Food Environmental Impact Data Sourcing & Estimation Methodology
To ensure transparency and replicability, this report adheres to a strict data sourcing and estimation hierarchy.
1. Data Hierarchy
1.1 Primary Source
The foundational data for land use and freshwater withdrawals for the majority of food items are the global mean values from the Poore & Nemecek (2018) meta-analysis (Poore and Nemecek #), as curated and presented by Our World in Data. This dataset provides the most robust, standardised, and globally representative values for the major food commodities it covers, which account for approximately 90% of global protein and calorie intake. The supplementary materials from the original study serve as the ultimate reference.
1.2 Specific Food Data
For food items not explicitly covered in the primary dataset, or where more granular analysis is beneficial (e.g., comparing different beef production systems or specific types of fish), our database draws on other peer-reviewed studies and expert reports provided in the research materials. For instance, detailed data on beef systems is sourced from Broom (2019), and specific water use for bananas is informed by FAO reports.
1.3 Proxy Assignment
For a number of food items listed in our database, no direct environmental impact data exists in any available literature. In these cases, a proxy assignment methodology is employed, consistent with the approach used by the UN FAO’s FoodEx2-environment dataset. A proxy value is assigned from the most similar food item for which data is available. For example, the impact of “Amaranth,” a minor grain, is proxied using the data for “Oatmeal.” All such assignments are made based on similarity in food type, cultivation profile, or processing, and are explicitly documented to maintain transparency.
1.4 Handling Composite Dishes
Many items in our database are composite dishes (e.g., “Lasagna,” “Chicken Curry,” “Spaghetti Bolognese”), for which direct LCA data is unavailable. For these items, a simplified estimation is performed by identifying the primary ingredient that drives the environmental impact. In most cases, this is the animal protein component. For example, the impact of “Beef Lasagna” is estimated using the data for its beef mince component, as this ingredient’s land use, water use, and GHG emissions overwhelmingly dominate the impacts of the pasta, cheese, and tomato sauce combined. This pragmatic approach provides a reasonable and directionally correct estimate for complex food items, mirroring the methodology suggested for such products in established databases.