Mapping AI and ESG Best Practices: A Comparative Typology of European Project Cases
DOI:
https://doi.org/10.15678/PG.2026.76.2.04Keywords:
artificial intelligence, ESG, best practices, typology, qualitative content analysis, higher education, sustainabilityAbstract
Objective: This article develops an original typology of artificial intelligence (AI) and environmental, social, and governance (ESG) best practices, examining how these two domains intersect in European organisational and educational contexts.
Research Design and Methods: Qualitative content analysis was applied to eleven best-practice cases collected across five countries (Estonia, France, Hungary, Slovenia, Poland) within the DEDYCATING Erasmus+ project, coded against seven dimensions.
Findings: AI is present in six of eleven cases but genuinely operational in only five: in one case AI is the subject of education rather than a tool in use, and the remaining five address substantive ESG objectives without current AI involvement. A 2×2 typology crossing AI implementation status with operational versus educational orientation yields four types: AI-Enabled ESG Operations, AI-Enabled ESG Pedagogy, ESG-Only Operations and Advocacy, and ESG-Only Education and Training.
Implications/Recommendations: “AI and ESG practice” functions as an umbrella label spanning fundamentally different configurations; future best-practice collections should require explicit AI-status disclosure and assess transferability against resource intensity and institutional dependency, not AI presence alone.
Contribution/Value Added: The study provides the first inductively grounded typology of AI-ESG best practices at the organisational and educational level in Europe, demonstrating that AI integration and ESG substance are largely independent design choices within this sample.
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