Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium  


Authors / CoAuthors

Lawley, C.J.M. | Haynes, M. | Chudasama, B. | Goodenough, K. | Eerola, T. | Golev, A. | Zhang, S. | Park, J. | Lèbre, E.

Abstract

<div>Disruptions to the global supply chains of critical raw materials (CRM) have the potential to delay or increase the cost of the renewable energy transition. However, for some CRM, the primary drivers of these supply chain disruptions are likely to be issues related to environmental, social, and governance (ESG) rather than geological scarcity. Herein we combine public geospatial data as mappable proxies for key ESG indicators (e.g., conservation, biodiversity, freshwater, energy, waste, land use, human development, health and safety, and governance) and a global dataset of news events to train and validate three models for predicting “conflict” events (e.g., disputes, protests, violence) that can negatively impact CRM supply chains: (1) a knowledge-driven fuzzy logic model that yields an area under the curve (AUC) for the receiver operating characteristics plot of 0.72 for the entire model; (2) a naïve Bayes model that yields an AUC of 0.81 for the test set; and (3) a deep learning model comprising stacked autoencoders and a feed-forward artificial neural network that yields an AUC of 0.91 for the test set. The high AUC of the deep learning model demonstrates that public geospatial data can accurately predict natural resources conflicts, but we show that machine learning results are biased by proxies for population density and likely underestimate the potential for conflict in remote areas. Knowledge-driven methods are the least impacted by population bias and are used to calculate an ESG rating that is then applied to a global dataset of lithium occurrences as a case study. We demonstrate that giant lithium brine deposits (i.e., >10&nbsp;Mt Li2O) are restricted to regions with higher spatially situated risks relative to a subset of smaller pegmatite-hosted deposits that yield higher ESG ratings (i.e., lower risk). Our results reveal trade-offs between the sources of lithium, resource size, and spatially situated risks. We suggest that this type of geospatial ESG rating is broadly applicable to other CRM and that mapping spatially situated risks prior to mineral exploration has the potential to improve ESG outcomes and government policies that strengthen supply chains. <b>Citation:</b> Haynes M, Chudasama B, Goodenough K, Eerola T, Golev A, Zhang SE, Park J and Lèbre E (2024) Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium. <i>Earth Sci. Syst. Soc. </i>4:10109. doi: 10.3389/esss.2024.10109

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document

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149059

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Commonwealth of Australia (Geoscience Australia)  
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Keywords

( Project )
  • EFTF – Exploring for the Future
( Project )
  • Australia’s Resources Framework
  • critical minerals
  • sustainable development
  • conflict
  • machine learning
  • deep learning
  • lithium
  • Environmental
  • Social
  • Governance
  • ESG
theme.ANZRC Fields of Research.rdf
  • Resource geoscienceEnvironmental SciencesData engineering and data scienceData mining and knowledge discoveryHuman geography not elsewhere classified
  • Published_External

Publication Date

2024-08-21T04:25:24

Creation Date

2023-11-17T14:00:00

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Australian Government Security Classification System    

Classification - unclassified

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Creative Commons Attribution 4.0 International Licence    

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completed

Purpose

Original research article investigating the potential to map global environmental, social and governance (ESG) characteristics to inform our understanding of lithium supply chain potential.

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notPlanned

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geoscientificInformation

Series Information

Earth Science, Systems and Society Volume 4, Article 10109, July 2024

Lineage

<div>Global-scale environmental, social, governance (ESG)&nbsp;datasets have been combined from multiple publicly available sources (including the United Nations, World Bank, and academia). These ESG criteria have been integrated to assess global ESG risks in the context of lithium supply chains. A global dataset of natural resources “conflict” (e.g., verbal disputes, protests, violence) news events has been data mined from the Political Event Classification, Attributes, and Types (POLECAT) dataset, with natural language processing used to identify and categorize historical conflict events. The resulting events dataset is used to train and validate predictive models for natural resources conflict, which then informs an analysis of global lithium supply chain potential.</div>

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[-60.00, 90.00, -180.00, 180.00]

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