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It is now well-known that the current food system is a major driver of unhealthy diets, climate change and environmental degradation. What is less known, however, are the concrete contributions of individual actors within the food system, such as food companies that produce and process food. The lack of attribution of impacts complicates the development of targeted approaches and policies that could reduce the food system’s impacts on public health and the environment.

This DPhil project will focus on attributing food-system impacts to specific agents. In particular, it will track and analyse the health and environmental impacts of food companies that are active in food production and processing. The work for the project will include:

  1. the collection, analysis and processing of business reports and statistics;
  2. the integration of those statistics into larger-scale datasets, such as food balance sheets;
  3. the analysis of the environmental footprint of food companies based on their attribution of production;
  4. the analysis of the burden-of-disease contribution of food companies based on their attribution of consumption; 
  5. the development of targeted approaches and policy proposals for reducing the health and environmental impacts of food companies.   


The successful candidate will gain experience in working with food-system data and models. The candidate will learn how to extract and process data, handle large-scale datasets, conduct environmental analyses using environmental footprints, and conduct burden-of-disease analyses using comparative risk assessments.


Most of the work will be desk-based, but the project allows for (critical) interaction with food companies and industry. Training in data handling, and in conducting environmental and health analyses will be provided. Additional training, e.g. in statistics and other relevant topics, will be available through departmental courses.

prospective candidate

Suitable candidates for this project could have a variety of backgrounds, but will have a strong interest in improving public health and environmental sustainability. They will have some experience in quantitative data analysis, and be comfortable with some amount of coding in relevant programming languages.