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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.
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.
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.