UQ-Exeter Institute joint PhD

Project team

UQ: Dr Skye Doherty

Exeter: Dr Iain Soutar

Project description

Background and context

A future of unreliable energy is plausible. Major shifts in peak demand, changing weather patterns and ageing infrastructure, combined with the insatiable demand of data centres, means disruption could become a feature of our networks as grids transition to renewable sources.

In both Australia and the United Kingdom, the shift from fossil fuels to renewables is underway; however, the cost of the transition is high, pushing up prices for households. Meanwhile, there are concerns that rising demand will result in energy shortages.    

Despite the urgency of the energy transition there is a fundamental misunderstanding among key players of the needs and values of others. Energy consumers, policymakers, and industry operators have different, and at times conflicting, priorities.

Research suggests that sensory and immersive virtual experiences can encourage deeper connection and empathy with environmental issues, which, in turn, can support behaviour change. As a result, there is an opportunity to design interventions that support diverse groups to engage with the multiple perspectives, complex trade-offs and potential consequences of building a sustainable energy future with a view to encouraging act in response.  

This project aims to explore how sensory experiences can be designed to support engagement with the need for and implications of energy transitions. It will engage communities and stakeholders in both countries. The key research questions are as follows: 

• What are the key tensions, values and opportunities related to energy transitions among communities in Australia and the UK?
• How can sensory experiences be designed to engage publics in understanding energy transitions? and taking positive action in response? 

Approach   

This is primarily a human-centred design project in which the PhD student will undertake both qualitative contextual and participatory research as well as technological prototyping and evaluation.

The project will have three phases:   

Contextual inquiry: a review of existing research on the intersection of immersive technology and social change will inform a series online focus group discussions with stakeholders in the energy sector, specifically policy makers, regulators, providers and consumers in both countries. This work, ideally done in both countries, will identify tensions and opportunities regarding energy transitions and build early engagement with the project.   

Design and initial evaluation: Initial design concepts, informed by phase one will be initially developed by the researcher and refined through co-design workshops and prototyping with groups in each country. One or more identified for further development, implementation and evaluation.   

Analysis and translation: This phase will focus on data analysis and translation. Key insights and findings will be presented back to participants, including industry groups, along with a public-facing report on the actions consumers, policymakers and operators might take, individually or collectively, to build a more resilient energy future.  

 The project will enhance our understanding the role of technology in building empathy among stakeholders in response to contested issues.  

Project outputs include: 

• A high-quality interdisciplinary thesis bridging technology design, energy transition and sustainability.
• Up to three peer-reviewed publications
• Public facing report and external engagement with stakeholder groups   

Contact

Questions about this project should be directed to Dr Skye Doherty at s.doherty@uq.edu.au ​​​​​

Project team

UQ: Dr Wayes Tushar

Exeter: Dr Carolyn Peterson

Project description

Research problem 

Despite the strong potential of agrovoltaics to improve sustainability in energy-intensive agricultural systems, current implementations are predominantly designed at the single-farm level. They do not adequately address community-scale integration, energy sharing, and participatory governance.

As a result, key challenges—such as perceived land-use competition, mismatches between local energy supply and demand, limited transparency in benefit distribution, and insufficient community acceptance—continue to hinder large-scale deployment and impact.

Integrated frameworks are lacking that combine technical design, economic optimisation, and socio-cultural considerations. These would enable networked, community-oriented agrovoltaic systems that reliably supply agricultural loads while equitably sharing benefits among stakeholders and support decarbonisation objectives and the transition toward more sustainable and equitable rural energy systems. 

Significance 

This research addresses a critical gap in current agrovoltaics scholarship by moving beyond predominantly single-farm implementations toward the design and evaluation of community-integrated, networked agrovoltaic systems.

Although agrovoltaics is widely recognised as a promising pathway for improving sustainability in energy-intensive agriculture, limited attention has been given to how multiple farms and rural households can be coordinated through energy sharing while ensuring technical reliability, economic viability, and social acceptance. 

The project will generate original contributions to knowledge by developing an integrated framework that jointly considers farm types, system architecture, energy management, and socio-economic design for community-oriented agrovoltaics.

In doing so, it will advance theoretical and methodological understanding of how distributed renewable generation can be co-optimised with agricultural energy demand and participatory benefit-sharing mechanisms at scale. 

From an applied perspective, the research will provide practical design and decision-support tools for:

• reducing irrigation energy costs,
• improving farm profitability, and
• enhancing resilience to grid disruptions through locally produced renewable energy.

The outcomes will also inform policy-makers and industry stakeholders on scalable models for deploying community-based agrovoltaic systems,. In this way, it will support decarbonisation objectives and the transition toward more sustainable and equitable rural energy systems. 

Research aim and research questions

The overall aim of this project is to design, develop, and evaluate a community-integrated agrovoltaic framework.

This framework will enable efficient energy self-sufficiency in irrigated agriculture by optimally coordinating electricity generation, distribution, and sharing among multiple farms and rural households, while addressing technical, economic, social, and cultural challenges to ensure sustainability, profitability, and resilience. 

In doing so, the project will address the following five research questions: 

• How can a community-oriented agrovoltaic system be technically designed to supply electricity for irrigation and on-farm processing while enabling energy sharing among neighbouring farms and rural households?
• What energy management and optimisation strategies can effectively balance local supply and demand in networked agrovoltaic communities under varying climatic and agricultural conditions?
• What are the economic impacts of community-based agrovoltaics with energy sharing on irrigation energy costs, farm profitability, and community-level revenue generation?
• How do different benefit-sharing and ownership models and farm types influence community acceptance, participation, and perceived fairness in agrovoltaic projects?
• What technical, economic, social, and cultural barriers and enablers affect the large-scale adoption of community-integrated agrovoltaic systems? 

The proposed research approach and methods 

The proposed research will be conducted in the following three phases.

Phase 1 – Techno-economic modelling of networked farms

In the first phase, we will develop a techno-economic model of networked agrovoltaic farms without energy-sharing capabilities. The objective is to determine optimal local solar configurations—either solar-only or solar-plus-battery—for each farm, taking into account farm type, size, energy demand, and socio-economic characteristics. 

This phase will generate scenarios identifying potential energy “gensumers” (farms that both generate and consume electricity) and energy consumers to inform the design of the energy-sharing framework in Phase 2.

A coalition formation game [1] will be employed to explore feasible groupings of farms for future energy sharing and to guide decisions on energy asset allocation. We will use agricultural data from the Department of Agriculture, Fisheries and Forestry (Australia) and the Centre for Rural Policy Research (CRPR), complemented by semi-structured interviews with farmers at the Sunday Farmers Market to capture in-depth insights into farming activities, energy requirements, and associated costs. 

Phase 2 – Design of community energy sharing framework

Phase 2 will focus on developing a novel, game theory-based energy-sharing algorithm [2] for the networked farms identified in Phase 1. The framework will enable farms and rural households to exchange electricity based on local sharing prices, supply-demand conditions, and grid energy costs.

We will apply cooperative game theory [3] to ensure fair benefit allocation and socially optimal outcomes for all participants. A key innovation of this phase is the integration of farm types and seasonal energy demand variations into the energy-sharing decision-making process. For example, farms with surplus energy may choose to share it with neighbours, while those anticipating higher future demand may store energy for later use, depending on current prices and community needs. 

Phase 3 – Model validation and stakeholder evaluation

The final phase will validate the networked agrovoltaic and energy-sharing model through extensive simulations across multiple crop types and climate scenarios.

We will conduct farmer surveys to evaluate feasibility, acceptance, and potential barriers from the stakeholder perspective.

This phase will be iterative, with feedback from farmers informing adjustments to the energy-sharing model to ensure alignment with practical needs and principles of participatory governance [4].

The outcomes will provide evidence-based recommendations for policymakers and industry stakeholders on scalable and socially acceptable models for community-integrated agrovoltaic systems. 

Project deliverables 

Techno-economic model of networked agrovoltaic farms

A robust modelling framework will determine optimal solar and battery sizing for different farm types, identify potential energy “gensumers” and consumers within networked farms. It will also capture the socio-economic and agricultural factors influencing participation in energy sharing. 

Game theory-based energy sharing framework

A novel cooperative game-theoretic algorithm will enable electricity sharing among farms and rural households by integrating farm type, seasonal energy demand, and dynamic pricing, supported by a decision tool that ensures equitable benefit distribution and maximises community-level energy efficiency. 

Simulation and policy-relevant recommendation

Validation of the agrovoltaic network and energy-sharing model across multiple crop and climate scenarios, combined with farmer surveys to assess feasibility and acceptance, leading to evidence-based recommendations for policymakers and industry on scalable community-integrated systems. 

Knowledge sharing 

This project is anticipated to contribute pioneering outcomes to networked agrovoltaic relevant literature through its techno-economic-driven, game-theoretic approach, with potential applications in agricultural settings beyond the selected test system.

The project’s early results will be shared with agricultural stakeholders and academic audiences through webinars, virtual seminars, international conferences, and targeted workshops.

You will also publish high-impact research outputs to leading Q1 journals in the energy and social science, such as Nature Energy, Energy and Social Science, and Energy and Environmental Science. In addition, the project's outcomes will be actively promoted through UQ and the University of Exeter’s social media channels to engage a broader, non-academic audience. 

References

[1] Tushar et al., "A coalition formation game framework for peer-to-peer trading," Applied Energy, vol. 261, pp. 114436:1-13, Mar 2020. 

[2] Tushar et al., "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, vol. 282, Part A, pp. 116131:1-19, Jan. 2021.

[3] Saad et al., "Coalitional game theory for communication networks," IEEE Signal Processing Magazine, vol. 26, no 5, pp. 77-97, Sept. 2009.

[4] Jager et al., “Pathways to implementation: Evidence on how participation in environmental governance impacts on environmental outcomes,” Journal of Public Administration Research and Theory, vol. 30, no 3, pp.383-399, 2020. 

Contact

Questions about this project should be directed to Dr Wayes Tusher at w.tushar@uq.edu.au ​​

Project team

UQ: Dr Kieren Lilly

Exeter: Professor Cornelia Guell

Project description

Climate change represents a critical global public health emergency, with escalating frequency and severity of climate hazards worldwide.

Systematic reviews have documented extensive adverse health outcomes following climate events—including heatwaves, floods, and droughts—spanning increased all-cause mortality, infectious disease incidence, hospitalisations for respiratory, neurological, and cardiovascular conditions, and deteriorating mental health.

Beyond direct physiological impacts, exposure to climate events significantly elevates health threat perceptions. With policy trajectories projecting global temperatures to rise by up to 4.0°C above pre-industrial levels by 2060, a substantial proportion of the global population faces imminent health risks. Notably, almost one-third of UK residents and two-thirds of Australians have experienced a climate event within the past five years, underscoring the urgency of this issue. 

Despite growing recognition of climate-related health threats, significant gaps remain in understanding how local health and social care provision can be compromised through climate hazards, and how communities prepare for and respond to these events.

Health-related preparedness represents a key strategy for building community resilience and informing government mitigation policies. However, the frequency, severity, and health impacts of climate events vary considerably across global regions and are not uniformly distributed.

Social, environmental, and physiological vulnerabilities create differential exposure and susceptibility patterns at both individual and population levels. This heterogeneity necessitates comparative research across diverse contexts to distinguish context-specific factors from universal determinants of climate resilience.

The specific research objectives are as follows:

• To systematically examine community experiences of climate events in the UK and Australia, identifying health impacts, preparedness practices, and infrastructure vulnerabilities
• To develop a place-based climate resilience framework to healthcare provision that integrates social, environmental, and health determinants across different geographical and cultural contexts
• To create a geospatial climate preparedness mapping tool that visualises vulnerability hotspots and resource allocation priorities
• To generate evidence-based recommendations for policy and practice that protect local healthcare delivery and enhance community-level climate resilience.

Research approach and methods

This study adopts a mixed-methods, comparative case study design that leverages the complementary climate experiences (flooding, wildfire, heatwave and drought) of UK and Australian communities across the following three stages:

• Case studies: Qualitative data collection to examine community experiences of climate events, identifying health impacts, preparedness practices, and infrastructure vulnerabilities.
• Desk top enquiry and user testing: Literature review to develop a place-based climate resilience framework.
• Quantitative GIS analysis: Create geospatial preparedness mapping tool to highlight vulnerability hotspots and resource allocation priorities.

Both nations are experiencing increasing climate event frequency and severity, yet differ in climate hazard profiles, healthcare infrastructure, and policy environments. This provides rich comparative contexts for identifying transferable versus context-dependent resilience factors.

The research will employ a sequential explanatory design informed through interdisciplinary perspectives of geography and public health. It will combine quantitative geospatial analysis with qualitative community engagement to develop a comprehensive understanding of climate preparedness needs and capacities.

A dissemination strategy will include Arc StoryMaps, Academic journals, policy briefs and stakeholder workshops.

Contact

Questions about this project should be directed to Dr Jonathan Olsen at j.olsen@uq.edu.au 

Project team

UQ: Professor Antje Blumenthal

Exeter: Professor Gordon Brown

Project description

Infections: a persistent global health challenge

Infectious diseases remain a major cause of illness and death worldwide. Chronic infections are especially challenging, often requiring long and complex treatment regimens that can lead to substantial side effects.

These challenges are intensified by rising antimicrobial resistance, which reduces the effectiveness of existing drugs and limits prospects for cure. The World Health Organization has identified tuberculosis, caused by Mycobacterium tuberculosis, and fungal infections, including those caused by Candida species, as high‑priority threats in urgent need of improved treatment strategies.

One promising frontier is the development of therapies that strengthen immune defences. To realise this potential, we must better understand how the immune system detects and controls pathogens.   

Project opportunity

This project builds on exciting new discoveries revealing a previously unrecognised mechanism through which the innate immune system identifies both mycobacteria and fungal pathogens.

For the first time, we will define the molecular and cellular features of this shared recognition pathway and determine how it contributes to early host defence. By clarifying how this mechanism operates across pathogen classes, the project lays the foundation for developing broad spectrum immune‑enhancing treatments that could shorten therapy and improve patient outcomes in hard-to-treat infections.   

Aims

The overarching aim is to characterise this newly discovered shared immune recognition pathway and its role in controlling infection. Specifically, the project will:   

1. map the host cell responses triggered by Mycobacterium tuberculosis and Candida albicans via this shared innate immune mechanism. Using in vitro experimental systems, this work will map cellular immune responses and effector pathways.   
2. determine how this new pathway interacts with established immune recognition pathways during mycobacterial and fungal infection.  This work will determine the cellular receptors and their intracellular signalling mechanisms.
3. define contributions of the new pathway to immune responses and pathogen control during fungal infection and compare these findings with its emerging roles in mycobacterial infections. Usingin vivo models, this work will dissect underlying immune mechanisms and their overlap with anti-mycobacterial immunity.   

Methods

To address these aims, we will:

• use in vitro and in vivo infection models
• generate and employ genetically modified cellular systems
• apply biochemical, cell biological, and immunological approaches to map intracellular signalling pathways and host responses to infection.

To enhance the translational potential of the project outcomes, clinical strains of Mycobacterium tuberculosis and Candida albicans will be integrated into the investigations.  

Outcomes and delverables

By uncovering a shared mechanism of immune activation, this project will deliver fundamental insights into how the host mounts early defences against mycobacterial and fungal pathogens.

These findings have the potential to identify new broad spectrum therapeutic targets and guide the development of host‑directed interventions that complement existing antimicrobial treatments. Such approaches could accelerate or enable novel approaches to treat tuberculosis and severe fungal infections, which impose substantial global health and economic burdens.

Because the pathways investigated include both validated and emerging drug targets, the project will provide a strong basis for future translational and therapeutic development. 

Contact

Questions about this project should be directed to Professor Antje Blumenthal at a.blumenthal@uq.edu.au   

Project team

UQ: Dr Leslie Roberson

Exeter: Associate Professor Kristian Metcalfe

Project description

Research problem and significance

Sharks and rays are among the most threatened vertebrates globally, with over one-third of species at risk of extinction, primarily due to overfishing.

While international attention has focused on the luxury fin trade, a growing share of shark mortality is driven by small-scale fisheries, where elasmobranchs support food security, livelihoods, and local economies [1]. These fisheries are widespread and poorly regulated, yet remain underrepresented in quantitative research [2].  

Current management approaches have struggled to curb declines, partly because they typically assume all fishers operate similarly and respond uniformly to regulations or market signals. Small-scale fisheries, however, are characterised by high levels of individual decision-making constrained by strong economic and social constraints linked to food security, market access, and risk.

Fishers make daily choices about species targeting, fishing locations, and gear use that directly influence shark and ray catch [2]. These choices are shaped not only by ecological conditions and regulations, but also by prices, social norms, and accumulated skill and experience [3]. Treating fishers as a uniform group obscures this behavioural complexity and limits intervention effectiveness, especially where enforcement capacity and resources are lacking. 

[1] Doherty, Metcalfe, et al. (2023) Artisanal fisheries catch highlights hotspot for threatened sharks and rays in the Republic of the Congo. Conservation Science and Practice, doi:10.1111/csp2.13017 

[2] Roberson, Klein, et al. (2022) Spatially explicit risk assessment of marine megafauna  vulnerability to Indian Ocean tuna fisheries. Fish and Fisheries doi:10.1111/faf.12676. 

[3] Roberson, Klein, et al. (2024) Opportunity to leverage tactics used by skilled fishers to address persistent bycatch challenges. Fish and Fisheries, doi:10.1111/faf.12873. 

[4] Roberson & Wilcox (2025). Fishery bycatch rates largely driven by variation in individual vessel behaviour. Nature Sustainability, doi:10.1038/s41893-025-01602-z 

Research aims 

This PhD project aims to characterise differences in fisher behaviour and performance to identify behaviourally informed pathways for reducing unsustainable shark and ray catch without undermining livelihoods.

Using four fishery case studies representing a range of shark fishing contexts, the project will: 

1. quantify individual variability in elasmobranch targeting within and across small-scale fisheries (Publication #1: Peru-Pakistan-Congo comparison)
2. assess how market prices interact with social and operational drivers of fishing behaviour (Publication #2: Congo/Cote d’Ivoire market and landings data)
3. examine whether and how fishers adjust targeting over time in response to species rarity, regulation, or environmental change (Publication #3: Congo longitudinal study)
4. Identify leverage points for management interventions aligned with fisher decision-making with stakeholders and local project partners (Publication #4: Perspective piece). 

Research approach and methods

Research for this project will be carried out under the supervision of Dr Leslie Roberson and Associate Professor Carissa Klein at the Centre for Biodiversity and Conservation Science at The University of Queensland and under the supervision of Dr Kristian Metcalfe at the Centre for Ecology and Conservation at the University of Exeter.

The project adopts a comparative, interdisciplinary research approach, integrating fisheries science, behavioural ecology, and applied economics.

Analyses will draw on high-resolution, longitudinal datasets from four complementary case studies: large “small-scale” multispecies gillnet fisheries in the northern Indian Ocean and South America; and multi-gear artisanal shark fisheries in West and Central Africa.

Advanced statistical models (e.g., mixed-effects models in both frequentist and Bayesian frameworks) will be used to disentangle individual behavioural effects from environmental, seasonal, and gear-related drivers of catch, and to assess behavioural responses to economic signals where market data are available. 

Project deliverables and contribution to knowledge

Key deliverables include three peer-reviewed publications in leading fisheries, sustainability, and conservation journals, alongside policy-relevant outputs for managers and conservation practitioners. 

The project will:

• advance understanding of behavioural heterogeneity in fisheries
• demonstrate how individual agency shapes exploitation patterns
• provide one of the most comprehensive comparative analyses of shark fishing in small-scale fisheries to date.

More broadly, it will generate transferable insights for managing vulnerable species in fisheries globally, including in resource-limited, food-security-dependent fishery contexts.

Contact

Questions about this project should be directed to Dr Leslie Roberson at l.roberson@uq.edu.au

Project team

UQ: Associate Professor Sarit Kaserzon

Exeter: Professor Edward Keedwell

Project description

Ensuring water security is a critical global challenge, underscored by UN Sustainable Development Goals 6 and 3 (Clean Water and Sanitation; Good Health and Well-being). The increasing incidence and diversity of drinking water contaminants necessitates advanced mitigation strategies. 

This project aims to pioneer a screening approach using High-Resolution Mass Spectrometry (HRMS) and Machine Learning (ML) to identify contaminant hazards in water sources for more rapid and comprehensive risk assessment and mitigation.

By integrating environmental science, analytical chemistry, data science, AI, and engineering, and collaborating with the UK and Australian water industries and the Queensland Health Department, this project aspires to develop a revolutionary water management risk assessment tool for enhanced water security. Anticipated outcomes will significantly impact societal, economic, environmental, and public health sectors, benefiting communities.  

The project concept emerged from the water and health industries’ need to address threats in drinking water supplies. It was conceived during the Brisbane G20 summit, where the water industry approached UQ to design a potable water monitoring and security framework for Brisbane’s drinking water supplies.

A proof-of-concept was developed, focusing on anomaly detection in water supplies, rather than investigating each, which is currently infeasible. The proposed solution included fingerprinting a ‘typical’ water sample and identifying ‘anomalies’, similar to automated tools used to detect cancerous cells in tissue samples. Advances in HRMS instruments and adaptive ML models, have made it possible to envision such a modernised water security tool. 

The project leverages UQ-Exeter strengths in Environmental Science and Data Science, through the Queensland Alliance of Environmental Health Science (QAEHS) and the Institute for Data Science and Artificial Intelligence, and the CIs' ongoing and substantive work within the Australian and UK water industry.

UQ is the preeminent Australian university in environmental sciences, ranked 15th in the 2025 QS world rankings, while Exeter is ranked 36th, and 33rd globally for SDG6 Clean Water and Sanitation.

QAEHS has established rigorous HRMS protocols while Exeter’s Institute provides proven computational solutions for the water industry and links with the centre for resilience in environment, water and waste (CREWW). 

The project aligns with UQ's and Exeter’s strategic goals: UQ’s Toward 2032, for delivering mission-driven research aligned with industry, government, and community priorities, fostering partnerships for research translation and commercialisation to create positive impact; and Exeter’s Strategy 2030 to work across teams and disciplines to tackle societal challenges and ensure research is translated for public good.

Contact

Questions about this project should be directed to Associate Professor Sarit Kaserzon at k.sarit@uq.edu.au

Project team

UQ: Dr Nathan Fox

Exeter: Professor Julian M. Ortiz

Project description

Global demand for the metals required for the green energy transition is expected to exceed supply by the mid‑2030s. Addressing this shortfall depends on reducing the uncertainty and risk that currently limit the technoeconomic, environmental, and social viability of mining projects.

Orebody knowledge (OBK), enabled by integrated technologies, provides a foundation for this by measuring geological heterogeneity at multiple scales to better understand the controls on uncertainty. Geometallurgy operationalises OBK by translating this heterogeneity into predictive spatial models that reduce technical risk and optimise strategic mine planning. 

Sensor‑based technologies, including XRF, hyperspectral imaging, LIBS and CT/XRT, enable rapid, multi‑scale analysis of intrinsic chemical, mineralogical and petrophysical variability. These tools are increasingly used in exploration for drill‑core characterisation, grade monitoring, and sensing/sorting applications.

However, their uptake in geometallurgy remains limited due to the high uncertainty associated with geostatistical modelling of sparse, multivariate datasets. Integrating these technologies across the mining value chain, from early characterisation through to processing, has the potential to generate critical geological information that strengthens geometallurgical and waste domaining.

Within a circular‑economy framework, this integration can reduce energy consumption, improve metallurgical recovery and lower environmental liabilities. 

This project will develop an open‑source framework for the systematic collection, validation and spatial geometallurgical modelling of sensor‑based characterisation data, enabling optimisation of the strategic mine plan from the earliest stages of the life‑of‑mine. 

The project’s objectives are to: 

1. develop a roadmap embedding advanced characterisation technologies across the life‑of‑mine to improve metallurgical performance and geoenvironmental stewardship.
2. use statistical and machine‑learning methods to correlate sparse, costly geometallurgical and geoenvironmental tests with variables measured rapidly and economically using sensor technologies.
3. quantify uncertainty in these variables and create geostatistical workflows and dynamic block models that propagate predicted geometallurgical and geoenvironmental responses across the strategic mine plan.
4. support waste‑reduction strategies by identifying ore‑sorting amenability that upgrades feed grade, reduces tailings footprints and creates reuse opportunities within a circular‑economy framework. 

With access to a dedicated Evolution study site, the project will integrate existing geological and metallurgical datasets with new core‑scanning data generated by Australian providers (CSIRO, Veracio, Corescan, GeologicAi).

Representative samples will be selected using multivariate geostatistical domaining and validated through mineralogical, chemical, and textural analyses (TIMA, SEM, XRD, XRF) and metallurgical/geoenvironmental tests (SMC, BBWi, flotation, ABA) at UQ’s CMM‑NRICH facilities. Sensor‑based ore‑sorting trials will be undertaken at the University of Exeter to assess upgrading potential and waste‑reuse opportunities. 

The University of Exeter will lead the development of machine‑learning‑enabled geostatistical approaches to quantify uncertainty in geological, textural, and geometallurgical variables derived from core scanning. These will be incorporated into spatial block and stochastic models to identify value drivers and risk factors linked to geological heterogeneity. 

The project will advance cross‑disciplinary knowledge by demonstrating the value of integrated sensor‑based technologies in geometallurgical modelling, reducing operational and geoenvironmental risk from exploration through to closure. Its open‑source outputs will ensure workflows and case studies are widely accessible, supporting technology uptake and contributing to a more sustainable mineral supply.

Contact

Questions about this project should be directed to Dr Nathan Fox at nathan.fox@uq.edu.au

Project team

UQ: Dr Kamila Svobodova

Exeter: Dr Yasser Mehrani

Project description

The global shift to sustainable energy is accelerating mineral extraction, with large-scale mining projects rapidly emerging, expanding, and closing across regions [1]. These dynamics drive demographic change through employment, land-use change and resettlement [2].

Despite their pervasiveness, the demographic changes remain difficult to identify and systematically assess. No spatially explicit datasets exist to map them, which limit the capacity of planning, regulation, and industry standards to anticipate and mitigate impacts. Existing research either maps broad global patterns [1,2] or isolated case studies [3], leaving mining-related demographic change largely invisible in decision-making. 

Project aims 

This project aims to develop an innovative assessment framework to identify and spatially localise mining-related demographic changes.

It will provide the first globally geolocated dataset of these changes. Using a two-stage approach, the project first undertakes global quantitative analysis to identify demographic changes associated with large mining projects.

These patterns then guide the selection of two study regions in Australia and the UK, where finer-scale quantitative analysis is combined with qualitative research. This mixed-methods approach enables interpretation of the mechanisms underlying observed global patterns. 

Project objectives

The project has four objectives: 

1. Build a global geospatial typology of large mining projects for comparative analysis: Operating large mining projects will be clearly defined to guide systematic data collection from global datasets (S&P Capital IQ Pro; ICMM; [4]). Key project attributes (e.g., method, commodity, life stage, size) will be classified into a coherent typology, producing a geodatabase as the primary outcome.
2. Model global mining-related demographic change using spatial and temporal indicators: The analysis will identify areas experiencing demographic change between 2014 and 2026 and statistically link observed patterns to mining projects. Infrastructure and settlement expansion and densification will be quantified using multi-temporal Sentinel-2, -1 and Landsat via machine learning and change-detection techniques (e.g., Random Forest) and statistically modelled against mining project characteristics using panel regression and econometric models. Where available, census and labour data will validate and calibrate these indicators. The analysis will be conducted using finer-scale data in one Australian and one UK study region.
3. Identify indirect demographic effects and functional mining regions in study regions: Company records, workforce mobility data and supply-chain analyses will be used to identify contractor catchments and commuting corridors to reveal how mining reshapes settlements and labour markets beyond host communities. The outcome is a transferable framework that delineates functional mining regions as empirically derived labour–economic zones.
4. Contextualise spatial-demographic evidence through participatory mapping (PPGIS) in study regions: Findings from O1-O3 will be contextualised through semi-structured interviews and PPGIS with ~30 stakeholders per region (industry, local governments, community members), with ethics approval secured prior to data collection. Sampling will aim for diversity across gender, age, stakeholder group, and proximity to mining. PPGIS will spatially identify perceived demographic vulnerabilities and lived experiences of mining-related change. O4 will deliver a decision-support framework translating spatial-demographic evidence and stakeholder knowledge into policy recommendations. 

[1] Svobodova et al. (2022). https://doi.org/10.1038/s41467-022-35391-2

[2] Owen et al. (2021). https://doi.org/10.1016/j.exis.2021.01.012

[3] Svobodova et al. (2021). https://doi.org/10.1016/j.erss.2020.101831

[4] Maus et al. (2022). https://doi.org/10.1038/s41597-022-01547-4

Contact

Questions about this project should be directed to Dr Kamila Svobodova at k.svobodova@uq.edu.au