Electrification and Lightweighting are key areas of interest for the automotive industry, driven by the goal of reducing carbon emissions to meet legislative and societal requirements, the phasing out of IC engines and the general drive towards Net Zero. However, the materials required to achieve these are heavily reliant on more advanced materials such as carbon fibre composites, rare earths or specifically engineered metallic alloys that are carbon intensive in terms of their manufacturing and ecological questionable in terms of their sourcing. Previously, although these concerns have been recognised, it was felt that the benefits outweighed the environmental and economic costs of their use.

There is now a real and growing concern over the full life environmental impact of these approaches, driven by increased consumer awareness, more vocal ecological voices (e.g. COP 26 & 27), and current (e.g. the Greenhouse Gas Protocol (GHGP)) and emergent (e.g. EU batteries regulation) policies/legislation. Without addressing these issues in a meaningful and ethical way, many companies risk losing competitive advantage, reputational damage and potential financial ruin.

Whilst everyone agrees that manufacturing processes and products need optimising, materials need ethically sourcing and products should be built using circular economy principles, the lack of understanding, data and more importantly a robust ‘system of systems’ model to enable informed decision making to de-risk change, means the linear ‘business as usual’ approach is prevalent.

This PhD will look at developing a new framework for data capture & analysis in the context of critical raw materials with a particular view to generating implementable assessments of the non-carbon environmental, social and economic impacts arising from decisions made within the automotive supply chain. It will further establish a full systems life cycle framework model for a vehicle platform that will enable better decision making and support transition to the more desirable circular end-product.

Essential

A good first degree (2:1 above or equivalent), a postgraduate degree/ or equivalent professional or research experience from a relevant STEM or Social Science background

Strong analytical capability, especially with large and diffuse data sets

Interest in Real World Sustainability Impacts

Ability to formulate a systems approach to the research question.

Desirable

Engineering and/or Data Science Background would be advantageous, but not essential.