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

Emerging technologies, such as Distributed Ledger Technology (DLT), face growing scrutiny for their environmental impact, especially when it comes to the energy use of the Proof of Work (PoW) consensus mechanism and broader Environmental, Social, and Governance (ESG) considerations. Yet, much of the existing systematic literature reviews of DLT rely on the limited analyses of citations, abstracts, and keywords, failing to fully capture the field's complexity and ESG concerns. To address these challenges, we analyze the full text of 24,539 publications using Natural Language Processing (NLP) with our manually labeled Named Entity Recognition (NER) dataset of 39,427 entities for DLT. This method identifies 505 key publications connecting DLT and ESG domains, providing a more comprehensive and nuanced understanding of the field. Our combined NLP and temporal graph analysis reveals critical trends in DLT evolution and ESG impacts, including the pivotal role of research in cryptography and peer-to-peer networks, Bitcoin's persistent impact on research and environmental concerns (a "Lindy effect"), Ethereum's influence on Proof of Stake (PoS) and smart contracts adoption, and a shift towards energy-efficient consensus mechanisms. Our contributions include the first DLT-specific NER dataset, addressing the scarcity of high-quality labeled NLP data for blockchain research; a methodology integrating NLP and temporal graph analysis for interdisciplinary literature review at large scale; and the first NLP-driven DLT literature review emphasizing ESG aspects.