Managerial Ability and ESG Risks: The Moderating Effect of Internal Control Quality

Abstract

The 2017 COSO framework highlights the increasing need to incorporate environmental, social, and governance (ESG) considerations into firms’ internal control and risk management practices. Top managers play a crucial role in risk management and control. However, it remains unclear whether managerial ability affects ESG risk management. This study investigates the relationship between firms’ ESG risk exposure and managerial ability, and examines whether the quality of internal control (ICQ) influences this relationship. Based on panel data from Chinese listed firms between 2008 and 2022, we found that firms led by more capable managers have lower overall ESG risk exposure, including reduced environmental, social, and governance risks. Furthermore, our findings indicate that higher ICQ strengthens the association between managerial ability and ESG risks. Specifically, an analysis of the five internal control components showed that the mechanisms through which ICQ influences this relationship involve enhancing control environments, risk assessment, control activities, and information and communication channels. Further analysis indicated that the moderating effect of ICQ on the relationship between managerial ability and ESG risks is influenced by management power and the effectiveness of external governance mechanisms.

1. Introduction

In recent years, firms have increasingly recognized and addressed environmental, social, and governance (ESG) risks due to heightened attention from investors, customers, and other stakeholders. ESG risks refer to the risks related to the environmental, social, and governance dimensions of business [1]. These include environmental issues such as climate change, greenhouse gas emissions, and pollution; social issues such as human rights violations, social discrimination, and impacts on communities; and governance issues such as corruption, fraud, and board independence. According to the World Economic Forum’s 2023 Global Risks Report, ESG-related risks have emerged as one of the top five global concerns for firms and significantly affect their daily operations. The literature indicates that these risks can undermine the credibility of firms’ ESG practices, posing significant threats to their reputation and potentially harming firm value [2,3,4,5,6]. In this context, the COSO 2017 Enterprise Risk Management Framework underscores the need for an updated internal control system that incorporates ESG factors into firms’ risk management objectives [7]. Previous research has identified several firm-level factors that affect ESG risk controls [8,9,10,11]. These studies suggest that stronger corporate governance [10], effective board monitoring [11], lower levels of shareholder litigation [9], and the provision of non-audit services [8] are correlated with less exposure to ESG risks.

Managers at the upper echelon are responsible for managing firm risks, including identifying, assessing and responding to firm risks related to ESG issues. While prior research has explored various firm-level factors that influence ESG risks, it remains unclear whether managerial ability affects these risks. Managerial ability refers to the knowledge, experience, and expertise that enable managers to utilize resources more efficiently [12]. The existing literature presents mixed findings on the relationship between managerial ability and firm risks, with some arguing that high-ability managers may expose firms to greater financial risks [13,14,15]. ESG risks are inherently more uncertain and complex than traditional firm risks, encompassing a broader range of issues that complicate their identification, assessment, and integration [16]. The evidence suggests that managers face considerable challenges in fully assessing and predicting ESG-related risks and allocating sufficient resources to address these risks due to the lack of clarity in the structure and rules governing ESG efforts [16,17]. Hence, the capability of top managers could be essential in determining the effectiveness of ESG risk management. This raises the following question: do more capable managers reduce ESG risks for firms? This is the first research question in this study.

As an internal governance mechanism, internal control is essential for guiding firms toward sustainable development [18]. Research indicates that firms with robust internal control are more likely to integrate ESG factors into their strategic frameworks and achieve better ESG ratings [19]. While the COSO framework emphasizes the role of internal control in managing ESG risks, evidence on the integration of internal control within firms’ ESG risk management frameworks remains limited. Internal control, as an institutional mechanism, and managerial ability, as a behavioral mechanism, are expected to influence firms’ ESG risks. Do they enhance the management of ESG risks and, if so, what mechanisms underpin this relationship? To fill this gap, this study further investigated whether internal control quality (ICQ) influences the relationship between managerial ability and firms’ exposure to ESG risks.

Based on a dataset comprising 12,723 firm–year observations from Chinese listed firms between 2008 and 2022, this study employed panel data estimation with industry and year fixed effects to examine the relationship between managerial ability and firms’ ESG risks. The findings revealed a significant negative relationship between managerial ability and ESG risk exposure, encompassing its subdimensions, namely, environmental (E), social (S), and governance (G) risks. This relationship remained robust across different endogeneity and robustness tests. Furthermore, our analysis showed that the negative relationship between ESG risk exposure and managerial ability becomes more pronounced in the presence of strong ICQ. A detailed examination of the five components of internal control demonstrated that the moderating effect is stronger in firms that excel in their control environment, risk assessment, control activities, and information and communication systems.

Finally, we conducted two additional tests to examine whether management power and external corporate governance mechanisms influence the moderating effect of ICQ on the relationship studied. Our findings indicated that the moderating role of ICQ diminishes when capable managers also serve as the chair of the board, hold higher ownership, or have shorter tenures in the firm. In addition, the moderating effect of ICQ diminishes in the presence of more robust external corporate governance mechanisms, as measured by audit quality and product market competition.

Our study makes several contributions to the existing literature. First, it extends research on managerial ability by demonstrating its impact on firms’ exposure to ESG risks. While prior studies have examined the relationship between managerial ability and firm risks [13,14,15] and between managerial ability and ESG-related performance [20,21,22], to the best of our knowledge, no study has specifically explored the effect of managerial ability on firm risks related to ESG issues. Moreover, we are among the few studies that differentiate between the types of ESG risks (except Newton et al. [23]), thus providing a more nuanced perspective on how managerial ability influences firms’ environmental, social, and governance risks individually.

Second, it enriches the literature on the determinants of firm-level ESG risks [8,9,10,11] by introducing two new significant internal mechanisms: managerial ability and ICQ. In addition, while most research on ESG risks has focused on Western markets such as the U.S. and Europe, our study examined the Chinese context. This issue is important because identifying strategies to mitigate ESG risks in Chinese firms is critical, given the significant ESG challenges posed by the country’s rapid economic development [24].

Third, it provides evidence of the role of internal control within firms’ ESG risk management framework by showing how ICQ may affect the relationship between managerial ability and ESG risks. It also meticulously examines how the components of internal control may exert varying impacts on this relationship. Furthermore, we investigated whether management power and the effectiveness of external governance influence the moderating effect of ICQ on this relationship. In this way, it enhances the research on the impact of internal control on firms’ ESG performance [19,25,26] and provides a direct assessment of the implementation of the COSO 2017 framework in firms’ ESG risk management.

This study is structured as follows: Section 2 reviews the theoretical background and develops the hypotheses. Section 3 outlines the research methodology. Section 4 presents the empirical results. Finally, Section 5 concludes the study.

2. Theoretical Foundation and Hypothesis Development

2.1. Theoretical Foundation

The upper echelons theory (UET) posits that the characteristics and cognitive biases of top executives significantly influence their decision making processes and, consequently, firm outcomes [27,28]. According to the UET, senior leaders formulate decisions and strategies based on their personal interpretations of the firm’s situational context. These interpretations are shaped by their personal biases, which include their cognitive biases (e.g., understanding or expectations regarding future events, alternatives, and their implications) and values (e.g., principles for evaluating alternatives and their potential impacts) [27]. Based on the UET, the prior literature has investigated the impact of different managerial characteristics on firm risks. Research indicates that managers’ age, gender, overconfidence, capabilities, and ethical considerations are critical factors influencing firms’ risk management strategies [14,29,30,31]. For instance, Faccio et al. [29] found that firms with female CEOs have less volatile earnings because these CEOs are generally more risk-averse. Serfling [30] showed that older CEOs, through their cautious strategies and prudent allocations, are likely to reduce firms’ financial risk exposure. Tang and Chang [31] suggested that overconfident CEOs may overestimate investment returns, which translates into increased risk-taking. Given that ESG risks represent a specific form of firm risk, it follows that managerial characteristics will affect how executives manage these risks, as the traits and biases of executives are likely to influence the firm’s strategy for ESG engagement and its ESG risk profile.

Despite this, limited research has examined the effects of managerial characteristics on managing ESG-related risks. As an important cognitive dimension of managerial characteristics, managerial ability, which refers to managers’ knowledge, expertise, and experience, is a critical factor in transforming firm resources into successful outcomes [32]. ESG risks distinguish themselves from traditional firm risks by encompassing a broader array of issues that impact multiple dimensions of firm performance, with some risks potentially taking years to materialize [16]. In the context of ESG risk management, the literature indicates that executives face considerable challenges in fully assessing and predicting ESG-related risks due to their inherent uncertainty and complexity [16,17,23]. As a result, executives tend to rely on their own knowledge and interpretations when formulating risk management strategies [33,34]. Given this reliance on individual interpretation, it is likely that managerial decisions regarding ESG risk management are shaped by executives’ managerial ability. This capability may lead some managers to view proactive ESG risk management as a strategic opportunity, while others may perceive it as a threat [17]. Thus, it is of theoretical and empirical importance to examine the effect of managerial ability on ESG risk management.

The theory of enterprise risk management (ERM) suggests that firms implementing ERM adopt a comprehensive approach to risk management and enhance shareholder value [35]. Jankensgård [36] indicated that risk management occurs within the context of agency relationships and information asymmetries, with ERM addressing these issues through risk governance and aggregation. On the one hand, managerial behavioral biases and incentive structures can lead to improper risk management. Effective risk governance in the ERM framework reveals how these factors influence risk management practices, thereby ensuring that risk management decisions align with the firm’s interests. On the other hand, addressing information asymmetries is critical for effective risk management. ERM ensures the collection and aggregation of high-quality risk information, facilitating timely and clear communication to support informed managerial decision making.

Internal control consists of a set of processes and procedures designed to manage and mitigate risks, ensuring the effective execution of risk management activities within the ERM framework [37]. These processes are structured around five key components: control environment, risk assessment, control activities, information and communication, and monitoring activities. The control environment establishes the foundation by shaping the organization’s culture, ethics, and governance structures. Risk assessment involves identifying and evaluating risks that may obstruct the achievement of organizational objectives. Control activities include policies and procedures that help ensure risk mitigation strategies are effectively implemented. Information and communication ensure that relevant and reliable data flow across the organization to support decision making. Finally, monitoring activities involve ongoing evaluations to assess the quality of internal controls over time, ensuring they remain aligned with the evolving risk landscape. The literature suggests that internal control helps mitigate behavioral biases and agency costs that could otherwise distort managerial behaviors. Implementing robust internal control encourages managers to align their actions more closely with the firm’s interests, thereby enhancing the effectiveness of risk management strategies [38]. Despite claims that internal control reduces firm risk exposure, there is limited evidence on its effects on firms’ ESG risks and how it interacts with managerial ability in managing these risks.

2.2. Managerial Ability and ESG Risks

Managerial ability refers to the ability of managers to more efficiently transform firm resources into profits [12]. This ability is developed and accumulated through managerial experience in managing firms, such as predicting market trends, formulating strategic plans, and implementing technologies to execute these strategies [32]. As managers develop and refine their skills and expertise, they become more proficient at aligning firm strategies with industry contexts, thereby improving firm performance by more effectively identifying opportunities and addressing threats [39].

Supporting this view, existing evidence suggests that firms led by more capable managers tend to achieve superior performance in different areas, including financial performance, investment decisions, accounting practices, and corporate innovation [40,41,42,43]. High-ability managers are more adept at crafting strategies that navigate complex business environments, guiding firms to success even amid crises and uncertainty [42,44]. Li et al. [45] contended that effective ESG risk management can create strategic advantages by enabling firms to capitalize on growth opportunities, such as developing innovative products, entering new markets, improving resource efficiency, and building resilient supply chains. More capable managers can strategically leverage these ESG initiatives to enhance their firms’ value [41]. Thus, it is expected that high-ability managers should be positioned to manage ESG risks by implementing ESG risk management policies and capitalizing on opportunities presented by ESG challenges. From the perspective of legitimacy theory, managers should align sustainability practices with societal values and expectations to ensure firm survival [46]. The increasing stakeholder attention and scrutiny concerning firms’ ESG risks [47,48] can be viewed as societal pressure that compels managers to address these risks in order to maintain legitimacy.

COSO [16] highlighted several challenges associated with managing ESG risks. For instance, it noted that “ESG-related risks are macro, multi-faceted, and interconnected, and can affect the business in many dimensions, making the assessment of ESG-related risks more complex” (p. 50). Additionally, COSO [16] pointed out that “because of the uncertainty associated with ESG-related risks, they tend to be influenced by organizational biases that exist when assessing and prioritizing risks” (p. 50). Hence, the nature of ESG risks implies that their effective management will depend on top managers’ abilities to anticipate and respond to a complex and interrelated range of risks and opportunities that could impact the firm’s goals and strategy.

Given the complexities inherent in ESG risk management, we expect that managerial ability, as an important cognitive trait of managers, influences how they perceive and respond to ESG risks. Hunter and Hunter [49] suggested that individuals with higher cognitive ability can use their enhanced processing capacities to adopt more advanced and sophisticated problem-solving strategies when addressing complex tasks. This stems from the fact that higher cognitive ability levels enable individuals to better monitor tasks, respond to changes in tasks, and actively implement new routines in complex situations [50,51]. In contrast, individuals with lower ability often need to devote a significant portion of their cognitive processing capacity to manage complex tasks. As a result, they may find it difficult to recognize and respond to changes in their environment. Additionally, the literature indicates that individuals with high cognitive ability are less vulnerable to the negative impacts of stress and uncertainty [52], which often accompany changes in the task environment. Supporting this view, Nelson and Simmons [53] found that those with greater cognitive ability are more likely to perceive increased task complexity as a motivating challenge, which enhances their intrinsic motivation to engage with the task, embrace learning opportunities, and ultimately achieve their goals. Based on this, we predict that the effectiveness of ESG risk management is positively correlated with the managerial abilities of individuals in the firms.

In line with the prior literature [12,17,20], we propose that high-ability managers are more likely to develop a holistic view of their firm’s ESG risk profile, make more informed judgments regarding potential ESG risks and opportunities that could impact the firm’s strategy and objectives, and align ESG considerations more effectively with business operations. Therefore, capable managers should have better knowledge and insights when formulating ESG risk management strategies. This includes improved accuracy in predicting and assessing ESG-related risks, as well as more efficient resource allocation for ESG risk mitigation. The hypothesis is outlined as follows:

H1: 

Managerial ability is significantly negatively associated with firms’ ESG risk exposure.

2.3. Managerial Ability and ESG Risks: The Moderating Role of ICQ

The literature suggests that weak internal control can impair managerial decision making, leading to suboptimal firm performance. For example, in firms with ineffective internal control, managers tend to provide less accurate guidance [54], which, in turn, results in poorer decision making [55]. In line with this perspective, D’Mello et al. [56] found that weak internal control hinders managers’ ability to allocate internal capital efficiently. Furthermore, firms with inadequate internal control typically invest less [57], invest less efficiently [55], and have lower operational efficiency [58]. Weak internal control can also exacerbate managerial agency problems related to ESG initiatives. Koo and Ki [59] indicated that such weaknesses lead firms to prioritize short-term investments, negatively affecting their willingness to engage in ESG activities, especially those requiring long-term commitments or stakeholder engagement. Therefore, this evidence leads to the argument that weak internal control may reduce managers’ motivation to engage in ESG activities and their capacity to manage ESG risks effectively.

A growing stream of evidence suggests that internal control is essential for integrating ESG practices and achieving desired ESG outcomes [19,25,26]. We expect that the relationship between ESG risk exposure and managerial ability will be stronger in the presence of robust internal control for two important reasons. First, from the perspective of risk assessment, managers rely on various sources of information to develop risk forecasts, including those related to ESG risks. Therefore, the quality of this information will significantly influence the accuracy of risk forecasts. Firms with robust internal control provide more reliable information and communication channels [60,61]. This enhances managers’ access to accurate information and effective feedback mechanisms in a timely manner, thereby improving the precision of the assessment of ESG risks. Second, in terms of risk prevention, robust internal control can curb managers’ self-interests and rent-seeking behaviors through stringent controls and monitoring [62]. They also help align managerial behaviors and incentives with the firm’s risk management practices [36], ensuring that decisions related to ESG risk management align more closely with the firm’s objectives. Therefore,

H2a: 

The quality of internal control significantly moderates the association between managerial ability and ESG risks.

Nevertheless, due to a lack of prior evidence on the mechanisms through which internal control may affect ESG risks, it remains unclear how internal control interacts with managerial ability to mitigate these risks. We expect that the moderating effect of ICQ on this relationship will operate through its five components. Hence, we decomposed the five components of internal control—control environment, risk assessment, control activities, information and communication, and monitoring—and propose that each component moderates the relationship between managerial ability and ESG risks. Thus, we developed the following hypothesis:

H2b: 

The control environment, risk assessment, control activities, information and communication, and monitoring components of internal control moderate the relationship between managerial ability and ESG risks.

3. Research Methods

3.1. Sample Selection

Our sample included a panel of Chinese listed firms from 2008 to 2022. This timeframe was selected because the RepRisk database provides ESG risk data for Chinese firms from 2008 through 2023. However, we excluded 2023 observations due to the absence of internal control quality data from the DIB database for that year. We excluded financial firms from our analysis due to differences in reporting methods and corporate governance structures compared to non-financial firms. Furthermore, firms identified as ST, *ST, and PT were excluded due to concerns regarding their ongoing concern status. We also excluded firms lacking data on control variables. Ultimately, our sample consisted of 12,723 firm–year observations, representing 1656 unique firms.

Table 1. Sample selection and distribution.

Panel (A): Sample selection
Coverage of Chinese listed firms in the RepRisk database: fiscal years 2008–2022					23,994
Excluding financial firms and firms labeled as ST, *ST, and PT					(2811)
Excluding firms with incomplete data on managerial ability scores					(3357)
Excluding firms with incomplete data on internal control quality					(1817)
Excluding firms with incomplete data on control variables					(3286)
Final sample firms					12,723
Unique firms					1656
Panel (B): Distribution of sample firms by industry and year
Industry Name	Frequency	Percentage	Year	Frequency	Percentage
Business	684	5.37	2008	559	4.39
Chemicals	1698	13.34	2009	598	4.70
Construction and materials	741	5.82	2010	589	4.63
Consumer	855	6.72	2011	699	5.49
Electronic	810	6.36	2012	837	6.58
Food and beverage	895	7.03	2013	888	6.98
Industrial and manufacturing	2175	17.08	2014	867	6.81
Mining, oil, and gas	722	5.67	2015	902	7.09
Personal and household	793	6.23	2016	961	7.55
Pharmaceuticals and biotech	1319	10.36	2017	745	5.86
Retail and services	1093	8.59	2018	804	6.32
Utilities	601	4.72	2019	827	6.50
Others	337	2.71	2020	815	6.40
			2021	1290	10.14
			2022	1342	10.56
Total sample	12,723	100	Total	12,723	100

details our sample, with Panel A describing the sample selection and Panel B presenting the industry and year representation within the sample. This Table indicates a significant increase in sample size, reflecting the expanding coverage of firms in the RepRisk database. The industries with the most severe ESG risk concerns in China include industrial and manufacturing, chemicals, pharmaceuticals and biotechnology.

3.2. Variable Measures

3.2.1. Measurement of Managerial Ability

This study utilized the managerial ability (MA) measure established by Demerjian et al. [12]. This measure evaluates how efficiently managers convert firm resources into productive outputs. Higher scores are given to managers who generate greater outputs from the same resources. Since managerial ability is indirectly measured through the outcomes of resource allocation, Demerjian et al. [12] introduced a two-step approach for precise measurement. The initial step involves applying data envelopment analysis (DEA) to assess the firm’s overall efficiency in transforming resources into revenue compared to its industry counterparts. Multiple factors contributing to revenue generation are considered input variables in the DEA, including fixed assets (FA), intangible assets (IA), goodwill (GW), cost of goods sold (COGS), research and development (R&D) expenses, operating costs (OC), and selling and administrative expenses (SG&A). Equation (1) is employed to calculate overall efficiency at the firm level.

$${\mathrm{M}\mathrm{a}\mathrm{x}}_{\mathrm{i},\mathrm{t}}={\displaystyle \frac{{\mathrm{R}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{u}\mathrm{e}}_{\mathrm{i},\mathrm{t}}}{{\mathrm{a}}_1{\mathrm{F}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_2{\mathrm{I}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_3{\mathrm{G}\mathrm{W}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_4{\mathrm{C}\mathrm{O}\mathrm{G}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_5{\mathrm{R}\&\mathrm{D}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_6{\mathrm{O}\mathrm{C}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_7{\mathrm{S}\mathrm{G}\&\mathrm{A}}_{\mathrm{i},\mathrm{t}}}},$$

(1)

${\mathrm{M}\mathrm{a}\mathrm{x}}_{\mathrm{i},\mathrm{t}}$ ranges from zero to one. Given that both management and the firm as a whole can contribute to a business’s success, the overall efficiency is then divided between them. Six characteristics were identified to account for firm-specific efficiency: firm size (SIZE), market share (SHARE), cash flows (CF), firm age (AGE), overseas businesses (OB), and business diversification (BD). These factors were then regressed against total firm efficiency using a Tobin model, computed as follows:

$${\mathrm{E}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}}_{\mathrm{i},\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{S}\mathrm{I}\mathrm{Z}\mathrm{E}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{S}\mathrm{H}\mathrm{A}\mathrm{R}\mathrm{E}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3\mathrm{C}{\mathrm{F}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{A}\mathrm{G}\mathrm{E}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{O}\mathrm{B}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{B}\mathrm{D}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}},$$

(2)

The residual component in this model represents the value of managerial ability at the firm level. A higher value indicates higher managerial ability.

3.2.2. Measurement of ESG Risks

This study employed the RepRisk Index (RRI) from the RepRisk database, known for its comprehensive coverage of ESG and business conduct risks, to evaluate a firm’s exposure to ESG risks. The RRI is provided monthly, and we aggregated it into yearly data by averaging the monthly values. The RRI score ranges between 0 and 100, with higher values reflecting greater ESG risk exposure. Following Asante-Appiah and Lambert [8], we used the natural logarithm of one plus the current RRI to measure firms’ current level of exposure to ESG risks (ESG_Risk). Additionally, RepRisk provides a breakdown of the RRI into components based on the number of incidents related to a firm’s environmental, social, and governance issues. This allowed us to analyze the subcategories of ESG risks: environmental risk (E_Risk), social risk (S_Risk), and governance risk (G_Risk). Following the methods of Newton et al. [23], environmental risk was calculated by first determining the percentage of environmental incidents in the current RRI, as provided by RepRisk. This percentage was then multiplied by the current RRI. The natural logarithm of this product plus one was then taken to obtain the value of environmental risk. The same approach was applied to measure social and governance risks.

3.2.3. Measurement of Internal Control Quality

Consistent with Chan et al. [63], data on internal control in this study were gathered from the DIB Business Risk Management Inc. database. This database utilizes an indexing system to collect data on internal control and its five key components from the annual reports, audit reports, and internal control evaluations of Chinese listed firms. The index of internal control spans from 0 to 66, where higher scores denote superior internal control quality. We used the natural logarithm of this index to represent the quality of internal control.

The COSO 2013 framework identifies five key components of internal control: the control environment, risk assessment, control activities, information and communication, and monitoring. While the overall internal control quality of a firm influences its risk management effectiveness, each component plays a unique role and can individually impact the firm’s risk management capabilities. The DIB database provides data on these five components of internal control. Therefore, we separately examined the moderating effect of each component to gain a more nuanced understanding of how internal control components influence the relationship between managerial ability and ESG risks.

3.2.4. Control Variables

We mitigated potential confounding factors affecting the association between managerial ability and ESG risks by incorporating various firm- and management-level controls, consistent with previous research [8,10]. The firm-level control variables consisted of firm size (Size), return on assets (ROA), leverage (Lev), sales growth (Growth), cash flow (Cash), firm age (FirmAge), audit opinion (Opinion), and ICQ.

For management-level controls, we included the number of board directors (Board), a dummy variable for management duality (Dual) that equals one if a manager also holds the position of the chair of the board of directors, and the manager’s ownership stake (Mshare) and tenure (Tenure). The measurement of these control variables is provided in Appendix A.

3.3. Research Models

H1 examines the association between ESG risk exposure and managerial ability. We employed the following regression model to test this association:

$${\mathrm{E}\mathrm{S}\mathrm{G}\_\mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k}}_{\mathrm{i},\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{M}\mathrm{A}\_\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\beta }}_{\mathrm{n}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\mathrm{Year}\_\mathrm{FE}+\mathrm{Industry}\_\mathrm{FE}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t},}$$

(3)

where i represents individual firms, t represents different years within the study period, ESG_Risk denotes the overall ESG risk exposure of the firm, and MA_Score is the measure of managerial ability. Additionally, we replaced ESG_Risk with E_Risk, S_Risk, and G_Risk and regressed each separately based on managerial ability. We included year and industry fixed effects and cluster standard errors at the firm level.

H2a states that the relationship between ESG risk exposure and managerial ability is stronger in firms with more robust internal control. We employed the following model to test this relationship:

$${\mathrm{E}\mathrm{S}\mathrm{G}\_\mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k}}_{\mathrm{i},\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1 {\mathrm{M}\mathrm{A}\_\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_2 {\mathrm{I}\mathrm{C}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3 {\mathrm{M}\mathrm{A}\_\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}_{\mathrm{i},\mathrm{t}}* {\mathrm{I}\mathrm{C}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\beta }}_{\mathrm{n}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\mathrm{Year}\_\mathrm{FE}+\mathrm{Industry}\_\mathrm{FE}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t},}$$

(4)

where i represents individual firms and t represents different years within the study period. The interaction coefficient of MA_Score * ICQ (MAICQ) signifies the moderating effect of ICQ on the association between ESG risks and managerial ability. We included year and industry fixed effects and clustered standard errors at the firm level.

H2b hypothesizes that the five internal control components moderate the relationship between managerial ability and ESG risks. To test this hypothesis, we replaced ICQ with the five individual components of internal control: control environment (Con. Env.), risk assessment (Ris. Ass.), control activities (Con. Act.), information and communication (Inf. Com.), and monitoring activities (Mon. Act.). A moderating effect exists when the coefficients on the interaction terms for MACE (MA_Score * Con. Env.), MARA (MA_Score * Ris. Ass.), MACA (MA_Score * Con. Act.), MAIC (MA_Score * Inf. Com.), and MAMA (MA_Score * Mon. Act.) are statistically significant. We re-ran Equation (4) to examine the moderating effect of each component separately on the association between managerial ability and ESG risks.

4. Empirical Results

4.1. Descriptive Statistics

Panel A of

Table 2. Descriptive statistics and correlation matrix.

Panel (A): Descriptive Statistics
Variable	N	Mean	SD		Min		P25		Median		P75		Max
ESG_Risk	12,723	0.771	1.173		0		0		0		1.934		4.134
MA_Score	12,723	−0.008	0.168		−0.806		−0.12		−0.01		0.076		0.596
ICQ	12,723	3.475	0.342		1.609		3.375		3.584		3.689		4.078
Size	12,723	22.582	1.37		19.406		21.576		22.459		23.489		26.444
Lev	12,723	0.485	0.202		0.027		0.332		0.494		0.639		0.925
ROA	12,723	0.036	0.066		−0.375		0.01		0.033		0.067		0.255
Cash	12,723	0.049	0.071		−0.226		0.01		0.047		0.09		0.283
Growth	12,723	0.168	0.41		−0.648		−0.031		0.109		0.271		3.705
FirmAge	12,723	2.349	0.696		0		1.946		2.485		2.89		3.401
Opinion	12,723	0.961	0.194		0		1		1		1		1
Board	12,723	2.151	0.205		1.609		1.946		2.197		2.197		2.708
Dual	12,723	0.234	0.423		0		0		0		0		1
Mshare	12,723	0.087	0.161		0		0		0.001		0.088		0.706
Tenure	12,723	1.52	0.594		0.693		1.099		1.386		1.946		3.091
Panel (B): Correlation Matrix
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
(1) ESG_Risk	1
(2) MA_Score	−0.056 ***	1
(3) ICQ	−0.021 ***	0.110 ***	1
(4) Size	0.281 ***	−0.017 **	0.248 ***	1
(5) LEV	0.086 ***	0.062 ***	−0.064 ***	0.421 ***	1
(6) ROA	−0.035 ***	0.149 ***	0.056 ***	0.061 ***	−0.366 ***	1
(7) Cash	0.042 ***	0.061 ***	0.015 *	0.057 ***	−0.203 ***	0.407 ***	1
(8) Growth	−0.039 ***	0.097 ***	−0.006	0.042 ***	0.011	0.265 ***	0.043 ***	1
(9) FirmAge	−0.121 ***	0.051 ***	0.075 ***	0.360 ***	0.314 ***	−0.148 ***	−0.013	−0.076 ***	1
(10) Opinion	−0.051 ***	0.029 ***	0.083 ***	0.083 ***	−0.102 ***	0.273 ***	0.087 ***	0.080 ***	−0.046 ***	1
(11) Board	−0.004	0.044 ***	−0.064 ***	0.216 ***	0.119 ***	0.060 ***	0.068 ***	−0.012	0.095 ***	0.020 **	1
(12) Dual	0.001	−0.074 ***	0.036 ***	−0.124 ***	−0.093 ***	0.008	−0.036 ***	0.029 ***	−0.184 ***	−0.003	−0.168 ***	1
(13) Mshare	−0.045 ***	−0.074 ***	0.102 ***	−0.249 ***	−0.277 ***	0.126 ***	−0.004	0.068 ***	−0.516 ***	0.020 **	−0.183 ***	0.206 ***	1
(14) Tenure	−0.003	−0.025 ***	0.050 ***	0.002	−0.066 ***	0.103 ***	0.055 ***	−0.028 ***	−0.035 ***	0.062 ***	−0.001	0.184 ***	0.102 **	1

Panel A of Table 2 presents the descriptive statistics of the study variables. Panel B displays the correlations among the multivariate regression variables. Statistical significance is denoted by ***, **, and *, indicating significance levels at 1%, 5%, and 10%, respectively.

presents summary statistics for the study variables. The dependent variable, ESG_Risk, has a mean of 0.771, with ESG risk values ranging from 0 to 4.134, indicating significant diversity in ESG practices among firms. MA_Score, the independent variable, has a mean (median) value of −0.008 (−0.01), which closely aligns with the observations of Chen et al. [41]. The mean ICQ stands at 3.475, which is close to the value of 3.350 reported by Chan et al. [60]. To mitigate the effect of outliers, continuous variables were winsorized at the 1st and 99th percentiles. The descriptive statistics for the other variables were within reasonable ranges and aligned with findings from previous studies.

Panel B of Table 2 displays the pairwise correlations between the study variables. The correlation matrix reveals a significant negative correlation between ESG risks (ESG_Risk) and managerial ability (MA_Score), providing preliminary evidence that higher managerial ability is correlated with lower ESG risk exposure. An unreported analysis suggested that all variables have variance inflation factor (VIF) scores below 5 (VIF = 2.13), indicating low multicollinearity in our study and ensuring the reliability of our regression results.

4.2. Multivariate Analyses

4.2.1. Test of Hypothesis 1

Hypothesis 1 posits a negative correlation between managerial ability and firms’ exposure to ESG risks.

Table 3. The association between managerial ability and ESG risks.

	(1)	(2)	(3)	(4)
	ESG_Risk	E_Risk	S_Risk	G_Risk
MA_Score	−0.220 ***	−0.089 **	−0.092 *	−0.145 **
	(−2.60)	(−1.98)	(−1.71)	(−2.06)
Size	0.196 ***	0.068 ***	0.103 ***	0.076 ***
	(12.02)	(7.33)	(9.09)	(5.97)
Lev	0.060	0.081 *	−0.019	−0.003
	(0.73)	(1.78)	(−0.39)	(−0.05)
ROA	−0.255	0.021	−0.134	−0.171
	(−1.12)	(0.17)	(−0.98)	(−0.99)
Cash	0.678 ***	0.349 ***	0.357 ***	0.157 **
	(4.06)	(3.93)	(3.65)	(1.20)
Growth	−0.070 **	−0.026 *	−0.030 **	−0.034 *
	(−2.51)	(−1.92)	(−2.05)	(−1.65)
FirmAge	−0.062 **	−0.055 ***	−0.025	−0.022
	(−2.58)	(−3.97)	(−1.59)	(−1.21)
Opinion	−0.275 ***	−0.043	−0.048	−0.203 ***
	(−4.05)	(−1.18)	(−1.32)	(−3.53)
ICQ	−0.069 **	−0.016 *	−0.014 *	−0.032 ***
	(−1.98)	(−0.84)	(−0.59)	(−1.38)
Board	−0.055	0.031	0.008	−0.060
	(−0.80)	(0.78)	(0.18)	(−1.16)
Dual	0.030	0.009	0.030 *	0.040 *
	(0.98)	(0.55)	(1.69)	(1.75)
Mshare	−0.163 *	−0.099 **	−0.014	−0.087
	(−1.87)	(−2.08)	(−0.24)	(−1.33)
Tenure	−0.026	0.010	0.001	−0.035 **
	(−1.26)	(0.92)	(0.05)	(−2.25)
Constant	−2.894 ***	−1.259 ***	−1.972 ***	−0.839 ***
	(−7.50)	(−5.59)	(−7.27)	(−2.88)
Observations	12,723	12,723	12,723	12,723
R-squared	0.172	0.064	0.073	0.071
Year FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES

T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

presents the regression results for Equation (3). Column (1) shows that the coefficient of MA_Score is −0.220 and significant at the 1% level, suggesting that higher managerial ability significantly decreases firms’ overall ESG risk exposure. In Columns (2) to (4), the coefficients for MA_Score are consistently negative and statistically significant (i.e., coef. = −0.089, p < 0.05 in Column (2); coef. = −0.092, p < 0.1 in Column (3); coef. = −0.145, p < 0.05 in Column (4)). These findings indicate a significant association between higher managerial ability and reduced risks in the environmental, social, and governance dimensions individually.

The results in Table 3 are in line with the existing literature regarding the influence of control variables on ESG risks. Specifically, a larger firm size (Size) is associated with higher ESG risk exposure, as the extensive scale of operations increases vulnerability to a broader range of ESG risks [64]. The significant positive association between cash flow and ESG risks suggests that firms with excess cash may experience more unfavorable assessments of ESG issues due to agency problems [65]. Firms with greater growth prospects (Growth) are linked to reduced ESG risks because they tend to focus on long-term value creation and are more likely to invest in sustainable practices as part of their growth strategies [66]. Firm age is negatively associated with ESG risks because older firms generally have more experience and have built greater community legitimacy over time [67]. Additionally, standard audit opinions (Opinion) and stronger internal control (ICQ) are associated with reduced ESG risks, consistent with the prior literature emphasizing their crucial roles in corporate governance and promoting sustainable performance [8,19]. Finally, management shareholding (Mshare) is negatively associated with ESG risks because managers with higher ownership are more inclined to align the firm’s interests with long-term value, thereby motivating them to reduce the firm’s ESG risks [10].

4.2.2. Endogeneity Test: Two-Stage Least Squares Instrumental Variable (2SLS IV) Analysis

We employed the 2SLS IV method to address potential omitted variable concerns. This method involves selecting an IV that has a strong relationship with managerial ability but does not influence a firm’s ESG risk management. Inspired by Demerjian et al. [68], this study employed the one-year lagged average industry-adjusted managerial ability of the city where each firm is headquartered (L.MA_AVG) as our instrumental variable. This choice was based on the argument by Demerjian et al. [68], which posits that firms located in regions with a higher availability of skilled managers are more likely to benefit from increased opportunities to recruit high-ability managers. Hence, the instrumental variable L.MA_AVG is expected to have a positive relationship with MA_Score. However, the average managerial ability in a city is less likely to directly influence a specific firm’s ESG risk management strategies. Thus, we considered this instrumental variable to meet the necessary criteria. We also conducted Hausman’s test on the IV. The resulting F-statistic is significant, and the corresponding p-value is less than 0.01 (p = 0.001), indicating that we cannot reject the null hypothesis of the IV being invalid.

Table 4. Regression results from the 2SLS IV analysis.

	(1)	(2)
	First Stage	Second Stage
	MA_Score	ESG_Risk
L.MA_AVG	0.823 ***
	(62.59)
Pred_MA_Score		−0.232 *
		(−1.86)
Size	−0.018 ***	0.213 ***
	(−12.32)	(18.29)
Lev	0.095 ***	0.063
	(10.50)	(0.83)
ROA	0.426 ***	−0.462 *
	(14.38)	(−1.94)
Cash	0.065 ***	0.540 ***
	(2.91)	(3.22)
Growth	0.028 ***	−0.071 **
	(6.96)	(−2.23)
FirmAge	0.020 ***	−0.050 **
	(6.70)	(−2.10)
Opinion	−0.001	−0.212 ***
	(−0.12)	(−3.21)
ICQ	−0.002	−0.100 **
	(−0.35)	(−2.55)
Board	0.007	−0.067
	(1.12)	(−1.15)
Dual	−0.012 ***	0.028
	(−3.76)	(1.02)
Mshare	−0.031 ***	−0.075
	(−2.97)	(−0.86)
Tenure	−0.002	−0.019
	(−1.04)	(−1.01)
Constant	0.295 ***	−2.497 ***
	(8.27)	(−8.44)
Hausman F-Statistic	34.33 (p < 0.01)
Observations	10,137	10,137
R-squared	0.437	0.164
Year FE	YES	YES
Industry FE	YES	YES

T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

presents the results. Column (1) presents the outcomes of the first-stage regression, where MA_Score was regressed on the IV (L.MA_AVG) and the control variables in Equation (3) to obtain the predicted values of MA_Score. The results indicate that our instrumental variable has a significant positive impact on managerial ability. Column (2) displays the findings from the second-stage regression, where firms’ ESG risks were regressed on the predicted values of managerial ability while controlling for the same variables as in the first stage. After adopting the IV regression, the results consistently show a significant negative relationship between managerial ability and ESG risks.

4.2.3. Endogeneity Test: Generalized Method of Moments (GMM) Analysis

We employed a two-step GMM model to account for potential omitted variable bias, measurement error, and endogeneity. The results presented in

Table 5. Generalized method of moments (GMM) analysis.

	ESG_Risk
L. ESG_Risk	0.365 ***
	(3.14)
MA_Score	−0.193 **
	(−0.65)
Size	0.038 *
	(0.80)
Lev	0.295
	(1.44)
ROA	−0.228
	(−0.59)
Cash	0.687 **
	(2.68)
Growth	−0.083 **
	(−2.01)
FirmAge	−0.014
	(−0.16)
Opinion	0.121
	(1.37)
ICQ	−0.091 *
	(−0.86)
Board	−0.132
	(−0.75)
Dual	0.078
	(1.22)
Mshare	−0.398 **
	(−0.90)
Tenure	−0.037
	(−1.04)
Constant	0.417 ***
	(0.38)
AR (2)	0.298
Hansen test	0.850
Observations	10,137
Year FE	YES
Industry FE	YES

T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

confirm the negative association between ESG risk exposure and managerial ability using the GMM estimation method. To assess the validity of the instruments, which included the lagged values of the control variables and year dummy variable, we used Hansen’s test for overidentification. The results indicate insufficient evidence to reject the null hypothesis of instrument exogeneity, as Hansen’s test statistic is insignificant. Moreover, the error terms of the difference equation exhibit no evidence of second-order serial correlation since AR (2) is statistically insignificant.

4.2.4. Robustness Test: Alternative Measure of ESG Risks

As an alternative measure of ESG risks, we used the Thomson Reuters Refinitiv Database to assess ESG controversies among the sample firms, consistent with prior research [3,10]. This database provides ESG controversy scores based on negative media stories related to 23 ESG controversy topics, including lawsuits, penalties, and legal disputes, indicating the firm’s exposure to ESG risks. The ESG controversy score (ESG_C) is expressed as a percentile, reflecting the firm’s involvement in ESG risks over a fiscal year, with scores ranging from 0 to 100. A score of 100 indicates that a firm has no recorded ESG risks; therefore, a higher score represents fewer ESG risks associated with the firm.

We took the natural logarithm of the value of ESG_C plus one and replaced it with ESG_Risk using Equation (3). The results are presented in

Table 6. Alternative measure of ESG risks.

	ESG_C
MA_Score	0.217 ***
	(3.65)
Size	0.012
	(0.48)
Lev	−0.066 *
	(−0.68)
ROA	−0.007
	(−0.05)
Cash	−0.089 *
	(−0.79)
Growth	0.004 *
	(0.23)
FirmAge	0.010
	(0.19)
Opinion	0.056 *
	(0.92)
ICQ	0.096 **
	(1.56)
Board	0.016
	(0.31)
Dual	0.020
	(0.91)
Mshare	0.137
	(0.88)
Tenure	0.009 *
	(0.86)
Constant	4.392 ***
	(7.49)
Observations	1574
R-squared	0.357
Year FE	YES
Industry FE	YES

T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

. The coefficient on ESG _C is significantly positive (coef. = 0.217, p < 0.01), indicating that higher managerial ability is associated with fewer ESG controversies (i.e., lower ESG risks) in firms.

4.2.5. Robustness Test: Alternative Measure of Managerial Ability

A potential endogeneity concern arises from the possibility that the measurement of managerial ability could introduce bias. To address potential measurement bias, we followed the approach of Demerjian et al. [68] by ranking the managerial ability scores (MA_Score) of all sampled firms in descending order and categorizing them into deciles based on industry and year. Firms with more capable managers (High Ability) were identified using an indicator variable, which equals 1 if MA_Score falls into the top quartile and 0 if MA_Score falls into the bottom quartile (Low Ability).

We re-examined the effect of managerial ability on ESG risks by applying the analysis outlined in Equation (3), with the results detailed in

Table 7. Alternative measure of managerial ability.

Dep. Var. = ESG_Risk	(1)	(2)
High Ability	−0.107 ***
	(−3.57)
Low Ability		0.060 *
		(1.89)
Size	0.198 ***	0.200 ***
	(12.25)	(12.32)
Lev	−0.000	−0.002
	(−0.00)	(−0.02)
ROA	−0.416 *	−0.431 *
	(−1.80)	(−1.87)
Cash	0.617 ***	0.598 ***
	(3.70)	(3.58)
Growth	−0.078 ***	−0.081 ***
	(−2.78)	(−2.88)
FirmAge	−0.065 ***	−0.062 **
	(−2.74)	(−2.58)
Opinion	−0.283 ***	−0.280 ***
	(−4.17)	(−4.12)
ICQ	−0.073 **	−0.075 **
	(−2.12)	(−2.17)
Board	−0.052	−0.052
	(−0.75)	(−0.75)
Dual	0.037	0.036
	(1.19)	(1.15)
Mshare	−0.158 *	−0.151 *
	(−1.82)	(−1.73)
Tenure	−0.027	−0.025
	(−1.29)	(−1.21)
Constant	−2.910 ***	−2.996 ***
	(−7.62)	(−7.80)
Observations	12,723	12,723
R-squared	0.173	0.172
Year FE	YES	YES
Industry FE	YES	YES

T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

. In Column (1), we present the regression outcomes related to the influence of high-ability managers on ESG risks, while Column (2) shows the effects associated with low-ability managers. The coefficient on High Ability is negative and statistically significant at the 1% level (coef. = −0.107, p < 0.01), indicating that high-ability managers effectively reduce ESG risks. In contrast, the coefficient on Low Ability is positive and significant at the 10% level (coef. = 0.060, p < 0.1), suggesting that firms led by low-ability managers may experience increased ESG risks.

4.2.6. Test of Hypothesis 2a

H2a posits that robust internal control strengthens the association between ESG risk exposure and managerial ability. We employed Equation (4) to test this hypothesis. H2 is supported if the interaction term MAICQ has a negative and statistically significant coefficient.

The results are presented in

Table 8. The moderating effect of ICQ on the association between managerial ability and ESG risks.

	(1)	(2)	(3)	(4)
	ESG_Risk	E_Risk	S_Risk	G_Risk
MA_Score	−0.220 ***	−0.089 **	−0.094 *	−0.146 **
	(−2.61)	(−1.99)	(−1.74)	(−2.08)
ICQ	−0.068 *	−0.016	−0.014	−0.032
	(−1.96)	(−0.83)	(−0.59)	(−1.38)
MAICQ	−0.143 **	−0.026 **	−0.028 *	−0.046 **
	(−1.99)	(−2.00)	(−1.79)	(−2.26)
Size	0.200 ***	0.068 ***	0.103 ***	0.075 ***
	(12.15)	(7.33)	(9.08)	(5.94)
Lev	0.061	0.082 *	−0.018	−0.000
	(0.74)	(1.79)	(−0.36)	(−0.00)
ROA	−0.234	0.022	−0.135	−0.166
	(−1.01)	(0.18)	(−0.99)	(−0.96)
Cash	0.695 ***	0.349 ***	0.360 ***	0.160
	(4.16)	(3.93)	(3.68)	(1.23)
Growth	−0.069 **	−0.026 *	−0.030 **	−0.033
	(−2.48)	(−1.91)	(−2.00)	(−1.61)
FirmAge	−0.051 **	−0.054 ***	−0.020	−0.017
	(−2.01)	(−3.73)	(−1.22)	(−0.92)
Opinion	−0.278 ***	−0.044	−0.049	−0.205 ***
	(−4.10)	(−1.18)	(−1.36)	(−3.55)
Board	−0.054	0.031	0.007	−0.060
	(−0.78)	(0.78)	(0.17)	(−1.16)
Dual	0.030	−0.009	0.030 *	0.040 *
	(0.96)	(−0.55)	(1.68)	(1.74)
Mshare	−0.186 **	−0.099 **	−0.011	−0.086
	(−2.00)	(−2.08)	(−0.20)	(−1.31)
Tenure	−0.025	0.010	0.001	−0.035 **
	(−1.19)	(0.92)	(0.08)	(−2.23)
Constant	−2.973 ***	−1.258 ***	−1.981 ***	−0.842 ***
	(−7.70)	(−5.58)	(−7.29)	(−2.90)
Observations	12,723	12,723	12,723	12,723
R-squared	0.172	0.064	0.073	0.071
Year FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES

T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

. Consistent with our hypothesis, the coefficient of the interaction term MAICQ is significantly negative (coef. = −0.143, p < 0.05) in Column (1), indicating that ICQ has a significant negative moderating effect on the association between ESG risks and managerial ability. Additionally, the coefficients of the interaction term remain significantly negative across Columns (2) to (4), suggesting that ICQ also strengthens the impact of managerial ability on the subcategories of ESG risks.

4.2.7. Test of Hypothesis 2b

H2b proposes that the five components of internal control moderate the relationship between managerial ability and ESG risks. The results are presented in

Table 9. The moderating effect of the five components of internal control on the association between managerial ability and ESG risks.

Dep Var. = ESG_Risk	(1)	(2)	(3)	(4)	(5)
MA_Score	−0.221 ***	−0.230 ***	−0.227 ***	−0.227 ***	−0.224 ***
	(−2.62)	(−2.73)	(−2.69)	(−2.68)	(−2.64)
Con. Env.	−0.017 ***
	(−3.29)
MACE	−0.056 **
	(−2.34)
Risk Ass.		0.016 ***
		(3.59)
MARA		−0.056 **
		(−2.38)
Con. Act.			−0.009 *
			(−1.85)
MACA			−0.060 **
			(−2.20)
Inf. and Com.				−0.015 *
				(−1.32)
MAIC				−0.149 *
				(−1.11)
Mon. Act.					0.012
					(1.42)
MAMA					−0.056
					(−1.07)
Size	0.199 ***	0.196 ***	0.196 ***	0.197 ***	0.195 ***
	(12.17)	(12.07)	(11.98)	(12.03)	(11.88)
Lev	0.046	0.057	0.058	0.067	0.059
	(0.55)	(0.69)	(0.70)	(0.81)	(0.72)
ROA	−0.253	−0.290	−0.270	−0.270	−0.259
	(−1.12)	(−1.28)	(−1.18)	(−1.19)	(−1.14)
Cash	0.691 ***	0.695 ***	0.690 ***	0.686 ***	0.677 ***
	(4.13)	(4.17)	(4.13)	(4.10)	(4.06)
Growth	−0.070 **	−0.063 **	−0.067 **	−0.066 **	−0.069 **
	(−2.50)	(−2.28)	(−2.40)	(−2.38)	(−2.47)
FirmAge	−0.053 **	−0.056 **	−0.051 **	−0.054 **	−0.055 **
	(−2.12)	(−2.22)	(−2.02)	(−2.15)	(−2.20)
Opinion	−0.283 ***	−0.305 ***	−0.280 ***	−0.279 ***	−0.273 ***
	(−4.18)	(−4.45)	(−4.13)	(−4.12)	(−4.03)
ICQ	−0.154 **	−0.171 ***	−0.015	−0.042	−0.097 **
	(−1.07)	(−4.28)	(−0.33)	(−1.03)	(−2.52)
Board	−0.050	−0.057	−0.058	−0.059	−0.056
	(−0.73)	(−0.83)	(−0.85)	(−0.86)	(−0.81)
Dual	0.020	0.030	0.032	0.033	0.031
	(0.65)	(0.96)	(1.03)	(1.06)	(1.00)
Mshare	−0.163 *	−0.143	−0.153 *	−0.151 *	−0.158 *
	(−1.87)	(−1.64)	(−1.75)	(−1.74)	(−1.81)
Tenure	−0.025	−0.027	−0.025	−0.027	−0.026
	(−1.17)	(−1.28)	(−1.20)	(−1.26)	(−1.22)
Constant	−3.265 ***	−2.694 ***	−3.011 ***	−2.967 ***	−2.838 ***
	(−8.18)	(−7.05)	(−7.73)	(−7.64)	(−7.30)
Observations	12,723	12,723	12,723	12,723	12,723
R-squared	0.173	0.173	0.172	0.172	0.172
Year FE	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES

T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

. The findings suggest that the moderating effect is more significant for firms with robust control environment (Con. Env.), risk assessment (Ris. Ass.), control activity (Control. Act.), and information and communication (Info. and Com.) components, as indicated by the significantly negative coefficients of the interaction terms for MACE, MARA, MACA, and MAIC. However, the moderating effect of monitoring activities (MAMA) is not significant.

Since prior evidence on the relationship between internal control and ESG risks is lacking, and inspired by Chan et al. [63] and COSO [37], we interpreted the results as follows:

The control environment reflects management’s general attitude, awareness, and commitment to the internal control system and its significance for the firm. It forms the foundation of the internal control system and includes elements such as the corporate governance framework, human resource policies, organizational structure, and accountability mechanisms. A robust control environment signals a commitment to integrity, a well-organized firm structure, and effective internal monitoring. Therefore, its influence on enhancing management’s motivations and efforts to mitigate ESG risks can be anticipated.

Risk assessment includes procedures for identifying and analyzing risks to ensure the firm’s objectives are achieved, providing the foundation for risk management. Effective risk assessment allows managers to identify and evaluate their firm’s exposure to ESG risks and their severity with reduced bias. Ultimately, it enables management to formulate more tailored strategies for mitigating ESG risks.

Control activities are mechanisms and procedures implemented within a firm to ensure the effective execution of management’s directives. Control activities can help mitigate ESG risks by providing a disciplined framework for monitoring these risks and conducting performance evaluations that inform management strategies related to potential ESG risks.

Information and communication ensure the collection and exchange of essential information necessary to achieve the firm’s objectives. This encompasses both internal communication within the firm and external communication with suppliers, regulators, and shareholders. Effective information and communication reduce information asymmetry and facilitate the timely transmission of ESG risk information. This supports informed decision making, builds stakeholder trust, and ultimately helps mitigate ESG risks.

Monitoring activities involve ongoing evaluations of the effectiveness of the internal control system. These activities support corporate governance and performance evaluation by ensuring regulatory compliance and operational efficiency. However, evidence suggests that extensive monitoring may inhibit high-uncertainty investments [69,70], including those related to ESG initiatives, which could increase exposure to ESG risks. Additionally, the literature indicates the potential adverse effects of extensive monitoring on highly capable managers [71]. As a result, the effect of monitoring activities on the relationship between managerial ability and ESG risks may not be significant.

4.3. Additional Analysis

4.3.1. The Influence of Management Power

We expect management power to affect managerial ability and internal control in addressing ESG risks. High-ability managers who gain increased power may enhance internal control to better align with ESG strategies. However, powerful managers may also engage in greater risk-taking, which can exacerbate agency problems and weaken the effectiveness of internal control [72]. Hence, we further investigated whether management power influences the moderating effect of ICQ on the relationship studied.

We built on the concepts of Ting et al. [73] using CEO–chairman duality, management ownership, and management tenure as proxy variables for management power in Chinese firms. Managers were considered to have greater power if they also served as the chairman of the board, if their ownership in the firm exceeded the industry median for management ownership, or if their tenure in the firm was longer than the industry median. Using these three proxies, we divided the sample firms into high and low management power subsamples and applied Model (4) for analysis.

Table 10. The influence of management power.

Dep Var. = ESG_Risk	(1)	(2)	(3)	(4)	(5)	(6)
	CEO–Chairman Duality	CEO–Chairman Separation	High Ownership	Low Ownership	Long Tenure	Short Tenure
MA_Score	−0.036	−0.257 ***	−0.272 **	−0.177 *	−0.214 **	−0.127 **
	(−0.24)	(−2.60)	(−2.31)	(−1.78)	(−2.49)	(−1.80)
ICQ	−0.108	−0.061 *	−0.021	−0.105 **	−0.073 **	−0.051
	(−1.34)	(−1.60)	(−0.37)	(−2.50)	(−2.07)	(−1.32)
MAICQ	−0.129	−0.148 **	−0.047	−0.186 **	−0.162 **	−0.141
	(−0.95)	(−1.75)	(−0.37)	(−2.08)	(−2.23)	(−1.15)
Control variables	YES	YES	YES	YES	YES	YES
Observations	2976	9747	5970	6753	8055	4668
R-squared	0.199	0.173	0.157	0.183	0.183	0.160
Year FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES

We do not report the coefficients of the control variables for conciseness. T-statistics in parentheses are adjusted for firm-level clustered standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

shows that the coefficients of the interaction term (MAICQ) are negative and significant only for the CEO–chairman separation subsample (coef. = −0.148, p < 0.05), the low ownership subsample (coef. = −0.186, p < 0.05), and the long tenure subsample (coef. = −0.162, p < 0.05). This suggests that internal control is less effective in facilitating ESG risk management when high-ability managers also serve as the chair of the board, hold higher ownership, or have shorter tenures in the firm.

4.3.2. The Influence of External Corporate Governance

The presence of external corporate governance may influence the moderating effect of ICQ on the relationship between managerial ability and ESG risks. Do external governance mechanisms complement or act as a substitute for the effect of ICQ in this relationship? Following Lee et al. [74], we identified auditor quality and product market competition as external governance mechanisms. When auditor quality is low or product market competition is weak, it suggests a lack of effective external corporate governance.

We denote better audit quality by the presence of a Big Four auditor. Additionally, we computed the Herfindahl–Hirschman Index (HHI) for the industry in which each firm operates. A high HHI indicates lower competition within that industry, and firms are subsequently classified based on the median of the full sample index. The results in

Table 11. The influence of external corporate governance.

Dep Var. = ESG_Risk	(1)	(2)	(3)	(4)
	Big Four	Non-Big Four	High Competition	Low Competition
MA_Score	−0.272 **	−0.146 *	−0.219 *	−0.168
	(−2.31)	(−1.95)	(−1.96)	(−1.36)
ICQ	−0.021	−0.083 **	−0.122 **	−0.016
	(−0.37)	(−2.41)	(−2.45)	(−0.35)
MAICQ	−0.047	−0.148 **	−0.155	−0.163 *
	(−0.37)	(−2.04)	(−1.47)	(−1.65)
Control variables	YES	YES	YES	YES
Observations	770	11,953	6422	6301
R-squared	0.360	0.159	0.163	0.193
Year FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES

We do not report the coefficients of the control variables for conciseness. T-statistics in parentheses are adjusted for firm-level clustered standard errors. **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

reveal that the MAICQ coefficients are negative and significant at the 5% and 10% level for the non-Big Four auditor subsample and the low product market competition subsample. Therefore, this suggests that in the context of weak external corporate governance, ICQ exerts a stronger moderating effect on the relationship between managerial ability and ESG risks.

5. Conclusions

This study explored the relationship between managerial ability, ICQ, and ESG risk exposure in Chinese listed firms from 2008 to 2022. ESG risk data were obtained from two reliable databases, RepRisk and Refinitiv. This study had two primary objectives: first, to examine whether managerial ability affects firms’ exposure to ESG risks, and second, to investigate whether ICQ moderates the relationship between managerial ability and ESG risks. The findings indicated that managerial ability significantly reduces firms’ exposure to ESG risks, including those related to the environmental, social, and governance dimensions. Moreover, ICQ strengthens the effect of managerial ability on ESG risk mitigation. A detailed analysis of the five internal control components revealed that the moderating effect is most pronounced in the control environment, risk assessment, control activity, and information and communication components.

In addition, we conducted two additional analyses. First, we explored whether management power influences the moderating effect of ICQ on the relationship between managerial ability and ESG risks. Our results suggested that the moderating effect of ICQ becomes less pronounced when high-ability managers are also the chair of the board, have higher ownership, or have shorter tenure in the firm. Furthermore, we examined whether external governance mechanisms influence the moderating effect of ICQ. Our findings indicated that this effect is weakened in the presence of strong external governance mechanisms, as evidenced by audit quality and product market competition.

5.1. Theoretical Implications

Our study contributes to the existing literature in the following ways: First, it extends research on managerial ability by suggesting that higher managerial ability is associated with lower firm exposure to ESG risks. Moreover, it is among the few studies that differentiate between the types of ESG risks, thereby enriching the understanding of the role of managerial ability in addressing firm risks related to ESG issues. Second, we enhance the literature on internal control by providing evidence of its effects on ESG risk management. Specifically, we showed that ICQ significantly influences the relationship between managerial ability and ESG risks. We further proposed that the mechanisms through which ICQ affects this relationship involve enhancing control environments, risk assessment, control activities, and information and communication channels, rather than monitoring activities. Third, we suggest that management power affects the positive influence of ICQ on the relationship between managerial ability and ESG risks. We also found that ICQ and external corporate governance mechanisms may be substituted for one another in influencing this relationship. Overall, our results enrich upper echelons theory and risk management theory.

5.2. Practical Implications

The results of our study have several practical implications. First, firms should invest in developing managerial capabilities, as higher managerial ability is associated with lower firm exposure to ESG risks, which is likely to attract investments and lead to long-term success. This also suggests that regulators may need to allocate more scrutiny and resources to the ESG issues of firms led by less capable managers. Second, firms should adopt a nuanced approach to applying an internal control framework for managing ESG risks, as the effectiveness of different components may vary when integrated with managerial ability. Third, firms should strategically balance the impact of management power on ESG risk management. Higher management ownership and CEO–chairman duality may weaken the effectiveness of the interaction between managerial ability and internal control in mitigating ESG risks; however, longer management tenure may prove beneficial. Fourth, firms should invest in internal control to enhance managerial decision making in ESG risk management, particularly in the absence of effective external governance mechanisms.

5.3. Limitations and Future Research Directions

Our study has certain limitations that warrant attention in future research. First, our empirical results are based on data from Chinese listed firms, which raises the possibility that country-specific factors may influence our findings. Future research could enhance the conceptual framework of this study by investigating the interactions among the study variables in a cross-country setting. Second, future research could further investigate the practical mechanisms through which internal control may affect ESG risks. Third, future studies could explore whether other managerial characteristics influence ESG risk management.