Economic Impact of Port Decarbonisation on Public Health: International Port of Portsmouth

About the Project

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Farzad Arabikhan, Professor David Hutchinson and Professor Mohamed Bader.

The work on this project will:

• Develop advanced machine learning models to link CO₂ emissions from port operations to public health outcomes.
• Quantify the economic impact of decarbonisation efforts, focusing on NHS cost savings and improved workforce productivity.
• Conduct scenario-based analyses to simulate the effects of different decarbonisation interventions at Portsmouth International Port.
• Produce a scalable, evidence-based framework to inform policy and investment in climate-resilient transport infrastructure.

Project description

Port cities are at the forefront of climate and public health challenges due to high concentrations of emissions from maritime operations, freight transport, and urban mobility. This PhD project is part of a larger research initiative supported by the DARe Hub Flexible Fund, focusing on the Portsmouth International Port—a major UK gateway undergoing a £19.8 million decarbonisation programme.

The PhD researcher will play a key role in advancing a data-driven framework that connects decarbonisation of port infrastructure with measurable public health and economic benefits. The project will build upon existing work (SEACHANGE project) that developed a predictive emissions model, and extend it to:

1. Model Health Impacts: Using environmental and health data, the candidate will develop machine learning models to predict the incidence of respiratory and cardiovascular diseases associated with varying levels of port-related air pollution.
2. Economic Valuation: Applying methodologies from HM Treasury’s Green Book and DEFRA, the researcher will translate health outcomes into quantifiable economic metrics, including NHS savings, reduced sick leave, and enhanced productivity.
3. Scenario Analysis: The PhD will explore multiple decarbonisation strategies (e.g., shore power, fuel switching) using simulation tools to assess their comparative impact under different assumptions, providing valuable insights for public sector decision-makers.

The research offers a unique opportunity to contribute to climate adaptation, transport decarbonisation, and public health modelling, with real-world implications for UK infrastructure policy. The candidate will collaborate closely with academic experts, policymakers, and stakeholders including Portsmouth City Council and the DARe Hub, and will have access to unique datasets and cutting-edge infrastructure at the University of Portsmouth.

This PhD is ideal for candidates with a strong background in data science, environmental science, transport modelling, or economics, and a passion for interdisciplinary, impact-driven research.

General admissions criteria

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in xxxxxxxxxxxxx or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

Must: Skills and experience in machine learning and programming

Must: Ability to work independently and collaboratively within a multidisciplinary team.

Desirable: Knowledge of cost-benefit analysis, public sector appraisal frameworks (e.g., HM Treasury Green Book), or health economics.

How to Apply

We’d encourage you to contact Dr Farzad Arabikhan (farzad.arabikhan@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

When applying please quote project code: CMP10161026

Funding Notes

Funding Availability: Self-funded PhD students only

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only - eligibility criteria apply).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.