2013 Floods post-disaster survey form for distribution to Bundaberg households

More than 17,000 dwellings in the Brisbane and Ipswich area were flood affected when, in January 2011, the Bremer and Brisbane Rivers exceeded major flood levels. In January 2013 the Brisbane and Ipswich area was again impacted by major flooding. During April and May 2012 Geoscience Australia conducted a postal survey of residents in the flood affected areas of Brisbane and Ipswich. Nearly 1,300 households responded. The survey covered a range of topics including preparation in the days leading up to the flood inundation, evacuation behaviour, economic impacts, subjective well-being and reconstruction and recovery in the days, weeks , and months following the flood event. The paper examines residential rebuilding following the floods and focuses on vulnerability and reconstruction. It discusses the composition of vulnerable households ( eg people with disabilities, no access to a motor vehicle, single parents with young children), household well being after the flood event ( eg physical, emotional and financial stress) and building fabric issues ( eg mould or warped timbers) during the reconstruction phase. Also examined are the steps taken to mitigate against future flood events. What lessons were there to be learned? The paper also compares two different socio-economic areas and looks at any differences in recovery between the two areas.

<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

<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