Refine
Year of Publication
Document Type
- Part of a Book (74)
- Report (54)
- Peer-Reviewed Article (43)
- Working Paper (40)
- Contribution to Periodical (31)
- Conference Object (17)
- Book (9)
Der Club of Wuppertal
(2005)
Domestic sustainability innovations are considered to play a key role for pathways to sustainable consumption. The paper shows how open innovation processes can lead to such sustainable innovations, by means of an experimental and interactive infrastructure. It presents how – based on results of the LivingLab project conducted at the Wuppertal Institute within a European Consortium (Lead TU Delft) - currently an extended Sustainable LivingLab approach is developed and applied in two joint research projects at national and international level. To conceptualise this approach, we refer to recent proceedings in innovation and sustainability research, i.e. practice theory to analyse sustainable product design. Focusing on technical solutions and individual behaviour while assuming people's needs as fixed entities, disregards the dynamics of everyday practices in which technologies themselves create needs. Therefore, the consumer's position should be strengthened through userdriven innovation. LivingLabs are combined lab-/household systems, which put the user, i.e. the home occupant, and value chain related actors (producer, handicraft, etc.) on centre stage in the innovation process. We introduce its research agenda and the Three Phases Model of research. We hypothesise that at the end of this userintegrated innovation process developed products have a higher chance of successful diffusion. To illustrate this, we show how the LivingLab infrastructure is employed for the German InnovationCity Ruhr and how it can promote the development of user-centred sustainable consumption strategies.
Green Information Systems in general, and footprint calculators in particular, are promising feedback tools to assist people in adopting sustainable behaviour. Therefore, a Material Footprint model for use in an online footprint calculator was developed by identifying the most important predictors of the Material Footprint of the calculator's users. By means of statistical learning, the analysis revealed that 22 of the 95 predictors identified accounted for 74% of the variance in Material Footprints. Ten predictors out of the 95, mainly from the mobility domain, were capable of showing a prediction accuracy of 61%. The authors conclude that 22 predictors from the areas of mobility, housing and nutrition, as well as sociodemographic information, accurately predict a person's Material Footprint. The short and concise Material Footprint model may help developers and researchers to enhance their information systems with additional items while ensuring the data quality of such applications.
The paper describes patterns of resource use related to German households' equipment. Using cluster analysis and material flow accounting, 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 and the related household energy use. 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 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.
Measure or management? : Resource use indicators for policymakers based on microdata by households
(2018)
Sustainable Development Goal 12 (SDG 12) requires sustainable production and consumption. One indicator named in the SDG for resource use is the (national) material footprint. A method and disaggregated data basis that differentiates the material footprint for production and consumption according to, e.g., sectors, fields of consumption as well as socioeconomic criteria does not yet exist. We present two methods and its results for analyzing resource the consumption of private households based on microdata: (1) an indicator based on representative expenditure data in Germany and (2) an indicator based on survey data from a web tool. By these means, we aim to contribute to monitoring the Sustainable Development Goals, especially the sustainable management and efficient use of natural resources. Indicators based on microdata ensure that indicators can be disaggregated by socioeconomic characteristics like age, sex, income, or geographic location. Results from both methods show a right-skewed distribution of the Material Footprint in Germany and, for instance, an increasing Material Footprint with increasing household income. The methods enable researchers and policymakers to evaluate trends in resource use and to differentiate between lifestyles and along socioeconomic characteristics. This, in turn, would allow us to tailor sustainable consumption policies to household needs and restrictions.
We present the results of a regression analysis of a large-scale integrated user online application that surveys natural resource use and subjective well-being in Germany. We analyse more than 44,000 users who provided information on their natural resource consumption (material footprint) as well as their personal socio-economic and socio-psychological characteristics. We determine an average material footprint of 26 tonnes per person per year. In addition, we endeavour to determine how much environment humans need by regressing natural resource use as well as relevant socio-economic and socio-psychological features on subjective well-being. We establish a slightly negative correlation between subjective well-being and material footprints. A higher material footprint is associated with lower subjective well-being. We conclude that consumer policies seeking to promote sustainable behaviour should highlight the fact that a lower material footprint may result in greater subjective well-being.
