Integrating AI-Driven Analytics for Enhanced ESG Mapping: Aligning Local and Global Perspectives

Fildisi, Buket and Vakaj, Edlira and Dridi, Amna and Imran, Ali Shariq and Azad, Muhammad Ajmal (2025) Integrating AI-Driven Analytics for Enhanced ESG Mapping: Aligning Local and Global Perspectives. Sustainable Futures, 10. ISSN 2666-1888

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Abstract

Sustainability remains a central global challenge, requiring a nuanced un- derstanding of how global policy frameworks align with localised priorities. However, analysing diverse data sources for sustainability assessment re- mains 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 struc- tured and unstructured sustainability data. We applied Latent Dirichlet Al- location (LDA), BERTopic, Generative AI (GenAI), and FinBERT-based co- sine similarity to extract macroeconomic trends from formal reports—Executive Summary of IMF’s Global Stability Reports—while identifying localised sus- tainability strategies from Greenstone’s UK-based newsletters on sustainable practice. GenAI outperformed topic models in producing more coherent, di- verse, and contextually relevant topics.
To further enhance GenAI’s performance, we applied MIPROv2—a Bayesian optimisation-based prompt tuning method—which improved topic distinc- tiveness across data sources.
Our key contribution lies in aligning global and territorial sustainabil- ity discourses through AI-enhanced topic modelling. The findings demon- strate 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 indus- try leaders to bridge sustainability knowledge gaps, align local priorities with global objectives, and drive innovative, targeted solutions.

Item Type:	Article
Identification Number:	10.1016/j.sftr.2025.101231
Dates:	Date Event 26 August 2025 Accepted 11 September 2025 Published Online
Uncontrolled Keywords:	Sustainable development, GenAI, Natural language processing (NLP), Topic modelling, ESGs, Sustainable innovations
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	02 Sep 2025 09:13
Last Modified:	17 Sep 2025 10:17
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16625

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