This paper examines the relationship between board gender diversity and Environmental, Social, and Governance (ESG) disclosure in developed economies. Using a sample of forty-five firms across developed countries between 2012 and 2023, the analysis employs Bloomberg’s ESG disclosure score as a proxy. In terms of methodology, both pooled ordinary least squares (OLS) and fixed effects regression models are employed. However, to mitigate potential endogeneity concerns, the study employs an instrumental variable approach and dynamic panel regression techniques to provide robust causal inference. The findings offer two significant insights. In accordance with critical mass theory, firms with a minimum of three female directors demonstrate a significant positive relationship between board gender diversity and ESG disclosure. This indicates that achieving a critical level of female representation is essential for fostering meaningful improvements in ESG disclosure scores. Second, firms with merely one or two female directors, often considered token representation, exhibit a negative significant impact on ESG disclosure. Additionally, within the UK context, board gender diversity is positively associated with ESG disclosure, suggesting that institutional frameworks and regulatory environment shape this relationship.

The study looks at the impact that the three dimensions of financial inclusion (FI) (i.e., access, usage, quality) may have on poverty alleviation. In doing so, the study relies on demand and supply-side data to measure Nigeria’s FI. The demand-side data were derived from the 2021 Global Findex data, and the supply side data were sourced from the IMF Access survey database (2004–2021). The supply-side data were analysed using the ordinary least squares regression (OLS), while the demand-side data were analysed using the probit regression model. The study outcomes revealed a negative and significant relationship between financial access and poverty rate, further indicating that those who use financial services are less likely to experience poverty. The study recommends that financial service providers tailor their financial products to align with the educational level of the target population to encourage savings.

This study empirically investigates the relationship between board composition, focusing on board size and board independence, and firm performance. It further examines how national governance quality moderates this relationship. Using a panel dataset of 1604 firms from 41 developed and emerging economies, the study employs pooled ordinary least squares (OLS) as the baseline regression method, alongside two-stage instrumental variable regression and system generalised method of moments (GMM) to address potential endogeneity concerns. Firm performance is measured using return on equity (ROE) and Tobin’s Q. Board size is captured by the number of directors on the board, while board independence is measured by the proportion of non-executive directors. The findings indicate that while board size and independence are positively associated with firm performance, the strength of these relationships weakens in countries with high governance quality. Our findings remain robust after controlling for dynamic endogeneity and unobserved time-invariant heterogeneity inherent in the corporate governance–performance nexus.

The prevailing view is that ESG performance contributes to corporate financial stability, particularly regarding long-term sustainability objectives. However, there is a notable lack of critical research exploring its short-term financial effects, especially within capital-intensive sectors experiencing green transformation. This study examines the theoretical gap by investigating whether increased ESG performance may unintentionally heighten the financial burden and default risk in the short run. To verify the stability of each variable in the series, we employed the short-panel unit root test on panel data from 234 Chinese energy industry companies covering the years 2015 to 2023. Including enterprise fixed effects as well as time fixed effects, we find that higher ESG ratings increase the possibility of default risk in the Chinese energy sector. This effect remains robust after controlling firm size, financial leverage, return on assets, return on equity, earnings per share, beta and firm age. In addition, we conduct robustness checks using alternative default risk measures, both endogeneity- and component-based, and the outcomes demonstrate that the impact is substantial and consistent. Consequently, we may draw the conclusion that raising the ESG rating has an adverse effect on reducing corporate default risk, which fills the knowledge gap regarding the influence of listed companies’ default risk on China’s energy sector. Moreover, it has been found that green innovation plays a strengthening role in the analysis of the interaction term between green innovation and ESG on default risk. This suggests that while green innovation is a strategic initiative aimed at long-term sustainability, it requires a significant amount of capital and resources in the short term, which may result in higher default risk in the beginning.

This study investigates the relationship between CEO characteristics and firm performance across emerging and developed economies using both panel regression and machine learning techniques. Drawing on Upper Echelons Theory, we examine whether CEO age, tenure, gender, founder status, and appointment origin influence Return on Assets (ROA), Return on Equity (ROE), and market-to-book ratio. We apply the fixed and random effects models for inference and deploy random forest and XGBoost models to determine the feature importance of each CEO trait. Our findings show that CEO tenure consistently predicts improved ROE and ROA, while CEO age and founder status negatively affect firm performance. Female CEOs, though not consistently significant in the baseline models, positively influence market valuation in emerging markets according to interaction models. Firm-level characteristics such as size and leverage dominate CEO traits in explaining performance outcomes, especially in machine learning rankings. By integrating machine learning feature importance, this study contributes an original approach to CEO evaluation, enabling firms and policymakers to prioritise leadership traits that matter most. The findings have practical implications for succession planning, diversity policy, and performance-based executive appointments.