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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.
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".
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.