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An AI-Based, Big Data Quantification of Corporate Alignment with SDGs in Emerging Economies

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  • Arnesh Telukdarie

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

  • Maddubailu Suresh Saivinod

    (Department of Management and Commerce, Sri Sathya Sai Institute of Higher Learning, Kadugodi, Bengaluru 515134, India)

  • Musawenkosi Hope Lotriet Nyathi

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

  • Rajour Jumfan Fabchi

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

Abstract

Despite widespread corporate endorsement of the Sustainable Development Goals (SDGs), systematic evidence on how top management in emerging economies prioritizes and frames SDG-related issues over time remains limited. Existing studies are often based on manual or single-year analyses, restricting comparability, scalability, and longitudinal insight. This study examines how corporate managerial communication aligns with and emphasizes SDGs across sectors and over time in two major emerging economies, India and South Africa. Using an AI-driven natural language processing (NLP) pipeline, we analyse 2400 annual reports from 600 publicly listed companies covering the period 2020–2023. A fine-tuned SDG-BERT multi-label classification model is applied to extract and classify SDG-related content from top management communications, enabling sectoral, temporal, and cross-country comparison of SDG relevance. The results reveal a strong and persistent emphasis on SDG 12 (Responsible Consumption and Production) across both countries, alongside sector-specific variation and differing patterns of SDG diversity over time. South African firms exhibit greater variation in SDG emphasis across years, while Indian firms display more concentrated and stable SDG framing. Overall, the findings highlight systematic imbalances in SDG-related managerial communication and persistent underrepresentation of several social SDGs. The study contributes methodologically by demonstrating the value of validated AI-assisted longitudinal text analysis for large-scale SDG research and empirically by providing comparative insights into how corporate SDG narratives evolve in emerging market contexts.

Suggested Citation

  • Arnesh Telukdarie & Maddubailu Suresh Saivinod & Musawenkosi Hope Lotriet Nyathi & Rajour Jumfan Fabchi, 2026. "An AI-Based, Big Data Quantification of Corporate Alignment with SDGs in Emerging Economies," Sustainability, MDPI, vol. 18(7), pages 1-36, March.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:7:p:3195-:d:1902554
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