This study focuses on the impact of the audit committee features on the quality of environmental, social, and governance (ESG) disclosure of the Saudi Stock Exchange-listed companies. Grounded in agency theory, stakeholder theory, and resource dependence theory, this research considers key audit committee characteristics, such as independence, expertise, and tenure, to determine the manner in which they contribute to the improvement of ESG disclosure through enhanced monitoring, accountability, and access to critical reporting-related resources. This study employed a regression model as a hypothesis-testing model using panel data of 78 Saudi-listed firms, which represent 234 firm-years until 2023. ESG disclosure quality is measured using the standardized ESG score obtained from the Refinitiv Eikon database. The results indicate that a positive and statistically significant relationship exists between ESG disclosure quality and audit committee independence and expertise. Conversely, the tenure of audit committees has a negative relationship with ESG disclosure quality. This research contributes to the ESG and corporate governance literature by extending audit committee research beyond traditional financial reporting oversight into ESG oversight in an emerging-market context, and by providing context-specific evidence from Saudi Arabia, where ESG reporting frameworks and enforcement mechanisms are still evolving. Practically, the implications of the findings provide useful recommendations to regulators and firms that intend to enhance their governance practices in accordance with the Saudi Vision 2030 and reforms at the Capital Market Authority.

Green financing represents a great untapped opportunity, especially in Saudi Arabia and the other Gulf Cooperation Council countries, which are characterized by their developed capital markets, attracting investors from all over the world to pump their capital into projects that have an environmental and social impact, in addition to activating strong governance processes.The study aimed to examine the impact of the dimensions of sustainable development: environmental, social, and economic on green finance in Saudi national banks and fill the gap in sustainability and green finance research in Arabian countries, specifically in the Kingdom of Saudi Arabia. The study population covered the national banks in Riyadh city. The data was collected through a questionnaire and the sample size was 125 participants. The study applied linear regression models and the result revealed that the environmental dimension of sustainable development, the social dimension of sustainable development, and the economic dimension have a positive impact on green finance in Saudi banks.

This study examines the impact of ESG disclosure, leverage, and profitability on firm value, measured by Tobin’s Q, among 67 non-financial Tadawul-listed companies in Saudi Arabia over the period 2015–2024. ESG disclosure is captured through a manual content-analysis index that scores the proportion of expected environmental, social, and governance items reported by each firm. The study further investigates whether board independence moderates these relationships while controlling for liquidity, firm size, current ratio, capital expenditure, and board size. Methodologically, the study employs the two-step system generalized method of moments (system GMM) estimator, which addresses dynamic persistence, endogeneity, and unobserved heterogeneity. The findings reveal that ESG disclosure has a positive and significant effect on firm value, indicating that the Saudi market increasingly rewards firms that provide broader sustainability-related information. Profitability also exerts a positive influence on Tobin’s Q, while leverage has a negative and significant effect, suggesting that higher debt weakens market valuation. Among the moderating effects, board independence significantly reduces the negative impact of leverage on firm value, although it does not significantly strengthen the positive ESG disclosure–firm value relationship. The results also show that liquidity, firm size, capital expenditure, and board size positively influence firm value. The study’s novelty lies in being the first, to our knowledge, to integrate ESG disclosure, financial structure, profitability, and board independence within a single dynamic firm-value framework over a decade-long panel that brackets the Saudi Exchange’s 2021 ESG disclosure guideline. In doing so, it advances emerging-market ESG research by showing that, under Saudi Arabia’s largely voluntary disclosure regime and concentrated-ownership structure, board independence operates primarily as a risk-monitoring mechanism rather than as an amplifier of disclosure value. The findings imply that regulators should strengthen and progressively mandate ESG reporting frameworks, that investors should treat ESG transparency as value-relevant information, and that firms should view ESG transparency and prudent governance as strategic tools for enhancing market value in line with Vision 2030.

This article defines a new distribution using a novel alpha power-transformed method extension. The model obtained has three parameters and is quite effective in modeling skewed, complex, symmetric, and asymmetric datasets. The new approach has one additional parameter for the model. Certain distributional and mathematical properties are investigated, notably reliability, quartile, moments, skewness, kurtosis, and order statistics, and several approaches of estimation, notably the maximum likelihood, least square, weighted least square, maximum product spacing, Cramer-Von Mises, and Anderson Darling estimators of the model parameters were obtained. A Monte Carlo simulation study was conducted to evaluate the performance of the proposed techniques of estimation of the model parameters. The actuarial measures are computed for our recommended model. At the end of the paper, two insurance applications are illustrated to check the potential and utility of the suggested distribution. Evaluation using four selection criteria indicates that our recommended model is the most appropriate probability model for modeling insurance datasets.