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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 "fuzzy front end" of innovation is argued to be crucial for the success and sustainability impact of a final product. Indeed, it is a promising area of focus in efforts to achieve the United Nations' 2015 Sustainable Development Goals (SDGs), which provide a globally accepted framework for sustainability. However, the usability of the 17 goals and the large number of sub-goals represent barriers to innovation practitioners. Moreover, this early innovation stage proves to be a challenge for corporate practitioners and innovators, largely due to the concept's intangible, qualitative nature and the lack of data. To help overcome these barriers, this article proposes a four-stage approach for structuring the innovation process using an online tool called the "SDG-Check", which help assess an innovator's sustainability orientation in the early phases of product and service development. It is a semi-quantitative tool to gather and combine assessments by experts involved in innovation processes with implications for the United Nations' SDGs. Furthermore, this article presents our first experiences in applying the SDG-Check based on three living lab innovation cases. The results indicate that the tools can support and inspire a dialogue with internal and external stakeholders with regards to sustainability considerations in the early design stages of product and service development.
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.
