Resumo

Sustainability is a key 21st-century concept, balancing economic development, environmental preservation, and social equity. It shapes public policies, corporate strategies, consumer behavior, and societal expectations. Sustainability reports are essential tools for organizations to transparently communicate their environmental, social, and governance (ESG) performance. However, the phenomenon of greenwashing, where companies make misleading claims about their environmental efforts, poses a significant challenge to transparency and integrity in sustainability practices.

Greenwashing undermines the credibility of corporate sustainability claims, eroding stakeholder trust. Despite technological advances, the application of AI and machine learning (ML) to detect greenwashing is still emerging. This study aims to systematically review the literature on using AI and ML to identify greenwashing in ESG reports. By analyzing articles from the Scopus database over the last five years, this research seeks to highlight trends, gaps, and opportunities for future research in developing robust methods to ensure the accuracy of sustainability disclosures.

Sustainability reports communicate business activities regarding environmental, social, and economic impacts, guided by various global standards. Greenwashing, a term coined in the 1980s, refers to companies falsely promoting their environmental efforts. It has become a prevalent issue as companies seek to attract environmentally conscious consumers without making substantial operational changes. AI and ML offer potential solutions by analyzing large volumes of data to detect patterns and inconsistencies indicative of greenwashing.

The systematic review shows a growing academic focus on greenwashing, with significant research concentrated in the United States, China, and Europe. AI and ML can enhance the detection of greenwashing by analyzing sustainability reports more rigorously and efficiently. However, the lack of standardized sustainability disclosure and specialized auditing institutions presents challenges. Current research indicates that AI and ML application in this domain is still nascent, suggesting a need for more comprehensive and integrated methodologies.

This review emphasizes the critical role of AI and ML in identifying greenwashing, highlighting the need for future research to develop more robust and integrated methods. It calls for collaboration between academics, technology professionals, and regulators to advance the accuracy and transparency of corporate sustainability disclosures. The findings suggest that addressing greenwashing effectively may require regulatory interventions to establish clear communication standards and accountability mechanisms.

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