This study examines the effect of monitoring mechanisms, specifically board attributes, audit committee characteristics and ownership types, on environmental disclosure environmental disclosure levels (ENDL) among listed firms in Nigeria, a context where such integrated analysis is lacking.
Using panel data from 95 firms (2012–2022), we apply the Global Reporting Index to assess ENDL. The analysis utilizes panel data regression techniques, including both fixed-effects and random-effects specifications, along with the Generalized Method of Moments to control for endogeneity in the estimation.
Firms with independent board, board environmental committees, environmental expertise, independent audit committees, audit committee financial expertise, audit committee gender, government and foreign ownership exhibit higher ENDL. However, chief executive officer’s gender and the audit committee meeting frequency negatively impact ENDL.
The generalizability of this study's conclusions is potentially constrained by its country-specific context, as the data are drawn solely from the Nigerian market. To enhance the external validity of the findings, subsequent studies should replicate this analysis in other national settings. This study also paves the way for future inquiry by suggesting that the theoretical model could be expanded through the inclusion of moderating or mediating variables, which would provide a more nuanced explanation of the mechanisms driving environmental disclosure.
Policymakers should strengthen governance mandates to enhance environmental transparency. For theory, it extends agency and stakeholder theory by demonstrating that general governance mechanisms are insufficient drivers of non-financial disclosure in emerging economies.
This study has the potential to positively impact social outcomes by promoting environmental sustainability, corporate accountability and informed decision-making in Nigeria.
This study uniquely integrates board, audit and ownership monitoring mechanisms into a single framework, offering insights for Nigerian regulators and firms.
1. Introduction and background
The global financial crisis and a series of corporate failures caused by misinformation to stakeholders, which led to misguided investment decisions, highlighted the need for a new approach to reporting that would ensure users of accounting information are adequately informed. Corporate reporting worldwide has evolved beyond merely disclosing financial performance; it now emphasizes revealing strategies that help firms navigate uncertainties in the business environment. Firms are increasingly presenting their performance holistically and articulating the value created, even if that value is not directly tied to their corporate objectives (Menike, 2020). As a result, environmental disclosure (ENDL) has become a critical tool for firms to communicate their environmental values (Ezejiorfor, 2018).
Traditional financial statements are increasingly seen as inadequate for stakeholder decision-making. This leads to growing demand for supplementary environmental and strategic disclosures (Elshabasy, 2018; Ezejiorfor, 2018; Soobaroyen and Ntim, 2013). Comprehensive corporate reporting must integrate environmental commitments in order to restore stakeholder confidence, enhance information quality and reliability (Tulung and Ramdani, 2018).
The disclosure of environmental information has been steadily increasing (Alhaj, 2019; Ellili, 2020). Over 100 multinational corporations, including the Coca-Cola Company and Microsoft Corporation, have voluntarily participated in the International Integrated Reporting Council (IIRC) pilot program (Udofia et al., 2023). Additionally, companies operating in countries such as Australia, India and Singapore have begun to embrace environmental disclosure (Ayoola, 2017).
Nigerian stakeholders increasingly demand greater environmental disclosure from firms, seeking transparency about operational impacts and responses to ecological challenges. Such disclosures demonstrate corporate environmental commitment while enabling constructive stakeholder dialogue on sustainability (Jizi, 2017). These reforms aimed to improve report comparability, transparency and credibility to restore stakeholder trust and attract foreign investment. Despite global trends toward sustainability reporting, empirical evidence indicates that Nigerian firms lag in environmental disclosures. As demonstrated by Emmanuel and Ifeanyichukwu (2021), Nigerian companies' annual reports remain disproportionately focused on financial and operational metrics, with environmental and social commitments receiving minimal attention.
Nigerian firms predominantly maintain traditional financial reporting practices with a strong focus on shareholder dividends and share price performance (Erin et al., 2022). While some companies have begun incorporating qualitative stakeholder reports, research indicates that comprehensive environmental and social disclosures remain limited (Ahmad et al., 2018; Ayoola, 2017).
Although there is a growing body of literature on ENDL, many previous studies have focused on corporate governance (CG) structures as drivers or incentives for ENDL, particularly Studies examine board attributes' impact on ENDL (Musa, 2024a, b, Augustine and Francis, 2023; Adeyemi et al., 2021; Akbas, 2016), audit committee characteristics (Khan et al., 2024; Arif et al., 2020), Ownership Structure (Acar et al., 2021; Al Amosh and Mansor, 2020; Akrout and Othman, 2016) and Firm-Specific Factors (Egbunike and Tarilaye, 2017). While prior studies identify ENDL drivers (firm attributes, governance, institutional factors) (Habib et al., 2019), none holistically examine monitoring attributes from board, ownership and audit committee attributes. This study bridges this gap through a Nigeria-centric analysis of all three dimensions, expanding knowledge of African environmental disclosure.
2. Literature review, theoretical framework and hypothesis development
ENDL reflects a firm's transparency in reporting environmental strategies, impacts and management (Gerged et al., 2021; Hossain et al., 2019). Theoretically, the agency Theory explain how boards mitigate agency costs by ensuring compliance and risk disclosure (Fama and Jensen, 1983), protects shareholder interests (Ezeani et al., 2022). While Stakeholder theory balances profit and sustainability demands (Freeman, 1984). This study examines board monitoring mechanisms' impact on ENDL through four key proxies: Board independence (BOIND) (Masud et al., 2018; Zaid et al., 2020), environmental committee presence (Biswas et al., 2018), board environmental expertise (Haque, 2017) and Chief Executive Officer (CEO) gender diversity (Cordeiro et al., 2020; Zahid et al., 2020). Aliyu (2019) argued that oversight functions performed by independent board members should be in line with approved standards, laws and regulations. This is in tandem with the agency theory view, where members would be able to monitor any self-interested actions by managers and hence lower agency cost. Mohammed Aly et al. (2024) posit that agency, legitimacy and stakeholder theories provide crucial insights that help explain the nexus between CG and ENDL. The agency theory emphasizes the need for good governance to enhance corporate transparency, including ENDL, while legitimacy and stakeholder theories explain the diverse corporate motivations to provide higher ENDL. Collectively based on these theories, it can be argued that better-governed firms can reduce information asymmetry (agency theory), fulfill social contracts (legitimacy theory) and cater to stakeholder interests (stakeholder theory) by providing higher disclosure about a firm's environmental impacts.
Furthermore, stakeholder theory posits that board gender diversity positively impacts environmental disclosure (Primacintya and Kusuma, 2025). In earlier research by Alazzani et al. (2017), it was found that female board members actively participate in environmental initiatives. This suggests that male and female board members can collaborate effectively to enhance the firm's environmental performance Xie et al. (2020).
Based on these theoretical foundations and empirical literature, the null hypothesis (H0) is formulated as follows:
There is no significant relationship between board monitoring mechanisms and environmental disclosure of listed firms in Nigeria.
2.1 Audit committee attributes and environmental disclosure
The Upper Echelons Theory links audit committee composition (gender diversity, expertise) to environmental disclosure quality (Agyapong et al., 2024; Nadeem et al., 2020) while Ethicality Theory explains how committee oversight ensures environmental compliance with accurate reporting (Abdeljawad et al., 2020) and Enhanced disclosure standards. Audit committees improve ENDL accuracy through enhanced oversight (Abdeljawad et al., 2020). This study examines their influence through independence (Namakavarani et al., 2021), gender diversity (Amin et al., 2021), financial expertise and meeting frequency (Masmoudi, 2021). Based on these theoretical foundations and empirical literature, the null hypothesis (H0) is formulated as follows:
There is no significant relationship between audit committee monitoring mechanisms and environmental disclosure of listed firms in Nigeria.
2.2 Ownership structure and environmental disclosure
Agency theory posits that dispersed ownership increases agency costs due to potential conflicts (Fama and Jensen, 1983). Ownership structures align interests and boost firm value through improved ENDL (Alkurdi and Mardini, 2020).The study evaluates ownership's impact on ENDL through: Government ownership (Akrout and Othman, 2016), foreign ownership (FROWN) (Yin and Wang, 2018), managerial ownership (Masum et al., 2020), institutional ownership (INOWN) (Kolk et al., 2018). Based on the theoretical foundations established by agency theory and supported by empirical evidence, the null hypothesis (H0) is formulated as follows:
Ownership structures have no significant effect on ENDL among listed firms in Nigeria.
3. Methodology
This study adopts a correlational study to examine monitoring mechanisms' impact on ENDL using Nigeria's NGX-listed firms (N = 162 across 11 sectors). The study covered period 2012–2022 and after applying continuous listing criteria, only firms continuously listed since 2012 were selected, yielding a final sample of 95 companies (67 excluded). Table 1 provides details of both the original and adjusted populations considered for analysis.
Population and adjusted population
| S/N | Sector | Population | Cumulative value (A) | Adjusted population | Cumulative value (B) |
|---|---|---|---|---|---|
| 1 | Agriculture | 5 | 5 | 4 | 4 |
| 2 | Conglomerates | 5 | 10 | 4 | 8 |
| 3 | Construction/real estate | 8 | 18 | 2 | 10 |
| 4 | Consumer goods | 20 | 38 | 16 | 26 |
| 5 | Financial services | 51 | 89 | 36 | 62 |
| 6 | Health care | 10 | 99 | 4 | 66 |
| 7 | ICT | 10 | 109 | 5 | 71 |
| 8 | Industrial goods | 13 | 122 | 6 | 77 |
| 9 | Natural resources | 4 | 126 | 1 | 78 |
| 10 | Oil and gas | 11 | 137 | 6 | 84 |
| 11 | Services | 25 | 162 | 11 | 95 |
| S/N | Sector | Population | Cumulative value (A) | Adjusted population | Cumulative value (B) |
|---|---|---|---|---|---|
| 1 | Agriculture | 5 | 5 | 4 | 4 |
| 2 | Conglomerates | 5 | 10 | 4 | 8 |
| 3 | Construction/real estate | 8 | 18 | 2 | 10 |
| 4 | Consumer goods | 20 | 38 | 16 | 26 |
| 5 | Financial services | 51 | 89 | 36 | 62 |
| 6 | Health care | 10 | 99 | 4 | 66 |
| 7 | ICT | 10 | 109 | 5 | 71 |
| 8 | Industrial goods | 13 | 122 | 6 | 77 |
| 9 | Natural resources | 4 | 126 | 1 | 78 |
| 10 | Oil and gas | 11 | 137 | 6 | 84 |
| 11 | Services | 25 | 162 | 11 | 95 |
The data for this study were extracted from the annual reports of the selected firms over a period of 11 years, from 2012 to 2022. This period was chosen due to the significant increase in global attention to issues related to environmental disclosure (ENDL) during these years. Additionally, this timeframe coincides with a period when regulatory authorities in Nigeria implemented various reforms to address global challenges, specifically concerning the inclusion of environmental information in annual reports (Baba and Abdulmanaf, 2017; Marshall, 2020).
3.1 Empirical models formulation
Board monitoring attributes and Environmental Disclosure.
To test the hypotheses on the nexus between monitoring mechanisms and ENDL, the following models were formulated (see Table 2).
Variables measurement
| Type | Construct | Label | Measurement | Apriori expectation | Source |
|---|---|---|---|---|---|
| Dependent variable | Environmental disclosure | ENDL | Unweighted GRI disclosure index, 1for disclosure and 0 for none | Yardimci and Durak (2022) | |
| Independent variable | Board environmental committee | BENVC | Presence of an environmental committee coded as 1 if a firm has one, otherwise 0 | + | Zahid et al. (2023) |
| Independent variable | Board independence | BOINDP | Ratio of independent non-executive directors to the number of directors on the board | + | Musa (2024b) |
| Independent variable | CEO gender diversity | CEOGD | Value of 1 if the firm has a female chief executive and 0 if otherwise | + | Ullah et al. (2020) |
| Independent variable | Environmental expert | ENVEXP | Where an environmental expert is present in the Board 1 point was awarded and 0 for none | + | Musa (2024a) |
| Independent variable | Managerial ownership | MGOWN | Proportion of shares held by Directors to the total number of ordinary shares | +/− | Xu et al. (2021) |
| Independent variable | Foreign ownership | FROWN | Proportion of shares owned by foreign investors to the total number of ordinary shares | + | Acar et al. (2021) |
| Independent variable | Government ownership | GOVOWN | Proportion of shares held by Government to the total number of ordinary shares | + | Abdeljawad et al. (2020) |
| Independent variable | Institutional ownership | INOWN | Proportion of shares held by corporate bodies such as foundations, banks, insurance companies, investment firms, pension funds and limited liability firms to the total number of ordinary shares | + | Adenugba et al. (2016) |
| Type | Construct | Label | Measurement | Apriori expectation | Source |
|---|---|---|---|---|---|
| Dependent variable | Environmental disclosure | Unweighted | |||
| Independent variable | Board environmental committee | Presence of an environmental committee coded as 1 if a firm has one, otherwise 0 | + | ||
| Independent variable | Board independence | BOINDP | Ratio of independent non-executive directors to the number of directors on the board | + | |
| Independent variable | CEO gender diversity | CEOGD | Value of 1 if the firm has a female chief executive and 0 if otherwise | + | |
| Independent variable | Environmental expert | ENVEXP | Where an environmental expert is present in the Board 1 point was awarded and 0 for none | + | |
| Independent variable | Managerial ownership | MGOWN | Proportion of shares held by Directors to the total number of ordinary shares | +/− | |
| Independent variable | Foreign ownership | Proportion of shares owned by foreign investors to the total number of ordinary shares | + | ||
| Independent variable | Government ownership | GOVOWN | Proportion of shares held by Government to the total number of ordinary shares | + | |
| Independent variable | Institutional ownership | Proportion of shares held by corporate bodies such as foundations, banks, insurance companies, investment firms, pension funds and limited liability firms to the total number of ordinary shares | + |
Board Monitoring Attributes and Environmental Disclosure Model:
Audit Committee Monitoring Attributes and Environmental Disclosure Model:
Ownership structures Monitoring Attributes and Environmental Disclosure Model:
4. Empirical findings
4.1 Descriptive statistics and univariate analysis
4.1.1 Dependent variable: environmental disclosure (ENDL)
Independent Variables: Board Attributes: BOIND (Independence), BENVC (Environmental Committee), BENEX (Environmental Expertise), CEOGD (CEO Gender Diversity); Audit Committee Attributes: ACIND (Independence), ACGD (Gender Diversity), ACFEX (Financial Expertise), ACMT (Meeting Frequency); Ownership Structure: GOVOW (Government), FROWN (Foreign), MGOWN (Managerial), INOWN (Institutional); Control Variables: LEVER (Leverage), FSZ (Firm Size).
4.1.2 Data source: sampled firms' annual reports (2012–2022)
Table 3 provides a summary of the descriptive statistics for the data obtained from the annual reports of the sampled firms over the 2012–2022 period, including the mean, standard deviation, minimum and maximum values.
Descriptive statistics
| Variable | Mean | Std. dev | Min | Max |
|---|---|---|---|---|
| ENDL | 0.0406 | 0.0748 | 0.0000 | 0.5130 |
| BOIND | 0.4523 | 0.2184 | 0.0693 | 0.8889 |
| BENVC | 0.0718 | 0.2582 | 0.0000 | 1.0000 |
| BENEX | 0.0287 | 0.1671 | 0.0000 | 1.0000 |
| CEOGD | 0.0449 | 0.2074 | 0.0000 | 1.0000 |
| ACIND | 0.2907 | 0.2064 | 0.2110 | 0.8333 |
| ACGD | 0.1444 | 0.1222 | 0.0000 | 0.4286 |
| ACFEX | 0.4137 | 0.1404 | 0.1667 | 0.8333 |
| ACMT | 3.7426 | 1.1089 | 0.0000 | 7.0000 |
| GOVOW | 0.0001 | 0.0001 | 0.0000 | 0.0006 |
| FROWN | 0.1733 | 0.0241 | 0.0000 | 0.8600 |
| MGOWN | 0.0289 | 0.0492 | 0.0000 | 0.2534 |
| INOWN | 0.2121 | 0.1759 | 0.0000 | 0.8667 |
| LEVER | 1.2694 | 0.7164 | 0.1230 | 3.3013 |
| FSIZE | 8.9915 | 0.2792 | 7.0127 | 9.7200 |
| Variable | Mean | Std. dev | Min | Max |
|---|---|---|---|---|
| 0.0406 | 0.0748 | 0.0000 | 0.5130 | |
| 0.4523 | 0.2184 | 0.0693 | 0.8889 | |
| 0.0718 | 0.2582 | 0.0000 | 1.0000 | |
| BENEX | 0.0287 | 0.1671 | 0.0000 | 1.0000 |
| CEOGD | 0.0449 | 0.2074 | 0.0000 | 1.0000 |
| 0.2907 | 0.2064 | 0.2110 | 0.8333 | |
| 0.1444 | 0.1222 | 0.0000 | 0.4286 | |
| 0.4137 | 0.1404 | 0.1667 | 0.8333 | |
| ACMT | 3.7426 | 1.1089 | 0.0000 | 7.0000 |
| GOVOW | 0.0001 | 0.0001 | 0.0000 | 0.0006 |
| 0.1733 | 0.0241 | 0.0000 | 0.8600 | |
| MGOWN | 0.0289 | 0.0492 | 0.0000 | 0.2534 |
| 0.2121 | 0.1759 | 0.0000 | 0.8667 | |
| LEVER | 1.2694 | 0.7164 | 0.1230 | 3.3013 |
| FSIZE | 8.9915 | 0.2792 | 7.0127 | 9.7200 |
Table 3 shows descriptive statistics for the variables. The dependent variable, ENDL, has a low mean of 0.0406, indicating that environmental disclosure averages just 4.1%. Values range from 0 to 0.513, showing some firms disclosed nothing while others disclosed up to 51.3%. For BOIND, the mean is 0.4523, meaning 45.23% of board members are independent. This is below the Nigerian CG code's recommendation for a majority of non-executive directors.
The average value for the presence of BENVC is 0.0717, indicating that only 7.17% of the sampled firms had a dedicated board environmental committee and the minimum value of zero reflects that many firms lacked such a committee. Similarly, board environmental expertise (BENEX) has a mean of 0.0287, suggesting that just 2.87% of the firms had at least one board member with environmental expertise.
The statistics show that female CEOs (CEOGD) were rare, averaging just 4.5% of firms. On average, 29.07% of audit committee members were independent (ACIND), with values ranging from 21.1% to 83.3%. For audit committees, an average of 41.37% of members were financial experts (ACFEX), with notable variability. Committees met an average of 3.74 times per year (ACMT), indicating general compliance with the minimum requirement of four meetings.
Government ownership (GOVOW) was negligible on average. For other ownership structures, the averages were: FROWN 17.35%, managerial ownership (MGOWN) 2.89% and INOWN 21.21%. Each showed significant variability across firms.
4.2 Correlation analysis
Tables 4–6 present the correlation coefficients among board attributes, audit committee attributes, ownership structure variables and other explanatory variables with ENDL.
Correlation matrix (board attributes)
| Variable | ENDL | BOIND | BENVC | BENEX | CEOGD | LEVER | FSIZE |
|---|---|---|---|---|---|---|---|
| ENDL | 1.000 | ||||||
| BOIND | 0.065 | 1.000 | |||||
| BENVC | 0.100 | −0.092 | 1.000 | ||||
| BENEX | 0.001 | −0.212 | 0.082 | 1.000 | |||
| CEOGD | 0.173 | 0.094 | 0.012 | −0.342 | 1.000 | ||
| LEVER | −0.046 | −0.001 | −0.027 | −0.039 | −0.005 | 1.000 | |
| FSZ | −0.029 | −0.044 | 0.060 | −0.049 | −0.064 | 0.017 | 1.000 |
| Variable | BENEX | CEOGD | LEVER | FSIZE | |||
|---|---|---|---|---|---|---|---|
| 1.000 | |||||||
| 0.065 | 1.000 | ||||||
| 0.100 | −0.092 | 1.000 | |||||
| BENEX | 0.001 | −0.212 | 0.082 | 1.000 | |||
| CEOGD | 0.173 | 0.094 | 0.012 | −0.342 | 1.000 | ||
| LEVER | −0.046 | −0.001 | −0.027 | −0.039 | −0.005 | 1.000 | |
| −0.029 | −0.044 | 0.060 | −0.049 | −0.064 | 0.017 | 1.000 |
Correlation matrix (audit committee)
| Variable | ENDL | ACIN | ACGD | ACFEX | ACMT |
|---|---|---|---|---|---|
| ENDL | 1.000 | ||||
| ACIN | 0.142 | 1.000 | |||
| ACGD | −0.068 | −0.029 | 1.000 | ||
| ACFEX | −0.003 | 0.024 | −0.009 | 1.000 | |
| ACMT | −0.028 | −0.019 | 0.063 | 0.045 | 1.000 |
| Variable | ACIN | ACMT | |||
|---|---|---|---|---|---|
| 1.000 | |||||
| ACIN | 0.142 | 1.000 | |||
| −0.068 | −0.029 | 1.000 | |||
| −0.003 | 0.024 | −0.009 | 1.000 | ||
| ACMT | −0.028 | −0.019 | 0.063 | 0.045 | 1.000 |
Correlation matrix (ownership structure)
| Variable | ENDL | GOVOW | FROWN | MGOWN | BIND |
|---|---|---|---|---|---|
| ENDL | 1.000 | ||||
| GOVOW | −0.113 | 1.000 | |||
| FROWN | 0.093 | −0.060 | 1.000 | ||
| MGOWN | −0.061 | 0.035 | −0.074 | 1.000 | |
| INOWN | 0.115 | 0.049 | −0.217 | −0.022 | 1.000 |
| Variable | GOVOW | MGOWN | BIND | ||
|---|---|---|---|---|---|
| 1.000 | |||||
| GOVOW | −0.113 | 1.000 | |||
| 0.093 | −0.060 | 1.000 | |||
| MGOWN | −0.061 | 0.035 | −0.074 | 1.000 | |
| 0.115 | 0.049 | −0.217 | −0.022 | 1.000 |
The results show varying correlations with environmental disclosure (ENDL). BOIND and BENEX have a weak positive association with ENDL, BENVC and CEOGD show a moderate positive relationship with ENDL, ACIND has a moderate positive correlation, while ACGD, ACFEX and ACMT show weak negative correlations with ENDL. Also, GOVOW shows a moderate negative correlation with ENDL, INOWN has a moderate positive correlation, FROWN has a weak positive correlation, while MGOWN has a weak negative correlation with ENDL. All correlations are weak to moderate, and diagnostic tests confirm no significant multicollinearity, supporting the reliability of the regression model.
Correlations among variables are weak-to-moderate, indicating no significant multicollinearity concerns. This is further confirmed by the results presented in Table 7, All values are <10 threshold, confirming the absence of multicollinearity and reliable regression results.
Multicollinearity test result (VIF)
| Variable | VIF | 1/VIF | Variable | VIF | 1/VIF | Variable | VIF | 1/VIF |
|---|---|---|---|---|---|---|---|---|
| BOIND | 1.07 | 0.93605 | ACMT | 1.02 | 0.98385 | FROWN | 1.07 | 0.93865 |
| BENEX | 1.06 | 0.94514 | FSIZE | 1.01 | 0.98573 | INOWN | 1.06 | 0.94472 |
| BENVC | 1.02 | 0.98066 | ACGD | 1.01 | 0.99283 | FSIZE | 1.02 | 0.97647 |
| CEOGD | 1.02 | 0.98461 | ACFEX | 1.01 | 0.99473 | MGOWN | 1.02 | 0.98413 |
| FSIZE | 1.01 | 0.98814 | ACIND | 1.00 | 0.99705 | GOVOW | 1.01 | 0.98817 |
| LEVER | 1.00 | 0.99718 | LEVER | 1.00 | 0.99779 | LEVER | 1.00 | 0.99673 |
| MEAN VIF | 1.03 | 1.01 | 1.03 |
| Variable | VIF | 1/VIF | Variable | VIF | 1/VIF | Variable | VIF | 1/VIF |
|---|---|---|---|---|---|---|---|---|
| 1.07 | 0.93605 | ACMT | 1.02 | 0.98385 | 1.07 | 0.93865 | ||
| BENEX | 1.06 | 0.94514 | FSIZE | 1.01 | 0.98573 | 1.06 | 0.94472 | |
| 1.02 | 0.98066 | 1.01 | 0.99283 | FSIZE | 1.02 | 0.97647 | ||
| CEOGD | 1.02 | 0.98461 | 1.01 | 0.99473 | MGOWN | 1.02 | 0.98413 | |
| FSIZE | 1.01 | 0.98814 | 1.00 | 0.99705 | GOVOW | 1.01 | 0.98817 | |
| LEVER | 1.00 | 0.99718 | LEVER | 1.00 | 0.99779 | LEVER | 1.00 | 0.99673 |
| MEAN VIF | 1.03 | 1.01 | 1.03 |
4.3 Multicollinearity test
Table 7 represents the results of multicollinearity tests for the three models.
Table 7 shows a VIF (1.00–1.07) and tolerance (0.936–0.997) values confirm no multicollinearity issues, as all fall within acceptable thresholds (VIF<10, tolerance>0.05). The results indicate minimal intercorrelation among explanatory variables.
4.4 Test for heteroskedasticity
Table 8 represent the results of Heteroskedasticity tests for the three models.
The heteroscedasticity results for the three regression models reveal that All models show significant heteroscedasticity (test stats: 34.08, 57.69, 163.65; p = 0.000), violating Ordinary Least Square (OLS) assumptions. Generalized least squares (GLS) and Fixed Generalized least squares (FGLS) methods are recommended for robust, unbiased estimates
5. Hausman specification test
Table 9 represents the results of the Hausman specification test.
Hausman specification test
| Models | χ2 (6) | Prob > χ2 | Model fitted |
|---|---|---|---|
| Model one | 29.61 | 0.0000 | Fixed |
| Model two | 20.12 | 0.0026 | Fixed |
| Model three | 44.29 | 0.0000 | Fixed |
| Models | χ2 (6) | Prob > χ2 | Model fitted |
|---|---|---|---|
| Model one | 29.61 | 0.0000 | Fixed |
| Model two | 20.12 | 0.0026 | Fixed |
| Model three | 44.29 | 0.0000 | Fixed |
The Hausman test results strongly justify using fixed effects models in all three cases. The significant chi-square values (29.61, 20.12 and 44.29) with p-values of 0.000, 0.0026 and 0.000, respectively, favor the fixed effects specification over random effects.
5.1 Multivariate analysis
The regression analysis results are presented in Tables 10–12.
Regression result (board attributes)
| Variable | Coefficient | t-value | p > t |
|---|---|---|---|
| Constant | −1.2453 | −8.370 | 0.000 |
| BOIND | 0.1048 | 3.770 | 0.000 |
| BENVC | 0.0333 | 1.820 | 0.068 |
| BENEX | −0.0313 | −1.790 | 0.074 |
| CEOGD | −0.0543 | −3.720 | 0.000 |
| LEVER | −0.048 | −0.540 | 0.551 |
| FSIZE | 0.1224 | 8.600 | 0.000 |
| R2 | 0.2343 | ||
| F (6, 943) | 25.60 | ||
| Prob > χ2 0.000 |
| Variable | Coefficient | t-value | p > t |
|---|---|---|---|
| Constant | −1.2453 | −8.370 | 0.000 |
| 0.1048 | 3.770 | 0.000 | |
| 0.0333 | 1.820 | 0.068 | |
| BENEX | −0.0313 | −1.790 | 0.074 |
| CEOGD | −0.0543 | −3.720 | 0.000 |
| LEVER | −0.048 | −0.540 | 0.551 |
| FSIZE | 0.1224 | 8.600 | 0.000 |
| R2 | 0.2343 | ||
| F (6, 943) | 25.60 | ||
| Prob > χ2 0.000 |
Regression result (audit committee)
| Variable | Coefficient | t-value | p > t |
|---|---|---|---|
| Constant | −1.2240 | −8.530 | 0.000 |
| ACIND | 0.0458 | 3.260 | 0.001 |
| ACGD | 0.0962 | 4.370 | 0.000 |
| ACFEX | 0.0297 | 2.220 | 0.027 |
| ACMT | −0.0065 | −2.060 | 0.039 |
| LEVER | 0.0051 | −0.580 | 0.563 |
| FSIZE | −0.0142 | 8.740 | 0.000 |
| R2 | 0.2534 | ||
| F (6,943) | 28.51 | ||
| Prob > χ2 0.0000 |
| Variable | Coefficient | t-value | p > t |
|---|---|---|---|
| Constant | −1.2240 | −8.530 | 0.000 |
| 0.0458 | 3.260 | 0.001 | |
| 0.0962 | 4.370 | 0.000 | |
| 0.0297 | 2.220 | 0.027 | |
| ACMT | −0.0065 | −2.060 | 0.039 |
| LEVER | 0.0051 | −0.580 | 0.563 |
| FSIZE | −0.0142 | 8.740 | 0.000 |
| R2 | 0.2534 | ||
| F (6,943) | 28.51 | ||
| Prob > χ2 0.0000 |
Regression result (ownership structure)
| Variable | Coefficient | t-value | p > t |
|---|---|---|---|
| Constant | −1.3920 | −9.92 | 0.217 |
| GOVOW | 0.0407 | 2.310 | 0.021 |
| FROWN | 0.0764 | 2.080 | 0.038 |
| MGOWN | −0.0027 | −0.050 | 0.962 |
| INOWN | 0.0036 | 0.110 | 0.510 |
| LEVER | −0.0067 | −0.740 | 0.461 |
| FSIZE | 0.1392 | 9.960 | 0.000 |
| R2 | 0.2201 | ||
| F(6, 943) | 21.54 | ||
| Prob > F 0.000 |
| Variable | Coefficient | t-value | p > t |
|---|---|---|---|
| Constant | −1.3920 | −9.92 | 0.217 |
| GOVOW | 0.0407 | 2.310 | 0.021 |
| 0.0764 | 2.080 | 0.038 | |
| MGOWN | −0.0027 | −0.050 | 0.962 |
| 0.0036 | 0.110 | 0.510 | |
| LEVER | −0.0067 | −0.740 | 0.461 |
| FSIZE | 0.1392 | 9.960 | 0.000 |
| R2 | 0.2201 | ||
| F(6, 943) | 21.54 | ||
| Prob > F 0.000 |
5.1.1 Board attributes and environmental disclosure
The estimated regression results, guided by the Hausman specification test, are presented in Tables 10–12.
BOIND and the existence of BENVC have a significant positive relationship with ENDL. This supports agency and stakeholder theories, indicating that independent boards improve oversight. In contrast, BENEX and CEOGD show a significant negative association with ENDL. The positive finding for BOIND aligns with mainstream theory but contradicts the findings of Chithambo and Tauringer (2014) and Trireksani and Djajadikerta (2016) but aligns with mainstream theoretical expectations.
The presence of BENVC has a significant positive association with ENDL, supporting the hypothesis that such dedicated committees promote disclosure. This aligns with agency and stakeholder theories, indicating these committees enhance oversight and engagement. The finding is consistent with prior empirical research studies of Peters and Romi (2012), Liao et al. (2015) showing that boards with specialized environmental oversight are more engaged in non-financial reporting.
BENEX has a significant negative effect on ENDL. This suggests firms with less expertise at the board level have difficulty managing and reporting on sustainability, contrary to the findings of Fahad and Rahman (2020), Bryan and Jose (2019) and Ofoegbu et al. (2018).
CEOGD are also significantly and inversely related to ENDL, challenging the hypothesis that gender diversity improves environmental disclosure. This finding contradicts Hussain et al. (2023), Oware et al. (2022), Tran et al. (2020). In summary, all four hypotheses (H1a, H1b, H1c, H1d) related to BOIND, BENEX and CEOGD are rejected, concluding that these factors significantly influence disclosure practices in Nigerian firms.
5.1.2 Audit committee attributes and environmental disclosure
The ACGD has a significant positive relationship with ENDL, supporting gender ethicality theory (Zalata et al., 2019). This finding is consistent with prior research (Omotoye et al., 2021; Abbasi et al., 2024). Conversely, the frequency of ACMTs has a significant negative relationship with ENDL, suggesting more meetings may indicate governance inefficiencies and detract from non-financial reporting. This aligns with Khan et al. (2024), however, this finding contradicts studies by Amin et al. (2021) and Arif et al. (2020).
Given these significant results, the study rejects hypotheses H2a, H2b, H2c and H2d, concluding that ACIND, gender diversity, financial expertise and meeting frequency all significantly influence environmental disclosure practices.
5.1.3 Ownership structure and environmental disclosure
Model 3 results indicate GOVOW and FROWN have a significant positive association ENDL, supporting hypotheses H3a and H3b. This suggests that greater government and FROWN increase disclosure, aligning with agency theory due to heightened monitoring needs and transparency expectations. These findings align with Agency Theory (Jensen and Meckling, 1976; Watts, 1977), where dispersed ownership heightens disclosure needs to reduce agency conflicts and with Fama and Jensen's (1983) view that ownership control separation necessitates transparency for monitoring. These results are consistent with those of Acar et al. (2021), Calza et al. (2016), Akrout and Othman (2016) and Haddad et al. (2015). In line with hypothesis H3b, greater FROWN increases ENDL, likely due to international investor pressure for enhanced sustainability reporting. This finding corroborates Alhazaimeh et al. (2014), who reported a similar positive relationship. However, it contradicts studies by Abu Qa’dan and Suwaidan (2019), Saini and Singhania (2019) and Bani Khalid et al. (2017), who observed a negative association between FROWN and ENDL.
The results show no significant relationship between MGOWN and ENDL, contradicting prior studies by Al Amosh and Mansor (2020) and Sufian and Zahan (2013). Similarly, INOWN has an insignificant positive relationship with ENDL, contradicting the hypothesis and previous research that reported a negative association. This suggests institutional investors do not actively pressure firms for greater transparency. However, this result contradicts previous studies by Acar et al. (2021), which reported a negative association between INOWN and environmental disclosure.
In summary, the study finds that government and FROWN significantly influence environmental disclosure, supporting hypotheses H3a and H3b. Conversely, managerial and INOWN do not have a significant effect, leading to a failure to reject hypotheses H3c and H3d.
5.2 Robustness test and additional analysis
Robustness tests, including generalized method of moments (GMM) estimation and the Hansen J-test, were conducted to address potential endogeneity, heteroskedasticity and omitted variable bias, ensuring the credibility and reliability of the findings. Table 13 presents these GMM results and a comparison with the baseline model estimates.
Robustness test and additional analysis
| Independent variables | Variables | Baseline | GMM | Interpretation |
|---|---|---|---|---|
| Fixed effect (FE) | ||||
| Board attributes | BOID | ***(3.77) | **(2.52) | Robustly significant |
| BENVC | *(1.82) | (−0.19) | FE-only significance | |
| BENEX | *(1.79) | (−0.82) | FE-only significance | |
| CEOGD | ***(3.72) | 0.0443 | GMM slightly weaker but still significant | |
| The instrumental variables are valid and uncorrelated with the error term | ||||
| Hansen J test L2 | PV 0.334 | |||
| Audit committee attributes | ACIND | ***(3.26) | ***(2.37) | Robustly significant |
| ACMT | ***(−2.49) | (−0.69) | FE-only significance | |
| ACFEX | **(2.22) | (−0.16) | FE-only significance | |
| ACGD | ***(3.49) | **(2.54) | GMM slightly weaker but still significant | |
| The instrumental variables are valid and uncorrelated with the error term | ||||
| Hansen J test L2 | PV 0.319 | |||
| Ownership attributes | INOWN | (0.11) | (−1.43) | Not robust in GMM |
| FROWN | **(2.08) | (−0.61) | FE-only significance | |
| GOVOWN | **(2.31) | (−0.00) | FE-only significance | |
| MGOWN | (−0.05) | *(1.70) | GMM slightly significant | |
| The instrumental variables are valid and uncorrelated with the error term | ||||
| Hansen J test L2 | PV 0.414 | |||
| Independent variables | Variables | Baseline | GMM | Interpretation |
|---|---|---|---|---|
| Fixed effect ( | ||||
| Board attributes | BOID | ***(3.77) | **(2.52) | Robustly significant |
| *(1.82) | (−0.19) | FE-only significance | ||
| BENEX | *(1.79) | (−0.82) | FE-only significance | |
| CEOGD | ***(3.72) | 0.0443 | GMM slightly weaker but still significant | |
| The instrumental variables are valid and uncorrelated with the error term | ||||
| Hansen J test L2 | PV 0.334 | |||
| Audit committee attributes | ***(3.26) | ***(2.37) | Robustly significant | |
| ACMT | ***(−2.49) | (−0.69) | FE-only significance | |
| **(2.22) | (−0.16) | FE-only significance | ||
| ***(3.49) | **(2.54) | GMM slightly weaker but still significant | ||
| The instrumental variables are valid and uncorrelated with the error term | ||||
| Hansen J test L2 | PV 0.319 | |||
| Ownership attributes | (0.11) | (−1.43) | Not robust in GMM | |
| **(2.08) | (−0.61) | FE-only significance | ||
| GOVOWN | **(2.31) | (−0.00) | FE-only significance | |
| MGOWN | (−0.05) | *(1.70) | GMM slightly significant | |
| The instrumental variables are valid and uncorrelated with the error term | ||||
| Hansen J test L2 | PV 0.414 | |||
Note(s): ***p < 0.01, **p < 0.05, *p < 0.10
The Hansen J-test results (board: p = 0.334; audit committee: p = 0.319; ownership: p = 0.414) consistently fail to reject the null hypothesis of instrument exogeneity, validating the GMM estimation approach. This confirms that the instruments are uncorrelated with the error term and the models are properly specified.
6. Conclusion and recommendations
In response to rising global demand for corporate environmental responsibility, this study examines how board, audit and ownership structures influence ENDL in Nigerian listed firms. Using a sample of 1,045 firm-year observations from 2012 to 2022, fixed effects estimation was applied to all three models, as supported by Hausman tests and supplemented by GMM estimation to address endogeneity. The results demonstrate significant positive associations between ENDL and several governance and ownership factors: BOIND, BENVC, ACIND, ACFEX, ACGD, government ownership (GOVOW) and FROWN. Conversely, CEO gender diversity (CEOGD) and excessive audit committee meetings (ACMT) show statistically significant negative relationships with ENDL, suggesting potential inefficiencies in environmental reporting oversight.
The results align with and extend several theoretical perspectives on CG and disclosure practices, this study supports and extends the premises of agency theory (Jensen and Meckling, 1976), stakeholder theory, upper echelons theory and ethicality theory. These frameworks collectively emphasize the importance of governance structures in aligning managerial actions with broader stakeholder expectations regarding environmental stewardship and disclosure.
The Nigerian-specific focus limits generalizability. Future studies should consider using cross-country data for broader validation; examine moderators such as environmental regulations, corporate culture and market competition in order to have a better understanding of governance monitoring mechanisms and ENDL relationships.

