DPhil research topics

Supervisors: Professor Jim Hall and Dr Yue Li

As climate change intensifies, traditional centralised infrastructure systems are increasingly vulnerable to disruptions from extreme weather events and long-term environmental shifts (Adapt, G. C. A., 2019). This project aims to explore decentralised planning as a transformative strategy that offers flexibility and resilience in infrastructure development (Hoffmann et al., 2020). By focusing on multiple sectors, such as water, energy, transportation, and telecommunications, the project seeks to identify how decentralised solutions can be integrated into existing systems to reduce the dependency on large-scale centralised infrastructures while creating more locally tailored, responsive solutions that bolster climate resilience and enhance sustainability. 

A key area of investigation will be how decentralised technologies can address climate challenges specific to individual sectors. For instance, in the water sector, decentralised water technologies such as rainwater harvesting, greywater recycling and water reuse, can contribute to water conservation and reduce the pressure on centralised water systems, ensuring safe access to all (including places sitting far and small islands, etc) during droughts and floods (Li et al., 2021); Similarly, distributed renewable generation and storage can transform energy system into active distribution networks, reducing reliance on large power grids and improving resilience during power outages (Wu et al., 2021). The project will also explore how cross-sector decentralised planning can enhance overall system resilience, particularly considering failure cascading effects (Pant et al., 2020). For example, decentralised energy solutions, such as solar microgrids, could support critical transportation and telecommunication infrastructure during climate hazard events, ensuring the continuity of services under adverse conditions.

In addition to enhancing resilience, decentralised solutions present opportunities for achieving sustainability goals. However, the adoption of these technologies must be done thoughtfully considering the potential increase in energy consumption (Li et al, 2021). Meanwhile, this project will explore how decentralised infrastructure planning can be optimised to balance access and equity, ensuring that vulnerable and underserved populations are not disproportionately affected by the transition to more decentralised systems. Moreover, it will address the trade-offs between different scales of decentralisation and assess how to optimise the design of technologies (such as type, location, and capacity) and integration with existing infrastructures across these different scales (Li et al, 2022). 

To achieve these objectives, this project will suit students with any quantified background, including environmental science, engineering, mathematics and physical science. Ideal candidates should be able to demonstrate their capabilities in geospatial data discovery and analysis, computer modelling, and enthusiasm to address interdisciplinary problems with system thinking. Candidates for this project from an engineering or physical sciences background would be eligible to apply for funding from Oxford University’s EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or partial funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford’s several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References

• Adapt, G. C. A. (2019). A Global Call for Leadership on Climate Resilience. Global Center on Adaptation and World Resources Institute.
• Hoffmann, S., Feldmann, U., Bach, P. M., Binz, C., Farrelly, M., Frantzeskaki, N., ... & Udert, K. M. (2020). A research agenda for the future of urban water management: exploring the potential of nongrid, small-grid, and hybrid solutions. Environmental science & technology, 54(9), 5312-5322.
• Li, Y., Mo, W., Derrible, S., & Lu, Z. (2022). Integration of multi-objective spatial optimization and data-driven interpretation to direct the citywide sustainable promotion of building-based decentralized water technologies. Water Research, 222, 118880.
• Li, Y., Khalkhali, M., Mo, W., & Lu, Z. (2021). Modeling spatial diffusion of decentralized water technologies and impacts on the urban water systems. Journal of Cleaner Production, 315, 128169. 
• Pant, R., Russell, T., Zorn, C., Oughton, E., & Hall, J. W. (2020). Resilience Study Research for NIC–Systems Analysis of Interdependent Network Vulnerabilities. Environmental Change Institute, Oxford University.
• Wu, R., & Sansavini, G. (2021). Active distribution networks or microgrids? Optimal design of resilient and flexible distribution grids with energy service provision. Sustainable Energy, Grids and Networks, 26, 100461. 

Supervisors: Professor Jim Hall, Professor Michael Obersteiner, and Dr Raghav Pant

There is growing recognition of the risk from wildfires (Jones et al., 2020), which is a threatening dimension of climate-related risks (Zscheischler et al., 2018). A number of wildfire risk assessment frameworks have been developed, though these tend to have been focussed at national and regional scales (Fiorucci, 2008, Scott, 2013, Thompson, 2011, 2016). Global wildfire models have been established and wildfire has been incorporated in the land surface schemes of Earth system models (Krause et al., 2014). There are also growing spatial datasets of wildfire observations, with accompanying statistical analysis (Parisien and Moritz, 2009). However, so far there has been limited analysis of the potential systemic impacts of wildfires and they ways in which they may compound with other climate-related hazards, including heat waves and droughts.

This DPhil project will take a spatial systems approach to fully appraising wildfire risks and the ways in which they may propagate through society and the economy. We are not aiming to generate a new wildfire model – we will use model simulations and projections from other sources, including the Inter-Sectoral Impact Model Intercomparison datasets and other sources. We will combine these with wildfire observations, to build up a statistical picture of wildfire hazard and supplement that with future projections. That will provide a wildfire hazard layer and associated uncertainties. The main focus of the research will be to properly characterise the impact of wildfires on communities, infrastructure and the economy. The direct damages from wildfires are documented, at least in countries that have high levels of insurance coverage. However, there is very little knowledge of the wider effects of wildfire on economic activities and how quickly communities recover. Wildfire damage to infrastructure can have widespread impacts on people and economic activities. Repairing damaged infrastructure can be a considerable burden on public finances. This study will explore these and other dimensions of wildfire impacts and develop datasets and models for damage assessment, including both direct and indirect damages. We aim to do this in a way which is scalable to global scales, so that we can generate much improved global estimates of the risks from wildfires and estimates of how these risks may change in the context of climate change, land use change, economic development and different wildfire management strategies.

The project will involve a combination of statistical analysis and geospatial modelling. It will suit students with a strong background in environmental sciences, engineering or another quantified subject. Students should be able to demonstrate aptitude for quantified geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from a natural sciences background would be eligible to apply for funding from Oxford University's NERC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References:

• Fiorucci, P., Gaetani, F. and Minciardi, R. 2008. Development and application of a system for dynamic wildfire risk assessment in Italy. Environmental Modelling & Software, 23: 690-702.
• Jones, M.W., Smith, A., Betts, R., Canadell, J.G., Prentice, I.C. and Le Quéré, C., 2020. Climate change increases the risk of wildfires. ScienceBrief Review, 116: 117.
• Krause, Andreas, et al. 2014. The sensitivity of global wildfires to simulated past, present, and future lightning frequency. Journal of Geophysical Research: Biogeosciences, 119.3 (2014): 312-322.
• Parisien, M-A, and Moritz, M.A. 2009. Environmental controls on the distribution of wildfire at multiple spatial scales. Ecological Monographs, 79.1 (2009): 127-154.
• Scott, J.H., Thompson, M.P. and Calkin, D.E. 2013. A wildfire risk assessment framework for land and resource management. General Technical Report RMRS-GTR-315. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p. doi: 10.2737/rmrs-gtr-315
• Thompson, M.P., Bowden, P., Brough, A., Scott, J.H., Gilbertson-Day, J., Taylor, A., Anderson, J. and Haas, J. R. 2016. Application of wildfire risk assessment results to wildfire response planning in the southern Sierra Nevada, California, USA. Forests 7, 64.
• Thompson, M.P., Calkin, D.E., Finney, M.A., Ager, A.A. and Gilbertson-Day, J.W. 2011 Integrated national-scale assessment of wildfire risk to human and ecological values. Stochastic Environmental Research and Risk Assessment, 25: 761-780.
• Zscheischler, J., Westra, S., Van Den Hurk, B.J., Seneviratne, S.I., Ward, P.J., Pitman, A., AghaKouchak, A., Bresch, D.N., Leonard, M., Wahl, T. and Zhang, X. 2018. Future climate risk from compound events. Nature Climate Change, 8(6): 469-477.

Supervisors: Professor Jim Hall, Professor Michael Obersteiner, and Dr Raghav Pant

Complex and systemic-scale risks will increase over the 21st century and threaten the well-being of millions of people, as well as national prosperity, security and competitiveness. Intensifying climate extremes, conflict, financial crises and the Covid-19 pandemic have illustrated the fragility of global systems to systemic risks. As evidenced by the Covid-19 pandemic, and the recent House of Lords Select Committee report (2021), current approaches to risk management at national and global scales are inadequate for responding to these risks.

The Oxford Martin Programme on Systemic Resilience aims to identify, appraise and advance solutions to managing systemic shocks in the 21st Century and thus improve the welfare of people and the resilience of the systems and institutions upon which they depend. The programme will examine where do complex crises fall through the cracks in our national and international systems, and how can crises be anticipated and targeted by interventions in order to enhance preparedness and system resilience. The focus is on shocks with a strong climate, social or environmental dimension, including climate, poverty and pandemics.

This DPhil project aims to complement, and become part of, the Oxford Martin Programme on Systemic Resilience. The question we wish to address is whether it is possible to develop better early warning indicators of systemic risks. What are the preconditions for systemic failures we can observe, for example based on the structure of networks that interconnect people, businesses and institutions? We will answer this question based on insights from general systems theory and network theory. Though there has been extensive research on systemic risks and early warning in the financial system (e.g. Billio et al., 2016, Tedeschi et al., 2020) this has seldom extended to examine the wide range of factors that might impact global systems and the real economy. We wish to understand whether there are indicators that can be monitored in real time that provide some early warning of systemic failures. These warnings may exist in trade flows and supply chains (e.g. based on our analysis of automatic ship tracking data (Verschuur et al. 2020)), may be based on monitoring of correlated climatic conditions (Gaupp et al. 2017), or on monitoring migration.

The research will involve in-depth literature review into theoretical understandings of systems failure and review of the empirical literature on the precursors and determinants of systemic failures, from a range of different disciplines. It will then involve collecting many different datasets and applying statistical methods to assess the extent to which these datasets are predictors of system failure and its consequences. Our aim is to develop a composite metric that can be used to inform anticipatory action. This will need to be tailored to a range of different contexts, e.g. humanitarian assistance, food security or financial system stability. It will build upon our extensive links with international organisations, central banks and humanitarian agencies.

The research will suit students with an interdisciplinary outlook. It will involve data analysis and advanced statistical methodologies, so will require a strong quantified background, but applicants will also need to demonstrate a capacity for deep conceptual thinking, along with a problem-solving attitude.

Applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References:

• Billio, M., Casarin, R., Costola, M. and Pasqualini, A., 2016. An entropy-based early warning indicator for systemic risk. Journal of International Financial Markets, Institutions and Money, 45: 42-59.
• Gaupp, F. Pflug, G. Hochrainer-Stigler, S. Dadson, S.J. and Hall, J.W. Dependency of crop production between global breadbaskets: A copula approach for the assessment of global and inter-regional risk pools, Risk Analysis, 37(11) (2017): 2212-2228. doi: 10.1111/risa.12761
• House of Lords, Preparing for extreme risks: Building a resilient society. House of Lords Select Committee on Risk Assessment and Risk Planning. December 2021.
• Tedeschi, G., Caccioli, F. and Recchioni, M.C., 2020. Taming financial systemic risk: models, instruments and early warning indicators. Journal of Economic Interaction and Coordination, 15(1): 1-7.
• Verschuur, J., Koks, E. and Hall, J.W. Port disruptions due to natural disasters: insights into port and logistics resilience. Transportation Research Part D: Transport and Environment, 85(2020): 102393. doi: 10.1016/j.trd.2020.102393.

Supervisors: Professor Jim Hall, Dr Yue Li and Tom Russell

Planning infrastructure, in all parts of the world, involves difficult choices about where and what to build to provide reliable, resilient services whilst minimising costs and negative impacts on people and the environment. Versions of this spatial allocation problem exist in many situations, for example in planning power grids, water supply systems, communications or transport networks to meet economic or development objectives, including many of those framed by the sustainable development goals (SDGs). New spatial datasets derived from satellites, sensors and crowdsourcing are providing information that can enable better mapping and navigation of the trade-offs associated with spatial allocation.

This project will explore the potential for using new data sources to inform the construction of large-scale models of infrastructure systems and the planning of new infrastructure. For example, we have carried out original research using AIS ship tracking data (Verschuur et al., 2020) to improve our understanding of transport networks and trade; we are now keen to advance methods for tracking road vehicles and trains. This information could be supplemented with crowdsourcing to provide new information on the condition of infrastructure assets and services. By combining these data sources, it should be possible to make a significant next step in the modelling and monitoring of infrastructure networks anywhere on Earth.

Given better data about infrastructure asset location, service provision and user needs, it will be possible to better target interventions to improve these systems. We have made significant progress in methodologies for this spatial allocation problem.

For example, in Bangladesh we have used new geospatial datasets to optimize the location of drinking water infrastructure (Garcia et al., 2021) - a version of this method could be up-scaled to much larger areas. In rural Bangladesh there has been a proliferation of private tube wells, demonstrating some of the potential for market forces in infrastructure service provision. One framing of the problem, for water or other utilities, would consider how market forces can be combined with targeted development assistance, public investment, and/or regulation for minimum universal service provision to provide infrastructure systems that leave no-one behind.

The electrification of transport is another possible application, widely regarded as an opportunity for developing countries to 'leapfrog' fossil-fuel dependent transport and associated infrastructure networks, by co-developing renewable energy supplies, vehicle charging, and transport fleet. There are however many different versions of how such systems might develop (e.g. with centralised electricity grids, or with micro-grids). What system is viable depends, in part, on local context (population density, building density, wealth, existing infrastructure), but is also subject to other uncertainties, such as the relative price of technologies and the business models that are adopted for service provision.

We have developed unique datasets of road infrastructure globally (Koks et al., 2019) and methodology for simulating electricity transmission and distribution networks all over the world. This is coupled with population datasets for analysing energy and transport demand and global datasets of potential for renewable energy supply. We propose to combine these datasets with different scenarios for costs and business models to explore and optimise scenarios for roll-out of these technologies.

These are just two examples of the sorts of problems that could be addressed with methodologies for spatial allocation and optimisation (Faiz and Krichen, 2012). We expect that other opportunities will materialise during the research, so the thesis will combine investigation of novel datasets with development of methods and case studies. Overall, the objective of the research would be to develop a broad framework to characterise different infrastructures and their relationship with the space and people around them.

The project will involve application of methods for spatial optimisation. The derived solutions need to take account of local economic, societal and governance conditions, so the student should also study these important contextual issues. It will suit students with a strong quantified background (e.g. engineering, economics, physics, geostatistics) but also a good appreciation of the wider societal context of infrastructure service provision.

Candidates for this project from an engineering, mathematics or physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References

• Faiz, S. and Krichen, S., 2012. Geographical information systems and spatial optimization. CRC Press.
• Flanagan, S.V., Johnston, R.B. and Zheng, Y. 2012. Arsenic in tube well water in Bangladesh: health and economic impacts and implications for arsenic mitigation. Bulletin of the World Health Organization, 90, 839-846.
• Garcia, O.R., Hoque, S.F., Ford, L., Salehin, M., Alam, M.M., Hope, R. and Hall, J.W. Optimizing rural drinking water supply infrastructure to account for spatial variations in groundwater quality and household welfare in coastal Bangladesh. Water Resources Research, 57(8) (2021): e2021WR029621. doi: 10.1029/2021WR029621
• Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1) (2019): 2677. doi: 10.1038/s41467-019-10442-3.
• Verschuur, J., Koks, E. and Hall, J.W. Port disruptions due to natural disasters: insights into port and logistics resilience, Transportation Research Part D: Transport and Environment, 85(2020): 102393. doi: 10.1016/j.trd.2020.102393.

Supervisors: Professor Jim Hall, Dr Raghav Pant and Dr Edward Oughton

Infrastructure systems that deliver essential services to society (e.g. energy, water, transport and telecommunications) are increasingly regarded as being cyber-physical systems, as they are controlled by digital networks and depend upon software and digital communication systems. The risks to these systems have been widely studied, but from rather different perspectives. There has been extensive research, much of it by our group, on physical risks to infrastructure networks, with a focus on weather-related extremes (Koks et al., 2019, Lamb et al., 2019) but also including terrorist threats (Oughton et al., 2019). Meanwhile, there has been extensive research on questions of cyber security for infrastructure networks, for example relating to the security of the Internet of Things (IoT). Our aim in this project is to bring these perspectives together.

In the first instance the focus will be on modelling the networks of interdependent electricity and telecommunications systems. We have a fairly complete model of electricity transmission and distribution networks in Britain, and recently as part of research with the National Infrastructure Commission we coupled this with a representation of telecommunications networks in Britain.

The DPhil project will involve modelling of electricity and digital communications networks (including SCADA systems), which we will seek to validate with data on faults in the electricity and telecommunications networks. This will be used to model possible interdependent and cascading failures. The analysis will be used to identify how these interdependent networks can be made more resilient. For example, what is the potential benefit of increased connectivity or backup capacity within the network? We also wish to examine how technological trends (like electrification of transport and the proliferation of renewable energy supply technologies) could impact the resilience of infrastructure networks.

The project will therefore involve using and adapting existing simulation models and creating new models of infrastructure systems and development of methods for vulnerability analysis and optimisation. It will suit students from any quantified background, including engineering, mathematics and the physical sciences. Students should be able to demonstrate aptitude for computer modelling and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References:

• Koks, E., Pant, R., Thacker, S., Hall, J.W. 2019. Understanding business disruption and economic losses due to electricity failures and flooding, International Journal of Disaster Risk Science, 10: 421-438. doi:10.1007/s13753-019-00236-y
• Lamb, R., Garside, P., Pant, R. and Hall, J.W. 2019. A network-scale analysis of the risk of railway bridge failure from scour during flood events in Britain. Risk Analysis, 39(11): 2457-2478. doi: 10.1111/risa.13370
• Oughton, E., Ralph, D., Leverett, E., Pant, R., Thacker, S., Hall, J.W., Copic, J., Ruffle, S. and Tuveson, M. 2019. Stochastic counterfactual analysis for the vulnerability assessment of cyber-physical attacks on electricity distribution infrastructure networks, Risk Analysis, 39(9): 2012-2031. doi: 10.1111/risa.13291
• Thacker, S., Barr, S., Pant, R., Hall, J.W., and Alderson, D. 2018. Geographic hotspots of critical national infrastructure. Risk Analysis, 11(1): 22-33. doi: 10.1111/risa.12840.
• Thacker, S., Kelly, S., Pant, R. and Hall, J.W. 2018. Evaluating the benefits of adaptation of critical infrastructures to hydrometeorological risks. Risk Analysis, 38(1): 134-150. doi: 10.1111/risa.12839.
• Thacker, S., Hall, J.W. and Pant, R. 2018. Preserving key topological and structural features in the synthesis of multi-level electricity networks for modeling of resilience and risk. Journal of Infrastructure Systems, ASCE, 24(1): 04017043. doi: 10.1061/(ASCE)IS.1943-555X.0000404
• Thacker, S., Pant, R. and Hall, J.W. 2017. System-of-systems formulation and disruption analysis for multi-scale critical national infrastructures, Reliability Engineering and Systems Safety, 167: 30-41. doi: 10.1016/j.ress.2017.04.023

Supervisors: Professor Jim Hall, and Dr Raghav Pant

There is growing recognition of the urgency and importance of adapting to climate change, especially in infrastructure networks which provide essential services and last for a long time (Hallegatte et al., 2019). In recent years, our OPSIS research group has made rapid progress in the development of methods for risk analysis of infrastructure systems at a global scale (Hall et al., 2019, Koks et al., 2019). This analysis combines climate hazard layers (e.g. floods, hurricanes, droughts) with high resolution data on infrastructure exposure and vulnerability and economic modelling of the impacts of infrastructure failure on supply chains and the economy. Analysis of this type provides estimates of the overall scale of climate risks to infrastructure and helps to pinpoint locations where climate risks are greatest, which should be a priority for adaptation. The next step is to use this information to inform the type, scale and location of adaptation planning. Should infrastructure networks be strengthened, relocated or made less vulnerable with the help of nature-based solutions (NbS)? Is it more efficient to wait for infrastructure to be rebuilt before factoring in adaptation? Answering these questions involves matching adaptation solutions to particular locations and then estimating what might be a reasonable level of investment in adaptation.

In principle this process is well understood, but applying the methods of cost-benefit analysis and spatial optimisation on a very large scale is a vast challenge. Designing adaptation pathways through time involves understanding the lifecycle of infrastructure assets and predicting future infrastructure needs. This DPhil project aims to address that challenge, by developing methodology to systematically explore adaptation options for different types of infrastructure networks across different geographies, and then match adaptation options to particular locations. By combining with information on the costs of individual adaptation options, it will be possible to come up with a much better estimate of the costs of adaptation than has previously been achieved (Global Commission on Adaptation, 2020, Neuman et al., 2021).

A critical aspect of this research will be dealing with the many inevitable uncertainties in the analysis. The project will therefore have a particular focus upon uncertainty and sensitivity analysis (Saltelli et al., 2004). Sensitivity analysis will help to identify the factors that are most influential in the model predictions. Is uncertainty in climate model projections most important, or are adaptation performance and cost estimates more significant? Conducting uncertainty and sensitivity analysis on a model of this scale will be a considerable challenge because of the very high dimensionality and computational expense of each model run. It will also be necessary to analysis the spatial statistics of the various factors that influence model outputs. We also wish to examine the robustness of solutions to deep uncertainty to establish whether there are categories of adaptation option that are more robust. This will involve methodologies for decision making under deep uncertainty (Marchau et al, 2019).

The project will involve a combination of geospatial analysis, decision analysis and multi-objective optimisation. It will suit students from any quantified background, including environmental sciences, engineering or economics. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References:

• Global Commission on Adaptation, 2020. Adapt Now: A global call for leadership on climate resilience
• Hall, J.W., et al. 2019. Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation Oxford: Environmental Change Institute, University of Oxford. December 2019, 64pp.
• Hallegatte, S., Rentschler, J. and Rozenberg, J. 2019. Lifelines: The Resilient Infrastructure Opportunity Washington, DC: World Bank.
• Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. 2019. A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1): 2677. doi: 10.1038/s41467-019-10442-3.
• Marchau, et al. 2019. Decision Making under Deep Uncertainty: from theory to practice. Springer.
• Neumann, J.E., P. Chinowsky, J. Helman, M. Black, C. Fant, K. Strzepek and J. Martinich (2021) Climate effects on US infrastructure: the economics of adaptation for rail, roads, and coastal development. Climatic Change, 166(44).
• Saltelli, et al. (2004) Sensitivity Analysis in Practice: a guide to assessing scientific models. Wiley.

Supervisors: Professor Jim Hall, Dr Raghav Pant and Dr Fred Thomas

Global energy networks are undergoing rapid transformations to meet growing needs for energy services and to rapidly transition away from fossil fuels. However, electric power networks are also vulnerable to the impacts of climate change. Transmission pylons and cables can be destroyed by hurricanes whilst substations can be flooded and thermo-electric power plants can be impacted by cooling water shortages (Hall et al., 2019). Climate change is also impacting demand for electricity supplies.

There have been several studies of climate impacts for power networks at local and national scales (e.g. Fant et al. 2020, Koks et al. 2019). Meanwhile, there is growing capability for energy systems modelling and network analysis at global scales, assisted by the existence of global power plant databases and Gridfinder (Arderne et al., 2020) a global model of transmission networks (Thomas et al., in review). There are also more country and continental specific datasets of detailed spatial representation of electricity transmission systems (HIFLD 2022, Hörsch et al., 2018). It is now possible to improve upon these methods in several significant respects: 

1. Improving the representation of power supply, though implementation of power flow models. Various Python tools exist, including PyPSA and Pandapower, which would enable better representation of how grids respond to disruption, and how this might evolve during hazard events. 
2. Improving validation, including through monitoring of the IP addresses of ‘sentinel’ sites: These are IP addresses that are typically reachable, except for in power outages (Bayat et al., 2021). This will improve upon previous research using Power Outage US and nighttime light datasets. 
3. Parameterising outage response from Power Outage US data and correlating these parameters with hazards and socioeconomic attributes.
4. Extending the variety of hazards that are modelled from flooding and hurricanes, which have been addressed in most previous studies, to include wildfires and extreme heat. 

By combining these datasets with statistical and model-based data on climate-related hazards, it will be possible to generate improved and better validated estimates of the climate risks to power networks on a global scale and the economic implications of their failure. This will also help to identify hotspots of vulnerability and prioritize locations for network adaptation. 

Looking into the future, the type and location of power generation, transmission and distribution networks will change significantly. The next step in the research will be to develop methods for assessing the vulnerability of these new energy and planning for resilience e.g. storage sizing for peak demand, ideal generator mixtures and optimisation of generator location given the spatial and temporal covariance of renewable resources. These problems have been examined in average conditions by other researchers, but in OPSIS our focus is also upon resilience in extreme conditions. Future climate scenarios will be imposed upon these networks to analysis their possible exposure to increasing climatic extremes.

The project will involve a combination of geospatial analysis and energy systems modelling. It will suit students with a strong background in engineer, physics or another quantified subject. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References:

• Arderne, C., Zorn, C., Nicolas, C. et al. 2020. Predictive mapping of the global power system using open data. Sci Data, 7:19. doi: 10.1038/s41597-019-0347-4
• Bayat, N., et al., Down for failure: Active power status monitoring, Future Generation Computer Systems, 125 (2021): 629-640
• Fant, C., Boehlert, B., Strzepek, K., Larsen, P., White, A., Gulati, S., Li, Y. and Martinich, J. 2020. Climate change impacts and costs to U.S. electricity transmission and distribution infrastructure. Energy, 195: 116899. doi: 10.1016/j.energy.2020.116899
• Hall, J.W., et al. 2019. Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation. Oxford: Environmental Change Institute, University of Oxford. December 2019, 64pp.
• Homeland Infrastructure Foundation Level Data 2022.
• Hörsch, J., Hofmann, F., Schlachtberger, D., & Brown, T. 2018. PyPSA-Eur: An open optimisation model of the European transmission system. Energy strategy reviews, 22: 207-215
• Koks, E., Pant, R., Thacker, S., Hall, J.W. 2019. Understanding business disruption and economic losses due to electricity failures and flooding, International Journal of Disaster Risk Science, 10: 421-438. doi: 10.1007/s13753-019-00236-y
• Thomas, F., Mo, Y., Rui, J., Russell, T., Robertson, M., Verschuur, J., Pant, R. and Hall, J.W. Tropical cyclone risk to global electricity supply, Nature Energy, in review. doi: 10.21203/rs.3.rs-4650238/v1

Supervisors: Professor Jim Hall and Dr Raghav Pant

Increasing frequency and severity of heatwaves is one of the most obvious consequences of climate change. This will impact people directly and will also have effects upon the built environment and infrastructure upon which we depend. There has been quite extensive research upon the impact of extreme heat on buildings and human comfort/health (e.g. Jenkins et al., 2014a), and analysis of specific instances of the impact of extreme heat on infrastructure systems, e.g. rail buckling (Mulholland and Feyen, 2021), road surfaces (Smoyer-Tomic et al., 2003), power transmission (Cadini et al, 2017) and underground metro systems (Jenkins at al., 2014b). However, there has not been a full global analysis of the risks of extreme temperatures for infrastructure networks. This is now becoming possible thanks to the assembly of global datasets of infrastructure assets and networks, assembled by the OPSIS team (Koks et al., 2019) and others. The research will combine (i) climatic information on temperature extremes (ii) geospatial analysis of the exposure of infrastructure assets and networks (iii) assessment of the vulnerability of these networks to temperature extremes, and (iv) quantification of the impacts of temperature-related infrastructure failures.

For the analysis of temperature extremes, particular attention will be paid to the spatial extent of heatwaves, given their potential to impact large areas at the same time. Statistical analysis of temperature extremes will use weather station observations and reanalysis data to assemble a global spatial catalogue of heatwave events. It may be possible to combine this analysis with climate attribution studies (Perkins-Kirkpatrick and Lewis, 2020) to understand the non-stationarity in the temperature record. Future spatial heatwave projections will be obtained from climate model outputs (e.g. CMIP6, CMIP7). To obtain a large ensemble of spatial heatwave events, it may be necessary to develop a statistical model of spatial extremes.

Analysis of the exposure of infrastructure assets will be based upon OPSIS’s ongoing activities to assemble global infrastructure asset and network data. A review of temperature vulnerability functions will cover mechanisms including: road/runway surface softening, railway line buckling, overheating of mechanical and electrical equipment, transmission line sagging, etc. Where appropriate these functions will be modified to reflect local conditions, e.g. local temperature rating/standards of equipment.

The research will also examine the impacts of heatwaves on infrastructure demand, notably on electricity demand for cooling. This can place additional demand upon electricity supplies and power networks which combine with other heatwave effects to result in systemic failures. Given satisfactory progress, it may also be possible to examine coincident risks e.g. wildfires and droughts, and their impacts on cooling water availability, hydropower and inland waterway navigation.

The research will quantify the impacts of heatwave-related failures in terms of the numbers of infrastructure users who are impacted. We will also seek to quantify the economic impacts of heatwave-related infrastructure failures. There have already been several studies of the impacts of heat on economic production (Burke et al., 2015) but none of these have sought to disentangle the specific effects of infrastructure failures in heatwave. The output will be a global analysis of hotpots of infrastructure heatwave vulnerability, taking into account the degree of local adaptation, and probabilistic quantification of the scale of potential disruptions at present and in future scenarios.

Finally, the research will examine the potential benefits of future adaptations, examining a range of adaptation options, which may be applied to existing infrastructure or incorporated when infrastructure is replaced or upgraded.

The project will involve a combination of geospatial analysis and spatial statistics. It will suit students with a strong background in engineer, physics or another quantified subject. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References

• Burke, M., Hsiang, S.M. and Miguel, E. 2015. Global non-linear effect of temperature on economic production. Nature, 527(7577): 235-239.
• Jenkins, K., Hall, J.W., Glenis, V., Kilsby, C.G., McCarthy, M., Goodess, C., Smith, D., Malleson, N. and Birkin, M. Probabilistic spatial risk assessment of heat impacts and adaptations for London. Climatic Change, 124(1-2): 105-117.
• Jenkins, K., Gilby, M., Hall, J.W., Glenis, V., Kilsby, C.G. Implications of climate change for thermal discomfort on underground railways. Transportation Research Part D: Transport and Environment, 30: 1-9.
• Cadini, F., Agliardi, G.L. and Zio, E., 2017. A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions. Applied energy, 185: 267-279.
• Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. 2019. A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1): 2677. doi: 10.1038/s41467-019-10442-3
• Mulholland, E. and Feyen, L. 2021. Increased risk of extreme heat to European roads and railways with global warming, Climate Risk Management, 34: 100365
• Perkins-Kirkpatrick, S.E. and Lewis, S.C., 2020. Increasing trends in regional heatwaves. Nature communications, 11(1): 3357.
• Smoyer-Tomic, K.E., Kuhn, R. and Hudson, A. 2003. Heat wave hazards: an overview of heat wave impacts in Canada. Natural hazards, 28: 465-486.

Supervisors: Professor Jim Hall and Dr Raghav Pant

Critical infrastructure networks of energy, transport, water, along with social infrastructure such as schools and hospitals are constantly under threat from extreme natural hazards which intensify with climate change (World Bank, 2019). While globally the impacts of these hazards are felt strongly, the most vulnerable regions of the world such as SIDS incur some of the largest losses from severe hazards due to lack of development and investment into climate resilience. For example, Hurricane Maria in 2017 destroyed 95% of Dominica’s housing stock, 90% of the island was without electricity for about 4 months, and the economic losses were estimated to be 226% of Dominica’s GDP (Government of the Commonwealth of Dominica, 2020). Integrating climate resilience with long-term sustainable development goals is therefore integral for SIDS. Given the scale of potential hazard impacts, the scale of climate adaptation investments needed for safeguarding very high infrastructure resilience might be unrealistic to finance in SIDS. Hence, the key challenge is to understand for how climate adaptation investments for infrastructures could be prioritised over space and time to guarantee varying levels of resilience, while meeting long-term SDG targets for infrastructure provision. This will be the objective of this DPhil.       

Our previous research has shown that infrastructure directly or indirectly influences the attainment of 72% of all SDG targets (Thacker et al., 2019). Research has also show how long-term infrastructure planning metrics aligned with meeting SDGs could be done effectively in small islands (Adshead et al., 2019). A conceptual framework has also mapped high-level linkages between climate impacts to ecosystems and socio-economic sectors and their effects on meeting SDGs (Fuldauer et al., 2022b), and demonstrated to a limited extent for some spatial climate risks and infrastructure assets (Fuldauer et al., 2022a). However, there is no research that has shown how infrastructure network failure impacts lead to losses in socio-economic service provision and how these hamper the ability to meet specific targets of the SDGs. This requires a spatial risk assessment approach integrating climate hazards datasets, infrastructure assets and network flow models, vulnerability assessment models, and impact assessment models to quantify population and economic service losses. This is followed by an assessment of asset and network level adaptation options, their costs and benefits of risk reduction, and prioritisation of options that provide the highest benefits in meeting given levels of socio-economic service. The overall climate adaptation needs at aggregated sector or regional scales can then be estimated and aligned with SDGs.    

The proposed research will propose a spatial climate adaptation assessment methodology to align resilience goals with SDGs in SIDS. It will involve undertaking compilation and quantitative assessment of datasets of SIDS-specific climate hazards, creation of unique datasets on infrastructure assets (energy, transport, water, schools, hospitals, buildings) from open or remote sensed data, compilation of hazard damage cost data and adaptation options and their costs and socio-economic datasets, and creation of risk indicators that align national adaptation and resilience plans with SDGs.     

The project will involve a combination of geospatial analysis, decision analysis and multi-objective optimisation. It will suit students from any quantified background, including environmental sciences, engineering or economics. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References

• Adshead, D., Thacker, S., Fuldauer, L.I., & Hall, J.W. 2019. Delivering on the Sustainable Development Goals through long-term infrastructure planning. Global Environmental Change, 59, 101975.
• Fuldauer, L.I., Adshead, D., Thacker, S., Gall, S., & Hall, J.W. 2022a. Evaluating the benefits of national adaptation to reduce climate risks and contribute to the Sustainable Development Goals. Global Environmental Change, 76: 102575.
• Fuldauer, L.I., Thacker, S., Haggis, R.A., Fuso-Nerini, F., Nicholls, R. J., & Hall, J.W. 2022b. Targeting climate adaptation to safeguard and advance the Sustainable Development Goals. nature communications, 13(1): 3579.
• Government of the Commonwealth of Dominica, 2020. Dominica Climate Resilience and Recovery Plan 2020-2030, pdf
•  Thacker, S., Adshead, D., Fay, M., Hallegatte, S., Harvey, M., Meller, H., ... & Hall, J.W. 2019. Infrastructure for sustainable development. Nature Sustainability, 2(4), 324-331.

• Hallegatte, S., Rentschler, J. and Rozenberg, J. 2019. Lifelines: The resilient infrastructure opportunity World Bank.

Supervisors: Professor Jim Hall, Dr Raghav Pant and Dr Yue Li

The risks posed by climate change to water supplies are well known. Droughts pose a risk to water availability, which combined with growing demand leads to intensifying risks of water shortages. This may be compounded by deteriorating water quality, e.g. through salinization of aquifers. Furthermore, extreme weather, including floods, storms and wildfires can impact water infrastructure. There has been extensive study of the occurrence of meteorological and hydrological droughts at a global scale, but these studies give little indication of the risk of water shortage which occurs when water availability is less than demand, not least because the supply-demand balance is mediated by water infrastructure systems. Simple measures of water stress do not provide an accurate picture of the risks of water shortage. There have also been important global studies of climate risks to hydropower production (Paltan et al., 2021) and cooling water (Zhou, 2018). However, providing a comprehensive picture of climate risks to water infrastructure at a global scale – including waste water treatment, as well as other forms of water infrastructure – has proved to be more challenging. One recent effort was by Olivia Becher in the OPSIS group (Becher et al., 2024) which used a large global water utility database. It is a major limitation that large-scale climate risk assessment to water infrastructure lags behind transport (Koks et al., 2019) and energy (Thomas et al., in review) infrastructure. 

This project aims to develop a complete global model of the resilience of water infrastructure. Fortunately, many of the components of such a model already exist. The CWatM global water systems model provides a version of the water balance in catchments worldwide. The water asset databases mentioned above provide wide, though incomplete, coverage, so methods for data filling will have to be developed. Global hydropower data and modelling can also be leveraged. A significant new development will be the analysis of concurrent hazards to water assets, including floods of various types. 

The PhD will begin with a review of these existing methodologies and development of a scalable framework. Existing models will have to be tested and integrated. A significant novel aspect will be the development of multivariate climate variable inputs, derived from a combination of reanalysis and climate model outputs. These will then be run through the integrated modelling system to provide new insights into vulnerabilities within the global water system, especially the risks from concurrent hazards. 

The project will involve computer model development, along with parameterization and validation using empirical data. Candidates must therefore be ready to take on a highly interdisciplinary analysis and modelling task. It will require a candidate with advanced computational and mathematical skills, coming from an engineering, economics or physical sciences background. Experience of water resource systems analysis is desirable but not essential. Students should be able to demonstrate aptitude for computer modelling and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK and EU applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References

• Becher, O., Smilovic, M., Verschuur, J., Pant, R., Tramberend, S. and Hall, J.W. The challenge of closing the climate adaptation gap for water supply utilities, Communications Earth & Environment, 5 (2024): 356. doi: 10.1038/s43247-024-01272-3.
• Becher, O., Verschuur, J., Pant, R. and Hall, J.W. Prioritising climate adaptation options to minimise financial and distributional impacts of water supply disruptions, Environmental Research: Infrastructure and Sustainability, 4(1) (2024): 015007. doi: 10.1088/2634-4505/ad0ff0
• Becher, O., Pant, R., Verschuur, J., Mandal, A., Paltan, H., Lawless, M., Raven, E. and Hall, J.W. A multi-hazard risk framework to stress-test water supply systems to climate-related disruptions, Earth’s Future, 11(1) (2023): e2022EF002946. doi: 10.1029/2022EF002946
• Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1) (2019): 2677. doi: 10.1038/s41467-019-10442-3.
• Paltán, H.A., Pant, R., Braeckman, J.P. and Dadson, S.J., 2021. Increased water risks to global hydropower in 1.5 C and 2.0 C Warmer Worlds. Journal of Hydrology, 599, p.126503.
• Thomas, F., Mo, Y., Rui, J., Russell, T., Robertson, M., Verschuur, J., Pant, R. and Hall, J.W. Tropical cyclone risk to global electricity supply, Nature Energy, in review. doi: 10.21203/rs.3.rs-4650238/v1
• Zhou, Q., Hanasaki, N., Fujimori, S., Yoshikawa, S., Kanae, S. and Okadera, T., 2018. Cooling water sufficiency in a warming world: projection using an integrated assessment model and a global hydrological model. Water, 10(7), p.872.

Supervisors: Professor Jim Hall, Dr Raghav Pant and Professor Rene Bañares-Alcántara

Economies worldwide face great challenges, and opportunities, in cutting carbon emissions from industrial sites. The UK has a target of achieving net zero carbon emissions by 2050 and a target for decarbonisation of the power sector by 2030. Decarbonisation of the power sector will entail continued rapid expansion of renewable energy supplies, major transmission investments, and growing deployment of energy storage technologies on a range of timescales – including e-fuels for longer-duration energy storage. Besides decarbonising the power sector, achieving net zero will entail decarbonisation of heavy industry (including steel, cement and refineries), the transport sector, buildings and implementation of negative emissions technologies that store CO2 in geological facilities. 

Port industrial clusters represent crucial nodes of investment and industrial transformation on the pathway to net zero. They bring together a combination of industrial activities that are crucial to achieving net zero: 

• Ports, which currently import/export fossil fuels, but are transitioning to importing/exporting e-fuels and biomass;
• Fuelling of shipping, which currently accounts for about 3% of global emissions;
• Manufacturing for, and servicing, the offshore renewable energy industry;
• Power plants, equipped with carbon capture;
• CO2 shipping, and pipeline connections to offshore geological storage facilities;
• Energy storage, including green hydrogen and green ammonia for long-duration energy storage;
• Refineries, which are energy intensive and will transition away from fossil fuels, and will in the future will synthesise e-fuels e.g. for aviation and feedstocks;
• Other heavy industry, including cement and steel industries, and energy-from-waste plants (equipped with CCS).

The fact that these activities are co-located in the same region provides opportunities for coordination of the use of resources and infrastructure. It also means that decarbonisation could result in regional economic transformation of places that can be geographically isolated and economically disadvantaged.

Researchers at the University of Oxford are specialists in systems analysis of renewable energy, chemical facilities and infrastructure systems. This includes expertise within: 

• OPSIS: the Oxford Programme for Sustainable Infrastructure Systems, which has developed the National Infrastructure Systems Model (NISMOD) of the UK and has particular expertise in the modelling of ports and shipping
• OXGATE: the Oxford Green Ammonia Technology (OXGATE) group, who are specialists in techno-economic optimisation of the global production and trade of green hydrogen and ammonia. Recent research has examined the economics of green ammonia imports and hydrogen storage in the UK. 

Recent research has examined the decarbonisation of shipping with green ammonia (Verschuur et al., 2024) and the use of green ammonia for long-duration energy storage (Palazzi, et al., 2024). 
Future research could work at two scales: (i) at a national scale examining the optimal configuration of industrial activities and infrastructure, and (ii) at an industrial cluster scale, to optimally sequence the decarbonisation process. 

The UK’s Future Energy Scenarios (ESO, 2024) indicate where largescale electricity supply (including offshore wind) and grid transmission will be located. However, this national picture of regional priorities for decarbonisation is still tentative, and has not yet been complete UK-scale spatial optimisation of the economic viability of technologies and the need for government regulatory/financial support. The optimal map will be sensitive to the relative costs of different technologies. The research would develop methodology for geospatial analysis on a national scale, which explores the geographical cost-effectiveness of decarbonisation, taking into account synergies between different technologies. The analysis will cover: 

• Electricity supply and transmission (based on ESO’s latest plans and scenarios);
• Hydrogen and green ammonia import, production, storage and use;
• CO2 capture, pipelines, use and storage. 

The outcome of the study will be an objective analysis of the relative merits of large-scale industrial decarbonisation and associated infrastructure across the UK. 

A cluster-specific systems analysis would create a detailed sequential optimisation mode, which would be a platform for testing, optimising and sequencing industrial cluster investments, at a site such as the Humber. Such a model would contain: 

• Simulation of production, costs (including the implications of variable renewable energy prices) and carbon emissions for the full range of green and legacy industrial activities taking place or planned on the Humber;
• Modelling the interdependencies between these facilities, including electric power (and storage), fuels, CO2 and heat;
• Modelling of networked infrastructure, including the electricity grid, and hydrogen and CO2 pipelines;
• Embedding the Humber within a model of shipping trade, including fossil fuels, e-fuels, biomass and CO2;
• Analysis of the resilience of the Humber facilities to sea level rise and shocks from a changing climate;
• Analysis of the uncertainties in cost and market support arrangements, nationally and globally;
• Optimisation of sequencing of investments, including optionality. Consideration of timescales for project delivery, including planning consent;
• Identification of employment and economic benefits for the region, and skills needs and opportunities;
• Identification of robust planning and investment strategies for the Humber region. 

Some elements of this type of analysis exist (e.g. Fletcher et al., 2023), but they have not hitherto been integrated in an integrated forward-looking systems analysis. 

The project will involve a combination of geospatial analysis and energy systems modelling. It will suit students with a strong background in engineer, physics or another quantified subject. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment

References

• Energy System Operator 2024 
• Fletcher, A. et al., Queensland green ammonia value chain: Decarbonising hard-to-abate sectors and the NEM. Centre for Applied Energy Economics & Policy Research: Working Paper Series 2023-16. 
• Palazzi, C., Verschuur, J., Hall, J.W. and Bañares-Alcántara, R. Green ammonia imports could supplement long-duration energy storage in the UK, Environmental Research: Energy, in review. 
• Verschuur, J., Salmon, N., Hall, J.W. and Bañares-Alcántara, R. Optimal fuel supply of green ammonia to decarbonise global shipping, Environmental Research: Infrastructure and Sustainability, 4 (2024): 015001. doi: 10.1088/2634-4505/ad097a.