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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.
The food system plays a crucial role in mitigating climate change. Even if fossil fuel emissions are halted immediately, current trends in global food systems may prevent the achieving of the Paris Agreement's climate targets. The high degree of variability and uncertainty involved in calculating diet-related greenhouse gas emissions limits the ability to evaluate reduction potentials to remain below a global warming of 1.5 or 2 degrees. This study assessed Western European dietary patterns while accounting for uncertainty and variability. An extensive literature review provided value ranges for climate impacts of animal-based foods to conduct an uncertainty analysis via Monte Carlo simulation. The resulting carbon footprints were assessed against food system-specific greenhouse gas emission thresholds. The range and absolute value of a diet carbon footprint become larger the higher the amount of products with highly varying emission values in the diet. All dietary pattern carbon footprints overshoot the 1.5 degrees threshold. The vegan, vegetarian, and diet with low animal-based food intake were predominantly below the 2 degrees threshold. Omnivorous diets with more animal-based product content trespassed them. Reducing animal-based foods is a powerful strategy to decrease emissions. However, further mitigation strategies are required to achieve climate goals.
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.
More and more companies are announcing their intention to become climate-neutral and numerous companies already offer climate-neutral products or services: From climate-neutral parcel delivery to air travel. But what exactly do the companies' net-zero targets mean? Is the target set ambitious? And what role does offsetting play, i.e., purchasing carbon credits that are accounted against the company's own climate target? The approaches behind the proclaimed targets are often difficult to understand. Against this background, this Zukunftsimpuls provides ten recommendations for the definition and implementation of neutrality targets. Among other things, the authors advocate the use of a robust database as the basis for net-zero targets, emphasize the importance of transparent communication, and highlight the role that offsetting should play. Purchased carbon credits should make as limited a contribution as possible for meeting climate targets and should only be used to offset emissions that cannot be reduced or avoided. More generally, net-zero targets should not be made the sole criterion for ambitious climate strategies. Rather, they are a building block of a much more comprehensive strategy of corporate climate action.
Financial institutions play a crucial role in achieving the 2015 Paris Climate Agreement. They can manage capital flows for financing the required transformation towards a decarbonized industry. Currently established policy programs and regulations at European and national level increasingly address financial institutions to make their climate warming impact measurable and transparent. However, required science-based assessment methods have not been sufficiently developed so far.
This paper discusses methodological opportunities and challenges for measuring carbon footprints of financial institutions. Based on a scientific case study undertaken with the German GLS Bank, the authors introduce an innovative method for quantifying greenhouse gas emissions from a bank's asset with a focus on loans. The authors apply an input/output database to calculate greenhouse gas (GHG) intensities and allocate them with bank's loans and investments.
Moreover, the paper provides insights of calculating avoided GHG emissions initiated by a bank's investment and loans. In conclusion, a high degree of consistent and standardized assessment methods and guidelines need to be developed and applied to promote comparability and transparency.
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.
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).