Integrating AI-driven analytics for enhanced ESG mapping: Aligning local and global perspectives

Sustainability remains a central global challenge, requiring a nuanced understanding of how global policy frameworks align with localised priorities. However, analysing diverse data sources for sustainability assessment remains a key challenge, as globally issued formally structured reports often lack localised granularity, while unstructured local data lacks structure and standardisation. Existing approaches fail to systematically integrate these heterogeneous sources, limiting their effectiveness in identifying actionable sustainability insights. This study presents an Artificial Intelligence (AI)-driven framework that leverages Natural Language Processing (NLP) techniques to integrate structured and unstructured sustainability data. We applied Latent Dirichlet Allocation (LDA), BERTopic, Generative AI (GenAI), and FinBERT-based cosine similarity to extract macroeconomic trends from formal reports — Executive Summary of IMF’s Global Stability Reports — while identifying localised sustainability strategies from Greenstone’s UK-based newsletters on sustainable practice. GenAI outperformed topic models in producing more coherent, diverse, and contextually relevant topics. To further enhance GenAI’s performance, we applied MIPROv2 — a Bayesian optimisation-based prompt tuning method — which improved topic distinctiveness across data sources. Our key contribution lies in aligning global and territorial sustainability discourses through AI-enhanced topic modelling. The findings demonstrate an integrated methodology that connects global policy directives with region- and industry-specific insights. This approach uncovers underexplored opportunities in the social and governance dimensions of ESG, enabling data-driven and adaptable strategies. By synthesising insights across multiple data sources, this research enables policymakers, financial institutions, and industry leaders to bridge sustainability knowledge gaps, align local priorities with global objectives, and drive innovative, targeted solutions.