| Summary: | Given the key role of households in driving global emissions and resource use, a change in their consumption behaviours towards more sustainable levels is essential to reduce worldwide adverse environmental impacts. Thereby, focusing on cities is especially important because of today’s large share of the global population living in cities and because local authorities are close to the needs of their residents. However, devising targeted and effective policy measures implies a thorough understanding of prevailing consumption patterns and associated environmental consequences. The goal of this article is to investigate and compare household behaviours and lifestyle-induced carbon footprints in Sydney and Melbourne in order to enhance today’s understanding of household consumption in cities of a high-income, high-emission country. For this purpose, we employed a two-stage clustering approach with a Self-Organising Map and a subsequent Ward-clustering. This allowed for including expenditure data along with socio-economic attributes and thus for recognising lifestyle-archetypes. These emerging archetypes represent households with similar characteristics and comparable consumption patterns. Analysing the archetypes in detail and performing a city-comparison based on multi-dimensional scaling revealed similarities and dissimilarities between the two metropoles. ‘Older’ archetypes seem to behave more alike across cities but show different carbon footprints emphasising the importance of regionalised environmental assessments and of city-specific supply chains. Distinct patterns especially emerged in the high- and low-income segments highlighting the different importance of different lifestyles in each city. Socio-economically similar family-archetypes were found in both cities, but some of them showed diverging consumption behaviours. This article showed that studying household-induced environmental impacts in cities should not rely on macro-trends but should rather be based on city-specific analyses that capture local peculiarities and consider socio-economic characteristics and consumption data simultaneously.
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