Join Us

Want to study in our group? We host a vibrant community of doctoral and taught masters students.

We typically recruit doctoral students to join our group each year. We are particularly keen to recruit students with very strong quantitative and computational skills, a problem-solving mentality and a commitment to doing research of the utmost societal and environmental significance. Further information on admission to our doctoral programme can be found here.

Funding may be available from Oxford’s NERC Doctoral Training Partnership in Environmental Research or from the EPSRC Doctoral Training Partnership allocation to our department.

We also support and advise upon research dissertations for Oxford’s MSc programmes in:

Below we list potential MSc/DPhil topics to which we are currently seeking qualified graduate students. Applicants are also free to propose their own topics, aligned with our research programme.

Spatial planning for nature and for infrastructure

Supervisors: Professor Jim Hall and Professor Nathalie Seddon

Click for description Construction often has severe impacts upon nature and biodiversity. Infrastructure fragments habitats and opens up previously inaccessible areas to exploitation. With infrastructure investment at an all-time high, there is potential for new infrastructure to cause ever-greater impacts upon already fragile environments. Though some of these impacts may be mitigated through thoughtful design, the most critical decisions relate to the location of new infrastructure projects. Once a project has been approved it is usually too late to deal with the environmental impacts. Thus there needs to be a shift in ‘up-front’ thinking so that the sustainability of nature is planned ahead of and at the same time as infrastructure, rather than afterwards.

One potential example of this approach is in the planning of Nature Recovery Networks in England. England’s natural habitats are highly fragmented, so need to be reconnected to enable species to adapt to the impacts of climate change (Smith et al., 2021). Nature Recovery Networks are a structured way of planning for nature on a large scale. However, the process of creating Nature Recovery Networks is disconnected from the process of infrastructure planning. We wish to explore how processes of spatial optimization for nature (e.g. Schuster et al., 2019) can be combined with infrastructure planning. To address this problem we propose to adapt the National Infrastructure Systems Model (NISMOD) developed by the Infrastructure Transitions Research Consortium (Hall et al., 2016) and combine it with methods for mapping and assessing biodiversity and ecosystem services under a range of different future scenarios – both of future land conversion for infrastructure and urbanisation, and also of land restoration for nature recovery.

Potential applications include infrastructure/nature planning in England, possibly with a focus upon the Oxford-Cambridge Arc, in collaboration with Natural England. There will also be interest in applying on a large scale in a developing country context (Africa, Asia or Latin America) where major new infrastructure developments are proposed. We would like to conduct a large-scale assessment so we can compare national infrastructure plans with integrated pathways that prioritise nature conservation and recovery. We wish to demonstrate how nature can be conserved and restored whilst delivering the infrastructure services that people need and plotting a pathway of climate-compatible development.

The research will involve analysis of the evidence for the spatial attributes of different habitats, their scale and interconnectedness, and how this contributes to biodiversity and ecosystem services. It will involve examining the spatial evolution of infrastructure networks and simulation of how they may evolve in future in different economic development scenarios. This will lead to development of an integrated spatial optimization methodology that seeks to address human needs for infrastructure services whilst conserving and restoring nature.

The project will involve a combination of evidence review, 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. This project is advertised as part of Oxford University’s Doctoral Training Partnership in Environmental Research, so 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:

  • Hall, J.W., Tran, M., Hickford, A.J. and Nicholls, R.J. (eds.) The Future of National Infrastructure: A System of Systems Approach, Cambridge University Press, 2016.
  • Hall, J.W. Using system-of-systems modelling and simulation to inform sustainable infrastructure choices, IEEE Systems, Man and Cybernetics Magazine, DOI:10.1109/MSMC.2019.2913565.
  • Schuster, R., Wilson, S., Rodewald, A.D. et al. Optimizing the conservation of migratory species over their full annual cycle. Nat Commun 10, 1754 (2019). https://doi.org/10.1038/s41467-019-09723-8
  • RJ Smith, SJ Cartwright, AC Fairbairn, DC Lewis, Developing a Nature Recovery Network using systematic conservation planning, https://ecoevorxiv.org/wqstj/
  • Thacker, S., Adshead, D., Fay, M., Hallegatte, S., Harvey, M., Meller, H., O’Regan, N., Rozenberg, J. and Hall, J.W. Infrastructure for Sustainable Development, Nature Sustainability, 2 (2019):324–331. DOI: 10.1038/s41893-019-0256-8.

National infrastructure system-of-systems modelling

Supervisors: Professor Jim Hall

Click for description National infrastructure systems (energy, transport, digital communications, water, waste management) provide essential services for people and the economy. They are increasingly interdependent, so performance of one system relies on others. Infrastructure is widely regarded as an essential pillar for economic competitiveness and as a contributor to sustainability (Thacker et al. 2019).

The Infrastructure Transitions Research Consortium has over the last ten years developed a unique modelling capability, called NISMOD (Hall et al., 2016), for simulating Britain’s infrastructure systems. NISMOD contains modules to simulate Britain’s energy, transport, digital and water supply systems. It uses scenarios of population and the economy to estimate future demand for infrastructure services and explore the performance of infrastructure policies and investments to meet those needs. The various simulation models are integrated with a model coupling framework called smif (Usher and Russell, 2019), which orchestrates model coupling, scenario analysis and optimisation.

Now that NISMOD is fully operational there are exciting opportunities for generating new scientific insights and results to guide decision making about national infrastructure systems in Britain. The types of questions that could be explored include:

  • Examination of the implications for infrastructure service provision of different scenarios for population and economic growth;
  • Evaluation of alternative strategies to achieve net zero carbon emissions from infrastructure;
  • Quantification of the most efficient strategies for providing essential services given constraints on infrastructure investments;
  • Examination of the implications of interdependencies between infrastructure sectors, for example due to the electrification of transport;
  • Strategic planning of infrastructure at a sub-national scale e.g. the Oxford-Cambridge Arc.

The research will particularly focus on the application of multi-objective optimisation methodologies to problems of infrastructure planning. We will explore the use of robust control methods and real options analysis to test and compare adaptive strategies for national infrastructure provision. The project will therefore involve using and adapting existing simulation models of infrastructure systems and development of methods for optimisation and adaptive planning. It will suit students from any quantified background, including engineering, mathematics, economics 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:

  • Hall, J.W., Tran, M., Hickford, A.J. and Nicholls, R.J. (eds.) The Future of National Infrastructure: A System of Systems Approach, Cambridge University Press, 2016.
  • Hall, J.W. Using system-of-systems modelling and simulation to inform sustainable infrastructure choices, IEEE Systems, Man and Cybernetics Magazine, DOI:10.1109/MSMC.2019.2913565.
  • Schuster, R., Wilson, S., Rodewald, A.D. et al. Optimizing the conservation of migratory species over their full annual cycle. Nat Commun 10, 1754 (2019). https://doi.org/10.1038/s41467-019-09723-8
  • RJ Smith, SJ Cartwright, AC Fairbairn, DC Lewis, Developing a Nature Recovery Network using systematic conservation planning, https://ecoevorxiv.org/wqstj/
  • Thacker, S., Adshead, D., Fay, M., Hallegatte, S., Harvey, M., Meller, H., O’Regan, N., Rozenberg, J. and Hall, J.W. Infrastructure for Sustainable Development, Nature Sustainability, 2 (2019):324–331. DOI: 10.1038/s41893-019-0256-8.

The fiscal implications of climate risks to infrastructure

Supervisors: Dr Nicola Ranger and Professor Jim Hall

Click for description The impacts of climate change on national infrastructure systems are one of the most severe risks faced by national governments. Climatic extremes can damage energy, transport and telecommunications networks, whilst more chronic risks like droughts and salinization of groundwater represent a risk to water supplies. Governments ‘own’ most of these risks, either because they are directly responsible for public assets, or because they hold some implicit responsibility to assist with emergency repair and recovery of privately owned assets. Losses from climate change will negatively impact public balance sheets and divert public resources from other important activities of governments. When societies and economies are hit by climate change, finance ministries receive fewer tax payments from businesses and employees, whilst being expected to step in to assist with emergency relief and recovery, including support to firms and the financial sector. Given these unexpected expenditures, governments in low-income countries will be less able to service sovereign debt. It’s no surprise that exposure to climate risks is already being felt in economic growth rates, sovereign credit ratings and the cost of capital (Buhr et al., 2018, Cevik and Jalles, 2020).

This project will combine quantified risk analysis of climate risks to infrastructure (Hall et al., 2019) with analysis of public financial systems and the ways in which climate risks may destabilise these systems. The analysis of climate risks to infrastructure will adapt tools developed in our research group for climate risk analysis of infrastructure at national and global scales. These combine 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. This will be combined with analysis of public finances within governments and relationships with other financial actors, including banks, ratings agencies and international financial institutions. A combination of stress tests and Monte Carlo risk analyses will be developed for case study locations to assess the robustness of financial systems to climate-related shocks. Mechanisms for enhancing financial stability, e.g. through budget contingencies or disaster risk finance, will be tested and explored.

The project will involve using and adapting existing risk analysis models of infrastructure systems as well as macro-economic modelling and more detailed analysis of fiscal flows. It will involve case study analysis in one or more jurisdiction. Students with a strong economics or engineering background would be equipped to conduct this research. Students should be able to demonstrate aptitude for computer modelling and quantified analysis. They should demonstrate 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:

  • Buhr, B., et al., Climate Change and the Cost of Capital in Developing Countries. 2018, Imperial College Business School and SOAS University of London.
  • Cevik, S. and J.T. Jalles, Feeling the Heat: Climate Shocks and Credit Rating, in IMF Working Paper. 2020, International Monetary Fund.
  • Hall, J.W., et al. Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation. Oxford: Environmental Change Institute, University of Oxford. December 2019, 64pp. https://gca.org/wp-content/uploads/2020/12/GCA-Infrastructure-background-paperV11-refs_0.pdf

The costs of adapting global infrastructure systems to the impacts of climate change

Supervisors: Professor Jim Hall and Dr Raghav Pant

Click for description In recent years, our 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)? Answering that question 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 optimization on a very large scale is a vast challenge. This DPhil project aims to address that challenge, by developing methodology to systematically explore adaptation options for infrastructure networks on a very large scale 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. Adapt Now: A global call for leadership on climate resilience. 2020 https://gca.org/reports/adapt-now-a-global-call-for-leadership-on-climate-resilience/
  • Hall, J.W., et al. Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation. Oxford: Environmental Change Institute, University of Oxford. December 2019, 64pp. https://gca.org/wp-content/uploads/2020/12/GCA-Infrastructure-background-paperV11-refs_0.pdf
  • 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.
  • Marchau, et al., Decision Making under Deep Uncertainty: from theory to practice, Springer 2019
  • 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) https://link.springer.com/article/10.1007%2Fs10584-021-03179-w
  • Saltelli et al., Sensitivity Analysis in Practice: a guide to assessing scientific models, Wiley, 2004.

The resilience of global energy networks

Supervisors: Professor Jim Hall and Dr Raghav Pant

Click for description 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). There have been several studies of climate impacts for power networks at local and national scales (e.g. Fant et al. 2020, Kos 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 the gridfinder model of transmission networks. By combining these datasets with statistical and model-based data on climate-related hazards, it will be possible to generate new 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 simulating how the energy systems may evolve into the future, given changing patterns of demand, investment and energy technologies. The research will combine socio-economic and technology scenarios to develop a range for scenarios for future energy networks. 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:

  • Fant, C., B. Boehlert, K. Strzepek, P. Larsen, A. White, S. Gulati, Y. Li and J. Martinich (2020): Climate change impacts and costs to U.S. electricity transmission and distribution infrastructure. Energy, 195, 116899 (doi: 10.1016/j.energy.2020.116899) (https://www.sciencedirect.com/science/article/pii/S0360544220300062)
  • Hall, J.W., et al. Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation. Oxford: Environmental Change Institute, University of Oxford. December 2019, 64pp. https://gca.org/wp-content/uploads/2020/12/GCA-Infrastructure-background-paperV11-refs_0.pdf
  • Koks, E., Pant, R., Thacker, S., Hall, J.W. Understanding business disruption and economic losses due to electricity failures and flooding, International Journal of Disaster Risk Science, 10(2019): 421–438. DOI:10.1007/s13753-019-00236-y

The resilience of cyber-physical infrastructure systems

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

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

Future exposure of transport networks to climate risks

Supervisors: Professor Jim Hall and Dr Raghav Pant

Click for description Transport networks, which include road, rail, ports and airports, are exposed to the impacts of natural hazards and climate change (Koks et al., 2019, World Bank, 2019). These networks are rapidly expanding, including through large investment initiatives such as the Belt and Road Initiative (BRI). Thus, to fully understand the scale of future risks to transport networks, we need to be able to able to predict where they will exist in the future, as well as using geospatial information on where they exist at the moment. This is a complex challenge, as there is a two-way relationship between the geography of economic activity and the provision of transport infrastructure. Because of the complexity of these processes, the empirical evidence of the effects is often inconclusive. Theoretically, the relationship has been addressed through the frameworks of New Economic Geography, input-output modelling and spatial computable general equilibrium models. Each of these approaches has their limitations as well as their strengths.

The aim of this research is to develop methods for projecting where transport networks will be located across the globe, under different scenarios of economic development. The results of this new model development will be used to understand the changing future vulnerability of transport networks to the impacts of climate change.

The proposed research will take a combination of a model-based and empirical approach to understanding the relationship between infrastructure and economic development at broad scales. The model-based analysis will start with stylised models, possibly reproducing the insights from NEG models. Meanwhile, we will seek datasets that can be used to characterise spatial changes. The analysis will be used to understand future demands for infrastructure services and how patterns of economic development may evolve in future. The work will be applied to a large geographical region, such as a national-scale or multi-national scale to see how infrastructure developments can create positive and negative effects for different regions.

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. 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:

  • Koks, E., Pant, R., Thacker, S., Hall, J.W. Understanding business disruption and economic losses due to electricity failures and flooding, International Journal of Disaster Risk Science, 10(2019): 421–438. DOI:10.1007/s13753-019-00236-y
  • World Bank, Lifelines: the resilient infrastructure opportunity. 2019. https://www.worldbank.org/en/news/infographic/2019/06/17/lifelines-the-resilient-infrastructure-opportunity
  • Venables, A., Laird, J. and Overman, H. Transport investment and economic performance: Implications for project appraisal. (Department for Transport, 2014).
  • Bird, J. H. and Venables, A.J. Growing a Developing City: A Computable Spatial General Equilibrium Model Applied to Dhaka. The World Bank, 2019.
  • Lall, S. V. and Mathilde S. M. L. “Who Wins, Who Loses? Understanding the Spatially Differentiated Effects of the Belt and Road Initiative.” 2019.
  • Hall, J.W., Tran, M., Hickford, A.J. and Nicholls, R.J. The Future of National Infrastructure: A System of Systems Approach, Cambridge University Press, 2016.

Using novel data sources to assist the planning and allocation of infrastructure

Supervisors: Professor Jim Hall and Dr Raghav Pant

Click for description Planning national infrastructure, in all parts of the world, involves difficult choices about where infrastructure is located in order to efficiently provide services whilst minimising negative impacts on people and the environment. Versions of this spatial allocation problem exist in many situations. New spatial datasets from satellites, sensors and crowd sourcing are providing information that can enable better 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 original research using AIS satellite ship tracing data (Verschuur et al., 2020); we are now keen to advance methods for tracking road vehicles and trains from satellites to improve our understanding of transport networks and trade. This information could be supplemented with crowd sourcing, which would also be used to provide new information on the condition of infrastructure assets and services. We are particularly concerned about the possible impacts of natural disasters on infrastructure systems and have experience of using satellite imagery and machine learning to detect these impacts. By combining these data sources, it should be possible to make a significant next step in the modelling 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. Another possible application would be electrification of transport, which is widely regarded as an opportunity for developing countries to ‘leapfrog’ fossil-fuel dependent transport and associated infrastructure networks, by co-developing renewable energy supplies and vehicle charging points. 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 big 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 of the costs and business models of renewable energy and electric vehicles to generate efficient 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 course of the research, so the thesis will combine investigation of new big datasets with methodological development and a series of case studies. Overall, we would like to develop a broad framework to characterise different infrastructures and their relationship with the space and people around them. We wish to incorporate multiple sustainability indicators which can help to inform decisions about infrastructure provision to achieve the SDGs. We aim to demonstrate how market forces in infrastructure service provision (for example the proliferation of private tube wells in rural Bangladesh) can be combined with targeted development assistance and public investment to provide networks that leave no one behind.

The project will involve statistical analysis of survey data and 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. Thus the student should have a strong quantified background (e.g. engineering, economics, physics, geostatistics) but should also have a good appreciation of the wider societal context of infrastructure service provision. 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:

  • 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.