A Data-Driven Approach to Mitigating ESG Risks in Rare Earth Mineral Supply Chains

• Author(s): Akash Kumar
• Paper ID: 1716255
• Page: 4133-4137
• Published Date: 06-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026
• DOI: https://doi.org/10.64388/IREV9I10-1716255

Abstract

The global shift toward clean energy and digital technology has made rare earth minerals critical to modern supply chains. Despite their importance, existing ESG assessment tools remain inadequate: corporate sustainability reports systematically underreport negative incidents, and major ESG rating agencies exhibit an average inter-agency correlation of only 0.54. This paper develops and validates a data-driven analytical framework using machine learning (ML) and natural language processing (NLP) to automatically detect, classify, and score ESG risks in rare earth mineral supply chains. Applied to a corpus of approximately 840 documents from five major rare earth producers across Australia, the USA, and China (2015–2025), the fine-tuned BERT classifier achieves an F1-score of 0.84. The framework detects ESG controversies an average of 127 days earlier than rating agency updates, reveals a 23.5-point composite ESG risk score gap between Chinese and Western producers, and confirms financial materiality with −3.2% average abnormal returns following controversy disclosure. All four research hypotheses are statistically supported. The study contributes a validated, sector-specific framework for investors, procurement managers, and regulators seeking improved ESG transparency in critical mineral supply chains.

Keywords

Rare Earth Minerals; ESG Risk; Machine Learning; Natural Language Processing; Supply Chain Transparency; BERT; Critical Minerals; Sustainable Supply Chain.

Citations

IRE Journals:
Akash Kumar "A Data-Driven Approach to Mitigating ESG Risks in Rare Earth Mineral Supply Chains" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 4133-4137 https://doi.org/10.64388/IREV9I10-1716255

IEEE:
Akash Kumar "A Data-Driven Approach to Mitigating ESG Risks in Rare Earth Mineral Supply Chains" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1716255

APA:
Akash Kumar (2026). A Data-Driven Approach to Mitigating ESG Risks in Rare Earth Mineral Supply Chains. Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1716255

MLA:
Akash Kumar "A Data-Driven Approach to Mitigating ESG Risks in Rare Earth Mineral Supply Chains" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1716255

BibTeX

@article{1716255,
author = {Akash Kumar},
title = {A Data-Driven Approach to Mitigating ESG Risks in Rare Earth Mineral Supply Chains},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {4133-4137},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716255.pdf},
abstract = {The global shift toward clean energy and digital technology has made rare earth minerals critical to modern supply chains. Despite their importance, existing ESG assessment tools remain inadequate: corporate sustainability reports systematically underreport negative incidents, and major ESG rating agencies exhibit an average inter-agency correlation of only 0.54. This paper develops and validates a data-driven analytical framework using machine learning (ML) and natural language processing (NLP) to automatically detect, classify, and score ESG risks in rare earth mineral supply chains. Applied to a corpus of approximately 840 documents from five major rare earth producers across Australia, the USA, and China (2015–2025), the fine-tuned BERT classifier achieves an F1-score of 0.84. The framework detects ESG controversies an average of 127 days earlier than rating agency updates, reveals a 23.5-point composite ESG risk score gap between Chinese and Western producers, and confirms financial materiality with −3.2% average abnormal returns following controversy disclosure. All four research hypotheses are statistically supported. The study contributes a validated, sector-specific framework for investors, procurement managers, and regulators seeking improved ESG transparency in critical mineral supply chains.},
keywords = {Rare Earth Minerals; ESG Risk; Machine Learning; Natural Language Processing; Supply Chain Transparency; BERT; Critical Minerals; Sustainable Supply Chain.},
month = {April}
}