The concept Material Input per Service Unit (MIPS) was developed 20 years ago as a measure for the overall natural resource use of products and services. The material intensity analysis is used to calculate the material footprint of any economic activities in production and consumption. Environmental assessment has developed extensive databases for life cycle inventories, which can additionally be adopted for material intensity analysis. Based on practical experience in measuring material footprints on the micro level, this paper presents the current state of research and methodology development: it shows the international discussions on the importance of accounting methodologies to measure progress in resource efficiency. The MIPS approach is presented and its micro level application for assessing value chains, supporting business management, and operationalizing sustainability strategies is discussed. Linkages to output-oriented Life Cycle Assessment as well as to Material Flow Analysis (MFA) at the macro level are pointed out. Finally we come to the conclusion that the MIPS approach provides relevant knowledge on resource and energy input at the micro level for fact-based decision-making in science, policy, business, and consumption.
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.
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 Wuppertal Institute conducted an impact analysis of the NRW Sustainability Bond #4 of 2018 on behalf of the State Government of North Rhine-Westphalia (NRW). The most recent bond has a volume of EUR 2.025bn, a term of 10 years and consists of 52 eligible projects from the State's 2017 general budget (sustainable value-added was confirmed in a second party opinion by oekom research1). This report analyses the contribution of the bond to climate mitigation, sustainable land use and social impacts. It also includes information on the impacts of the previous three bonds (NRW Sustainability Bond #1 to #3).
Die Modellierungs-Studie "Reparaturkosten-Empfehlung" wurde vom Wuppertal Institut im Auftrag der Verbraucherzentrale Nordrhein-Westfalen erstellt. Die Studie zielt auf die Berechnung von maximalen Reparaturkosten, die für eine Reparatur aus finanzieller Sicht sinnvoll für Verbraucherinnen und Verbraucher sind. Zusätzlich wird die ökologische Vorteilhaftigkeit von Reparaturen beispielhaft diskutiert.