Ranking animal foods based on suffering and GHG emissions
I created a simple web-based tool which ranks animal species according to the harm caused by consuming them. The user can specify the relative priority of two subscales of harm: animal suffering and greenhouse gas emissions.
Numerous analyses have been published on how much suffering is caused by eating various animals. For example by Peter Hurford, Brian Tomasik, Charity Entrepreneurship and Dominik Peters. Results of these analyses hint at the small animal replacement problem which is the concern that advocating for reduced meat consumption for environmental reasons leads people to replace beef with smaller animals such as chicken and fish. This increases total suffering because more farmed animals are consumed for the same amount of calories.
I was inspired by Dominik Peters’ tool and was wondering if a similar ranking could be developed which accounts for animal suffering, greenhouse gas emissions and human health. My main motivation was to better understand the climate change/animal welfare trade-off when deciding which animals to leave off our plates. Due to difficulties with modeling health effects I eventually narrowed down the harms to just animal suffering and greenhouse gas emissions and developed a tool based on Dominik’s model.
A simple model is used to calculate the animal suffering and greenhouse gas emissions subscale scores of each species in the data set. The subscale scores are then combined into a single score which is used to rank the species.
The animal suffering subscale estimates the number of hours spent on a farm to produce 2000 kcal of food energy. The climate change subscale estimates CO2-equivalent greenhouse gas emissions produced per 2000 kcal of food. The suffering subscale can be adjusted according to the relative suffering intensity of the species and brain size/neuron count. Both subscales can be adjusted by supply and demand elasticity.
The subscale scores are exponentiated using the subscale priorities that the user has provided and then multiplied to get a single score (weighted product model). The combined scores are normalised to the 0-100 range and used to display a ranking of the species based on the estimated harm.
The user interface allows the user to set the subscale priorities, toggle the adjustments, change the relative suffering intensities and choose the brain weighting function. The goal is to enable the user to specify their beliefs if they don’t agree with the default parameters.
When playing around with the sliders it seems that the model is generally consistent with the welfare/climate trade-off. If climate is prioritised, ruminants rank higher on the combined scale. If welfare is prioritised, smaller animals rank higher on the combined scale.
The model does not consider indirect effects on wild animal welfare. The suffering of wild animals could significantly exceed that of farmed animals. Indirect effects of farming contribute to wild animal suffering. It would be interesting to also analyse how changes in animal consumption affect wild animals through indirect effects on feed crop production.
It is difficult to come up with meaningful subscale priorities. It would make sense to measure the disvalue of emissions and suffering based on the underlying values which cause us to be concerned about these issues in the first place. If, for example I am motivated by improving welfare, it would be helpful to estimate the welfare impacts of climate change and factory farming on a common scale which seems difficult.
Heather Browning’s doctoral thesis outlines several issues with common methods of measuring animal welfare. This includes the hours lived on a farm and relative suffering intensity methods that were used in this work. Jason Schukraft has also written about difficulties in measuring the intensity of valenced experiences.
More detailed information about the website is provided on the methods page.
I want to thank Dominik Peters for providing the data and model that he used for ethical.diet.