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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.
Die Sustainable Development Goals (SDGs) schlagen zur Indikation verantwortungsvoller Konsum- und Produktionsstrukturen bzw. zum nachhaltigen Management und der effizienten Nutzung natürlicher Ressourcen den Material Footprint pro Kopf vor. Zudem sollen SDG-Indikatoren prinzipiell in der Lage sein, zwischen verschiedenen Bevölkerungsgruppen (etwa nach Einkommen oder Alter) unterscheiden zu können. Wir stellen einen Indikator aus der Nachhaltigkeitsstrategie NRW zum Ressourcenverbrauch des privaten Konsums auf der Grundlage von Mikrodaten vor. Der größte Ressourcenverbrauch der privaten Haushalte in NRW bleibt Wohnung, Nahrungsmittel und Verkehr vorbehalten. Dabei ist zwischen 2003 und 2013 die größte Steigerung des Ressourcenverbrauchs in Post und Telekommunikation zu verzeichnen, wobei sich insgesamt der Ressourcenverbrauch leicht reduziert hat. Der Indikator zum Ressourcenverbrauch der privaten Haushalte erfüllt die Anforderungen an Indikatoren der Sustainable Development Goals sowie der Nachhaltigkeitsstrategie des Landes NRW. Gleichzeitig empfehlen wir eine weitere Disaggregierung des Material Footprints nicht nur nach Bevölkerungsgruppen, sondern auch in Gütergruppen auf der Basis von Lebenszyklusanalysen.
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 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".