Reducing the environmental impact of household consumption is widely recognised to be essential for achieving sustainable societies. Designing targeted policies requires detailed data on which households contribute how much to environmental footprints.
While many studies look into average environmental footprints of households, assessments across household income remain limited and focused on carbon, energy or material footprints. Furthermore, the existing literature is dominated by Input-Output Analysis despite increasing data availability making it possible to use process-based Life Cycle Assessment (pLCA) as well, with the latter offering potentially much higher levels of detail.
This study addresses this gap by estimating the environmental footprints from the consumption of household goods, appliances and food for 200,000 European households from 24 European countries and the European Union (EU) using a pLCA approach. To do so, pLCAs for the EU covering 16 impact categories are mapped to expenditure data from the EU Household Budget Survey (HBS).
The research question is: How are the environmental footprints from the consumption of household goods, appliances and food distributed across household income groups in the EU?
The study focused on carbon, water use, land use and resource use (minerals and metals) footprints, which are considered to be headline indicators. The results show that the carbon, water use and land use footprints at EU-level (without Austria, Italy and Germany), are dominated by the consumption of food and therefore largely independent from household income. The resource use footprint is dominated by appliances. The 10% households with the highest income have, on average, 2.8 times the carbon footprint, 6.4 times the water use footprint, 3.4 times the land use footprint and about 8.2 times the resource footprint of households belonging to the lowest income decile. Income inequality (with about 28.6 times the mean household income for top decile vs bottom decile households at EU-level) is much larger. At the same time, the range of household environmental footprints is large in all impact categories due to factors other than household income. The difference to existing literature, which generally finds a stronger connection between income and impacts, particularly for the carbon footprint, mainly stems from the exclusion of mobility and housing.
The study furthermore discusses the significant limitations of HBS data and outlines what
improvements are required to get more robust, comprehensive and detailed estimates of household environmental footprints. Ideally, future HBS would record physical quantities and/or prices along with the expenditures, which would make it possible to consider product quality differences. Using digital tools might make it possible to record consumption all year round or even for multiple years and thus mitigate the bias from infrequent purchases.
Future research should include services, housing and mobility and use regionalised impact data and find mitigation strategies for infrequent purchases and the product quality problem. The pLCA approach should be upheld due to the granularity it offers, for example for the assessment of the distribution of EFs under scenarios.