Product catalogue
Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium
<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 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
Simple
Identification info
- Date (Creation)
- 2023-11-17T14:00:00
- Date (Publication)
- 2024-08-21T04:25:24
- Citation identifier
- Geoscience Australia Persistent Identifier/https://pid.geoscience.gov.au/dataset/ga/149059
- Cited responsible party
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Role Organisation / Individual Name Details Author Lawley, C.J.M.
External Contact Author Haynes, M.
Internal Contact Author Chudasama, B.
External Contact Author Goodenough, K.
External Contact Author Eerola, T.
External Contact Author Golev, A.
External Contact Author Zhang, S.
External Contact Author Park, J.
External Contact Author Lèbre, E.
External Contact Publisher Elsevier Ltd
External Contact
- Name
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Earth Science, Systems and Society
- Issue identification
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Volume 4, Article 10109, July 2024
- Purpose
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Original research article investigating the potential to map global environmental, social and governance (ESG) characteristics to inform our understanding of lithium supply chain potential.
- Status
- Completed
- Point of contact
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Role Organisation / Individual Name Details Resource provider Minerals, Energy and Groundwater Division
External Contact Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Point of contact Haynes, M.
Internal Contact
- Spatial representation type
- Topic category
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- Geoscientific information
Extent
))
- Maintenance and update frequency
- Not planned
Resource format
- Title
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Product data repository: Various Formats
- Website
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Data Store directory containing the digital product files
Data Store directory containing one or more files, possibly in a variety of formats, accessible to Geoscience Australia staff only for internal purposes
- Project
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EFTF – Exploring for the Future
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- Project
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Australia’s Resources Framework
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- Keywords
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critical minerals
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sustainable development
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conflict
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machine learning
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deep learning
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lithium
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Environmental
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Social
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Governance
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ESG
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- theme.ANZRC Fields of Research.rdf
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Resource geoscience
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Environmental Sciences
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Data engineering and data science
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Data mining and knowledge discovery
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Human geography not elsewhere classified
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Published_External
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Resource constraints
- Title
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Creative Commons Attribution 4.0 International Licence
- Alternate title
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CC-BY
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4.0
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Role Organisation / Individual Name Details User Any
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- Use constraints
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© 2024 Lawley, Haynes, Chudasama, Goodenough, Eerola, Golev, Zhang, Park and Lèbre
Resource constraints
- Title
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Australian Government Security Classification System
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
- Classification system
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Australian Government Security Classification System
- Language
- English
- Character encoding
- UTF8
Distribution Information
- Distributor contact
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Role Organisation / Individual Name Details Distributor Commonwealth of Australia (Geoscience Australia)
Voice facsimile
- OnLine resource
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Link to Journal
Link to Journal
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Resource lineage
- Statement
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<div>Global-scale environmental, social, governance (ESG) 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>
Metadata constraints
- Title
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Australian Government Security Classification System
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
Metadata
- Metadata identifier
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urn:uuid/c26ae25c-54cc-4549-947b-ba55fd9f7cc7
- Title
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GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
- Contact
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Role Organisation / Individual Name Details Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Point of contact Haynes, M.
Internal Contact
Type of resource
- Resource scope
- Document
- Name
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Journal Article / Conference Paper
Alternative metadata reference
- Title
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Geoscience Australia - short identifier for metadata record with
uuid
- Citation identifier
- eCatId/149059
- Date info (Creation)
- 2024-08-21T02:28:09
- Date info (Revision)
- 2024-08-21T02:28:09
Metadata standard
- Title
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AU/NZS ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-3
- Title
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Geoscience Australia Community Metadata Profile of ISO 19115-1:2014
- Edition
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Version 2.0, September 2018
- Citation identifier
- http://pid.geoscience.gov.au/dataset/ga/122551