Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature

• Main Authors: Hernandez, Walter, Tylinski, Kamil, Moore, Alastair, Roche, Niall, Vadgama, Nikhil, Treiblmaier, Horst, Shangguan, Jiangbo, Tasca, Paolo, Xu, Jiahua
• Format: Journal Article
• Language: English
• Published: 23.08.2023
• Subjects: Computer Science - Computation and Language; Computer Science - Information Retrieval; Computer Science - Learning
• DOI: 10.48550/arxiv.2308.12420

As Distributed Ledger Technologies (DLTs) rapidly evolve, their impacts extend beyond technology, influencing environmental and societal aspects. This evolution has increased publications, making manual literature analysis increasingly challenging. We address this with a Natural Language Processing (NLP)-based systematic literature review method to explore the intersection of Distributed Ledger Technology (DLT) with its Environmental, Social, and Governance (ESG) aspects. Our approach involves building and refining a directed citation network from 107 seed papers to a corpus of 24,539 publications and fine-tuning a transformer-based language model for Named Entity Recognition (NER) on DLT and ESG domains. Applying this model, we distilled the corpus to 505 key publications, enabling an inaugural literature review and temporal graph analysis of DLT's evolution in ESG contexts. Our contributions include an adaptable and scalable NLP-driven systematic literature review methodology and a unique NER dataset of 54,808 entities, tailored for DLT and ESG research. Our inaugural literature review demonstrates their applicability and effectiveness in analyzing DLT's evolution and impacts, proving invaluable for stakeholders in the DLT domain.