Interlocking Director Network and Sustainable Information Disclosure: Evidence from Climate Risk Reporting in China

Abstract

Corporate climate risk disclosure has become increasingly important globally in combating climate change and achieving sustainable development goals. This study examines the impact of interlocking director networks on corporate climate risk disclosure in emerging markets. Using a sample of Chinese listed companies from 2009 to 2023, we measure interlocking director networks in terms of network breadth and brokerage position. Our results demonstrate that interlocking director networks have a positive and significant impact on corporate climate risk disclosure, and these findings remain robust across multiple specifications. Mechanistically, interlocking director networks promote climate risk disclosure through information transparency and reputational capital. Furthermore, environmental regulation and media attention positively moderate this relationship. We also find important heterogeneity: the positive impact is more pronounced in state-owned enterprises and regions with high physical climate risk exposure. This study provides empirical evidence on the role of director networks in corporate climate risk disclosure and offers practical insights for firms, investors, and regulators in emerging markets to improve disclosure practices and promote sustainable development goals.

1. Introduction

The threat of climate change to sustainable development is becoming more urgent. The IPCC AR6 indicates a 1.1 °C increase in global surface temperatures, predicting a more than 50% chance of warming hitting or surpassing 1.5 °C from 2021 to 2040 [1]. This escalating crisis has significantly changed stakeholders’ expectations regarding corporate environmental responsibility. Investors increasingly demand accurate climate risk data to assess long-term corporate value [2], while regulators enhance transparency on climate risks through strengthened disclosure requirements, thereby directing capital toward sustainable projects [3]. However, climate risk disclosure practices remain significantly inadequate, particularly in emerging economies. In 2023, only 10.96% of China’s top 500 listed companies disclosed climate-related information. Although global initiatives such as the TCFD and ISSB have introduced sustainability reporting frameworks, their voluntary nature limits their ability to promote meaningful corporate transparency on climate issues. Although global initiatives such as the TCFD and ISSB have introduced sustainability reporting frameworks, their largely voluntary nature means they fall short of driving meaningful corporate transparency on climate issues. Anticipating potential adverse market reactions, corporate management frequently adopts selective or minimal disclosure strategies [4]. In this context, a systematic examination of the factors driving climate risk disclosure is essential for strengthening regulatory frameworks and improving the quality of corporate disclosures.

Previous studies have primarily focused on the economic consequences of climate risk disclosure, including stock price synchronicity, market value, corporate green innovation, and carbon reduction effects [5,6,7,8]. While scholarly attention to the determinants of climate risk disclosure has grown substantially in recent years, existing research has predominantly concentrated on formal institutional frameworks and organizational capacity variables, including governmental regulations, board composition, external monitoring mechanisms, and technological capabilities [9,10,11,12,13,14]. Research examining the informal institutional dimension remains limited, with only a few scholars investigating the influence of Confucian culture [15]. During transitional periods characterized by underdeveloped regulatory frameworks and voluntary disclosure practices, the constraining power of formal institutions is relatively weak, potentially rendering informal institutions more influential. This is especially relevant in China, a relationship-oriented society, where corporate social networks (CSNs) formed through director interlocks and business interactions play a crucial role as informal institutional arrangements [16,17,18]. Among these, interlocking director networks, a key form of CSNs, may play a significant role, but existing research has not fully explored this phenomenon.

An interlocking directorate network arises when individuals hold board positions in multiple listed firms simultaneously, creating inter-organizational links. This governance structure is common in China’s capital markets. According to social network theory, these connections serve as channels for information exchange, facilitating knowledge diffusion and behavioral imitation among network members [19,20]. Climate risk disclosure is highly specialized and marked by significant uncertainty. Companies must identify risk exposures, understand evolving disclosure standards, and anticipate policy changes. These tasks require specialized expertise and analytical capabilities that many firms lack. Without adequate technical tools, in-house expertise, and cross-functional integration, companies struggle to meet disclosure requirements effectively. Given these challenges, interlocking director networks may serve as a valuable mechanism for overcoming information asymmetries and capability gaps in climate risk disclosure. However, empirical evidence on this relationship remains limited. this study seeks to shed light on the following inquiries: (1) whether interlocking director networks enhance corporate climate risk disclosure levels; (2) through what channels these networks influence disclosure practices; and (3) whether their impact varies across different corporate structures and regional contexts.

To address the above questions, this study develops an interlocking director network metric using Chinese listed company data from 2009 to 2023 to evaluate its impact on climate risk disclosure. Focusing on Chinese listed companies offers several advantages. First, China faces severe climate risk challenges. Its geographical characteristics, particularly the influence of monsoons and diverse topography, contribute to persistent weather-related hazards such as flooding and water scarcity, which pose significant challenges to economic growth and business operations. As a result, there is an urgent need for Chinese enterprises to identify and disclose climate risks. Second, China’s climate risk disclosure is undergoing a critical phase of policy-driven evolution and practical advancement. Following the emphasis on ecological development at the 20th CPC National Congress, China’s securities regulators have gradually introduced ESG reporting frameworks. However, corporate disclosure practices remain inconsistent. The regulatory landscape features guidelines rather than strict mandates, allowing firms considerable discretion in climate-related disclosures. This creates an ideal setting to examine how informal governance structures, such as interlocking directorates, influence disclosure behavior. Third, compared to mature Western markets, China’s formal corporate governance framework is still evolving, with informal mechanisms, such as interlocking director networks, playing a more prominent supplementary role. Additionally, Chinese listed companies exhibit significant heterogeneity in their director network characteristics, offering an ideal empirical context for examining how different network attributes influence disclosure practices.

This study makes three contributions. First, it examines new drivers of climate risk disclosure. Prior research focused on regulatory pressure [21], media attention [22], institutional investors [23], board diversity [24], and sustainability committees [25]. We examine interlocking director networks from a social structural perspective and identify two mechanisms: information transparency and reputational capital that promote climate risk disclosure. Second, we extend research on director networks and governance. Existing studies examined how director networks affect innovation, M&A, risk-taking, and performance [26,27,28,29,30], but few examine their impact on environmental disclosure, especially climate risk reporting. We address this gap and show how director networks influence sustainability governance. Third, we show that director networks’ effects vary by context. Their impact is stronger in state-owned firms and varies with firms’ physical climate risk exposure. Environmental regulations and media pressure strengthen these effects. These findings reveal how institutional and environmental contexts shape climate disclosure practices.

2. Literature Review

2.1. Interlocking Director Network and Corporate Decision-Making

Based on Granovetter’s (1973) social embeddedness theory, listed companies, as socialized economic entities, are inevitably embedded within diverse social relationship networks [19]. Interlocking director networks, as a typical informal institutional arrangement, form connections through directors holding concurrent positions across multiple firms [31], emerging as a significant research topic in corporate governance. Existing research shows that interlocking director networks influence corporate decision-making through two main mechanisms: information transmission and resource provision. In terms of information transmission, board interlocks serve as channels for knowledge diffusion, enabling firms to observe and adopt successful practices from their network partners. Haunschild (1993) highlighted that these networks facilitate the cost-effective dissemination of acquisition strategies [32], while Davis (1991) documented how governance innovations, such as poison pills, spread through mimetic processes [33]. Beckman and Haunschild (2002) further found that experience diversity among interlocking partners enhances organizational learning quality [34]. Building on these insights, recent studies show that such information transmission mechanisms drive organizational homogenization across various domains, including green innovation, digital transformation, and ESG reporting practices [35,36,37,38,39]. Regarding resource provision, interlocking directors act as resource brokers who facilitate the transfer of both tangible and intangible assets between organizations, including specialized expertise, financing access, political connections, and institutional legitimacy. Empirical research has confirmed that interlocking directors can enhance innovation performance through knowledge integration [40] and improve investment efficiency through more effective capital allocation [41].

Beyond strategic and financial domains, interlocking director networks have emerged as key mechanisms in corporate environmental governance. Evidence shows that such interlocks enhance environmental performance, particularly when connected to larger firms or with diverse ties [42], and are significantly associated with lower greenhouse gas emissions [43]. Recent studies have further explored how network centrality influences specific environmental outcomes. Li et al. (2024) found that board network centrality gives Chinese heavy-polluting companies an advantage in environmental information disclosure [44]. Similarly, Feng et al. (2024) revealed that firms in central network positions exhibit superior ESG performance, attributed to stronger internal controls [45]. Collectively, these studies underscore the governance value of board interlocks in corporate environmental management. However, whether and how these networks influence climate risk disclosure remains an open question.

2.2. Climate Risk Disclosure

As global climate change accelerates, corporations face increasing threats from climate-related risks. The first involves tangible physical impacts, such as extreme weather that disrupts supply chains, damages facilities, and threatens operational continuity. The second relates to transition pressures, including regulatory changes, technological shifts, and evolving market preferences, all of which require firms to strategically adapt as the transition to a low-carbon economy progresses. Both types of risks present systemic challenges to corporate financial stability, operational continuity, and long-term value creation [46]. In response, global climate governance frameworks, including the Kyoto Protocol, UNFCCC and the Paris Agreement, have encouraged the widespread adoption of climate disclosure regulations to enhance transparency in corporate climate risk management. However, existing regulations often provide management with considerable discretion, resulting in substantial variations in the content, quality, and comprehensiveness of disclosures across firms [47]. This variability has generated significant academic interest in identifying the key factors that drive these differences in disclosure practices.

Previous research on the determinants of climate risk disclosure has largely focused on corporate governance, particularly board characteristics and composition. Scholars generally agree that board composition and operational methods, as the core mechanisms of corporate governance, directly influence corporate disclosure strategies [48]. Research indicates a positive correlation between board size and climate risk disclosure, as expanded board membership typically brings greater collective expertise and organizational capacity to navigate intricate climate-related challenges [49]. The inclusion of female board members similarly contributes to enhanced environmental reporting practices, a pattern often explained by women directors’ greater responsiveness to stakeholder concerns [50]. Furthermore, the establishment of environmental committees [51] and stronger climate governance capabilities of boards [52] have been found to promote both the breadth and depth of corporate climate risk disclosure. In addition to board characteristics, other factors, such as institutional investor field visits, CEO compensation, and corporate governance quality, also significantly influence climate risk disclosure [10,11,53]. While these studies offer valuable insights into corporate climate risk disclosure, they primarily focus on internal board characteristics and governance mechanisms, with little attention given to the role of external networks, such as interlocking director networks.

2.3. Research Gap

Prior research has examined the influence of director networks on corporate environmental performance and ESG disclosure, these studies predominantly focus on general environmental information disclosure and have paid insufficient attention to climate risk disclosure, a highly specialized and technically complex disclosure task. Climate risk disclosure requires not only identifying physical and transition risks but also understanding the technical requirements of international frameworks such as the TCFD, characteristics that distinguish it from conventional environmental disclosures. Under voluntary reporting regimes where formal regulatory enforcement is limited, firms often rely on inter-organizational networks to acquire specialized expertise and learn disclosure practices. As a critical channel for inter-organizational information exchange and learning, interlocking director networks may therefore play a pivotal role in facilitating climate risk disclosure. However, the specific mechanisms through which these networks influence disclosure decisions and the boundary conditions that shape their effectiveness remain unexplored.

3. Theoretical Analysis and Research Hypotheses

3.1. Interlocking Director Network and Corporate Climate Risk Disclosure

Unlike traditional financial disclosures, climate risk reporting requires integrating interdisciplinary knowledge, such as climate science, scenario analysis, and strategic planning. However, the lack of standardized frameworks and mature disclosure paradigms makes it challenging for companies to independently determine what and how to disclose. This uncertainty can be mitigated through network ties that cross organizational boundaries, as these ties break down information silos and facilitate the exchange of diverse knowledge across firms [54]. Interlocking directors, in particular, play a crucial role in enhancing transparency within corporate networks [55]. By serving on multiple boards, they facilitate the sharing and comparison of best practices, making previously opaque behaviors more visible to network members. Directors in intermediary positions, especially, bridge otherwise disconnected groups, gaining access to unique and non-redundant information [56], which fosters a more transparent information environment.

This increased transparency creates conditions for corporate climate risk disclosure in several ways. First, observing and emulating peers is an effective strategy for firms navigating disclosure uncertainties [57,58]. A transparent information environment allows companies to observe the risks identified and the frameworks adopted by their peers, turning vague disclosure tasks into concrete, actionable references. Additionally, climate risk disclosure concerns not only what to disclose but also how to disclose. This involves making complex assumptions about future climate scenarios, carbon pricing, and both physical and transition risks [59]. Given the wide variation in scenario analysis and risk assessment methodologies across firms, the underlying judgments and assumptions are often difficult to share through formal channels [60]. A transparent information environment enables firms to observe how others translate abstract climate risks into concrete disclosures, facilitating the transfer of tacit knowledge and reducing cognitive and capability barriers to disclosure. Second, transparency amplifies normative pressure and legitimacy concerns. In emerging markets, where mandatory disclosure regulations are absent, firms assess the legitimacy of their actions by comparing themselves to peer practices [61]. When a firm realizes that its peers are engaging in climate risk disclosure while it is not, this gap creates legitimacy pressure. As stakeholders’ concerns about climate risks grow, firms that do not disclose face increasing scrutiny, compelling them to engage in proactive climate risk disclosure.

Beyond information transparency, interlocking director networks enhance corporate climate risk disclosure through reputational capital mechanisms. Directors accumulate reputational capital across multiple boards, which is highly valuable in corporate governance. When a board fails to effectively identify and manage climate-related risks, stakeholders such as investors, regulators, and customers may question the board’s governance capabilities, resulting in negative assessments of both the board and individual directors’ competence [62]. This reputational damage can have serious consequences, reduce a director’s future opportunities, and threaten the long-term success of the firms they are associated with. Prospect theory suggests that individuals tend to react more strongly to losses than to equivalent gains [63], which means directors are particularly motivated to protect both their own reputations and those of the companies they oversee. In an interlocking director network, this reputational risk is amplified. A climate governance failure at one firm can cast doubt on the director’s competence across all affiliated companies, creating a contagion effect. To safeguard their reputational capital and prevent network-wide devaluation, interlocking directors have a strong incentive to ensure robust climate risk disclosure across all their board positions. By advocating for transparent reporting, they not only mitigate future reputational crises but also demonstrate their governance expertise to the market. Based on these insights, we propose the following hypothesis:

H1. 

Interlocking director networks positively impact corporate climate risk disclosure.

H2. 

Interlocking director networks positively influence climate risk disclosure by enhancing information transparency.

H3. 

Interlocking director networks positively influence climate risk disclosure by enhancing corporate reputation.

3.2. Interlocking Director Network, Environmental Regulation, and Corporate Climate Risk Disclosure

Interlocking director networks facilitate information transfer, but their influence on corporate climate risk disclosure may be strengthened or limited by the intensity of formal institutional pressures. Studies have shown that stricter environmental regulations can notably improve the quality of corporate disclosures [64]. By contrast, in lenient regulatory environments, firms face fewer external mandates for climate risk assessment and disclosure and therefore invest fewer resources in collecting and voluntarily disclosing climate risk information. In such contexts, the director may fail to leverage the environmental expertise available through these networks fully. The 2015 revision of China’s ‘Environmental Protection Law’ offers a valuable opportunity to examine this dynamic. The revised law established a more stringent environmental regulatory framework, introducing stricter enforcement measures, including daily consecutive penalties for ongoing violations (with the potential for unlimited fines), expanded liability provisions for polluters, and reinforced environmental monitoring and information disclosure mechanisms. This revision marked a significant step towards stricter enforcement and higher transparency in China’s environmental governance. Enhanced institutional pressure amplifies the role of interlocking director networks in two ways. On one hand, more stringent regulations prompt investors and creditors to pay greater attention to corporate environmental risk management [65], creating market pressure for firms to enhance their disclosure practices. Faced with these heightened stakeholder expectations but lacking established frameworks for comprehensive disclosure, firms increasingly turn to their interlocking directors as conduits for learning disclosure strategies from peer organizations. On the other hand, the penalties and professional consequences associated with environmental failures expose interlocking directors to significant personal risk. These consequences can include reputational damage and potential challenges in maintaining or securing future board positions. This creates incentives for directors to take a proactive stance in promoting superior governance standards, with particular emphasis on more robust climate risk reporting, thereby reducing potential exposures. Based on the above analysis, we hypothesize that:

H4. 

The revised 2015 Environmental Protection Law strengthens the positive effect of interlocking director networks on corporate climate risk disclosure.

3.3. Interlocking Director Network, Media Attention, and Corporate Climate Risk Disclosure

As previously discussed, interlocking directors incentivize firms to proactively disclose climate risk information through information transparency and reputational mechanisms. We argue that media attention amplifies the role of interlocking directors in promoting climate risk disclosure. First, media coverage accelerates information dissemination, reducing information asymmetry between companies and the public [66]. Companies receiving greater media attention gain higher public visibility [14]. When firms proactively disclose high-quality climate risk information, positive media coverage signals effective board governance to capital markets and stakeholders, boosting investor confidence in corporate environmental stewardship. This positive signaling incentivizes interlocking directors to promote similar disclosure practices across their portfolio of board positions, creating a cross-firm demonstration effect that encourages climate risk disclosure across their network. Second, the media, as a key information intermediary, supervises corporate governance issues through negative reporting, thereby imposing constraints on directors [67]. Higher media attention increases stakeholder awareness of companies’ climate disclosure practices while also raising the probability that inadequate disclosures will be detected and publicized. When the media point out that a company fails to disclose its climate-related information adequately, the individuals who also serve as directors of that company will not only face questions about the failure of their current company’s governance, but also damage their reputations on other boards they serve on. To prevent disclosure shortcomings from tarnishing their reputation, interlocking directors are motivated to strengthen climate risk disclosure across all companies in their network. Therefore, we propose the following hypothesis:

H5. 

Media attention positively moderates the relationship between the interlocking director network and corporate climate risk disclosure.

Figure 1 illustrates the ways through which interlocking director networks influence climate risk disclosure.

4. Research Methods

4.1. Data and Sample Selection

We test our hypotheses using panel data from Chinese A-share listed corporations over the 2009–2023 interval, providing cross-sectional variation. Director network data and financial variables come from CSMAR, while CRD metrics are derived through Python 3.10-based content analysis of annual report MD&A sections. After excluding financial firms, ST/*ST companies, and incomplete records, the final sample contains 26,945 firm-year observations. To mitigate the influence of outliers, we winsorize all continuous variables at the 1st and 99th percentiles. Standard errors are clustered at the firm level to account for potential within-firm correlation

4.2. Key Variables

4.2.1. Explanatory Variable: Interlocking Director Networks (Inde)

Network centrality can be operationalized through multiple dimensions per Freeman (1979) [68]: degree (volume of direct ties), betweenness (brokerage positioning), closeness (access efficiency), and eigenvector (connection quality weighted by partner prominence). Degree centrality quantifies a node’s direct linkages, while betweenness captures its structural advantage in bridging disparate network segments and controlling information flows. Closeness measures geodesic distances to all nodes, and eigenvector weights connections by the centrality of alters. Given our focus on governance mechanisms and resource mobilization within director networks, degree and betweenness centrality offer superior analytical precision. Closeness and eigenvector centrality, though valuable for assessing reachability and prestige, are less germane to our research questions. Accordingly, we operationalize network position using Degree and Between, calculated as follows.

(1)

Degree centrality (Inde_dc) measures the total number of other firms directly connected to a focal firm through shared directors. The formula is:

$$Inde\_d{c}_i={\sum }_i{\displaystyle \frac{X_{ji}}{g-1}}$$

(1)

Equation (1) defines several key components: director $i$ is the focal individual, while $j$ represents the other directors on the board for that year. The term $X_{ji}$ indicates whether a network tie exists between directors. We denote $g$ as the total number of directors in the firm’s network for a given year. To account for temporal variation in board size, we normalize by $g-1$, creating a scale-invariant measure. This standardized metric quantifies each director’s direct connections, with higher values indicating stronger centrality within the network structure.

(2)

Betweenness Centrality (Inde_bc) measures the extent to which independent directors serve as bridges between other independent directors within the network. The formula is:

$$Inde\_b{c}_i={\displaystyle \frac{{\sum }_{j<k}{g}_{jk\left(ni\right)}/g_{jk}}{(g-1)(g-2)/2}}$$

(2)

Equation (2) specifies the following elements: $g_{jk}$ represents the count of the shortest paths connecting any two directors, $j$ and $k$, while $g_{jk}\left(ni\right)$ indicates the frequency with which director $i$ appears on these geodesic paths. To ensure comparability across networks of varying sizes, we normalize by ${\displaystyle \frac{(g-1)(g-2)}{2}}$, which accounts for all possible director pairs excluding the focal individual. The resulting metric reflects a director’s structural advantage in information brokerage, with higher scores indicating a superior position for controlling communication flows and coordinating resources across otherwise disconnected network clusters.

4.2.2. Dependent Variable: Climate Risk Disclosure

The dependent variable in our analysis captures corporate climate risk disclosure (CRD) intensity. We construct this measure following Wang et al. (2024) [8], utilizing a CBOW word embedding algorithm to identify semantically related climate terminology, which produces 43 thematically coherent keyword clusters. CRD is calculated as the proportion of these climate-specific terms within the MD&A narrative, expressed as a percentage (multiplied by 100). This continuous indicator gauges the comprehensiveness of climate-related reporting, with elevated values signaling more thorough disclosure practices. Descriptive evidence on keyword frequency distributions is provided in

Table 1. Climate Change Risk Word Frequency.

Transition Risk
Carbon Dioxide	Carbon Trading	Natural Gas	Coal	Fossil Fuels	Clean Energy
Carbon Emissions	Carbon Sink	Energy Conservation	Energy Storage	Biomass Energy	Wind Energy
Peak Carbon Emissions	Low-Carbon	Coal-Fired Power	Hydropower Generation	Paris Agreement	Optoelectronics
Carbon Neutrality	Carbon Reduction	Green	petroleum	Wind Energy	Energy Consumption
Dual Carbon	Calcium Carbonate	Solar Renewable	Reduce Consumption	Geothermal Energy
Carbon Emission Reduction	Zero Carbon	Renewable Energy	Energy Consumption	Nuclear Energy
Physical risk
Climate	Air Temperature	Hailstone	Frozen	Hurricane	Frost
Weather	Rainfall	Torrential Rain	Drought	High Temperature	Snowfall
Flood	Low Temperature

.

4.2.3. Mediating Variable

Our empirical framework examines two critical mechanisms: corporate information transparency and firm reputation. Drawing on the approach of Qin et al. (2024) [69], we construct a multidimensional measure of information transparency that incorporates four complementary perspectives: earnings quality, regulatory disclosure ratings, analyst forecast characteristics, and auditor assessments. To operationalize this construct, we employ the following component indicators:

(1)

Earnings Quality: The earnings quality index (DD) is derived using the adjusted version of the Dechow and Dichev (2002) model [70]. The final index is multiplied by negative one, so that a higher value of DD indicates better earnings quality.

(2)

Information Disclosure Evaluation: The disclosure score (DSORCE) of listed companies on the Shenzhen Stock Exchange is used as an indicator of information transparency. Since 2001, the information disclosure quality of listed companies has been assessed annually and classified into four grades, with scores ranging from 1 to 4. A higher score reflects better disclosure quality.

(3)

Analyst Perspective: We employ two measures of information transparency from the analysts’ viewpoint: analyst coverage (ANALYST) and forecast accuracy (ACCURACY). ANALYST equals the natural logarithm of the number of analysts issuing earnings forecasts for firm i in year t. ACCURACY is computed as the negative logarithm of the median absolute forecast error, where forecast error is defined as |predicted EPS—actual EPS|/prior year EPS. Higher ACCURACY values indicate smaller forecast deviations, thus greater information precision.

(4)

Auditor Perspective: The indicator (BIG4) examines whether the company hires one of the Big Four accounting firms to audit its annual report. Since audits by the Big Four are considered to enhance the quality of financial reporting, their involvement may improve the transparency of the company.

Finally, we calculate the average percentile values of these five indicators to derive the composite indicator of enterprise information transparency (TRANS). A larger value of TRANS indicates higher overall information transparency.

For the reputation construct, we adapt the framework developed by Bigus et al. (2023) [71], operationalizing corporate reputation through a composite index based on 12 indicators across four stakeholder perspectives. The first dimension focuses on consumer and social standing, incorporating firm size and market position, with indicators such as total assets (logged), operating revenue (logged), net profit (logged), and industry rank by market capitalization. The second dimension reflects creditor perceptions, measured through leverage and liquidity ratios: the asset-liability ratio (total liabilities divided by total assets), current ratio (current assets divided by current liabilities), and long-term debt ratio (non-current liabilities relative to total assets). The third dimension assesses shareholder value, using metrics such as earnings per share (net profit per share outstanding), dividends per share (cash or stock distributions per share), and external audit quality (a binary indicator equal to one if audited by a Big Four firm, zero otherwise). The fourth dimension captures governance characteristics, including the sustainable growth rate (proxied by operating revenue growth) and board independence (the proportion of independent directors).

We apply principal component analysis to derive a unified reputation score from these 12 variables, reducing dimensionality while preserving underlying variation. The resulting scores are ranked in ascending order, partitioned into deciles, with each firm assigned a REP value from 1 (lowest reputation) to 10 (highest reputation). This decile-based measure serves as our proxy for corporate reputation, where higher values indicate stronger reputational standing.

4.2.4. Moderating Variable

Environmental Regulation (ENL): We exploit China’s 2015 Environmental Protection Law as an exogenous shock to regulatory stringency. This legislation substantially raised penalties for environmental violations, intensifying compliance pressure on polluting firms. Following a standard difference-in-differences design, ENL = Treat × Post, where Post equals 1 from 2015 onward (effective 1 January 2015) and Treat equals 1 for firms in 16 heavily polluting sectors (e.g., thermal power, steel). The interaction captures differential regulatory exposure.

Media Attention (Media): Data are sourced from the CNRDS database, which aggregates coverage across traditional and digital media platforms. Adopting Li’s (2024) [14] approach, Media = ln (1 + total news mentions), where mentions include both headlines and article content referencing the firm. Higher values reflect greater media visibility.

4.2.5. Control Variables

Ten firm-level controls are included: Size, Lev, ROA, Growth, Loss, Board, Indep, Dual, Top1, ListAge, and Big4, capturing firm characteristics, financial health, and governance attributes. Variable definitions appear in

Table 2. Variable Description.

Variable Categories	Variable Names	Variable Symbols	Measurement
Dependent variable	Climate Risk Disclosure	CRD	The ratio of the word count for “climate change risks” to the total word count in the MD&A text, multiplied by 100.
Independent variable	Degree centrality	Inde_dc	See Equation (1).
	Betweenness centrality	Inde_bc	See Equation (2).
Control variables	Size	Size	Natural logarithm of total enterprise assets at the end of the year.
	Leverage Ratio	Lev	The ratio of total liabilities to total assets at the end of the year.
	Return on Assets	ROA	Ratio of net profit to total assets.
	Growth Opportunity	Growth	The growth rate of total operating income.
	Net Profit Loss	Loss	Equals one if net profit is negative, and 0 Otherwise.
	Board Size	Board	Natural logarithm of the number of board members.
	Independent Director Ratio	Indep	Ratio of independent directors to total directors.
	CEO Duality	Dual	Are the chairman and CEO the same person?
	Largest Shareholder Ownership	Top1	Ratio of shares held by the largest shareholder to total shares.
	Age	ListAge	The natural logarithm of the current year minus the year when the company was first listed plus 1.
	Audit Quality	Big4	Big4 equals one if the firm is audited by one of the Big Four auditing firms and zero otherwise.

.

4.3. Empirical Framework

To examine the impact of director networks in corporate chains on climate risk disclosure, this study constructs Model (3) to test Hypothesis H1:

$$CR{D}_{i,t}={\alpha }_0+{\alpha }_{1}Ind{e}_{i,t}+\sum Controls+{\delta }_i+{\theta }_t+{\epsilon }_{i,t}$$

(3)

The dependent variable CRD represents climate risk disclosure. Inde denotes network position, operationalized through degree centrality (Inde_dc) and betweenness centrality (Inde_bc). Controls denote control variables. ${\delta }_i, {\theta }_t$ represent firm-level and time-level fixed effects, respectively, while ε is the error term. We employed panel data regression, accounting for firm-level and year-level controls, and utilised firm-level cluster-robust standard errors. This approach reduces potential biases arising from unobserved industry-specific and time-specific factors, ensuring more robust and credible estimation results.

To test Hypotheses H2 and H3, we constructed mediation effect testing models for analysis, as shown in Equations (4) and (5):

$$M{V}_{i,t}={\beta }_0+{\beta }_{1}Ind{e}_{i,t}+\sum Controls+{\delta }_i+{\theta }_t+{\epsilon }_{i,t}$$

(4)

$$CR{D}_{i,t}={\gamma }_0+{\gamma }_{1}Ind{e}_{i,t}+{\gamma }_{2}M{V}_{i,t}+\sum Controls+{\delta }_i+{\theta }_t+{\epsilon }_{i,t}$$

(5)

where MV is the mediating variable, which will be replaced by the corresponding variable in subsequent sections, for model (4), this paper primarily focuses on the coefficient ${\beta }_1$ of the interlocking directorate network (Inde) on the mediating variable (MV). If ${\beta }_1$ is statistically significant, it indicates that the interlocking directorate network (Inde) exerts a substantial effect on the mediating variable (MV). For Equation (5), this paper primarily examines the coefficient ${\gamma }_1$ for the interlocking directorate network (Inde) and the coefficient ${\gamma }_2$ for the mediating variable (MV). If coefficient ${\gamma }_1$ is significantly positive and coefficient ${\gamma }_2$ is significant, it indicates that the mediating effect holds.

To test Hypotheses H4 and H5, we introduced interaction terms between the moderator variable and the core explanatory variable, constructing the following models in Equations (6) and (7). This paper primarily focuses on the coefficients of the interaction terms. If these coefficients are significant, it indicates that the moderator variable exerts a significant moderating effect on the relationship between interlocking shareholders (Inde) and climate risk disclosure (CRD):

$$CR{D}_{i,t}={\delta }_0+{\delta }_{1}Inde\ast EP{L}_{i,t}+{\delta }_{2}Ind{e}_{i,t}+{\delta }_{3}EP{L}_{i,t}+\sum Controls+{\delta }_i+{\theta }_t+{\epsilon }_{i,t}$$

(6)

$$CR{D}_{i,t}={\delta }_0+{\epsilon }_{1}Inde\ast Medi{a}_{i,t}+{\epsilon }_{2}Ind{e}_{i,t}+{\epsilon }_{3}Medi{a}_{i,t}+\sum Controls+{\delta }_i+{\theta }_t+{\epsilon }_{i,t}$$

(7)

5. Empirical Analysis

5.1. Descriptive Statistics

Table 3. Descriptive statistics.

Variable	N	Mean	Std. Dev.	Min	Max	Median
CRD	26,945	0.003	0.006	0.000	0.025	0.000
Inde_dc	26,945	0.165	0.030	0.000	0.213	0.168
Inde_bc	26,945	0.001	0.002	0.000	0.009	0.001
Size	26,945	22.267	1.299	19.956	26.367	22.060
Lev	26,945	0.425	0.203	0.050	0.896	0.419
ROA	26,945	0.040	0.063	−0.210	0.210	0.039
Growth	26,945	0.155	0.345	−0.518	1.884	0.104
Loss	26,945	0.125	0.331	0.000	1.000	0.000
Board	26,945	2.123	0.194	1.609	2.639	2.197
Indep	26,945	37.660	5.326	33.330	57.140	36.360
Dual	26,945	0.302	0.459	0.000	1.000	0.000
Top1	26,945	0.343	0.150	0.083	0.746	0.322
ListAge	26,945	1.957	0.916	0.000	3.367	2.079
Big4	26,945	0.062	0.241	0.000	1.000	0.000

reports descriptive statistics. The mean CRD of 0.003 (SD = 0.006, range: 0–0.025) reveals substantial heterogeneity in climate disclosure practices, with most firms exhibiting minimal disclosure. For network centrality measures, Inde_dc averages 0.165 (SD = 0.030, range: 0–0.213), indicating moderate variation in direct board connections, while Inde_bc averages 0.001 (SD = 0.002, range: 0–0.009), suggesting few firms occupy brokerage positions within the network. The mean VIF of 3.290 confirms the absence of problematic multicollinearity, supporting model reliability.

5.2. Basic Regression Results

Table 4. Baseline Regression.

Variable	(1)	(2)	(3)	(4)
	No Control Variable		Add Control Variables
	CRD
Inde_dc	0.009 *** (6.942)		0.007 *** (5.564)
Inde_bc		0.102 *** (4.156)			0.070 *** (3.001)
Constant	0.002 *** (8.762)	0.003 *** (90.798)	0.001 (0.518)		0.002 (0.857)
Controls	NO	NO	YES		YES
Year FE	YES	YES	YES		YES
Firm FE	YES	YES	YES		YES
Observations	26,945	26,945	26,945		26,945
R-squared	0.497	0.497	0.512		0.512

Note: Standard errors, clustered at the firm level, are reported in parentheses. *** Significant at the 1% level.

reports the regression results of the relationship between the interlocking directorate network and climate risk disclosure (CRD). Columns (1) and (2) show the regression results for degree centrality (Inde_dc) and betweenness centrality (Inde_bc), respectively. Columns (3) and (4) present the regression results after adding control variables. All regressions control for year and firm fixed effects.

The results show that the coefficient of degree centrality (Inde_dc) is significantly positive at the 1% level in both columns (1) and (3) (Coefficients = 0.009 and 0.007, p < 0.01), indicating that firms with higher network centrality exhibit greater climate risk disclosure. Similarly, the coefficient of betweenness centrality (Inde_bc) is significantly positive at the 1% level in columns (2) and (4) (Coefficients = 0.102 and 0.070, p < 0.01). This suggests that firms occupying “bridging positions” in the network play a stronger role in information control and resource transmission, leading to more proactive climate risk disclosure. The regression results are consistent with Hypothesis H1.

5.3. Mediating Effect Test Results

Table 5. The results of the mediation effect test.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	TRANS	CRD	TRANS	CRD	REP	CRD	REP	REP
Inde_dc	0.198 *** (4.983)	0.006 *** (4.693)			0.012 *** (3.825)	0.007 *** (5.699)
Inde_bc			1.815 *** (2.834)	0.072 *** (2.889)			0.178 *** (3.186)	0.074 *** (2.968)
TRANS		0.001 ** (2.108)		0.001 ** (2.193)
REP						0.013 *** (3.333)		0.013 *** (3.391)
Constant	−0.811 *** (−12.230)	0.003 (0.998)	−0.787 *** (−11.904)	0.003 (1.341)	−0.394 *** (−49.329)	0.007 ** (2.226)	−0.392 *** (−48.976)	0.008 ** (2.546)
Controls	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES	YES	YES
Observations	26,358	26,358	26,358	26,358	26,592	26,592	26,592	26,592
R-squared	0.765	0.514	0.765	0.513	0.919	0.514	0.919	0.513

Note: Standard errors, clustered at the firm level, are reported in parentheses. *** Significant at the 1 % level, ** Significant at the 5 % level.

reports the mediation analysis examining how the interlocking director network affects climate risk disclosure through information transparency (TRANS) and corporate reputation (REP).

Information transparency mechanism (Columns (1)–(4)). Both network measures significantly enhance TRANS: Inde_dc has a coefficient of 0.198 (p < 0.01) and Inde_bc has a coefficient of 1.815 (p < 0.01). TRANS positively predicts CRD with a coefficient of 0.001 (p < 0.05). These findings support Hypothesis H2, confirming that network centrality enhances climate disclosure through improved information transparency.

Reputation mechanism (Columns (5)–(8)). Similarly, both network measures significantly enhance REP: Inde_dc has a coefficient of 0.012 (p < 0.01) and Inde_bc has a coefficient of 0.178 (p < 0.01). REP positively predicts CRD with a coefficient of 0.013 (p < 0.01). These findings support Hypothesis H3, confirming that network centrality enhances climate disclosure through reputational capital accumulation. Overall, the interlocking director network influences climate risk disclosure directly and indirectly through information transparency and corporate reputation. Network connections provide access to information resources, while accumulated reputational capital improves responsiveness to external pressures, promotes the disclosure of climate risks by enterprises.

5.4. Moderating Effect Test Results

Table 6. The test results for the moderating effect. The moderating variables are Environmental regulation and media attention.

Variable	(1)	(2)	(3)	(4)
Inde_dc	0.006 *** (4.579)		0.003 *** (2.687)
Inde_bc		0.066 *** (2.719)		0.022 (0.986)
Inde_dc * EPL	0.009 *** (2.991)
Inde_bc *EPL		0.195 ** (2.146)
EPL	0.000 (0.396)	0.000 (0.530)
Inde_dc * Media			0.007 *** (8.050)
Inde_bc * Media				0.068 *** (5.359)
Media			0.000 (0.281)	0.000 (0.904)
Constant	0.002 *** (8.762)	0.003 *** (90.798)	0.001 (0.518)	0.002 (0.857)
Controls	NO	NO	YES	YES
Year FE	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Observations	26,169	26,169	26,064	26,064
R-squared	0.523	0.523	0.511	0.510

Note: Standard errors, clustered at the firm level, are reported in parentheses. *** Significant at the 1 % level, ** Significant at the 5 % level.

examines how environmental regulation and media attention moderate the network centrality-disclosure relationship.

Environmental Regulation (Columns (1) and (2)): The interaction between degree centrality and regulatory stringency (Inde_dc × ENL) yields a coefficient of 0.009 (p < 0.01), indicating that heightened regulatory pressure amplifies the disclosure effect of network connections. This suggests that well-connected firms leverage knowledge spillovers within their networks to navigate compliance demands better, thereby increasing climate transparency. For betweenness centrality, the interaction term (Inde_bc × ENL) exhibits a coefficient of 0.195 (p < 0.05), demonstrating that firms occupying bridging positions translate regulatory pressure into enhanced disclosure through superior information flow and resource mobilization capabilities. Notably, the standalone ENL coefficient remains statistically insignificant, implying that regulatory enforcement alone proves insufficient; its disclosure impact materializes primarily through network-embedded firms, supporting H4.

Media Attention (Columns (3) and (4)): The Inde_dc × Media interaction displays a coefficient of 0.007 (p < 0.01), while Inde_bc × Media shows a coefficient of 0.068 (p < 0.01). Both results confirm that intensified media scrutiny strengthens the positive association between network centrality and disclosure practices. Firms with greater connectivity or brokerage advantages appear more responsive to reputational concerns, as media visibility incentivizes them to signal environmental responsibility through expanded climate reporting.

5.5. Robustness Tests

5.5.1. Endogenous Problem

(1)

IV Method

To address potential endogeneity, we apply an instrumental variables approach (IV-2SLS), using board members’ professional diversity (Div_Major) as the instrument. Div_Major categorizes directors into five professional groups, reflecting board knowledge heterogeneity, which influences network centrality without directly affecting climate risk disclosure.

Columns (1) and (2) of

Table 7. The results of the instrumental variable method and the propensity score matching test.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	IV Method				PSM
Inde_dc		0.162 *** (2.797)			0.010 *** (4.863)
Inde_bc				3.176 ** (2.457)		0.087 * (1.931)
div_Major	0.006 *** (4.414)		0.000 *** (3.402)
Controls	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES
Kleibergen-Paap rk LM	19.024		11.363
Cragg-Donald Wald F	34.380		22.553
Observations	26,945	26,945	26,945	26,945	11,188	11,188
R-squared		−0.670		−1.081	0.575	0.575

Note: Standard errors, clustered at the firm level, are reported in parentheses. *** Significant at the 1 % level, ** Significant at the 5 % level, * Significant at the 10 % level.

show the results from the two-stage least squares estimation with degree centrality treated as endogenous. The first-stage regression reveals a significant link between Div_Major and Inde_dc (β = 0.006, p < 0.01), confirming the instrument’s validity. In the second stage, the coefficient for Inde_dc remains positive and significant (β = 0.162, p < 0.01), supporting the conclusion that network centrality improves climate risk disclosure, even after addressing endogeneity. Diagnostic tests further confirm the validity of the instrument: the Kleibergen-Paap rk LM statistic (19.024) rejects the null hypothesis of underidentification, and the Cragg-Donald Wald F-statistic (34.380) exceeds the Stock-Yogo critical value, eliminating concerns about weak instruments.

Columns (3) and (4) present results for betweenness centrality (Inde_bc). In the first stage, the coefficient for Div_Major on Inde_bc is significantly positive, suggesting that professional diversity affects a director’s position as a network bridge. The second-stage results show a coefficient of 3.176 (p < 0.05) for Inde_bc in the climate risk disclosure regression, indicating that the bridge position also positively influences climate risk transparency.

(2)

Propensity Score Matching Test

To address potential sample selection bias, we use propensity score matching (PSM) for robustness testing. Firms are categorized into treatment and control groups based on whether they disclose climate risks (CRD > 0), with 1:1 nearest neighbor matching using control variables from the main regression. The post-matching regression results, presented in Table 7, Columns (5) and (6), show estimated coefficients of 0.010 for Inde_dc (p < 0.01) and 0.087 for Inde_bc (p < 0.10), which align with the unmatched estimates. These results suggest that our findings are not driven by compositional differences between firms that disclose and those that do not, reinforcing a causal interpretation.

(3)

Heckman test

This study employs a Heckman two-stage selection model to address potential sample selection bias. Specifically, the first stage uses a probit model to estimate firms’ probability of disclosing climate risk information. Board member stability (Stability) is selected as the exclusion restriction to ensure model identification. The construction of this variable follows Liu et al. (2025) [72], mirroring the approach used to measure executive team stability: it assesses overall board stability by examining the retention, exit, and new appointments of board members across years. We argue that while board stability affects the decision to disclose, it does not directly determine disclosure intensity conditional on the decision to disclose and other firm characteristics. Once disclosure is initiated, the extent of disclosure depends primarily on climate risk exposure, resource availability, and external pressures rather than board composition stability.

Table 8. Heckman test results.

Variable	(1)	(2)	(3)
	Phase One	Phase Two
Inde_dc		0.006 *** (5.394)
Inde_bc			0.063 *** (2.693)
Stability	0.459 *** (9.690)
imr		0.003 *** (7.599)	0.003 *** (7.563)
Constant	−0.860 * (−1.920)	−0.000 (−0.018)	0.001 (0.300)
Controls	YES	YES	YES
Year FE	YES	YES	YES
Firm FE	YES	YES	YES
Observations	26,945	26,945	26,945
R-squared		0.514	0.514

Note: Standard errors, clustered at the firm level, are reported in parentheses. *** Significant at the 1 % level, * Significant at the 10 % level.

reports the Heckman two-stage results. Column (1) shows board stability significantly predicts climate disclosure in the first-stage probit model (β = 0.459, p < 0.01). We calculate the inverse Mills ratio (IMR) from this stage and include it in the second-stage regressions. Columns (2) and (3) show that IMR is significant (p < 0.01), confirming selection bias. After correction, both Degree and Between remain significantly positive in CRD.

5.5.2. Other Robustness Tests

(1)

Alternative Dependent Variable

To mitigate concerns about measurement bias stemming from reliance on a singular metric for climate risk disclosure, we replace the dependent variable, Climate Risk Disclosure Index (CRD), with the natural logarithm of climate risk keyword frequency (lnCRD).

Table 9. Other robustness tests.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Replace the Explained Variable		One-Period-Lagged Variable		Add Fixed Effects		Change the Regression Model
	lnCRD	lnCRD	CRD	CRD	CRD	CRD	CRD	CRD
Inde_dc	0.346 *** (4.128)		0.004 *** (2.977)		0.007 *** (5.444)		0.024 *** (6.253)
Inde_bc		3.264 ** (2.236)		0.072 *** (2.905)		0.069 *** (3.019)		0.267 *** (4.407)
Constant	−0.842 *** (−4.952)	−0.800 *** (−4.662)	−0.001 (−0.373)	−0.000 (−0.063)	0.002 (0.800)	0.003 (1.112)	0.004 (1.031)	0.007 * (1.678)
Controls	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES	YES	YES
Observations	26,945	26,945	20,203	20,203	26,936	26,936	26,945	26,945
R-squared	0.593	0.593	0.512	0.512	0.527	0.526

Note: Standard errors, clustered at the firm level, are reported in parentheses. *** Significant at the 1 % level, ** Significant at the 5 % level, * Significant at the 10 % level.

columns (1) and (2) show that Inde_dc and Inde_bc remain significant at the 1% and 5% levels, respectively. The results confirm that director networks promote climate risk disclosure regardless of the measurement approach.

(2)

Lagged Explanatory Variables

To address potential reverse causality concerns, we re-estimate the model by including one-period lagged values of the primary explanatory and control variables. This temporal adjustment helps mitigate endogeneity issues arising from simultaneous relationships between disclosure practices and board characteristics. The results in Columns (3) and (4) of Table 9 show that both lagged Inde_dc and Inde_bc coefficients remain statistically significant with positive signs. These findings confirm that the director network continues to positively influence climate risk disclosure, even after accounting for temporal sequencing.

(3)

Industry-Year Fixed Effects

Considering potential institutional differences and external constraints across industries in addressing climate risks, as well as possible standard policy shocks across periods, this study further incorporates industry-by-year interaction fixed effects. As shown in Columns (5) and (6) of Table 9, the director network retains its significantly positive association with climate risk disclosure (p < 0.01). These results indicate that industry-specific time-varying factors do not drive the observed relationships, reinforcing the reliability of our main conclusions.

(4)

Tobit Regression Model

Given that the Climate Risk Disclosure Index (CRD) exhibits left-censoring at zero, conventional OLS estimation risks generating biased coefficients. Consequently, we adopt a Tobit specification as a robustness check. This approach effectively handles dependent variables with censored distributions or bounded ranges, yielding more precise parameter estimates for the association between independent and dependent variables. As presented in Columns (7) and (8) of Table 9, both Inde_dc and Inde_bc display statistically significant positive coefficients (p < 0.01). These findings reinforce our core conclusion that the interlocking directorate network positively drives corporate climate risk disclosure across different econometric specifications.

5.6. Heterogeneity Analysis

5.6.1. Ownership Structure

The analysis also examines variations by ownership structure, comparing state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). The results in

Table 10. Heterogeneity Analysis.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	SOE	Non-SOE	SOE	Non-SOE	High Climate Risk	Low Climate Risk	High Climate Risk	Low Climate Risk
Inde_dc	0.008 *** (4.411)	0.005 *** (3.112)			0.006 *** (3.376)	0.007 *** (4.015)
Inde_bc			0.092 *** (3.232)	0.012 (0.325)			0.063 ** (2.099)	0.056 (1.585)
Constant	−0.012 *** (−3.496)	0.012 *** (3.431)	−0.011 *** (−3.150)	0.012 *** (3.549)	0.003 (0.851)	−0.002 (−0.580)	0.003 (1.088)	−0.001 (−0.362)
Controls	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES	YES	YES
Observations	9503	17,442	9503	17,442	13,068	13,117	13,068	13,117
R-squared	0.403	0.549	0.403	0.549	0.529	0.552	0.529	0.551

Note: Standard errors, clustered at the firm level, are reported in parentheses. *** Significant at the 1 % level, ** Significant at the 5 % level.

(Columns (1)–(4)) show that the impact of interlocking director networks is more pronounced in SOEs, where both degree and betweenness centrality significantly enhance climate risk disclosure at the 1% level. In the non-SOE subsample, Inde_dc remains significantly positive, while Inde_bc is not significant. This suggests that SOEs, facing stronger policy pressures and regulatory constraints, are more inclined to leverage the social capital and resource advantages of centrally positioned directors to enhance climate risk disclosure. In contrast, non-SOEs are driven more by market mechanisms in climate risk disclosure, with a more limited role for interlocking directorate networks.

5.6.2. Regional Climate Risk Exposure

Following the methodology of Guo et al. (2024) [73], we partition the sample based on the climate risk exposure level of the firm’s registered city. Results in Table 10 (Columns (5)–(8)) show that in high-risk regions, both degree centrality (Inde_dc) and betweenness centrality (Inde_bc) significantly promote climate risk disclosure (Inde_dc at the 1% level; Inde_bc at the 5% level). In contrast, only Inde_dc remains significant in low-risk regions. This suggests that heightened physical climate risks intensify external pressure, thereby amplifying the role of interlocking director networks in motivating disclosure.

6. Conclusions and Discussion

6.1. Conclusions

Although existing research has preliminarily explored the relationship between interlocking directorate networks and information disclosure, most studies have concentrated on general disclosure behavior or environmental performance analysis. However, research on underlying transmission mechanisms and contextual boundaries remains insufficient, particularly lacking systematic evidence in the emerging field of climate risk disclosure. To fill this gap, this study adopts a dual-pathway analytical framework of “information transmission and reputation building” and conducts an empirical examination of Chinese A-share listed companies from 2009 to 2023, providing an in-depth exploration of the mechanisms through which interlocking directorate networks influence corporate climate risk disclosure. The findings are as follows:

First, this study confirms the significant role of interlocking directorate networks (degree centrality and betweenness centrality) in promoting climate risk disclosure. This finding echoes Li et al. (2024), who examined the impact of board centrality on corporate environmental disclosure in heavily polluting industries [44]. This study extends the perspective from heavily polluting industries to a full industry sample, enhancing the generalizability of director network governance effects across different industry contexts. Furthermore, the research focus of this study is not limited to general environmental disclosure but extends to climate risk disclosure as a specific strategic issue. By employing a cross-industry sample, we demonstrate that interlocking directorate networks can effectively promote climate risk information disclosure through information transparency and reputation mechanisms, even without the constraint of industry environmental sensitivity. This highlights the universal value of director networks as an informal governance mechanism. This finding provides new empirical support for the applicability of social network theory across different contexts and issue domains, expanding the theoretical boundaries of drivers for corporate climate governance.

Second, the moderating effects of environmental regulation intensity and media attention are significant, indicating that the influence of director networks requires an appropriate institutional environment and external monitoring to be fully translated into disclosure behavior. This aligns with the research logic of Christensen et al. regarding how external pressures strengthen corporate social responsibility practices [74], emphasizing the critical role of contextual factors in activating network effects. The study finds that environmental regulation not only exerts direct influence through mandatory constraints but also serves as a “catalyst,” driving director networks to pay greater attention to climate risk management. Meanwhile, media attention amplifies the normative pressure transmitted through networks via reputation pressure mechanisms. These findings extend the research perspective on the interactive effects between institutional pressure and external monitoring pressure within social networks, reminding future research to pay closer attention to the boundary conditions of network governance effects and their contextual dependencies.

Finally, heterogeneity analysis reveals differentiated impact patterns of ownership nature and regional physical risk exposure. Director networks exhibit stronger effects in state-owned enterprises (SOEs), which aligns with the institutional reality that SOEs face higher legitimacy pressures in China’s transitional economy context [75]. Meanwhile, the network effect is enhanced in regions with high physical risk, suggesting that when climate threats transform from abstract risks to concrete exposures, the pressure transmitted through director networks is more readily converted into disclosure actions. These findings not only enrich the heterogeneity research on climate risk disclosure but also provide new evidence for understanding the boundary conditions of network governance across different institutional and risk contexts. Overall, this study reveals the critical role of board interlocking networks as an informal governance mechanism in driving corporate climate risk disclosure, provides systematic evidence from emerging markets for the integration of social network theory and climate governance research, and expands the social embeddedness explanation pathway for corporate climate behavior.

6.2. Policy Implications

Based on the research findings, we propose the following policy recommendations to encourage and promote corporate climate risk disclosure:

For Firms: First, firms should systematically cultivate the social networks of their board members while actively recruiting independent directors with extensive external network connections to broaden and deepen their networks. In director selection, emphasis should be placed on candidates’ professional capabilities, industry experience, and their position and influence within industry networks, thereby leveraging the positive governance effects of network embeddedness. Second, firms should establish robust disclosure systems to enhance financial and non-financial information transparency. Simultaneously, firms should prioritise accumulating and maintaining reputational capital by consistently fulfilling environmental responsibilities and proactively communicating information to build a positive social image. These measures will create a favorable environment for leveraging director network effects, strengthening directors’ intrinsic motivation to drive climate risk disclosure. Third, considering the critical moderating role of environmental regulation and media attention, firms should proactively adapt to increasingly stringent ecological oversight requirements. Concurrently, firms should proactively engage with media, voluntarily disclose climate risk information, and reduce information asymmetry by enhancing transparency to earn stakeholder trust and support. Finally, differentiated strategies should be adopted by different firm types. State-owned enterprises should leverage their interlocking directorate networks to pioneer climate risk disclosure systems and serve as exemplary leaders. Non-state-owned enterprises should expand their director networks more actively, using market-based mechanisms to compensate for resource disadvantages. Firms in regions with high physical climate risk exposure should emphasise the role of the interlocking director network, enhancing climate risk response capabilities through network learning and resource sharing.

For Policymakers: First, policymakers should refine the interlocking director network’s regulatory framework. While encouraging the development of director networks, robust independence oversight mechanisms should be established to prevent potential adverse effects such as collusion. Second, recognising the positive synergistic effect of environmental regulation on the interlocking director network and climate risk disclosure, policymakers should continue advancing the refinement and implementation of environmental laws and regulations. Policy design should balance regulatory intensity, strengthening corporate disclosure incentives through strict legal accountability, while avoiding excessive compliance costs. Third, supervision and evaluation of corporate climate risk disclosure should be strengthened by establishing unified disclosure standards and assessment frameworks.

6.3. Research Limitations and Prospects

Although this study systematically explored how the interlocking director network affects climate risk disclosure and provided an empirical analysis, there are still some limitations, and it offers further scope for future research. First, this study relies on climate risk information disclosed by companies, which is often voluntarily provided. This introduces the potential for self-reporting bias, as companies may selectively disclose information that enhances their image while omitting less favorable details. This could affect the reliability of the results. Future studies could mitigate this bias by using third-party data sources or conducting case studies to provide more objective assessments. Second, the sample in this study is mainly concentrated on companies from specific countries or regions. Future research could explore climate risk disclosure practices across different cultural and regional contexts, particularly in countries with differing policy environments. For example, the disparity in climate risk disclosure between developed and developing countries may be influenced by varying governance structures, regulations, and market mechanisms. Cross-national comparative studies would offer valuable insights into the role of cross-regional director networks in climate risk disclosure in a globalized context.