The Geological and Bioregional Assessments (GBA) Program is a series of independent scientific studies undertaken by Geoscience Australia and the CSIRO, supported by the Bureau of Meteorology, and managed by the Department of Agriculture, Water and the Environment. The Program consists of three stages across three regions with potential to deliver gas to the East Coast Gas Market. Stage 1 was a rapid regional prioritisation conducted by Geoscience Australia, to identify those sedimentary basins with the greatest potential to deliver shale and/or tight gas to the East Coast Gas Market within the next five to ten years. This prioritisation process assessed 27 onshore eastern and northern Australian basins with shale and/or tight gas potential. Further screening reduced this to a shortlist of nine basins where exploration was underway. The shortlisted basins were ranked on a number of criteria. The Cooper Basin, the Beetaloo Sub-basin and the Isa Superbasin were selected for more detailed assessment. Stage 2 of the program involved establishing a baseline understanding of the identified regions. Geoscience Australia produced regional geological evaluations and conceptualisations that inform the assessment of shale and/or tight gas prospectivity, ground- and surface-water impacts, and hydraulic fracturing models. Geoscience Australia’s relative prospectivity assessments provide an indication of where viable petroleum plays are most likely to be present. These data indicate areal and stratigraphic constraints that support the program’s further work in Stage 3, on understanding likely development scenarios, impact assessments, and causal pathways. <b>Citation:</b> Hall Lisa S., Orr Meredith L., Lech Megan E., Lewis Steven, Bailey Adam H. E., Owens Ryan, Bradshaw Barry E., Bernardel George (2021) Geological and Bioregional Assessments: assessing the prospectivity for tight, shale and deep-coal resources in the Cooper Basin, Beetaloo Subbasin and Isa Superbasin. <i>The APPEA Journal</i><b> 61</b>, 477-484. https://doi.org/10.1071/AJ20035

<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