Artificial Intelligence-Enabled Environmental, Social and Governance (ESG) Carbon Accounting and Reporting in Zimbabwe: Expert Perspectives on Opportunities, Challenges, and Future Directions

Abstract

This paper examines the role of Artificial Intelligence (AI) in enhancing Environmental, Social, and Governance (ESG) carbon accounting and reporting, focusing on opportunities, challenges, and future directions. As regulatory pressure and stakeholder demand for high-quality climate disclosures increase, organizations face growing complexity in measuring, validating, and reporting carbon emissions across operational and value chain activities. AI technologies offer scalable solutions through automated data capture, anomaly detection, predictive analytics, and integrated reporting systems. The study adopts a qualitative methodology based on expert interviews conducted with sustainability professionals, auditors, financial managers, and regulators in Zimbabwe. A purposive sampling approach and thematic analysis were used to generate insights into current practices and emerging adoption patterns. A sample of 15 participants was used. Findings indicate that AI significantly improves efficiency, data consistency, and analytical capability in carbon accounting processes, particularly through the automation of emissions data aggregation and intelligent estimation models. Experts highlighted the value of AI in scenario modeling and forward-looking carbon forecasting linked to strategic planning. However, the results also reveal important constraints, including model transparency concerns, auditability challenges, skills shortages, data quality risks, and reliance on external technology vendors. Governance and assurance implications emerged as central adoption conditions, with explainable models and documented controls viewed as critical safeguards. The paper contributes empirical-style insight from an emerging market context and shows that AI can strengthen ESG carbon reporting quality when supported by appropriate governance, professional capacity, and regulatory guidance. It recommends phased adoption, explainable AI frameworks, and targeted skills development. Future research should extend mixed-method and cross-country analyses of AI-enabled ESG reporting systems.