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Das Vorhaben analysiert 1.) die Argumente verschiedener Positionen im Wachstumsdiskurs und formuliert eine idealtypische "vorsorgeorientierte Postwachstumsposition". Er präsentiert zudem Ursachen von Wirtschaftswachstum und identifiziert gesellschaftliche Bereiche, deren Funktion vom Wirtschaftswachstum abhängen könnte. Darüber hinaus werden Reformvorschläge diskutiert, um diese Wachstumsabhängigkeit zu verringern. Das Vorhaben untersucht 2.) die Relevanz der Postwachstumsdebatte für Ressourcenpolitik und eine entsprechende Instrumentierung. Außerdem werden 3.) konstitutive Kernelemente einer nachhaltigen (Postwachstums-)Gesellschaft bestimmt. Das Vorhaben setzt damit Impulse zur gesellschaftlichen Debatte über die Ausgestaltung und Instrumentierung von Transformationspfaden für "gesellschaftliches Wohlergehen innerhalb planetarer Grenzen".
Recent research on the natural resource use of private consumption suggests a sustainable Material Footprint of 8 tons per capita by 2050 in industrialised countries. We analyse the Material Footprint in Germany from 2015 to 2020 in order to test whether the Material Footprint decreases accordingly. We studied the Material Footprint of 113,559 users of an online footprint calculator and predicted the Material Footprint by seasonally decomposed autoregressive (STL-ARIMA) and exponential smoothing (STL-ETS) algorithms. We find a relatively stable Material Footprint for private consumption. The overall Material Footprint decreased by 0.4% per year between 2015 and 2020 on average. The predictions do not suggest that the Material Footprint of private consumption follows the reduction path of 3.3% per year that will lead to the sustainable consumption of natural resource
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.
Footprint calculators are efficient tools to monitor the environmental impact of private consumption. We present the results of an analysis of data entered into an online Material Footprint calculator undertaken to identify the socioeconomic drivers of the Material Footprint in different areas of consumption, from housing to holidaymaking. We developed regression models to reveal (1) the impact of socioeconomic characteristics on Material Footprints of private households and (2) correlations between the components of Material Footprints for different arrays of consumption. Our results show that an increasing Material Footprint in one array of consumption comes with an increasing Material Footprint in all other arrays, with the exception of housing and holidaymaking. The socioeconomic characteristics of users have a significant impact on their Material Footprints. However, this impact varies by the array of consumption. Households only exhibit generally bigger Material Footprints as a result of higher incomes and larger dwellings. We conclude that indicators which strive to monitor resource efficiency should survey disaggregated data in order to classify the resource use to different population groups and arrays of consumption.
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.
In the face of growing popularity of eco-feedback innovations, recent studies draw attention to the relevance of the human factor for a more effective design of eco-feedback. This paper explores these challenges more deeply by employing a mixed methods approach. We provide in-situ insights from a Living Lab experiment on the effect of smart home systems and traffic light feedback on heating energy consumption in private households. Our results from an interrupted time series analysis of logged data on indoor room temperature, CO2 concentration and consumption of natural gas show that the interventions do not affect heating as expected, neither for automating behaviour via high-tech smart home systems nor via low-tech traffic light feedback. Smart home systems do not promise a significant reduction of heating energy consumption and a traffic light feedback on indoor air quality does not lead to a reaction of indoor CO2 concentrations, but may reduce heating energy consumption. Qualitative interviews on heating practices of participants suggests that comfort temperatures, lack of competences and inert heating systems do override expected effects of the feedback interventions. We propose that high-tech smart home systems should carefully consider the handling competences of users. Low-tech feedback products on the other hand should by design stronger address user experience factors like comfort temperatures.
The effectiveness of sustainable product and service innovations is often restricted by limited market acceptance or unexpected consumption patterns. The latter includes rebound effects, which occur when resources liberated by savings are used for further consumption. Recently emerging research from the Living Lab is striving to address and anticipate challenges in innovation design by integrating users in prototyping and field testing product and service innovations. The paper presents findings from a literature review on rebound effects and expert interviews identifying methods to monitor and measures to mitigate rebound effects in early innovation design via Living Lab research.
We find that monitoring and mitigating rebound effects in Living Lab research includes technological and behavioural triggers as well as socio-psychological and time use effects in addition to economic re-spending effects. The experts have confirmed that Living Labs contain the potential to observe complex demand systems of users within experimental designs, encompassing indirect rebound effects in terms of expenditure as well as time use. In this respect, Living Lab research can facilitate support for sustainable innovations, which aim to encourage changes in consumer behaviour, considering re-spending and time use effects simultaneously.
The transformation processes towards a sustainable development are complex. How can science contribute towards new solutions and ideas leading to change in practice? The authors of this book discuss these questions along the energy transition in the building sector.
A transformative research that leaves the neutral observer position needs appropriate concepts and methods: how can knowledge from different disciplines and from practice be integrated in order to be able to explain and understand complex circumstances and interrelations? What role do complex (agent-based) models and experiments play in this respect? Which mix of methods is required in transformative science in order to actively support the actors in transformation processes?
Theses questions are illustrated by the example of the BMBF funded project "EnerTransRuhr".