A Household's Burden – The Embodied Resource Use of Household Equipment in Germany

Abstract

We use cluster analysis and material flow accounting to describe patterns of resource use in German households. Data on socio-demographic characteristics, and expenditures on fuel, electricity and household equipment allow for a differentiation of seven different household types. The corresponding resource use, expressed in Material Footprint per person and year, is calculated based on cradle-to-gate material flows of average household goods. Our results show that patterns of resource use are mainly driven by the use of fuel and electricity and the ownership of cars. The quantified Material Footprints correlate to higher social status and are also linked to city size, age and household size. Affluent, established and/or younger families living in rural areas typically show the highest amounts of durables and expenditures on non-durables, thus exhibiting the highest use of natural resources.

Carbon Footprints and Consumer Lifestyles: An Analysis of Lifestyle Factors and Gap Analysis by Consumer Segment in Japan

“…This could be partly explained by the energy requirement to operate these products, but also by the tendency for materialistic consumer lifestyles to have higher carbon footprints overall, as a sum of footprints across all consumption domains. The findings in this study on the impacts of materialistic lifestyles with multiple product ownership confirmed the relevance of ownership of household equipment [36]. To confirm the absence of multicollinearity, the variance influence factor (VIF) test of independent variables was used for each model, which revealed that none of the above models had multicollinearity problems based on the criteria of a VIF not greater than 10.…”

“…Households with this factor tended to use large amounts of fuel at home for heating but might also consume less meat and more fruit and vegetables. These underlying lifestyle factors confirmed the relevance of carbon-intensive leisure activities, car driving, home energy consumption, meat and dairy product consumption [10], and ownership and use of goods [36] for addressing environmental impacts of consumer lifestyles. The identified lifestyle factors were beyond a single product category or domain, as illustrated by a factor referring to car driving, leisure, and product consumption.…”

“…A recent study from Switzerland also applied the clustering method to identify major consumer segments and examined their characteristics in terms of environmental footprints [33]. Other compelling applications of cluster analysis include a study of dietary habits in Canada [34] and Hungary [35], and household appliances in Germany [36], but their scope is limited to particular consumption domains rather than being applicable to the environmental impact of lifestyles as a whole. Combining these two methods of factor analysis and cluster analysis was an approach taken by a previous comparative study, which concluded that using the results of exploratory factor analysis as inputs for cluster analysis improves the quality of clusters [37].…”

Informing Sustainable Consumption in Urban Districts: A Method for Transforming Household Expenditures into Physical Quantities

“…Furthermore, information from small-scale studies cannot be extrapolated to approximate broader consumption patterns. Some authors identify that it is important to provide households with information that can help them make more environmentally responsible choices, but certain groups may need more support or even incentives to adopt more sustainable lifestyles [15,16].…”

Measure or Management?—Resource Use Indicators for Policymakers Based on Microdata by Households

“…Its indicator Material Footprint can also be adapted to the currently suggested SDG 12 indicator with the same name. Recent methodological developments make use of improved LCA data [21][22][23][24][25].…”