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Sustainable consumption policies affect households differently, in particular when they are confronted with limitations on income, time or freedom of movement (e.g. driving to work). And although it is possible to assess either the average or individual material footprint (per capita or via surveys), we lack methods to describe different types of households, their lifestyles and footprints in a representative manner.
We explore possibilities to do so in this article. Our interest lies in finding an applicable method that allows us to describe the footprint of households regarding their socio-demographic characteristics but also find the causes consumption behaviour. This type of monitoring would enable us to tailor policies for sustainable consumption that respect people's needs and restrictions.
Mit der Nachhaltigkeitsanleihe NRW erschließt das Land das international wachsende Segment des nachhaltigen Investments für die Kreditfinanzierung des Landeshaushalts. Ziel der vorliegenden Studie ist es, die Bewertung positiver Nachhaltigkeitseffekte für die in 2016 emittierte Nachhaltigkeitsanleihe NRW zu erproben und Handlungsempfehlungen zur Bewertung zukünftiger Nachhaltigkeitsanleihen zu entwickeln. Für eine umfassende Nachhaltigkeitsanalyse ist aufgrund der Vielschichtigkeit der finanzierten Projekte und fehlender Standards zur Bewertung sozialer Kriterien eine umfangreiche Untersuchung erforderlich, wodurch sie für eine regelmäßige und standardisierte Wirkungsanalyse zukünftiger Anleihen nicht in Frage kommt. Stattdessen verfolgt die vorliegende Studie einen pragmatischen Ansatz zur Bewertung. Der Fokus liegt auf der Evaluation der potenziellen Treibhausgas-Minderung (THG-Minderung), da deren Bewertung sich derzeit als Standard für eine Wirkungsanalyse solcher Anleihen abzeichnet. Darüber hinaus werden die nächsten Schritte für eine Weiterentwicklung der Methodik und des Indikatorensets im Rahmen zukünftiger Wirkungsanalysen aufgezeigt.
Ziel der vorliegenden Studie ist es, positive Nachhaltigkeitseffekte für die in 2017 emittierte Nachhaltigskeitsanleihe NRW zu bewerten. Die Studie baut dabei auf der Wirkungsanalyse der zweiten Nachhaltigskeitsanleihe NRW auf. Diese Studie war auf die Abschätzung von Treibhausgas-Minderungspotenzialen (THG-Minderungen) fokussiert. Der Fokus der vorliegenden Studie liegt ebenfalls auf der Evaluation der potenziellen THG-Minderung, da deren Bewertung sich derzeit als Standard für eine Wirkungsanalyse solcher Anleihen abzeichnet.
Im Forschungsprojekt "Landscaping" untersuchte das Wuppertal Institut die für Nordrhein-Westfalen aus heutiger Sicht denkbaren Technologieansätze, die dafür nötigen politischen Rahmenbedingungen sowie mögliche Innovationen entlang der Wertschöpfungsketten. Bestandteil des Berichts sind Steckbriefe, in denen die möglichen Technologien für treibhausgasneutrale Industrieprozesse samt offener Forschungsfragen und Infrastrukturbedarfe dargestellt sind. Das Projekt entstand im Auftrag des Ministeriums für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen.
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 implementation of energy efficiency improvement actions not only yields energy and greenhouse gas emission savings, but also leads to other multiple impacts such as air pollution reductions and subsequent health and eco-system effects, resource impacts, economic effects on labour markets, aggregate demand and energy prices or on energy security. While many of these impacts have been studied in previous research, this work quantifies them in one consistent framework based on a common underlying bottom-up funded energy efficiency scenario across the EU. These scenario data are used to quantify multiple impacts by energy efficiency improvement action and for all EU28 member states using existing approaches and partially further developing methodologies. Where possible, impacts are integrated into cost-benefit analyses. We find that with a conservative estimate, multiple impacts sum up to a size of at least 50% of energy cost savings, with substantial impacts coming from e.g., air pollution, energy poverty reduction and economic impacts.
The Wuppertal Institute conducted an impact analysis of the NRW sustainability bond #5 of 2019 on behalf of the State government of North Rhine-Westphalia (NRW). The most recent bond has a volume of EUR 2.25 bn, a term of 15 years and consists of 52 eligible projects from the State's 2018 general budget (sustainable value-added was confirmed in a second party opinion by ISS-oekom). 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 four bonds (NRW sustainability bond #1 to #4).
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).
Causal strands for social bonds : a case study on the credibility of claims from impact reporting
(2022)
The study investigates if causal claims based on a theory-of-change approach for impact reporting are credible. The authors use their most recent impact report for a Social Bond to show how theory-based logic models can be used to map the sustainability claims of issuers to quantifiable indicators. A single project family (homeownership loans) is then used as a case study to test the underlying hypotheses of the sustainability claims. By applying Bayes Theorem, evidence for and against the claims is weighted to calculate the degree to which the belief in the claims is warranted. The authors found that only one out of three claims describe a probable cause–effect chain for social benefits from the loans. The other two claims either require more primary data to be corroborated or should be re-defined to link the intervention more closely and robustly with the overarching societal goals. However, all previous reported indicators are below the thresholds of the most conservative estimates for fractions of beneficiaries in the paper at hand. We conclude that the combination of a Theory-of-Change with a Bayesian Analysis is an effective way to test the plausibility of sustainability claims and to mitigate biases. Nevertheless, the method is - in the presented form - also too elaborate and time-consuming for impact reporting in the sustainable finance market.