Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913896849211392 |
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| author | Cruz, Walter Hernandez Tylinski, Kamil Moore, Alastair Roche, Niall Vadgama, Nikhil Treiblmaier, Horst Shangguan, Jiangbo Tasca, Paolo Xu, Jiahua |
| author_facet | Cruz, Walter Hernandez Tylinski, Kamil Moore, Alastair Roche, Niall Vadgama, Nikhil Treiblmaier, Horst Shangguan, Jiangbo Tasca, Paolo Xu, Jiahua |
| contents | Distributed Ledger Technology (DLT) faces increasing environmental scrutiny, particularly concerning the energy consumption of the Proof of Work (PoW) consensus mechanism and broader Environmental, Social, and Governance (ESG) issues. However, existing systematic literature reviews of DLT rely on limited analyses of citations, abstracts, and keywords, failing to fully capture the field's complexity and ESG concerns. We address these challenges by analyzing 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 methodology identified 505 key publications at the DLT/ESG intersection, enabling comprehensive domain analysis. Our combined NLP and temporal graph analysis reveals critical trends in DLT evolution and ESG impacts, including cryptography and peer-to-peer networks research's foundational influence, Bitcoin's persistent impact on research and environmental concerns (a "Lindy effect"), Ethereum's catalytic role on Proof of Stake (PoS) and smart contract adoption, and the industry's progressive shift toward energy-efficient consensus mechanisms. Our contributions include the first DLT-specific NER dataset addressing the scarcity of high-quality labeled NLP data in blockchain research, a methodology integrating NLP and temporal graph analysis for large-scale interdisciplinary literature reviews, and the first NLP-driven literature review focusing on DLT's ESG aspects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_12420 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature Cruz, Walter Hernandez Tylinski, Kamil Moore, Alastair Roche, Niall Vadgama, Nikhil Treiblmaier, Horst Shangguan, Jiangbo Tasca, Paolo Xu, Jiahua Information Retrieval Computation and Language Machine Learning Distributed Ledger Technology (DLT) faces increasing environmental scrutiny, particularly concerning the energy consumption of the Proof of Work (PoW) consensus mechanism and broader Environmental, Social, and Governance (ESG) issues. However, existing systematic literature reviews of DLT rely on limited analyses of citations, abstracts, and keywords, failing to fully capture the field's complexity and ESG concerns. We address these challenges by analyzing 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 methodology identified 505 key publications at the DLT/ESG intersection, enabling comprehensive domain analysis. Our combined NLP and temporal graph analysis reveals critical trends in DLT evolution and ESG impacts, including cryptography and peer-to-peer networks research's foundational influence, Bitcoin's persistent impact on research and environmental concerns (a "Lindy effect"), Ethereum's catalytic role on Proof of Stake (PoS) and smart contract adoption, and the industry's progressive shift toward energy-efficient consensus mechanisms. Our contributions include the first DLT-specific NER dataset addressing the scarcity of high-quality labeled NLP data in blockchain research, a methodology integrating NLP and temporal graph analysis for large-scale interdisciplinary literature reviews, and the first NLP-driven literature review focusing on DLT's ESG aspects. |
| title | Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature |
| topic | Information Retrieval Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2308.12420 |