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

arXiv: https://arxiv.org/abs/2308.12420 · HTML: https://mlsanity.com/paper/2308.12420/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 176 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Scaling Data-Constrained Language Models](https://mlsanity.com/paper/2305.16264/) (2023)
- [Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?](https://mlsanity.com/paper/2207.10551/) (2022)
- [Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://mlsanity.com/paper/2305.05862/) (2023)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols](https://mlsanity.com/paper/2103.12732/) (2021)
- [Blockchain for Internet of Things: A Survey](https://mlsanity.com/paper/1906.00245/) (2019)
- [Validating Label Consistency in NER Data Annotation](https://mlsanity.com/paper/2101.08698/) (2021)
- [Taxonomy of Centralization in Public Blockchain Systems: A Systematic Literature Review](https://mlsanity.com/paper/2009.12542/) (2020)
- [From banks to DeFi: the evolution of the lending market](https://mlsanity.com/paper/2104.00970/) (2021)
- [An Analysis of Energy Consumption and Carbon Footprints of Cryptocurrencies and Possible Solutions](https://mlsanity.com/paper/2203.03717/) (2022)
- [Ergo -- a programming language for Smart Legal Contracts](https://mlsanity.com/paper/2112.07064/) (2021)
- [Conversion of Legal Agreements into Smart Legal Contracts using NLP](https://mlsanity.com/paper/2210.08954/) (2022)
- [Carbon Trading with Blockchain](https://mlsanity.com/paper/2005.02474/) (2020)
- [Bitcoin's Carbon Footprint Revisited: Proof of Work Mining for Renewable Energy Expansion](https://mlsanity.com/paper/2304.04578/) (2023)
- [Analysis of the XRP Ledger Consensus Protocol](https://mlsanity.com/paper/1802.07242/) (2018)
- [Triple-entry Accounting, Blockchain and Next of Kin: Towards a Standardisation of Ledger Terminology](https://mlsanity.com/paper/2101.02632/) (2021)
- [Jack The Rippler: Arbitrage on the Decentralized Exchange of the XRP Ledger](https://mlsanity.com/paper/2106.16158/) (2021)

## Built on by (most cited first, 1 of 1)

- [DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain](https://mlsanity.com/paper/2602.22045/) (2026)

## Abstract (arXiv)

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.