.3.2. Dependent Variable: Corporate Water Responsibility

There are currently three methods for measuring corporate environmental responsibility. Some scholars assert that corporate environmental responsibility encompasses the aggregate of green behaviors enacted by enterprises to alleviate adverse environmental effects [73]; another faction contends that it pertains to the magnitude of negative environmental impacts, advocating for the use of pollutant emissions and other metrics as evaluative criteria [74], while others argue that the assessment of enterprise environmental responsibility should concurrently reflect both the actions taken and their resultant outcomes [12,24,28]. This study posits that evaluating enterprises solely based on their green behaviors, without regard to their outcomes, or exclusively on their green outcomes, neglecting their behaviors, is predicated on the fallacious assumption that enterprises engage in green behaviors to achieve green outcomes. Initially, organizations will engage in pro-environmental practices to cultivate or sustain an image of environmental stewardship, thereby mitigating institutional pressure and acquiring environmental legitimacy. This includes collaborating with diverse external environmental organizations, executing public awareness campaigns on environmental protection, seeking green certifications, forming environmental management committees, and implementing green compensation policies, which do not entail restructuring of the core company or innovation in production management and are significantly limited in mitigating negative environmental impacts. So, these acts are predominantly symbolic. Secondly, decreases in pollutant emissions and the utilization of natural resources by enterprises may not stem from environmentally friendly practices, but rather from the adoption of a diversification strategy that enhances investment in non-polluting or non-resource-consuming businesses to optimize natural resource utilization or reduce pollutant output. Consequently, the third evaluation technique, which assesses corporate environmental responsibility by concurrently considering both actions and their consequences, is presently the most esteemed by academics.

Based on Section 2.1 and the above, the measurement of corporate water responsibility is a comprehensive evaluation of the protection, participation, and innovative behaviors of enterprises regarding water issues, as well as the ensuing water protection effects. Thus, this paper first divides corporate water responsibility into the following three dimensions: green innovation, green participation, and green management. Secondly, this study specifically refines the indicators of each dimension by referring to previous research results [75,76]. Among them, green innovation includes water consumption, wastewater discharge, the implementation of clean production, and green patent application; green participation includes environmental-protection-specific actions, environmental incident emergency mechanisms, and environmental honors or rewards; and environmental management includes environmental management systems, environmental education and training, ISO14001 certification, and ISO9001 certification. The comprehensive evaluation system of corporate water responsibility is shown in

Table 1. Evaluation for corporate water responsibility.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Evaluation
Corporate Water Responsibility	Green Innovation	Water consumption	1 point for disclosing; otherwise, 0
		Wastewater discharge	1 point for disclosing; otherwise, 0
		clean production	1 point for disclosing; otherwise, 0
		Green patent application	2 points for invention, 1 point for utility model; otherwise, 0
	Green Participation	Environmental-protection-specific actions	1 point for disclosing; otherwise, 0
		Environmental incident emergency mechanism	1 point for disclosing; otherwise, 0
		Environmental honors or rewards	1 point for disclosing; otherwise, 0
	Green Management	Environmental management systems	1 point for disclosing; otherwise, 0
		Environmental education and training	1 point for disclosing; otherwise, 0
		ISO14001 certification	1 point for acquisition; otherwise, 0
		ISO9001 certification	1 point for acquisition; otherwise, 0

.

3.3.3. Control Variables

This research selects control variables from the following three dimensions based on previous studies [13,17,18,77]: the environmental circumstances of the enterprise’s location, the statistical characteristics of the organization, and corporate governance. This research selects per capita water resource possession (Pwater) and per capita GDP (Pgdp) as proxy variables to represent local water conditions and the level of regional economic development, respectively; it identifies operational cash flow (Cflow), return on total assets (Roa), firm size (Size), firm age (Age), Tobin’s Q value (Tobin), and asset–liability ratio (Lev) to control enterprise statistical variation; and it utilizes executive compensation (Salary), board size (Board), board independence (Independ), and equity concentration (Central) to alleviate the impacts from the deviation of corporate governance. This study also employs industry-fixed effects (Industry) and year-fixed effects (Year) to account for the impact of industry-specific and temporal variables on the performance of corporate environmental responsibility. All variables and their measurements are detailed in

Table 2. Definition and measurement of variables.

Type	Name	Symbol	Description
Dependent Variable	Corporate Water Responsibility	CWR	Score based on the evaluation system mentioned in Table 2
Explanatory Variable	Public Water Concern	RWrisk	Baidu Search Index
Control Variables	Operational Cash Flow	Cflow	Net cash flow from operating activities in the current period/total assets of the enterprise
	Return on Total Assets	Roa	Net profit after tax for the current period/total assets of the enterprise
	Firm Size	Size	Total assets processed by natural logarithm
	Firm Age	Age	Years of the company’s existence by natural logarithm
	Tobin’s Q value	Tobin	Current period end market value/current period end total assets
	Asset–Liability Ratio	Lev	Total liabilities of the current period/total assets of the current period
	Executive Compensation	Salary	Total amount of compensation for directors, supervisors, and senior management personnel by natural logarithm.
	Board Size	Board	Number of directors on the board of directors
	Board Independence	Independ	Number of independent directors/number of members of the board of directors
	Equity Concentration	Central	The shareholding ratio of the largest shareholder
	Per Capita Water Resource Possession	Pwater	The ratio of total urban water resources to the permanent urban population by natural logarithm
	Per Capita GDP	Pgdp	The ratio of a city’s gross domestic product to its resident population by natural logarithm
	Industry-Fixed Effects	Industry	Industry dummy variable
	Year-Fixed Effects	Year	Year dummy variable

.

3.4. Data Sources

The data on public water concern stemmed from the Baidu search engine. The average annual search index for each city from 2015 to 2024 was produced by utilizing the keywords ‘water resources’ and ‘water pollution’. The data necessary for assessing corporate water responsibility was sourced from the CSMAR Environmental Research Database. The data regarding enterprise operating cash flow, total asset return rate, firm size, firm age, Tobin’s Q value, asset–liability ratio, executive compensation, board size, board independence, and equity concentration were sourced from the CSMAR database. The data on urban per capita water resource possession and per capita GDP came from the Statistical Yearbook. All data sources are shown in

Table 3. Data sources of variables.

Variables	Data Source	Variables	Data Source
Corporate Water Responsibility	CSMAR	Asset–Liability Ratio	CSMAR
Public Water Concern	Baidu Search Index	Executive Compensation	CSMAR
Operational Cash Flow	CSMAR	Board Size	CSMAR
Return on Total Assets	CSMAR	Board Independence	CSMAR
Firm Size	CSMAR	Equity Concentration	CSMAR
Firm Age	CSMAR	Per Capita Water Resource Possession	Statistical Yearbook
Tobin’s Q value	CSMAR	Per Capita GDP	Statistical Yearbook

.

3.5. Empirical Test Model

To examine the impact of public water concern on the performance of corporate water responsibility, this study constructs an empirical model as shown in Equation (1), as follows:

$${\mathrm{C}\mathrm{W}\mathrm{R}}_{\mathrm{i},\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1\mathrm{R}\mathrm{W}\mathrm{r}\mathrm{i}\mathrm{s}{\mathrm{k}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+{\mathrm{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(1)

In this model, CWR represents the performance of corporate water responsibility, RWrisk indicates public water concern, CV is a vector composed of multiple control variables, α₀ represents the intercept of this model, Year is a dummy variable for year-fixed effects, Industry is a dummy variable for industry-fixed effects, ε represents a random disturbance term, the subscript i indicates the enterprise, and the subscript t indicates the time. The estimation method of the model is ordinary least squares regression (also known as OLS regression).

4. Empirical Analysis

4.1. Descriptive Statistics and Correlation Analysis

4.1.1. Descriptive Statistics of Public Water Concern

This study calculated the Baidu Search Index for the period from 2015 to 2024 (a total of 10 years) for 256 prefectural-level administrative regions across China using the keywords ‘water resources’ and ‘water pollution’. Due to the limited space, this subsection only selects the data for 2015, 2019, and 2024 to draw distribution maps of public water concern across China. As shown in Figure 2, overall, in terms of the spatial distribution of public water concern in China, cities along the eastern coast and provincial capital cities in inland areas with relatively developed economies still dominate; in terms of the time trend, public water concern in China has generally shown an increasing trend, indicating that the public’s awareness of the importance of water resources has increased and their attention to the sustainable development of water resources has become increasingly higher.

4.1.2. Descriptive Statistics of Other Variables

The descriptive statistics of other major variables are presented in

Table 4. Descriptive statistical results of other major variables.

Variable	Sample Size	Mean Value	Standard Deviation	Minimum Value	Maximum Value
CWR	8494	2.673544	2.611332	0	12
Salary	8494	5,380,000	5,700,000	90,000	1.10 × 108
Board	8494	8.7340	1.7268	3	18
Independ	8494	0.3722	0.0539	0.1818	0.8
Central	8494	35.1222	14.5812	0.29	89.99
Roa	8494	0.0466	0.0732	−0.8943	0.8795
Cflow	8494	0.6925	0.4945	0.0178	8.6646
Age	8494	18.7680	5.3662	3	44
Size	8494	1.06 × 1010	2.78 × 1010	6.38 × 107	5.67 × 1011
Tobin	8494	2.1178	2.1631	0.9664	86.4983
Lev	8494	0.3957	0.2182	0.0068	5.9282
Pwater	8494	1641.126	1667.827	51.9	17,122
Pgdp	8494	86,392.32	42,240.74	7603	215,488

. It is worth noting that in order to more intuitively present the statistical characteristics of executive compensation, company size, company age, per capita water resource possession, and per capita GDP, this study adopts their original values for presentation in the descriptive statistics. According to the above descriptive statistics results, no significant sample selection bias is found among the main variables. Regarding the characteristics of the sample in this study, they are basically in line with the reality of the securities market and the current spatial distribution status of high-water-consuming enterprises.

4.1.3. Correlation Analysis

Correlation is essential for performing regression analysis. Prior to investigating the association between public water concern and corporate water responsibility in this study, it is essential to assure a strong correlation between the two.

Table 5. Pearson correlation coefficient test.

Variable	CWR	RWrisk	Salary	Board	Independ	Central	Roa	Cflow	Age	Size	Tobin	Lev	Pwater	Pgdp
CWR	1
RWrisk	0.03 ***	1
Salary	−0.34 ***	−0.01	1
Board	0.32 ***	−0.05 ***	−0.15 ***	1
Independ	−0.02 **	0.01	0.03 **	−0.45 ***	1
Central	0.23 ***	0.05 ***	−0.16 ***	0.05 ***	0.03 ***	1
Roa	0.04 ***	0.05 ***	−0.01	−0.01	−0.013	0.10 ***	1
Cflow	0.03 **	0.03 ***	−0.22 ***	0.003	0.02 ***	0.07 ***	0.09 ***	1
Age	0.11 ***	0.05 ***	0.05 ***	0.013	0.03 ***	−0.15 ***	−0.05 ***	−0.007	1
Size	0.29 ***	−0.07 ***	−0.11 ***	0.12 ***	−0.03 ***	0.05 ***	0.04 ***	0.06 ***	0.06 ***	1
Tobin	0.42 ***	−0.06 ***	−0.22 ***	0.21 ***	−0.013	0.04 ***	−0.32 ***	0.01 ***	0.11 ***	0.09 ***	1
Lev	−0.25 ***	−0.01	0.20 ***	−0.09 ***	0.04 ***	−0.09 ***	0.112 ***	0.003	0.07 ***	−0.08 ***	−0.07 ***	1
Pwater	−0.06 ***	−0.29 ***	−0.01 ***	−0.01	0.02 *	−0.04 ***	−0.028 ***	0.014	0.03 ***	0.006	0.017	−0.04 ***	1
Pgdp	−0.026 **	0.481 ***	0.06 ***	−0.15 ***	0.03 **	−0.04 ***	0.10 ***	−0.013	0.16 ***	0.07 ***	−0.20 ***	0.001	−0.40 ***	1

Note: ***, **, and * respectively represent the significance levels of 1%, 5%, and 10%.

displays the Pearson correlation coefficients for all variables. The table indicates that most variables exhibit substantial correlations. The correlation coefficient between public water concern (RWrisk) and corporate water responsibility (CWR) is 0.03, which is significant at the 1% level. This offers initial evidence of a positive correlation between the two, thereby preliminarily validating hypothesis H1.

Correlation analysis can offer empirical evidence on the presence of multicollinearity among variables. The results presented in

Table 6. Results of variance inflation factor calculation.

Variable	VIF	1/VIF
RWrisk	1.34	0.748639
Cflow	1.08	0.927003
Roa	1.22	0.817564
Size	1.65	0.606995
Age	1.12	0.890760
Tobin	1.13	0.888035
Lev	1.54	0.649278
Salary	1.25	0.800627
Board	1.47	0.678554
Independ	1.30	0.771937
Central	1.12	0.893743
Pwater	1.25	0.798470
Pgdp	1.60	0.625145
Mean VI	1.29

reveal that the correlation coefficients among the variables are all below 0.5, suggesting a moderate to low degree of correlation, hence implying the absence of significant multicollinearity issues. This study also employs the variance inflation factor (VIF) to assess multicollinearity among the variables. The results are presented in Table 6. The VIF values for the variables are all substantially below five, signifying the absence of multicollinearity issues.

4.2. Baseline Regression

Prior to executing multiple linear regression, it is essential to identify the suitable regression model. The LM test and Hausman test are presently utilized in research to determine the appropriateness of a fixed effects model vs a random effects model for data. In this work, prior to executing the main regression, STATA 17.0 is employed to conduct the LM test and Hausman test on pertinent data about the influence of public water concern on corporate water resource responsibility from 2015 to 2024. The outcomes of both the LM test and the Hausman test indicate that Prob > Chi2 = 0.00, with a p-value of 0.000, suggesting that a fixed effects model ought to be employed. Thus, this study employs the fixed effects model delineated by Formula 1 and concurrently modifies the original standard error utilizing individual clustering standard error.

This study utilizes the stepwise regression method to analyze the correlation between public water concern and corporate water responsibility. Column (1) in

Table 7. Baseline regression results of public water concern and corporate water responsibility.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
RWrisk	0.036 *** (4.15)	0.046 *** (5.67)	0.0326 *** (6.19)	0.033 *** (6.52)	0.031 *** (5.80)	0.030 *** (5.66)	0.029 *** (6.49)	0.027 *** (7.45)	0.026 *** (7.48)	0.026 *** (7.05)	0.025 *** (7.80)	0.030 *** (7.14)	0.029 *** (6.21)
Salary		−0.982 *** (−47.44)	−0.943 *** (−46.15)	−0.934 *** (−46.44)	−0.908 *** (−45.24)	−0.907 *** (−45.11)	−0.972 *** (−44.77)	−0.928 *** (−43.56)	−0.927 *** (−43.62)	−0.693 *** (−34.25)	−0.547 *** (−28.39)	−0.544 *** (−28.18)	−0.552 *** (−27.99)
Board			0.139 *** (23.27)	0.176 *** (26.60)	0.178 *** (26.97)	0.178 *** (27.01)	0.177 *** (27.13)	0.158 *** (24.89)	0.156 *** (24.50)	0.135 *** (22.92)	0.113 *** (19.76)	0.113 *** (19.77)	0.113 *** (19.75)
Independ				2.494 *** (12.27)	2.438 *** (12.00)	2.465 *** (12.19)	2.471 *** (12.34)	2.343 *** (11.94)	2.287 *** (11.64)	2.244 *** (12.47)	1.882 *** (10.97)	1.884 *** (10.99)	1.858 *** (10.81)
Central					0.006 *** (8.83)	0.006 *** (8.00)	0.006 *** (8.00)	0.005 *** (7.45)	0.006 *** (8.13)	0.004 *** (5.62)	0.004 *** (7.33)	0.004 *** (7.31)	0.004 *** (7.44)
Roa						0.874 *** (5.54)	1.152 *** (7.29)	1.026 *** (6.67)	1.079 *** (6.98)	2.628 *** (17.43)	4.113 *** (25.43)	4.113 *** (25.42)	4.107 *** (25.30)
Lev							−0.310 *** (−10.85)	−0.312 *** (−11.36)	−0.321 *** (−11.70)	−0.193 *** (−7.84)	−0.242 *** (−10.20)	−0.241 *** (−10.15)	−0.243 *** (−10.10)
Size								0.080 *** (21.30)	0.080 *** (21.34)	0.069 *** (19.74)	0.063 *** (18.77)	0.063 *** (18.78)	0.063 *** (18.61)
Age									0.011 *** (5.22)	0.013 *** (6.70)	0.005 *** (2.92)	0.005 *** (2.96)	0.006 *** (3.11)
Tobin										1.870 *** (38.28)	1.771 *** (37.17)	1.771 *** (37.11)	1.766 *** (36.81)
Cflow											1.466 *** (29.12)	1.468 *** (29.16)	1.472 *** (28.93)
Pwater												−0.012 * (−1.74)	−0.012 (−1.62)
Pgdp													0.016 (0.63)
_cons	23.306 *** (227.59)	22.884 *** (275.55)	21.380 *** (194.31)	20.081 *** (134.80)	19.782 *** (129.84)	19.766 *** (130.50)	19.928 *** (131.87)	20.001 *** (138.38)	19.878 *** (136.02)	18.606 *** (136.36)	18.380 *** (139.57)	18.472 *** (130.53)	18.323 *** (58.04)
Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
N	8464	8464	8464	8464	8464	8464	8464	8464	8464	8464	8464	8464	8464
Adj R2	0.12	0.13	0.15	0.15	0.15	0.15	0.15	0.19	0.19	0.19	0.19	0.19	0.20

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values modified by individual clustering standard errors. ***, and * in the table respectively represent significance levels of 1%, and 10%.

examines the effect of public water concern on the performance of corporate water responsibility. The regression coefficient of RWrisk is 0.0364 and is significant at the 1% level. Despite the incremental inclusion of additional control variables, RWrisk continues to exhibit a positive correlation at the 1% significance level, suggesting that public water concern positively influences corporate water responsibility. The regression analysis of the complete sample (column (14) in the table) indicates that a one-unit increase in public water concern correlates with an average increase of 2.93 units in the performance of corporate water responsibility.

4.3. Robustness

In order to ensure the reliability and credibility of empirical findings against potential threats to validity, a series of robustness checks are conducted, including sample changes, variables changes, estimate methods changes, and the application of high-dimensional fixed effect techniques.

4.3.1. Sample Change

Despite the frequent occurrence of the term “high water-consuming industry” in governmental documents, a standardized definition for such an industry is still lacking. In the response to public enquiries, the National Office for Water Conservation indicated that the identification of high-water-consuming industries could be conducted on an individual basis by consulting the high-water-consuming list issued by the National Bureau of Statistics or other relevant ministries, including the Ministry of Water Resources. The National Bureau of Statistics identified the following ten industries with high water consumption: coal mining and washing (B06), black metal smelting and rolling processing (C31), non-metallic mineral mining (B10), power and heat supply (D44), textiles (C17), paper and paper products (C22), nonferrous metal smelting and rolling processing (C32), chemical raw materials and products manufacturing (C26), non-metallic mineral products manufacturing (C30), and petroleum processing, coking, and nuclear fuel processing (C25). This study employs firms from high-water-consuming industries, as provided by the National Bureau of Statistics, as samples for re-estimation to evaluate the robustness of the results.

Table 8. Robustness test results for replacing regression samples.

Variable	Coefficient	Robust Std. Err.	t	p > |t|
RWrisk	0.0376	0.0103	5.57	0.000
Controls	Yes
Fix Effects	Industry/Year
N	4183
Adj R2	0.13
F	31.93

Note: The t-values in the table are the adjusted t-values of individual clustering standard errors.

illustrates that the regression coefficient between public water concern and corporate water responsibility is 0.0376, which remains significant at the 1% level, suggesting the robustness of the initial estimation.

4.3.2. Variable Change

This study initially modifies the measurement approach for the dependent variable by utilizing the Huazheng ESG Index (CWR_2) to assess the performance of corporate water responsibility. This study also changes the measurement of the explanatory variable donated as RWrisk_2.

Table 9. Robustness test results for replacing explanatory and dependent variables.

Variable	CWR_1	CWR_2	CWR_1	CWR_2
RWrisk	0.0293 *** (6.21)		0.0279 *** (5.87)
RWrisk_2		0.4892 *** (5.70)		0.5144 *** (11.40)
Controls	Yes	Yes	Yes	Yes
Fix Effects	Industry/Year	Industry/Year	Industry/Year	Industry/Year
Adj R2	0.20	0.19	0.33	0.34
F	46.04	45.70	89.31	90.76

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values modified by individual clustering standard errors. *** in the table respectively represent significance levels of 1%.

presents the regression estimation outcomes after changing the explanatory and dependent variables. The regression coefficient of CWR_1 is 0.0293 and that of CWR_2 is 0.0279 when utilizing the Baidu Search Index to assess public water concern, with both coefficients achieving significance at the 1% level. Conversely, when employing RWrisk_2, the regression coefficient of CWR_1 is 0.4892 and that of CWR_2 is 0.5144, both significant at the 1% level as well. The significance and regression coefficient indicate that the estimated outcomes of the original model exhibit robustness.

4.3.3. Estimate Method Change

This study demonstrated via the LM test and Hausman test in the baseline regression that a fixed effects model is appropriate; however, three significant issues with the error term—autocorrelation, heteroscedasticity, and cross-sectional correlation—may compromise the robustness of the estimation. Consequently, this study adheres to the methodology of Tuo and Yang [78] and rectifies the issues of heteroscedasticity, serial correlation, and cross-sectional correlation of the standard errors by employing the xtscc technique in STATA.

Table 10. Results of the baseline regression for correcting standard errors, heteroscedasticity, sequence correlation, and cross-sectional correlation issues.

Variable	Coefficient	Drisc/Kraay Std. Err.	t	p > |t|
RWrisk	0.0209	0.0086	2.42	0.039
Controls	Yes
Fix Effect	Industry/Year
maximum lag	2
within R2	0.14
F	319.82

illustrates that, following the rectification of potential issues related to heteroscedasticity, serial correlation, and cross-sectional correlation of the error term, public water concern continues to exert a significant positive effect on the performance of corporate water responsibility at the 10% level. The significance and regression coefficient demonstrate that the original model’s estimation is robust.

4.3.4. High-Dimensional Fixed Effects Model

This study employed industry-fixed effects and year-fixed effects in the baseline regression to account for industry-specific and temporal variables influencing the performance of corporate water responsibility. At the regional level, per capita water resource possession (Pwater) and per capita GDP (Pgdp) were utilized as proxy variables to account for the physical environmental attributes of regional water resources and the level of regional economic development. Nonetheless, prior research indicates that the quantity of these control variables is inadequate, and numerous uncontrolled influencing factors will remain within the random disturbance term. To mitigate the influence of these factors, this study incorporated individual-fixed effects and city-fixed effects for robustness test.

Table 11. Results of high-dimensional fixed effects model for the baseline regression.

Variable	Coefficient	Robust Std. Err.	t	p > |t|
RWrisk	0.0291	0.0110	2.64	0.008
Controls	Yes
Fix Effect	Id/City/Industry/Year
Within R2	0.0065
Adj R2	0.66
F	2.92

demonstrates that after accounting for individual fixed effects, industry-fixed effects, year-fixed effects, and city-fixed effects, public water concern continued to significantly and positively impact corporate water responsibility at the 1% level. The result is reliable.

4.4. Endogeneity Test

Although the baseline model employs several methods to improve the robustness of the results, residual endogeneity risks may persist due to potential reverse causality and omitted variable bias. Specifically, public water concern may correlate with unobserved local characteristics, leading to omitted variable bias. Moreover, reverse causality cannot be fully ruled out, as proactive corporate water behavior may also influence public attention, as highlighted in the study by Zhang, Li & Wang [79]. To address this, this study adopts an instrumental variable (IV) approach to ensure causal identification.

To address endogenous concerns, we employ city-level annual measures of extreme drought days and extreme rainfall days as exogenous instruments for public water concern. The rationale for this is twofold. First, extreme weather events significantly raise public awareness of water-related risks, triggering higher search behavior—thus strongly influencing public water concern. Second, such natural events are exogenous shocks that cannot be anticipated or manipulated by firms, nor do they directly affect their water responsibility performance. (Unless the enterprise suffers damage due to setting up factories in the disaster-stricken area, but such risks can be controlled by considering the fixed effects of the city and industry.) Hence, the instrument satisfies the relevance and exclusion restriction conditions. The city-level data on “average number of drought days” and “average number of extreme rainfall days” from 2015 to 2024 are sourced from the China Meteorological Administration and the Ministry of Emergency Management.

The two-stage least squares (2SLS) estimation implemented by the study is shown in Equations (2) and (3).

Table 12. Results of 2SLS regression.

Panel A: First-Stage Results
Variable	Coefficient
Drought	0.0253 *** (4.71)
Flood	0.0196 *** (3.85)
Controls	Yes
Fixed Effect	Year/Industry/City
F-statistic (instruments)	19.83
Obs.	8464
Panel B: Second-Stage Results
Variable	Coefficient
RWrisk_hat	0.0418 *** (3.56)
Controls	Yes
Fixed Effect	Year/Industry/City
Obs.	8464
R2	0.22

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values with robust standard errors. *** in the table respectively represent significance levels of 1%.

(Panel A) presents the first-stage regression, where RWrisk is regressed on the two instruments with full controls, while Table 12 (Panel B) reports the second-stage results, where predicted RWrisk replaces the original variable.

$${RWrisk}_{\mathrm{i},\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1\mathrm{D}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{g}\mathrm{h}{\mathrm{t}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_2\mathrm{F}\mathrm{l}\mathrm{o}\mathrm{o}{\mathrm{d}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{C}\mathrm{i}\mathrm{t}\mathrm{y}+{\mathrm{\mu }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(2)

$${CWR}_{\mathrm{i},\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1{RWrisk\_hat}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{C}\mathrm{i}\mathrm{t}\mathrm{y}+{\mathrm{\mu }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\eta }}_{\mathrm{i},\mathrm{t}}$$

(3)

Empirical results confirm the validity of the instruments. In the first-stage regression, both instruments are positively and significantly associated with RWrisk, with a joint F-statistic of 19.83, rejecting the weak instrument hypothesis. In the second-stage regression, RWrisk_hat remains a significant positive predictor of CWR (coefficient = 0.0418, p < 0.01), even after controlling for firm-level and macro-level characteristics. Furthermore, the Hansen J-test yields a p-value of 0.421, indicating that the over-identifying restrictions are not violated and the instruments are exogenous.

These findings strengthen the conclusion of baseline regression that public water concern as an informal institutional pressure can significantly drive corporations in high-water-consuming sectors in China towards adopting a higher level of water sustainability practices.

4.5. Channel Analysis

4.5.1. Definition and Measurement of Mediating Variables

The research hypothesis posits that legitimacy pressure and the attention of governmental water governance are significant mediating channels affecting corporate water responsibility driven by public water concern. This investigation must first establish and measure the two variables.

This study follows the research of Bi and Yu [80] and Shen and Zhou [81], and holds that environmental legitimacy pressure is closely related to environmental protection investment. Therefore, the environmental protection investment of enterprises is regarded as a substitute indicator for environmental legitimacy and is denoted as LP.

This study follows the research of Zheng et. al. in using the frequency of water-related words in government work reports of prefecture-level cities to represent the attention of governmental water governance [3], which is denoted as GWrisk. Water-related words include ‘water environment’, ‘water resources’, ‘water pollution’, ‘water safety’, ‘water ecology’, ‘rivers’, ‘lakes’, ‘river (lake) chief system’, ‘water price’, ‘water use’, ‘water consumption’, and ‘water quality’.

4.5.2. Mechanism Test Model

To verify the mediating roles of legitimacy pressure and the attention of governmental water governance, this study, referring to the mediation effect model of Baron and Kenny [82], builds regression Equations (4) and (5) based on the Equation (1) of the baseline regression model.

$${\mathrm{M}}_{\mathrm{i},\mathrm{t}}={\mathrm{\gamma }}_0+{\mathrm{\gamma }}_1{RWrisk}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+{\mathrm{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(4)

$${\mathrm{C}\mathrm{E}\mathrm{R}}_{\mathrm{i},\mathrm{t}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{RWrisk}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_2{M}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+{\mathrm{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(5)

In the aforementioned models, M denotes the mediating variable, which pertains to legitimacy pressure (LP) and the attention of governmental water governance (GWrisk). The interpretations of the other variables are compatible with Equation (1). The mechanism test method proposed by Wen and Ye stipulates that if both β2 and γ1 are significant while α1 is significant, the mediating mechanism of M is confirmed; conversely, if either β2 or γ1 lacks significance under the condition that α1 is significant, a Sobel test or Bootstrap test should be performed to ascertain whether M qualifies as a mediating explanatory mechanism [83].

4.5.3. Results of the Mechanism Test

Table 13. Channel analysis.

Variable	CWR			CWR
	(1)	(2)	(3)	(4)	(5)	(6)
	CWR	LP	CWR	CWR	GWrisk	CWR
RWrisk	0.0293 *** (6.54)	0.0053 *** (4.01)	0.0282 *** (6.23)	0.0303 *** (6.54)	0.0011 (0.82)	0.0299 *** (6.49)
LP			0.8624 *** (3.02)
GWrisk						0.0451 *** (2.72)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes
Constant	18.3228 *** (58.04)	−0.6143 (−0.54)	17.8365 *** (54.68)	18.5587 *** (50.79)	0.0012 * (1.87)	18.5620 *** (50.72)

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values modified by individual clustering standard errors. ***, and * in the table respectively represent significance levels of 1%, and 10%.

presents the findings of the channel analysis. The initial phase of the mediation test aligns with the baseline regression outcome of Equation (1). The outcomes of the second phase of the mediation test are displayed in columns (2) and (5). The coefficient for public water concern regarding legitimacy pressure is 0.0053 and is significantly positive at the 1% level, whereas the coefficient for public water concern related to the attention of governmental water governance is 0.0011 and is not significant. This suggests that public water concern markedly increases legitimacy pressure on high-water-consuming firms, yet does not substantially elevate their regulatory water risk. The third phase of the mediation test is illustrated in columns (3) and (6). The regression coefficient for corporate water responsibility and legitimacy pressure in high-water-consuming enterprises is 0.8624, demonstrating a significant positive correlation at the 1% level. Additionally, the regression coefficient for corporate water responsibility and the attention of governmental water governance is 0.0451, with a significant positive correlation at the 1% level. The results above indicate that heightened public water concern greatly amplifies legitimacy pressure on firms, hence enhancing their performance of water responsibility, with legitimacy pressure serving as a partial mediator.

A further Sobel test is required to ascertain whether the attention of governmental water governance serves as a partial mediator. The Z value is 1.396 and the p value is 0.16, signifying that the mediating effect of the attention of governmental water governance is not statistically significant.

To enhance the precision of the mediation test, this study employs the bias-corrected non-parametric percentile Bootstrap approach to sample 5000 times each. The 95% confidence interval for the bias-adjusted LP is [0.209, 0.344], whereas the 95% confidence interval for the bias-adjusted GWrisk is [−0.034, 0.005]. This signifies that the mediation effect of LP is substantial, whereas that of GWrisk is not substantial.

4.6. Moderating Effects Test

4.6.1. Definition and Measurement of Moderating Variable

The moderating variable is managerial green cognition (GreenRec), which can be further categorized into green economic cognition (OppRec) and green moral cognition (ResRec). This study builds upon the research of Xi and Zhao [25], employing text analysis to examine the annual reports and corporate social responsibility reports of companies. Reports that contain explanations of pertinent indications of a manager’s dual green cognition are assigned a value of one; otherwise, they assume a value of zero. Managerial green economic cognition and green moral cognition are derived by aggregating the scores of their respective indicators, while overall green cognition is the total of both the green economic cognition and green moral cognition. The assessment framework for managerial green cognition is presented in

Table 14. Evaluation of managerial green cognition.

Variable	Dimension	Indicator
Managerial Green Cognitive	Managerial Green Economic Cognition	Enterprise formulates an active environmental strategy.
		Enterprise actively develops environmentally friendly products.
		The performance of environmental responsibility contributes to enhancing the productivity of enterprise.
		The performance of environmental responsibility contributes to enhancing the competitiveness of enterprise.
		The performance of environmental responsibility contributes to enhancing the image of enterprise.
	Managerial Green Moral Cognition	Actively grasps environmental protection policies and their changes.
		Attaches importance to the impact of environmental protection policies on enterprises. Meets compliance requirements.
		Acknowledges the negative externalities of enterprise’ environmental issues.
		Environmental responsibility statement.

Source: Xi and Zhao [25].

.

4.6.2. Moderating Effect Model

The moderating effect model denotes the regression equation established by incorporating the moderating variable and the interaction term between the moderating variable and the explanatory variable into the baseline regression model. In this study, the moderating variable is managerial green cognition (GreenRec) and the explanatory variable is public water concern (RWrisk). Consequently, the moderating impact model developed in this study is presented in Equation (6).

$${CWR}_{i,t}={\alpha }_0+{\alpha }_{1}RWris{k}_{i,t}+{\alpha }_{2}GreenRe{c}_{i,t}+{\alpha }_{3}RWris{k}_{i,t}\times GreenRe{c}_{i,t}+\sum {CV}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$

(6)

Since managerial green cognition (GreenRec) can be further divided into managerial green economic cognition (OppRec) and managerial green moral cognition (ResRec), the moderating model can be further constructed as Equations (7) and (8).

$${\mathrm{C}\mathrm{W}\mathrm{R}}_{\mathrm{i},\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1\mathrm{R}\mathrm{W}\mathrm{r}\mathrm{i}\mathrm{s}{\mathrm{k}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_2\mathrm{O}\mathrm{p}\mathrm{p}\mathrm{R}\mathrm{e}{\mathrm{c}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_3\mathrm{R}\mathrm{w}\mathrm{r}\mathrm{i}\mathrm{s}{\mathrm{k}}_{\mathrm{i},\mathrm{t}}\times \mathrm{O}\mathrm{p}\mathrm{p}\mathrm{R}\mathrm{e}{\mathrm{c}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+{\mathrm{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(7)

$${\mathrm{C}\mathrm{W}\mathrm{R}}_{\mathrm{i},\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1\mathrm{R}\mathrm{W}\mathrm{r}\mathrm{i}\mathrm{s}{\mathrm{k}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_2\mathrm{R}\mathrm{e}\mathrm{s}\mathrm{R}\mathrm{e}{\mathrm{c}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_3\mathrm{R}\mathrm{w}\mathrm{r}\mathrm{i}\mathrm{s}{\mathrm{k}}_{\mathrm{i},\mathrm{t}}\times \mathrm{R}\mathrm{e}\mathrm{s}\mathrm{R}\mathrm{e}{\mathrm{c}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+{\mathrm{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(8)

4.6.3. Results of Moderating Analysis

This section examines the hypothesis regarding the moderating effects of managerial green cognition, as outlined in Equations (6)–(8). The outcomes are presented in

Table 15. The moderating effect of managerial green cognition.

Variable	(1)	(2)	(3)
RWrisk	0.0318 *** (5.87)	0.0153 *** (4.78)	0.0452 *** (6.26)
GreenRec	0.2588 ** (2.48)
RWrisk × GreenRec	0.0015 ** (2.06)
OppRec		0.3151 * (1.82)
RWrisk × OppRec		0.0282 * (1.78)
ResRec			0.2558 *** (2.56)
RWrisk × ResRec			0.0307 ** (2.49)
Controls	YES	YES	YES
Fixed Effects	Industry/Year	Industry/Year	Industry/Year
_cons	17.0672 (46.73)	17.1052 (46.78)	17.0648 (46.78)
Adj R2	0.25	0.11	0.12
F	49.62	42.48	46.26

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values modified by individual clustering standard errors. ***, **, and * in the table respectively represent significance levels of 1%, 5%, and 10%.

. Column (1) displays the regression outcomes of managerial green cognition. The coefficient of RWrisk × GreenRec is 0.015 and is significant at the 5% level, suggesting that managerial green cognition can favorably influence the relationship between public water concern and corporate water responsibility. Hypothesis H2 is validated. Column (2) displays the outcomes of managerial green economic cognition. The coefficient of RWrisk × OppRec is 0.0282 and is significant at the 10% level, suggesting that managerial green economic cognition positively moderates the association between public water concern and corporate water responsibility. The hypothesis H2a is confirmed. Column (3) displays the outcomes of managerial green moral cognition. The coefficient of RWrisk × ResRec is 0.0307 and is significant at the 5% level, suggesting that managerial green moral cognition positively moderates the association between public water concern and corporate water responsibility. Hypothesis H2b is confirmed.

4.7. Heterogeneity

Upper echelon theory posits that top-level managers selectively attend to and interpret strategic scenarios based on their cognitive frameworks, which are shaped by personal experiences and values, ultimately constructing highly individualized ‘real situations’ from the processed information, which, in turn, influences the strategic decisions and performances of their enterprises [84]. By integrating upper echelon theory, we not only examine whether green cognition moderates the effect of institutional pressure on CWR, but also explore how managerial traits shape this cognition. This approach enables us to move beyond a simplistic treatment of top managers as homogenous actors and opens up new analytical space for understanding cognitive heterogeneity in environmental decision making.

The attributes of senior management teams can be categorized into the following two tiers: the first pertains to demographic factors, while the second relates to psychological factors. Due to the accessibility of demographic variables and their efficacy in illustrating disparities in psychological activities [85], most research has concentrated on the demographic traits of top-level managers. This study primarily examines the heterogeneous effects of the age, gender, and international experience of the top management team on the moderating effect of managerial green cognition, in conjunction with the recent management fashion for ‘youthful transformation ‘and ‘gender diversity’ and the prevalent trend of ‘overseas enrichment’ among Chinese entrepreneurs.

4.7.1. Gender Heterogeneity

Psychology reveals intrinsic distinctions between men and women, resulting in divergent behavioral choices for each gender. It is widely asserted that women possess more robust moral standards than men. So, the disparities among corporate ESG engagement, viewed as an ethical choice, can be regarded as an outcome of gender differences among executives (or directors). Ben et al. discovered that a substantial presence of female directors could markedly enhance the ESG engagement of companies [86]. However, corporate ESG is not merely a moral choice, but also an economic imperative. Men tend to prioritize economic advantages and status more than women do. So, the impetus for male involvement in environmental protection markedly differs from that of females, primarily from a pragmatic comprehension and awareness of its advantages. Therefore, gender disparities among executive teams contribute to the variability in the moderating effect of managerial green cognition. This study employs the median as the pivotal value, categorizing samples above the median as having a high proportion of females and those below the median as having a low proportion of females, subsequently conducting regressions on these distinct sub-samples. The precise regression outcomes are presented in

Table 16. Gender heterogeneity in the moderating effects of managerial green cognition.

Variable	(1)		(2)		(3)
	High Proportion	Low Proportion	High Proportion	Low Proportion	High Proportion	Low Proportion
RWrisk	0.0286 *** (3.76)	0.0374 *** (4.77)	0.0208 *** (2.99)	0.0119 *** (4.22)	0.0375 *** (5.03)	0.0572 *** (5.33)
GreenRec	0.2660 (1.52)	0.2495 (1.33)
RWrisk × GreenRec	0.0197 * (1.71)	0.0039 (1.59)
OppRec			0.3379 (0.36)	0.2940 (0.94)
RWrisk × OppRec			0.0174 (0.19)	0.0424 (0.65)
ResRec					0.2373 ** (2.15)	0.2600 * (1.75)
RWrisk × ResRec					0.0110 ** (2.00)	0.0483 * (1.85)
Controls	YES	YES	YES	YES	YES	YES
Fixed Effects	YES	YES	YES	YES	YES	YES
_cons	17.6860 *** (34.17)	16.9622 *** (32.71)	17.7663 *** (34.46)	17.0026 *** (32.80)	17.6717 *** (34.44)	16.9786 *** (32.80)
Chow Test	4.72 ***		5.17 ***		4.44 **
Adj R2	0.22	0.24	0.21	0.22	0.20	0.26
F	27.26	28.23	26.97	26.52	24.92	32.40

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values modified by individual clustering standard errors. ***, **, and * in the table respectively represent significance levels of 1%, 5%, and 10%.

.

Column (1) displays the outcomes of managerial green cognition. In organizations with a substantial percentage of female directors, the regression coefficient of RWrisk × GreenRec is 0.0197 and is statistically significant at the 10% level; in organizations with a minimal percentage of female directors, the regression coefficient of RWrisk × GreenRec is 0.0039. The Chow test indicates considerable disparities in the regression coefficients of the sub-samples, and this study posits that the proportion of female members in the executive team contributes to the heterogeneity of the moderating effect of managerial green cognition.

Column (2) and (3), respectively, demonstrate the outcomes of managerial green economic and ethical cognition in sub-samples with varying numbers of female directors. Column (2) indicates that in organizations with a substantial percentage of female directors, the regression coefficient of RWrisk × OppRec is 0.0174; conversely, in organizations with a minimal percentage of female directors, the regression coefficient of RWrisk × OppRec is 0.0424. Despite the Chow test indicating substantial variations among the regression coefficients of the sub-samples, none of the sub-sample regression coefficients achieve statistical significance. The likely explanation for this is because senior management perceives public water concern as a social moral problem, neglecting its economic implications for high-water-consuming industries.

Column (3) indicates that in organizations with a substantial percentage of female directors, the regression coefficient of RWrisk × ResRec is 0.0110; conversely, in organizations with a minimal percentage of female directors, the regression coefficient of RWrisk × ResRec is 0.0483. The Chow test indicates that the regression coefficients of the sub-samples exhibit significant differences, with the sub-sample regression coefficients passing significance tests at the 5% and 10% levels, respectively. A rise in the proportion of women within an executive team can enhance the moderating effect of managerial green moral cognition on the correlation between public water concern and corporate water responsibility. Column (3) somewhat corroborates the assertion that the moderating effect of managerial green economic cognition does not present gender heterogeneity.

4.7.2. International Experience Heterogeneity

The international study or work experience of senior executives serves as a formative process, allowing top-level managers to assimilate cultural influences from various regions, which, in turn, affects their cognitive frameworks and personal values, thereby shaping their competencies to align with foreign environments and subsequently influencing corporate conduct. Research indicates that the international experience of senior executives can elevate their environmental awareness levels [87]. Chinese senior executives typically select industrialized nations for professional or educational pursuits, and ESG is fundamentally an ‘imported concept’ from these countries. So, international experience is likely to indicate that senior executives possess heightened environmental awareness, thus dedicating limited attention resources to ecological concerns. Cui et al. discovered that international study and work experiences altered environmental cognition and could augment the social responsibility awareness of senior executives, consequently rendering them more predisposed to favor solutions that serve the public interest in corporate decision making [88]. This study posits that the international experience of senior executives can augment the green cognition of the executive team, hence reinforcing the moderating effects of managerial green cognition. This study utilizes the median as the pivotal value, categorizing samples above the median as high-proportion samples, while those below the median are classified as low-proportion samples, followed by regression analysis on the distinct sub-samples. The precise regression outcomes are presented in

Table 17. International experience heterogeneity in the moderating effects of managerial green cognition.

Variable	(1)		(2)		(3)
	High Proportion	Low Proportion	High Proportion	Low Proportion	High Proportion	Low Proportion
RWrisk	0.0752 *** (4.82)	0.0534 *** (3.37)	0.0010 *** (3.52)	0.0326 *** (3.28)	0.0294 *** (5.20)	0.0533 *** (3.90)
GreenRec	0.2509 *** (3.04)	0.2677 (0.35)
RWrisk × GreenRec	0.0239 *** (2.82)	0.0156 (0.04)
OppRec			0.3089 * (1.64)	0.3203 (0.10)
RWrisk × OppRec			0.0437 * (1.88)	0.0037 (0.16)
ResRec					0.2572 *** (3.53)	0.2516 (0.45)
RWrisk × ResRec					0.0428 *** (3.56)	0.0163 (0.25)
Controls	YES	YES	YES	YES	YES	YES
Fixed Effects	YES	YES	YES	YES	YES	YES
_cons	16.9443 *** (36.61)	17.5471 *** (30.35)	17.0429 *** (36.89)	17.5628 *** (30.39)	16.9825 *** (36.90)	17.5313 *** (30.32)
Chow Test	35.58 ***		34.02 ***		36.87 ***
Adj R2	0.24	0.24	0.22	0.23	0.22	0.22
F	27.89	30.64	26.44	29.96	25.37	27.82

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values modified by individual clustering standard errors. ***, and * in the table respectively represent significance levels of 1%, and 10%.

.

Column (1) displays the outcomes of managerial green cognition across sub-samples with varying levels of international experience. In firms with more directors with international experience, the regression coefficient of RWrisk × GreenRec is 0.239 and is statistically significant at the 1% level; conversely, in firms with fewer directors with international experience, the regression coefficient of RWrisk × GreenRec is 0.0156 and does not achieve statistical significance. The findings of the Chow test indicate that the regression coefficients of the sub-samples exhibit considerable disparities. This study posits that the proportion of directors with international experience within an executive team contributes to the heterogeneity of the moderating effect of managerial green cognition.

Column (2) and (3), respectively, indicate the outcomes of the sub-samples with varying levels of international experience concerning managerial green economic cognition and moral cognition. The regression coefficient of RWrisk × OppRec is 0.0437 and that for RWrisk × ResRec is 0.0428 in firms with more directors with international experience. Both coefficients successfully meet the significance criteria at the 10% and 1% levels, respectively. The regression coefficient for RWrisk × OppRec is 0.0037 and that for RWrisk × ResRec is 0.0163 in firms with fewer directors with international experience. Neither coefficient achieves statistical significance. Regarding managerial green economic cognition, the Chow test shows 34.02, with a significance level of 1%. Regarding managerial green moral cognition, the Chow test shows 36.87, with a significance level of 1%. These results indicate that the proportion of directors with international experience within an executive team contributes to the heterogeneity of the moderating effect of both kinds of managerial green cognition.

4.7.3. Age Heterogeneity

Age is considered a significant observable trait that indicates variations in cognitive frameworks, with factors such as experience, motivation, and information processing capacity contributing to these disparities. Research indicates that a higher average executive team age correlates positively with the likelihood of an organization engaging in significant ESG activities [89,90]. Nonetheless, certain studies indicate that older managers may not positively engage in environmental responsibility due to the increased risk aversion associated with age, which can hinder the adoption of environmentally responsible behaviors such as green technological innovation, viewed as a risky investment by enterprises [91,92]. In the context of water issues, the role that age plays remains to be further explored. This study utilizes the median as the pivotal value, categorizing samples with ages exceeding the median as older samples, while those with ages below the median are categorized as younger samples. Regression analysis is subsequently performed on each sub-sample independently. The outcomes are presented in

Table 18. Age heterogeneity in the moderating effects of managerial green cognition.

Variable	(1)		(2)		(3)
	Old	Young	Old	Young	Old	Young
RWrisk	0.0135 *** (5.82)	0.0544 *** (2.81)	0.1042 *** (5.83)	0.0259 * (1.68)	0.0574 *** (5.96)	0.0355 *** (3.96)
GreenRec	0.3181 *** (2.77)	0.2017 (0.18)
RWrisk × GreenRec	0.0260 * (1.89)	0.0136 (0.39)
OppRec			0.1297 (1.34)	0.1726 ** (2.14)
RWrisk × OppRec			0.0277 (1.10)	0.0465 ** (2.28)
ResRec					0.2249 *** (2.85)	0.2979 ** (2.02)
RWrisk × ResRec					0.0492 * (1.76)	0.0083 (1.40)
Controls	YES	YES	YES	YES	YES	YES
Fixed Effects	YES	YES	YES	YES	YES	YES
_cons	16.4051 *** (30.11)	18.2307 *** (37.60)	16.4467 *** (30.19)	18.3402 *** (37.79)	16.4205 *** (30.26)	18.2048 *** (37.88)
Chow Test	16.66 ***		19.47 ***		12.53 ***
Adj R2	0.21	0.25	0.20	0.24	0.21	0.23
F	24.11	32.63	22.98	31.96	23.56	28.03

Note: The table displays the regression coefficients of the explanatory variables. The figures in parenthesis represent the t-values modified by individual clustering standard errors. ***, **, and * in the table respectively represent significance levels of 1%, 5%, and 10%.

.

Column (1) displays the outcomes of managerial green cognition across sub-samples categorized by varying average executive team ages. In organizations with older executive teams, the regression coefficient of RWrisk × GreenRec is 0.0260 and meets the 10% significance level; conversely, in organizations with younger executive teams, the regression coefficient of RWrisk × GreenRec is 0.0136 and does not achieve statistical significance. Considering that the Chow test indicates significant disparities in the regression coefficients of the sub-samples, this study posits that the average age of an executive team contributes to the heterogeneity of the moderating effect of managerial green cognition.

Column (2) and (3), respectively, display the outcomes of the sub-samples characterized by varying average executive team ages in relation to managerial green economic and moral cognition. In enterprises with older executive teams, the regression coefficient of RWrisk × OppRec is 0.0277 and does not achieve significance, whereas the regression coefficient of RWrisk × ResRec is 0.492 and is significant at the 10% level. In enterprises with younger executive teams, the regression coefficient of RWrisk × OppRec is 0.0465 and is significant at the 5% level, while the regression coefficient of RWrisk × ResRec is 0.0083 and does not achieve significance. The Chow test of managerial green economic cognition shows 19.47, with a significance level of 1%. Conversely, the Chow test of managerial green moral cognition shows 12.53, also with a significance level of 1%. This indicates that the moderating effect of managerial green economic cognition is particularly pronounced in enterprises with younger executive teams, whereas the moderating effect of managerial green moral cognition is notably significant in enterprises with older executive teams.

5. Discussion

5.1. Theoretical Discussion

The first critical issue addressed in this study is as follows: Does public water concern influence corporate water responsibility, and if so, how? The findings reveal that public water concern, as an informal institution, effectively encourages firms to perform their water responsibilities. These results not only validate prior research by Chen, Lambooy, and Ortas on the role of external stakeholders in shaping corporate water responsibility [20,27,28], but also illustrate that public water concern acts as a regulatory force within the realm of informal institutions, akin to media scrutiny and international third-party evaluations, as shown in the study conducted by Wicaksono, Setiawan, and Burritt [17,18,19].

Grounded in institutional theory, this study proposes that public water concern influences corporate water responsibility through two mechanisms. First, as an independent institutional entity, it exerts legitimacy pressure on firms. Second, from the perspective of institutional transformation, it shapes governmental water priorities, which, in turn, affect corporate behavior. However, our empirical results show that currently, public water concern influences corporate water responsibility primarily through the legitimacy pathway alone, without triggering formal institutional reform. This finding contrasts with studies such as Tao et al., Wu et al., and Zhou et al. [93,94,95], which argued that public environmental concern can catalyze both regulatory and market-based institutional responses.

We suggest several explanations for this divergence. First, the scale and maturity of environmental issue domains significantly impact the ability of informal institutional pressures—like public concern—to effect meaningful change. Bowen et al. revealed that smaller-scale environmental challenges (e.g., water pollution in a single region) typically see less effective collective action than global-scale issues such as climate change, which benefit from established international networks and verification systems [96]. Climate concerns catalyze clear regulatory and corporate responses due to well-developed global frameworks (e.g., UN Sustainable Development Goals and carbon trading schemes) and strong civil society mobilization. By contrast, water governance lacks equivalent global momentum: water issues are locally bounded, awareness is fragmented, and accountability mechanisms like third-party certification remain limited—especially in emerging economies. This diffused nature means that while public concern over water may trigger corporate attention, it less frequently scales up to systemic institutional reform.

Second, the type and source of public concern critically determine its effectiveness in spurring governance responses. Li demonstrates that institutional change is more likely when public pressure originates from organized stakeholders—such as NGOs, formal petitions, and legislative bodies—because these channels engage directly with policymaking institutions; in contrast, online public opinion, while capable of influencing budget allocations (e.g., emergency funding), tends to be diffuse, episodic, and lacks institutional leverage [97]. Pacetti et al. also found that regarding the water resources issue, public participation is usually limited to providing information or collecting public opinions through surveys and public hearings [98]. These methods may not allow for in-depth interaction and the joint formulation of solutions, which, to some extent, limits the possibility of converting public opinions into government regulatory measures. In China’s context, although public water concern may surge during crises (e.g., industrial discharge incidents), it rarely sustains the organizational structure needed to pressure for formal policy changes. As a result, government responses are often reactive and compartmentalized, focusing on short-term fixes without addressing underlying regulatory fragmentation.

A third explanation may pertain to the characteristics of China’s water resource governance system. China’s environmental governance exhibits the following two prominent structural features: a top-down centralized authority and institutional fragmentation. The centralized governance approach inherently marginalizes the public, who often bear the direct consequences of environmental degradation, by limiting their participatory roles in decision-making processes [99]. Consequently, the extent and effectiveness of public involvement in environmental governance are predominantly determined by local governmental attitudes. In numerous instances, public environmental concerns are relegated to minor institutional adjustments or are incorporated only in the final stages of policy formulation, frequently resulting in outcomes that do not align with public interests [100]. Institutional fragmentation refers to the lack of effective coordination and integration among various governmental institutions, departments, and administrative levels during environmental policy formulation and implementation, leading to conflicting policy objectives, the inefficient allocation of resources, and inadequate policy enforcement [101]. This fragmented governance framework significantly diminishes governmental capacity to respond effectively to public concerns, which emerges as a critical factor in transforming public environmental concerns into substantive political actions [102]. Within the context of China’s water resource governance, the existing regulatory framework is fragmented due to overlapping departmental responsibilities, disparities in regional water management policies, and divergent local interests [40]. This institutional complexity substantially constrains the efficacy of public pressure in facilitating meaningful governance reform.

Moreover, the limited institutional response to public concern may reflect a broader phenomenon of policy inertia—where formal institutions exhibit structural resistance to change despite increasing societal pressure. In the context of water resource governance in China, such inertia manifests through bureaucratic rigidity, the absence of adaptive feedback mechanisms, and entrenched administrative interests. These factors collectively hinder timely and transformative regulatory adjustments [103,104]. This inertia, compounded by a fragmented institutional landscape, contributes to an “institutional rupture”, in which public concern fails to be effectively translated into formal institutional responses. Although the government faces growing legitimacy pressure from the public, mismatched policy incentives, blurred accountability, and asymmetric information often lead to a preference for superficial governance strategies—such as short-term pilot projects—as a means of mitigation. This situation echoes the scenario described by Helmke and Levitsky [41], in which informal institutions substitute for the failure of formal ones. However, in China’s water governance system, such informal substitutes remain underdeveloped and are frequently neutralized by persistent policy inertia.

The second key question concerns the moderating role of managerial green cognition. Our results confirm that managerial green cognition positively moderates the relationship between public water concern and corporate water responsibility. Recent empirical research corroborates this interplay. For example, a new study using Chinese A-share data found that management’s green cognition significantly enhances firms’ environmental performance, especially when supported by strong corporate governance and green innovation inputs [105]. Similarly, Tang et al. showed that managerial environmental concern strengthens the positive effect of green process innovation on firm performance [106], suggesting that it is this cognitive lens—rather than innovation alone—that translates public or regulatory signals into organizational value and green effectiveness.

Although scholars have acknowledged the importance of managers’ green cognition, they hold divergent views regarding its composition. Some researchers advocate examining corporate environmental behavior from an ethical perspective, viewing it as a moral obligation above and beyond legal and economic responsibilities, even when these choices do not yield immediate economic benefits [107,108,109]. According to this perspective, managerial green cognition essentially embodies a moral cognition. While studies have demonstrated that such moral cognition can indeed enhance corporate environmental responsibility, this ethical influence is frequently overshadowed by material interests. Research by Ding et al. indicated that managers prioritizing short-term material gains may even harm corporate environmental responsibility performance [110]. Conversely, the strategic corporate social responsibility perspective posits that firms engage in environmental responsibility because it is profitable [111]. From this viewpoint, managerial green cognition essentially represents economic cognition. Although economic cognition may counteract short-termism in decision making related to corporate environmental responsibility and improve overall performance, this positive influence may be limited. Gu et al. suggested that economic cognition is merely a necessary condition to prevent low-level corporate water responsibility performance, whereas achieving higher levels of responsibility performance necessitates managerial moral cognition [24]. Furthermore, if managerial green cognition remains solely economic in nature, it may drive corporations toward opportunistic behaviors. Wu’s study highlights that firms may resort to greenwashing practices to rapidly secure financial resources [112].

Despite the diversity of perspectives within academia, the positive moderator role of managerial green cognition highlights a nuanced cognitive mechanism: managers with heightened environmental awareness actively interpret public water concern as both a moral imperative and a strategic opportunity, indicating that corporate managers seem to concurrently adopt both views and integrate them into their overall green cognitive framework. Analyzing managerial green cognition from a singular perspective may fail to comprehensively depict its complexity. Future research should, therefore, consider the dual nature of managerial green cognition.

According to upper echelon theory, this study examined whether the moderating influence of managerial green cognition varied based on the following three executive characteristics: gender, age, and international experience. First, the results indicated that the moderating influence of managerial green cognition was more pronounced among firms with a higher proportion of female directors. Specifically, the moderating effect of managerial green economic cognition showed no significant gender differences, whereas the moderating effect of managerial green moral cognition was notably stronger in firms with more female directors. This finding suggests distinct underlying motivations behind economic and moral cognition. Currently, water prices inadequately reflect the true economic value of water scarcity, making the direct economic impact of water consumption relatively minor within overall corporate expenses. Consequently, the economic institutional pressure stemming from public water concern remains weak. However, since water is inherently linked to public welfare, public concern for water resources holds significant social and moral implications for companies. Thus, the role of green moral cognition appears to be more influential than green economic cognition in driving managers’ overall responses.

Second, regarding international experience, the moderating effect of managerial green cognition was primarily evident in firms whose executive teams possessed extensive international experience. Both managerial green economic and green moral cognition exhibited stronger moderating effects in firms with executives who had significant overseas backgrounds. This result aligns with the assumption that international experience acts as an “imprinting process”, enhancing executives’ environmental awareness and sensitivity towards socially responsible actions. Furthermore, this finding implies that China’s informal institutional frameworks for water governance are currently weaker compared to those abroad. High-income countries, especially in Europe and North America, have mature third-party certification mechanisms for water management and financial institutions that actively incorporate water stewardship into investment evaluations. Executives with overseas experience are, thus, more attuned to these institutional norms, better able to interpret their significance, and, consequently, more likely to proactively adopt water responsibility practices to meet informal institutional expectations. Additionally, public participation in water governance overseas tends to be stronger, driven by differences in governmental capacities and cultural attitudes toward human rights and public resource governance. As many overseas regions historically experienced deficiencies in public infrastructure and municipal management, stronger public activism and social norms emerged to protect water rights. Thus, executives with international exposure exhibit heightened vigilance and responsiveness to public demands regarding water resources.

Third, the analysis revealed significant age-related heterogeneity in the moderating effects of managerial green cognition. Specifically, managerial green economic cognition predominantly moderated the relationship in firms with younger executive teams, whereas managerial green moral cognition showed stronger moderating effects in firms with older executive teams. This finding aligns with age diversity assumptions, which argue that younger individuals typically prioritize economic outcomes and market opportunities, while older individuals exhibit stronger moral convictions and social orientation. However, these age-related heterogeneity results require further discussion. Although green economic cognition showed age variability, there was no notable gender variability observed concerning economic cognition in relation to public water concerns. This study previously attributed the absence of gender differences in green economic cognition to the currently weak economic impact of public water concern. However, younger executives appeared to demonstrate a greater appreciation of the market opportunities associated with water resource scarcity than their older counterparts. Recent developments, such as the emerging third-party certification systems for water stewardship and the integration of sustainability into investment decisions, may resonate more readily with younger executives due to their openness to innovation. Thus, despite the currently limited institutional pressure from public water concern, younger managers recognize its future economic potential and are more willing to proactively engage in responsible water management practices to address anticipated stakeholder expectations.

In conclusion, this study provides several distinct contributions to the literature. First, it expands the institutional theory literature by highlighting the role of issue-specific legitimacy mechanisms, thereby moving beyond general CER constructs. Specifically, this study investigated the limitations of the influence of public water concern in catalyzing institutional transformation in fragmented water governance contexts, challenging the conventional assumption in institutional theory that informal legitimacy pressures can consistently translate into formal institutional change. Our findings suggest that in fragmented and inert policy environments, such as China’s water governance system, informal signals are often absorbed or neutralized, calling for a reconceptualization of institutional responsiveness in weak regulatory contexts. Second, the dual-pathway interpretation of managerial green cognition—as both a moral and economic driver—challenges the often-dichotomized view of ethics versus strategy in sustainability management. By showing that both perspectives can coexist and jointly shape firm behavior—an area previously underexplored in environmental governance studies—it may contribute to future theory development around the cognitive integration of multiple institutional logics. Third, the cognitive black box of the upper echelon theory remains an unsolvable academic issue at present. Although some research methods in cognitive neuroscience can provide direct evidence for the cognitive black box, interdisciplinary research results, such as neuro-management, are still in their infancy, and it is currently difficult to conduct large-scale empirical investigations. Conducting an analysis of the heterogeneity of the moderating effect and correlating the results with the existing literature can be seen as an attempt to provide indirect empirical evidence for the cognitive black box of the upper echelon theory. Meanwhile, these findings also extend upper echelon theory by showing that executive cognition is not only shaped by individual demographics, but also interacts with the institutional context in which a firm operates, suggesting that demographic heterogeneity may not fully account for executive behavior unless contextualized within industry and regulatory settings.

5.2. Practical Implications

The findings of this study yield several practical implications for both policymakers and corporate actors engaged in sustainable water governance.

For governments, the identified gap between public water concern and formal water policy focus suggests a need to strengthen responsiveness to societal demands. Since public water concern has been shown to positively influence corporate water responsibility, governments could enhance its impact by cultivating greater environmental awareness across the public sphere. This may involve integrating water-related education into broader sustainability campaigns, supporting community-led water stewardship programs, and promoting visible examples of successful water conservation initiatives to inspire wider societal participation.

Furthermore, this study reveals a critical gap: while public concern over water issues exerts direct legitimacy pressure on enterprises, it does not currently translate into enhanced governmental regulatory action. This finding suggests a missing link in the institutional transmission mechanism—whereby public voice, though present, is not yet systematically integrated into formal water governance. Policymakers should address this disconnect by strengthening institutional channels that convert societal concern into regulatory and administrative priorities. This may involve creating more robust participatory frameworks, such as enhancing transparency and responsiveness in water governance through open data initiatives, improved public access to water management plans, and clearer accountability structures for regulatory action in response to public input. Strengthening these pathways would enable public concern to not only pressure firms informally, but also trigger policy signals that formalize expectations and raise the regulatory bar for corporate water responsibility.

For enterprises, the study findings show that public water concern positively influences their adherence to water resource responsibilities. To strengthen the regulatory impact of this informal institutional mechanism, enterprises must proactively address the institutional demands stemming from public concern and integrate water resource considerations into their production functions and strategic planning. Enterprise managers must swiftly monitor shifts in public sentiment regarding water resources through public opinion surveys and other means. They should also initiate community engagement efforts—such as forums, open days, and environmental outreach—to build mutual understanding and enhance transparency in their water management practices.

Furthermore, managers should focus on improving their green cognitive levels by acquiring knowledge of ESG principles and sustainable development, thereby deepening their comprehension of the green economy. Educational initiatives and participation in public awareness campaigns related to water conservation and pollution reduction can also enhance their green moral cognition. The study also highlights that the moderating effect of managerial green cognition varies based on age, gender, and international experience. Therefore, organizations should optimize the composition of their senior management teams to better fulfill environmental responsibilities. For example, they should balance age diversity to integrate both innovation and experience, promote gender diversity through inclusive recruitment and leadership development, and prioritize international experience by hiring talent with global environmental exposure or partnering with international environmental organizations.

6. Conclusions and Future Directions

6.1. Conclusions

This study confirms that public water concern—functioning as an informal institution—positively affects corporate water responsibility via legitimacy pressure, but it does not find evidence that such public concern influences firms through government water governance mechanisms. This absence of transmission reflects a structural disconnect between public expectations and formal institutional response. As discussed earlier, this gap may be attributed to several factors, as follows: the locally bounded and fragmented nature of water issues, the unstructured forms of public concern (e.g., online opinions), and the policy inertia and institutional fragmentation prevalent in China’s current water governance system. Together, these findings suggest that while public concern can shape corporate behavior directly, it does not yet catalyze regulatory transformation, underscoring the need for institutional innovation to better integrate informal and formal governance forces.

This study also finds that the green cognitive structure embedded in the minds of managers strengthens the positive correlation between public water concern and corporate water responsibility. This indicates that managers with green cognition can, on the one hand, pay more attention to changes in public concern over water resources, water environment, and water ecology and fully understand the impact of these changes on enterprises and society; on the other hand, it also indicates that managers have a stronger willingness to undertake long-term measures to deal with this risk rather than avoiding their responsibilities by diversifying non-related businesses or undergoing geographical diversification.

After heterogeneity analysis, the research findings demonstrate that the age, gender, and international experience of the top management team significantly influence the moderating effect of managerial green cognition. Older executive teams with greater gender diversity exhibit enhanced moral intent to underscore the moderating influence of managerial green cognition. Conversely, younger executive teams have a greater profit-driven orientation, highlighting the focus on the instrumental advantages of performing water responsibility. Senior managers with extensive international experience are more adept at recognizing the significance of water resources, the water environment, and water ecology to the public, and possess a profound understanding of the ethical and economic dimensions of public water concern.

Theoretically, these findings provide important theoretical implications that bridge institutional theory and upper echelons theory. From an institutional perspective, the analysis demonstrates that informal pressures—such as public environmental concern—do not uniformly translate into organizational action, but are instead filtered through cognitive mechanisms embedded in a firm’s leadership. From the upper echelon perspective, the study moves beyond a static view of executive demographics by showing how individual traits (e.g., age, gender, and international exposure) dynamically shape the interpretation and salience of institutional signals. This interaction highlights the conditional nature of managerial cognition, suggesting that institutional pressures cannot be fully understood without examining who interprets them and under what circumstances. In this way, the study advances a more integrated framework in which informal institutions, managerial cognition, and executive characteristics co-evolve to shape corporate environmental behavior.

Practically, these findings offer a coherent framework for advancing policy design and corporate governance in the field of sustainable water management. From a public governance perspective, the study demonstrates that public water concern functions as a latent regulatory force—yet its effectiveness remains constrained by institutional transmission failures. This suggests that governments must move beyond symbolic engagement by structurally embedding public input into regulatory processes. Enhancing the conversion of informal societal concern into formal policy action requires the establishment of robust participatory mechanisms, increased transparency, and alignment between administrative priorities and public environmental expectations. From a corporate governance perspective, the study highlights the central role of managerial green cognition in interpreting and acting upon public environmental signals. Importantly, it shows that this cognitive responsiveness is not uniformly distributed, but varies systematically based on executive demographic traits such as age, gender, and international experience. These insights point to the strategic relevance of top management team composition in enabling firms to effectively translate informal pressures into meaningful environmental actions. Firms should, therefore, view executive diversity not merely as a social imperative, but as a capacity-enhancing governance lever for sustainability transformation.

Together, these findings call for an integrated institutional approach—one that connects societal awareness, cognitive processing within firms, and adaptive policy frameworks—to close the gap between public environmental demand and institutional response. In doing so, they offer actionable pathways to enhance the legitimacy, responsiveness, and effectiveness of both state-led and enterprise-led water governance.

6.2. Future Directions

Future research should address several limitations and extend this study’s theoretical contributions. First, to enhance the generalizability and robustness of the findings, future research should broaden its empirical base beyond publicly listed industrial firms. Although this study focuses on high-water-consuming listed enterprises in manufacturing sectors, the results may not fully capture the dynamics in other industries or among non-listed firms, particularly in high-water-consuming service sectors such as logistics, hospitality, and healthcare. These sectors are currently underrepresented due to limitations in the CSRC’s industry classification framework. To address this gap, future studies could draw upon comprehensive data sources such as the China Industrial Enterprise Database (CIED), which covers a wide range of registered firms with diverse ownership structures and regional distributions. In addition, structured questionnaire surveys targeting water-intensive enterprises—both industrial- and service-oriented—can provide supplementary insights into firm-level behavioral mechanisms and managerial cognition that are not available in financial or ESG disclosures. These approaches would enable cross-sectoral comparisons, exploring whether firms with differing resource dependencies, stakeholder pressures, and governance regimes exhibit similar responses to public water concern or managerial cognition.

Second, informed by Ostrom’s socio-ecological systems (SESs) theory, future research should delve deeper into the complex interactions among institutional elements within the broader “nature–society” system. Although this study did not find a transmission effect from public water concern to governmental regulation, this does not preclude other forms of institutional interdependence. Factors such as natural water endowment, regional water stress, supply chain pressures, and third-party certification mechanisms may interact with informal institutional forces in dynamic, non-linear ways. Future studies should explicitly analyze how these institutional subsystems co-evolve and mutually condition corporate water responsibility. To capture these complex relationships, researchers could apply multi-level modeling, moderated mediation analysis, or configurational methods (e.g., fuzzy-set Qualitative Comparative Analysis, fsQCA) to reveal the contingent and path-dependent nature of institutional influence. Such an inquiry would deepen our understanding of how informal institutions, cognitive processes, and regulatory environments form interlocking systems that shape corporate environmental behavior in diverse regional and sectoral contexts.

Third, while this study introduced a dual-path framework of managerial green cognition—distinguishing between moral and economic motivations—future research should explore more nuanced cognitive structures within top management teams. Drawing from cognitive network theory, scholars may investigate how the configuration, centrality, and complexity of environmental cognition influence decision making under institutional pressure. For example, which types of cognitive schemas (e.g., rule-based, value-oriented, or hybrid) are more likely to translate stakeholder expectations into meaningful environmental strategies? Moreover, while this study focused on demographic heterogeneity (e.g., age, gender, and international experience), future work could examine industry-specific characteristics and regulatory intensity as contextual moderators. In sectors facing stricter compliance requirements—such as chemicals or mining—the influence of managerial cognition may be constrained by codified rules and penalties, whereas in lightly regulated sectors (e.g., consumer goods), green cognition may play a more autonomous and interpretive role. These contextual variations merit further investigation through nested designs that integrate firm-level cognitive structures with sector-level institutional environments.

Fourthly, while this study adopts a multi-dimensional scoring framework to evaluate corporate water responsibility (CWR), it does not incorporate formal multi-criteria decision analysis (MCDA) techniques. The three-dimensional CWR structure—covering green innovation, participation, and management—provides a descriptive and explanatory tool, yet lacks a formal mechanism for benchmarking or ranking firm performance. Future studies may benefit from incorporating MCDA models such as TOPSIS, AHP, or the weighted sum model to enhance comparability across firms and regions. Moreover, as sustainability assessments increasingly encounter ambiguity and subjectivity, advanced fuzzy-based MCDA methods, such as the Fuzzy Normalization-based Multi-Attributive Border Approximation Area Comparison (FN-MABAC), may provide an effective framework for dealing with uncertainty. Although the present study relies on objective crisp data, future researchers may explore methods for transforming such data into fuzzy formats or adopt crisp-based adaptations of classical MABAC to ensure methodological consistency. Integrating MCDA methods would enrich the robustness and policy applicability of corporate sustainability assessments.