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Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review

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  • Wayne Moodaley

    (Johannesburg Business School, University of Johannesburg, Johannesburg 2092, South Africa)

  • Arnesh Telukdarie

    (Johannesburg Business School, University of Johannesburg, Johannesburg 2092, South Africa)

Abstract

The rise of stakeholder interest globally in sustainable business practices has resulted in a rise in demands from stakeholders that companies report on the environmental and social impacts of their business activities. In certain cases, however, companies have resorted to the practice of providing inaccurate disclosures regarding sustainability as part of their corporate communications and sustainability reporting—commonly referred to as “greenwashing”. Concurrently, technological improvements in artificial intelligence have presented the means to rapidly and accurately analyze large volumes of text-based information, such as that contained in sustainability reports. Despite the possible impacts of artificial intelligence and machine learning on the fields of greenwashing and sustainability reporting, no literature to date has comprehensively and holistically addressed the interrelationship between these three important topics. This paper contributes to the body of knowledge by using bibliometric and thematic analyses to systematically analyze the interrelationship between those fields. The analysis is also used to conjecture a conceptual and thematic framework for the use of artificial intelligence with machine learning in relation to greenwashing and company sustainability reporting. This paper finds that the use of artificial intelligence in relation to greenwashing, and greenwashing within sustainability reporting, is an underexplored research field.

Suggested Citation

  • Wayne Moodaley & Arnesh Telukdarie, 2023. "Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review," Sustainability, MDPI, vol. 15(2), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1481-:d:1033646
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    References listed on IDEAS

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    Cited by:

    1. Doaa Hafez Emam Abdellattif & Amina Ramadan Mouhamed Nomer & Dalida Mohamed Adel Eldawayaty, 2025. "Sustainability Risk Management and Financial Distress: The Moderating Role of Financial Performance in Saudi Firms," Sustainability, MDPI, vol. 17(21), pages 1-37, October.
    2. Nan Shang & Guori Huang & Yuan Leng & Jihong Zhang & Angxing Shen, 2023. "Time Limit of Environmental Benefits of Renewable Energy Power Projects—Analysis Based on Monte Carlo Simulation," Sustainability, MDPI, vol. 15(20), pages 1-14, October.
    3. Sneideriene, Agne & Legenzova, Renata, 2025. "Greenwashing prevention in environmental, social, and governance (ESG) disclosures: A bibliometric analysis," Research in International Business and Finance, Elsevier, vol. 74(C).
    4. David Hay & Noel Harding & Nives Botica Redmayne & Jahanzeb Khan & Harjinder Singh & Nigar Sultana & Jean You, 2024. "Comments on recent International Ethics Standards Board for Accountants Exposure Drafts regarding sustainability assurance and the use of external experts," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(4), pages 4197-4216, December.
    5. Kurniawati, 2023. "Sustainable Textile Practices by Integrated Viscose Rayon and Yarn Producers: An Empirical Study," GATR Journals jfbr210, Global Academy of Training and Research (GATR) Enterprise.
    6. Muhammad Kaleem Khan & Chunhui Huo & R. M. Ammar Zahid & Umer Sahil Maqsood, 2024. "The automated sustainability auditor: Does artificial intelligence curtail greenwashing behavior in Chinese firms?," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 9015-9039, December.
    7. Adriana AnaMaria Davidescu & Marina-Diana Agafiței & Mihai Gheorghe & Vasile Alecsandru Strat, 2025. "A Machine Learning and Econometric Framework for Credibility-Aware AI Adoption Measurement and Macroeconomic Impact Assessment in the Energy Sector," Mathematics, MDPI, vol. 13(19), pages 1-31, September.

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