Modelling the health impact of environmentally sustainable dietary scenarios in the UK

Eur J Clin Nutr. 2012 Jun;66(6):710-5. doi: 10.1038/ejcn.2012.34. Epub 2012 Apr 11.


Background/objectives: Food is responsible for around one-fifth of all greenhouse gas (GHG) emissions from products consumed in the UK, the largest contributor of which is meat and dairy. The Committee on Climate Change have modelled the impact on GHG emissions of three dietary scenarios for food consumption in the UK. This paper models the impact of the three scenarios on mortality from cardiovascular disease and cancer.

Subjects/methods: A previously published model (DIETRON) was used. The three scenarios were parameterised by fruit and vegetables, fibre, total fat, saturated fat, monounsaturated fatty acids, polyunsaturated fatty acids, cholesterol and salt using the 2008 Family Food Survey. A Monte Carlo simulation generated 95% credible intervals.

Results: Scenario 1 (50% reduction in meat and dairy replaced by fruit, vegetables and cereals: 19% reduction in GHG emissions) resulted in 36,910 (30,192 to 43,592) deaths delayed or averted per year. Scenario 2 (75% reduction in cow and sheep meat replaced by pigs and poultry: 9% reduction in GHG emissions) resulted in 1999 (1739 to 2389) deaths delayed or averted. Scenario 3 (50% reduction in pigs and poultry replaced with fruit, vegetables and cereals: 3% reduction in GHG emissions) resulted in 9297 (7288 to 11,301) deaths delayed or averted.

Conclusion: Modelled results suggest that public health and climate change dietary goals are in broad alignment with the largest results in both domains occurring when consumption of all meat and dairy products are reduced. Further work in real-life settings is needed to confirm these results.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cardiovascular Diseases / mortality*
  • Conservation of Natural Resources*
  • Dairy Products*
  • Diet*
  • Food Industry
  • Goals
  • Greenhouse Effect*
  • Humans
  • Meat*
  • Models, Biological
  • Monte Carlo Method
  • Neoplasms / mortality*
  • Public Health
  • United Kingdom