This report provides information on the activities of Geoscience Australia during the 2022-23 financial year. Content for the Annual Report was developed in accordance with the requirements set out in the Public Governance, Performance and Accountability Rule 2014 and Department of Finance's Resource Management Guide 135, To view the annual report on our website: https://www.ga.gov.au/about/corporate-documents/annual-report

<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>Our Corporate Plan 2024–25 explains who we are, what we do, how we do it and where we are going. The accountable authority of a Commonwealth entity must prepare a corporate plan for the entity at least once each reporting period in accordance to section 16E(2) of the PGPA Rule.</div> The plan can also be viewed on our internet - https://www.ga.gov.au/about/corporate-plan

Our Corporate Plan 2023–24 explains who we are, what we do, how we do it and where we are going. The structure is reflected in Figure 1 and in keeping with the requirements of the Public Governance, Performance and Accountability Rule 2014, the plan outlines our: • purpose and vision • strategic priorities • key activities • operating environment • capability • cooperative relationships • governance and risk management • performance measures. To view the plan on our website: https://www.ga.gov.au/about/corporate-plan

The Geoscience Australia Data Strategy 2018-2021 is the enterprise strategy that outlines the initiatives that should be followed in order to maximise data potential.

<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract:</strong> Under the Exploring for the Future (EFTF) program, Geoscience Australia staff and collaborators engaged with land-connected stakeholders that managed or had an interest in land comprising 56% of the total land mass area of Australia. From 2020 to 2023, staff planning ground-based and airborne geophysical and geological data acquisition projects consulted farmers, National Park rangers and managers, Native Title holders, cultural heritage custodians and other land-connected people to obtain land access and cultural heritage clearances for surveys proposed on over 122,000 parcels of land. Engagement did not always result in field activities proceeding. To support communication with this diverse audience, animations, comic-style factsheets, and physical models, were created to help explain field techniques. While the tools created have been useful, the most effective method of communication was found to be a combination of these tools and open two-way discussions.</div><div><br></div><div><strong>Citation: </strong>Sweeney, M., Kuoni, J., Iffland, D. &amp; Soroka, L., 2024. Improving how we engage with land-connected people about geoscience. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra. https://doi.org/10.26186/148760</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