Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Abstract

Normalisation is an optional step of a life cycle assessment, supporting the interpretation of the results of the characterization in terms of relative environmental relevance of the impacts. Normalisations factors (NFs) are calculated as results of regional/global inventories of emission and resources characterized through impact assessment methods. Several methodological assumptions are needed for building the inventory, as presented in Sala et al. (Int J Life Cycle Assess 20:1568–1585, 2015). NFs for EU27 have been calculated for 2010 compliant with ILCD recommendations defining a methodological approach for sources selection and the use of proxy indicators. Qualitative and quantitative uncertainty evaluation is needed for assessing the robustness of final figures. The present work aims at quantifying the influence of key methodological choices on the variability of the normalisation factors. Five sources of uncertainty have been analyzed in this work: (F1) the selection of the sources of data; (F2) the classification of data as life cycle inventory (LCI) elementary flows; (F3) the classification of substances for characterization; (F4) the specification of the emission compartments and (F5) the use of spatially differentiated characterization factors. The sensitivity of the normalization factors to such uncertainties were assessed through a global sensitivity method, for the impact categories acidification (ACID), terrestrial eutrophication (ET), marine eutrophication (EM), photochemical ozone formation (POF), respiratory inorganics/particulate matter (RIPM) and water depletion (WD). The results demonstrate the need of thorough uncertainty and sensitivity analysis for supporting the use of NFs. Uncertainties are high for the impact categories respiratory inorganics (RIPM) and water depletion (WD) and improvement of these categories is a priority. For RIPM this is explained by the high variability amongst the characterization factors for PM2.5 and PM10, together with the contextual lack of information about the height of the emission source in the inventory. For WD this is explained by variability of the regionalized factors available within the ILCD. For ACID, ET and EM the uncertainty is relatively low and generally completely led by factors F1 and F2. However, regionalized characterization factors were not tested for ACID and ET, therefore the results might be underestimating the overall uncertainty. For what concerns POF, the main source of uncertainty—amongst those included in the analysis—is the selection of the data source. Overall, improvements in the spatial resolution of the inventory are needed in order to confine uncertainty. This would allow the use of characterization factors specific for emission source typology and geographical location. The uncertainty associated with the methodological choices made for calculating normalization factors (Sala et al. in Int J Life Cycle Assess 20:1568–1585, 2015) was assessed. Generally, the value calculated by Sala et al. (Int J Life Cycle Assess 20:1568–1585, 2015) compare well against average and median values estimated in this analysis for ACID, ET, EM and POF. Instead, the impact categories RIPM and WD show different patterns. For RIPM, although the average value is very similar to the normalization factor reported by Sala et al. (Int J Life Cycle Assess 20:1568–1585, 2015), the median value is far lower. For what concerns WD, the median value is much higher. Future improvements of the normalization factors should therefore prioritize the development of more detailed inventories of emissions by including higher substance resolution, height of emission as well as the use of spatially differentiated characterization factors. To support the interpretation of normalized results, we recommend that the normalization factors from Sala et al. (Int J Life Cycle Assess 20:1568–1585, 2015) are applied together with two additional sets of normalization factors i.e. the ‘median values’ and the set of ‘average + standard deviation’ values, so to better capture their uncertainty. Similarly, the interpretation of the results should build on the qualitative estimates of robustness provided by Sala et al. (Int J Life Cycle Assess 20:1568–1585, 2015).