PhD in Transitional DeCarbonisation Scenario Modelling for Value Chain Emissions Mitigation (TransCarb)

Industrial manufacturing activities generate roughly two-thirds of global CO2 emissions, with over 90% of these emissions generated in the value chain, which encompasses all the individual steps taken to manufacture a product, driving climate change, and climate action. Legal requirements and obligations (e.g., the UK Carbon Budget: Net Zero by 2050 legislation), the need to demonstrate proactivity on climate change action, satisfy stakeholder/customer expectations, and boost performance ranking in sustainability indices, constitute drivers encouraging companies to reduce emissions. Companies are therefore taking steps to decarbonise emissions under their operational control and direct ownership (Scope 1 and 2). Due to several challenges to overcome, a remaining aspect difficult to decarbonise and often left unabated relates to value chain emissions (VCEs), indirect up/downstream Scope 3 emissions, which can be as high as 98% of the total, depending on the economic sector. Yet, without decarbonising VCEs, transitioning to a net zero carbon economy would be significantly hampered.

To address the challenges and characterise plausible transition to VCE decarbonisation, this PhD Studentship will utilise sustainable systems engineering and data science principles, within a spatial ecological-economic framework, to deliver a step change in methodology and application. TransCarb’s overall vision is to advance the theory, method, and practice in VCEs to accelerate global progress towards net zero by aligning company-specific decarbonisation strategies and country-specific industrial decarbonisation pathways with a data-centric approach informed by industrial ecology & sustainable systems engineering principles. It is conceived that this PhD studentship will make original contributions to knowledge in the field of sustainable systems engineering through (i) methodological advancement in computational sustainability for VCE measurement and assessment; (ii) identification of practical steps for future development of transitional VCE decarbonisation plans; and (iii) scenario development enabled by the interactions between technical and strategic levers for change, towards VCE decarbonisation.

As a PhD candidate, you will join the Computational Sustainability Research Team, as part of the wider Sustainable Materials and Manufacturing Group, comprising excellent and leading academics, researchers, project engineers and fellow PhD candidates. The group adopts an interdisciplinary approach across the full technology supply chain, focusing on the development of sustainable materials and manufacturing processes to combat environmental damage.

For further enquiries regarding the position and application procedure, contact Dr Taofeeq Ibn-Mohammed (t.ibn-mohammed@warwick.ac.uk) or Professor David Greenwood (d.greenwood@warwick.ac.uk).

Essential and Desirable Criteria

Candidates with a First or Second High Upper Class Degree in Engineering, Computer Science, Environmental Science, Economics, or a closely related subject are invited to apply. An MSc degree in relevant subject area will constitute an added advantage. The successful candidate will be highly numerate and quantitatively driven, with working knowledge of sustainable systems engineering tools/techniques such as life cycle assessment, input-output analysis, and a strong understanding of general carbon accounting principles, alongside data science techniques such as programming with Python or MATLAB. Burning passion and demonstrated ability of independent academic research and contribution to scientific publications is essential.

Funding and Eligibility

This project is funded via the EPSRC Doctoral Training Programme (DTP), University of Warwick. Under this funding scheme, UK citizens are eligible, as are UK domicile EU students. The studentship is for 3.5 years starting in October 2024 and will provide full coverage of tuition fees and an annual tax-free stipend of circa £19,237.