The Exploring for the Future program Showcase 2024 was held on 13-16 August 2024. Day 3 - 15th August talks included: <b>Session 1 – Hydrogen opportunities across Australia</b> <a href="https://youtu.be/pA9ft3-7BtU?si=V0-ccAmHHIYJIZAo">Hydrogen storage opportunities and the role of depleted gas fields</a> - Dr Eric Tenthorey <a href="https://youtu.be/MJFhP57nnd0?si=ECO7OFTCak78Gn1M">The Green Steel Economic Fairways Mapper</a> - Dr Marcus Haynes <a href="https://youtu.be/M95FOQMRC7o?si=FyP7CuDEL0HEdzPw">Natural hydrogen: The Australian context</a> - Chris Boreham <b>Session 2 – Sedimentary basin resource potential – source rocks, carbon capture and storage (CCS) and groundwater</b> <a href="https://youtu.be/44qPlV7h3os?si=wfQqxQ81Obhc_ThE">Australian Source Rock and Fluid Atlas - Accessible visions built on historical data archives</a> - Dr Dianne Edwards <a href="https://youtu.be/WcJdSzsADV8?si=aH5aYbpnjaz3Qwj9">CO2: Where can we put it and how much will it cost?</a> - Claire Patterson <a href="https://youtu.be/Y8sA-iR86c8?si=CUsERoEkNDvIwMtc">National aquifer framework: Putting the geology into hydrogeology</a> - Dr Nadege Rollet <b>Session 3 – Towards a national inventory of resource potential and sustainable development</b> <a href="https://youtu.be/K5xGpwaIWgg?si=2s0AKuNpu30sV1Pu">Towards a national inventory of mineral potential</a> - Dr Arianne Ford <a href="https://youtu.be/XKmEXwQzbZ0?si=yAMQMjsNCGkAQUMh">Towards an inventory of mine waste potential</a> - Dr Anita Parbhakar-Fox <a href="https://youtu.be/0AleUvr2F78?si=zS4xEsUYtARywB1j">ESG mapping of the Australian mining sector: A critical review of spatial datasets for decision making</a> - Dr Eleonore Lebre View or download the <a href="https://dx.doi.org/10.26186/149800">Exploring for the Future - An overview of Australia’s transformational geoscience program</a> publication. View or download the <a href="https://dx.doi.org/10.26186/149743">Exploring for the Future - Australia's transformational geoscience program</a> publication. You can access full session and Q&A recordings from YouTube here: 2024 Showcase Day 3 - Session 1 - <a href="https://www.youtube.com/watch?v=Ho6QFMIleuE">Hydrogen opportunities across Australia</a> 2024 Showcase Day 3 - Session 2 - <a href="https://www.youtube.com/watch?v=ePZfgEwo0m4">Sedimentary basin resource potential – source rocks, carbon capture and storage (CCS) and groundwater</a> 2024 Showcase Day 3 - Session 3 - <a href="https://www.youtube.com/watch?v=CjsZVK4h6Dk">Towards a national inventory of resource potential and sustainable development</a>

<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

<div><strong>Output type: </strong>Exploring for the Future Extended Abstract <strong> </strong></div><div><br></div><div><strong>Short abstract: </strong>There is an increased international focus on achieving high environmental, socio-economic, and governance (ESG) outcomes within mineral supply chains, in addition to delivering positive economic results. Mineral exploration and development projects must balance these disparate objectives to the satisfaction of separate stakeholders. However, the challenge of reconciling distinct preferences can obscure viable outcomes and confound project selection, particularly in the early stages of project development. Here, we discuss how such investment decisions can be treated as multicriteria optimization problems. In appraising the pre-competitive potential for nickel sulphide developments, we show how this approach can be used to effectively evaluate competing objectives and to locate regions that perform best under a range of different metrics. We outline a mapping framework that identifies Australian regions that optimally balance geological potential, economic value, and environmental impact. Our workflow creates a new capability within Australia to incorporate high-level, holistic information into the earliest stages of exploration. While this abstract focuses on mineral exploration, the modelling could be extended to other Australian resource development applications. Importantly, our results further underscore the need to compile baseline ESG datasets across Australia to help drive sustainable exploration decisions.</div><div><br></div><div><strong>Citation:</strong> Walsh S.D.C., Haynes M.W. &amp; Wang C., 2024. Multicriteria resource potential mapping: balancing geological, economic &amp; environmental factors. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra. https://doi.org/10.26186/149250</div>

<div>Mineral exploration and development involves the selection of potential projects which must be evaluated across disparate characteristics. However, the distinct metrics involved are typically difficult to reconcile (e.g. geological potential, environmental impact, jobs created, value generated, etc.). Separate stakeholders—with different goals and attitudes—will reasonably differ in their preferences as to which categories to prioritize and how much weight to give to each. These conflicting preferences can obscure optimal outcomes and confound project selection.</div><div><br></div><div>In this presentation, we will discuss how early-stage exploration decisions can be treated as multi-criteria optimization problems. We show how this approach can be used to effectively evaluate and communicate competing criteria, and locate regions that perform best under a range of different metrics. We then outline a mapping framework that identifies regions that perform best in terms of geological potential, economic value and environmental impact and demonstrate this approach in a real-word example that highlights new exploration targets in the Australian context. Abstract presented at the American Geophysical Union (AGU) Fall Meeting 2023 (AGU23) https://www.agu.org/fall-meeting