HENRY EMIFE MONYE-EMINA

This study examines the factors influencing environmental disclosure among oil and gas companies in Nigeria. It adopts an ex-post facto research design with a longitudinal approach, utilizing panel data spanning eleven (11) financial years (2014–2024) from oil companies listed on the Nigerian Exchange (NGX). The variables investigated include leverage, firm size, profitability, audit firm type, financial constraint, and firm age. The findings reveal that leverage, profitability, firm size, audit firm type, firm age, and financial constraint all have no significant effect on the level of environmental accounting disclosure by oil and gas companies in Nigeria. Based on these results, the study recommends that future research should consider a broader sample of companies and incorporate additional variables beyond those used in the current model, to provide a more comprehensive understanding of the determinants of environmental disclosure in the Nigerian oil and gas sector.

This study examines the factors influencing environmental disclosure among oil and gas companies in Nigeria. It adopts an ex-post facto research design with a longitudinal approach, utilizing panel data spanning eleven (11) financial years (2014–2024) from oil companies listed on the Nigerian Exchange (NGX). The variables investigated include leverage, firm size, profitability, audit firm type, financial constraint, and firm age. The findings reveal that leverage, profitability, firm size, audit firm type, firm age, and financial constraint all have no significant effect on the level of environmental accounting disclosure by oil and gas companies in Nigeria. Based on these results, the study recommends that future research should consider a broader sample of companies and incorporate additional variables beyond those used in the current model, to provide a more comprehensive understanding of the determinants of environmental disclosure in the Nigerian oil and gas sector.

This study explores the extent to which AI-driven tools such as machine learning, natural language processing, and data analytics enhance auditors’ ability to detect anomalies, assess risks, and provide deeper insights into financial statements. AI’s capacity to process vast datasets in real time reduces human error, strengthens fraud detection, and enables auditors to focus on judgment-intensive tasks, thereby improving audit quality. Moreover, automation of repetitive audit procedures accelerates workflow, minimizes costs, and enhances overall efficiency. However, the adoption of AI also raises concerns about data security, auditor independence, ethical implications, and the need for continuous skill development. This paper argues that while AI does not replace professional skepticism and human judgment, it serves as a powerful enabler that reshapes auditing practices toward greater reliability, transparency, and efficiency. The findings contribute to ongoing debates on the future of auditing and provide practical insights for regulators, practitioners, and stakeholders.

This study explores the extent to which AI-driven tools such as machine learning, natural language processing, and data analytics enhance auditors’ ability to detect anomalies, assess risks, and provide deeper insights into financial statements. AI’s capacity to process vast datasets in real time reduces human error, strengthens fraud detection, and enables auditors to focus on judgment-intensive tasks, thereby improving audit quality. Moreover, automation of repetitive audit procedures accelerates workflow, minimizes costs, and enhances overall efficiency. However, the adoption of AI also raises concerns about data security, auditor independence, ethical implications, and the need for continuous skill development. This paper argues that while AI does not replace professional skepticism and human judgment, it serves as a powerful enabler that reshapes auditing practices toward greater reliability, transparency, and efficiency. The findings contribute to ongoing debates on the future of auditing and provide practical insights for regulators, practitioners, and stakeholders.