Refine
Document Type
- Peer-Reviewed Article (12)
- Report (7)
- Conference Object (3)
- Part of a Book (2)
- Working Paper (2)
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).
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).
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.
Improvements in energy efficiency have numerous impacts additional to energy and greenhouse gas savings. This paper presents key findings and policy recommendations of the COMBI project ("Calculating and Operationalising the Multiple Benefits of Energy Efficiency in Europe").
This project aimed at quantifying the energy and non-energy impacts that a realisation of the EU energy efficiency potential would have in 2030. It covered the most relevant technical energy efficiency improvement actions in buildings, transport and industry.
Quantified impacts include reduced air pollution (and its effects on human health, eco-systems), improved social welfare (health, productivity), saved biotic and abiotic resources, effects on the energy system and energy security, and the economy (employment, GDP, public budgets and energy/EU-ETS prices). The paper shows that a more ambitious energy efficiency policy in Europe would lead to substantial impacts: overall, in 2030 alone, monetized multiple impacts (MI) would amount to 61 bn Euros per year in 2030, i.e. corresponding to approx. 50% of energy cost savings (131 bn Euros).
Consequently, the conservative CBA approach of COMBI yields that including MI quantifications to energy efficiency impact assessments would increase the benefit side by at least 50-70%. As this analysis excludes numerous impacts that could either not be quantified or monetized or where any double-counting potential exists, actual benefits may be much larger.
Based on these findings, the paper formulates several recommendations for EU policy making:
(1) the inclusion of MI into the assessment of policy instruments and scenarios,
(2) the need of reliable MI quantifications for policy design and target setting,
(3) the use of MI for encouraging inter-departmental and cross-sectoral cooperation in policy making to pursue common goals, and
(4) the importance of MI evaluations for their communication and promotion to decision-makers, stakeholders, investors and the general public.
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.
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.
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.
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.
Nowadays, the main impetus to apply additive manufacturing (AM) of metals is the high geometric flexibility of the processes and its ability to produce pilot or small batch series. In contrast, resource and energy intensities are often not considered as constraints, even though the turnout of additive manufacturing is high, at least compared to chip removing processes.
The study at hand analyses the material characteristics and environmental impacts of a hose nozzle as an example of a commercial product of simple geometry. The production routes turning (conventional manufacturing) and laser beam melting (additive manufacturing) are compared to each other in terms of natural resource use, climate change potential and primary energy demand. It is found, that the product shows a lower demand for natural resources when produced via AM, but higher carbon emissions and energy demand when using a steel, that is mainly (80%) produced from high-alloyed steel scrap. However, different case studies during the sensitivity analyses showed that a number of factors highly influence the results: the steel source as well as the source of electricity play a major role in determining the environmental performance of the production routes. The authors also found that other production processes (here cold forging of tubes) might be an eco-friendly alternative to both routes, if feasible from an economic point of view.
In regard to the material characteristics, experimental testing revealed that the material advantages of AM produced hose nozzles (in particular higher yield strength) are reduced after a solution heat treatment is applied to the as-produced material, in order to increase corrosion resistance. However, products that do not require this production step might benefit from the higher yield strength, as a lower wall thickness could be realised.
