FoodPrints of households

Purpose

Food consumption is one of the main drivers of environmental impacts. To develop meaningful strategies for the reduction of impacts, food consumption patterns need to be understood on the household level, as purchasing decisions are taken on this level. The goals of this study were to develop a model that estimates food demand and environmental impact as a function of household characteristics, to assess variability between households, and to provide a basis for the development of consumer-targeted political interventions. We titled the study “FoodPrints of households,” as we assessed food consumption in terms of carbon footprint (in analogy to (Stoessel et al. Environ Sci Technol 46(6):3253–3262 2012)).

Methods

We used data from the Swiss household budget survey and applied multiple linear regressions based on generalized linear models to quantify food and beverage demand of individual households. Seven household characteristics, such as size, income, and educational level, served as input variables for the regressions. In a case study, food and beverage demand of 3238 individual households of a Swiss municipality was environmentally assessed with life cycle assessment, and scenarios for different reduction strategies were evaluated.

Results and discussion

We found that the carbon footprints of in-home food consumption per household member and year vary from 0.08 t CO₂ eq. to 5 t CO₂ eq. with a median value of 1 t CO₂ eq. This variability is significantly smaller than the carbon footprint variability for the consumption areas of housing and mobility, where 25 % of the people are responsible for 50 % of the environmental impacts. Differences between high- and low-impact households can be primarily explained by differences in meat and dairy consumption.

Conclusions

This paper presents a model for quantifying food demand and impacts on a household level in Switzerland and represents a basis for developing targeted political measures to mitigate food consumption impacts. Household budget data is also available for many other countries, and the methods presented in this paper could therefore also be applied to other geographical regions.