Artificial Intelligence for Sustainability Reporting: A Systematic Literature Review

Abstract

Growing demand for high-quality sustainability and ESG reporting has added pressure to employ Artificial Intelligence (AI) to enhance efficiency, accuracy, and credibility. However, research on AI-driven sustainability reporting remains unsatisfactory, with many theoretical works and few empirical experiments. To fill this gap, a comprehensive and systematic review of 38 academic articles published over the past five years (2005-2025) on the application of AI in sustainability reporting, ESG reporting, carbon accounting, and sustainability accounting will be conducted as part of this thesis. The study uses a systematic literature review design, in which the authors organize the papers by research methodology, AI application, results presentation, and limitations to implementation. As the findings show, the most common examples of AI applications are the automation of data collection and processing, the simplification of the process of measuring carbon emissions, the increased timeliness and consistency of reporting, and the assistance of machine learning, natural language processing, big data analytics, and IoT-based systems to analyze the ESG disclosures. A small number of empirical studies show that reporting speed, data quality, and the utility of decisions have been increased in measurable ways. Enabling conditions are very important in determining the success of AI, and they include data readiness, governance structures, digital platforms, employee competency, and the incorporation of current accounting and reporting platforms. The audit's willingness to provide assurance and acceptance of AI-produced sustainability information is an untested matter that raises issues of credibility, transparency, and the production of digital-like sustainability reports that do not create any substantive enhancement. The paper concludes that AI has the. potential to significantly improve sustainability reporting, yet only possible under conditions of strong data. system of management, transparent procedures, and control. The gaps in the research are evident in the thesis, particularly in the areas most significant to emerging economies. Empirical studies are invited in the future to investigate SMEs and high-impact sectors, contextual moderators, and high-impact assurance practices, thereby building stronger, more generalizable research.