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A reduction in working hours is being considered to tackle issues associated with ecological sustainability, social equity and enhanced life satisfaction - a so-called triple dividend. With respect to an environmental dividend, the authors analyse the time use rebound effects of reducing working time. They explore how an increase in leisure time triggers a rearrangement of time and expenditure budgets, and thus the use of resources in private households. Does it hold true that time-intensive activities replace resource-intensive consumption when people have more discretionary time at their disposal? This study on environmental issues is complemented by introducing the parameters of voluntary social engagement and individual life satisfaction as potential co-benefits of rebound effects. In order to analyse the first dividend, a mixed methods approach is adopted, enabling two models of time use rebound effects to be applied. First, semi-standardised interviews reveal that environmentally ambiguous substitutions of activities occur following a reduction in working hours. Second, estimates for Germany from national surveys on time use and expenditure show composition effects of gains in leisure time and income loss. For the latter, we estimate the marginal propensity to consume and the marginal propensity to time use. The results show that time savings due to a reduction in working time trigger relevant rebound effects in terms of resource use. However, both the qualitative and quantitative findings put the rebound effects following a reduction in working time into perspective. Time use rebound effects lead to increased voluntary social engagement and greater life satisfaction, the second and third dividends.
Das Ziel dieser Basisstudie ist es Ursachen für Rebound-Effekte und potentielle Gegenmaßnahmen aufzuzeigen. Zudem sollen Möglichkeiten zur Beobachtung und Verringerung von Rebound-Effekten in Living Labs beschrieben werden.
Dieses Arbeitspapier ist ein Ergebnis aus dem Arbeitspaket 1 "Bestandsaufnahme des Innovationsumfeldes für Living Labs" im Rahmen des Projektes "Living Labs in der Green Economy: Realweltliche Innovationsräume für Nutzerintegration und Nachhaltigkeit" (INNOLAB), das im Rahmen der Sozial-ökologischen Forschung zum Themenschwerpunkt "Nachhaltiges Wirtschaften" vom Bundesministerium für Bildung und Forschung gefördert wird.
Die Basisstudie stützt sich auf eine Literaturanalyse von ausgewählten Schlüsselstudien sowie auf fünf Experteninterviews und deren Inhaltsanalyse.
Es zeigt sich, dass sowohl technologische Innovationen als auch Verhaltensänderungen als Auslöser von Rebound-Effekten unterschieden werden. Von diesen Auslösern ausgehend, entstehen zunächst unmittelbare Effekte, die dann Rebound- Effekte über drei unterschiedliche Mechanismen bewirken können: über monetäre Effekte (also aufgrund von Geldeinsparungen), über Zeiteffekte (also aufgrund von Zeiteinsparungen) und über sozial-psychologische Effekte. Rebound-Effekte können sich durch die Reinvestition eingesparter Geld- und Zeitbudgets im Bedarfsfeld der ursprünglichen Einsparung (als direkte Rebound-Effekte) oder in einem anderen Bedarfsfeld (als indirekte Rebound-Effekte) ergeben, siehe nachfolgende Abbildung.
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.
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.
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.
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
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.