Community Research and Development Information Service - CORDIS

H2020

RELIEF Report Summary

Project ID: 641459
Funded under: H2020-EU.1.3.1.

Periodic Reporting for period 1 - RELIEF (RELIability of product Environmental Footprints)

Reporting period: 2015-01-01 to 2016-12-31

Summary of the context and overall objectives of the project

Life Cycle Assessment (LCA) is widely accepted as a sophisticated tool to support decisions on environmental footprint measurement and reduction and in making unbiased product comparisons. However, the reliability of state-of-the-art LCA is often unknown. The main goal of the European Industrial Doctorate proposal RELIEF, a cooperation between Radboud University and Unilever, is to train early stage researchers in how to assess and improve the reliability of product environmental footprints. RELIEF addresses the operationalization of both uncertainty and variability, bringing the accuracy of LCA to a higher level. The results will lead to the development and testing of an open source environmental decision support tool to optimise the environmental footprint assessment of a wide variety of products (see Figure 1).

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

In the first phase of the RELIEF project, new methods and tools were developed to quantify uncertainty and variability in the following environmental analyses:
- The variability in Greenhouse Gas (GHG) footprints of washing clothes associated with various detergent-related and washing machine-related parameters was quantified across 23 European countries via Monte Carlo simulation. Between-country variability is mainly caused by the differences in the background electricity mixes of the countries, while within-country variability is to a large extent due to the behavioral parameters.
- The influence of bio-physical conditions on GHG footprints of field tomato production in a number of countries around the globe over three years (i.e. 2013, 2014, 2015) was quantified using multi-level variability and regression analysis. Of the biophysical variables, crop area has the strongest negative relationship with GHG footprints. We, however, found that the majority of the variance is contributed by the differences between countries and farms which may be related to differences in farm-management practices.
- Chemical emissions from wastewater treatment plants (WWTPs) were estimated with a newly developed methodology, based on consumer use surveys of personal care products (PCPs). The methodology was applied to estimate spatially explicit chemical emissions from WWTPs related to the use of PCPs in four countries (US, France, Netherlands, South Korea) and quantifies the uncertainty via Monte Carlo simulation. Spatial representation of chemical emissions identified large point discharge sources with confidence intervals spreading up to 5 times around the mean value.
- Mean annual stream flows (MAF) were predicted at the global scale, which is essential for assessments of global water supply, ecosystem integrity and water footprints. To this end, a hydro-statistical model was developed via linear regression, explaining about 90% of the variance in MAF, based on catchment area and catchment-average mean annual precipitation, air temperature, slope and elevation.
- To develop effective strategies to minimize biodiversity impacts resulting from consumption, the terrestrial biodiversity footprints of 140 countries in 2011 resulting from global agricultural land use were quantified. The results of this analysis not only highlights the role of consumption in contributing to global biodiversity loss, but they also allow us to identify the pathways in the global economy that are contributing most to this decline.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We showed how to apply methods to quantify uncertainty and variability in environmental analyses in practice. Our results provide new insights in the main drivers of uncertainty and variability. With these findings RELIEF enhances the reliability and robustness of decision making in sustainable product development. RELIEF also provides the analytical basis for supporting the credibility and acceptability of product environmental footprinting.

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