3730af18-ca2d-4cc7-b2a7-468962224069 urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-10-16T22:10:27 creation ISO 19115 2016 Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium 3730af18-ca2d-4cc7-b2a7-468962224069 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. Citation: 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. Earth Sci. Syst. Soc. 4:10109. doi: 10.3389/esss.2024.10109 {"type": "Polygon", "coordinates": [[[-180.0, -60.0], [180.0, -60.0], [180.0, 90.0], [-180.0, 90.0], [-180.0, -60.0]]]} -180.0 180.0 -60.0 90.0 Australia’s Resources Framework Data engineering and data science Data mining and knowledge discovery EFTF – Exploring for the Future ESG Environmental Environmental Sciences Governance Human geography not elsewhere classified Published_External Resource geoscience Social conflict critical minerals deep learning lithium machine learning sustainable development Link to Journal documentDigital Link to Journal HTML