About the Project
An opportunity has arisen for an outstanding Ph.D. student to join the industrial ecology team at the University of Edinburgh to work on Materials and Energy Requirements for Sustainable Development.
In 2015, the United Nations published the Sustainable Development Goals (SDGs) – 17 ambitious goals for society to aim for to achieve sustainable development – which have since achieved widespread agreement and significantly directed work across academia, industry, and government. However, the consensus achieved, in part due to the broad/general appeal of the SDGs, goes hand in hand with their more qualitative rather than quantitative nature. Consequently, it is still unknown how much materials and energy are needed to achieve the SDGs in practice, and thus whether or not they are achievable.
Sustainable energy generation and storage are fundamental to many of the SDGs. Whilst renewable energy generation technologies – wind, solar, tidal, etc. – and the scalable energy storage systems that are needed to apply these technologies in practice – electrochemical (batteries), thermal, mechanical etc. – are both well quantified in terms of their physical performance (MW and MWh), both are poorly assessed in terms of their holistic long-term material requirements. Significantly, this lack of understanding around material requirements may lead to a dangerous ‘lock-in’ scenario – a scenario in which society has overinvested its intellectual and physical capacity into developing/deploying technologies that are exciting in terms of performance but cannot realise the SDGs due to limited materials availability (both primary, e.g., ores, and secondary, e.g., wastes).
At the time of the last major transition in energy generation technologies (early 1950’s), atomic power seemed to provide a practically limitless source of affordable energy. However, society is still struggling to deal with the whole life cycle implications/consequences of this technology, even after decades of deployment and £bns invested. This situation – of great technological promise but challenging implementation – seems to have recently recurred, with the UK Government twice investing £mns in failed carbon capture and storage projects in the last decade. Will society stumble again with other technologies in the future, e.g., in mass uptake of Li-ion batteries?
This Ph.D. project aims to develop quantitative data around materials and energy requirements to achieve sustainable energy generation and storage. These data will provide a basis to guide today’s decision makers, researchers, and industry into making more informed choices and investments around long-term energy generation and storage without repeating the mistakes of the past. It will answer questions such as:
1. How much and what types of materials are needed to achieve sustainable energy generation and storage?
2. Is this scenario achievable with current or ‘business as usual’ technologies or must transformative technological change occur?
3. If so, which technologies require major innovation, and what are the most promising options?
4. How can researchers, engineers, and designers harness these insights to develop/improve one or more such technologies?
We envisage that the Ph.D. student will develop quantitative scenarios for energy generation and storage, working on a highly transdisciplinary basis with experts across the School of Engineering and University of Edinburgh. The Ph.D. student will work to develop and implement their methodology in energy generation and storage in the first instance, but potentially also to other major sectors such as transport, infrastructure and buildings, water provision, and food provision.
Start date: Flexible – applications will be considered on a rolling and individual basis. Applicants are encouraged to apply well before February 1st 2019 to meet the deadline for School of Engineering PhD scholarships beginning in 2019.
Funding Notes
Applicants must hold an undergraduate or masters degree in one or more of the following areas: engineering, economics, science (informatics, physics, chemistry, materials, environmental science, forestry, etc.). Experience in statistical data analysis including regressions and coding in R, Python, etc. is highly desirable but not essential.
Applicants must be enthusiastic and highly motivated to learn and work across traditional discipline boundaries.
Applications are welcomed from students who are applying for, or have been awarded, a scholarship or similar from the University of Edinburgh or elsewhere.