Lorenzo Benini

Rethinking the Area of Protection “Natural Resources” in Life Cycle Assessment

Life cycle impact assessment (LCIA) in classical life cycle assessment (LCA) aims at analyzing potential impacts of products and services typically on three so-called areas of protection (AoPs): Natural Environment, Human Health, and Natural Resources. This paper proposes an elaboration of the AoP Natural Resources. It starts with analyzing different perspectives on Natural Resources as they are somehow sandwiched in between the Natural Environment (their cradle) and the human-industrial environment (their application). Reflecting different viewpoints, five perspectives are developed with the suggestion to select three in function of classical LCA. They result in three safeguard subjects: the Asset of Natural Resources, their Provisioning Capacity, and their role in Global Functions. Whereas the Provisioning Capacity is fully in function of humans, the global functions go beyond provisioning as they include nonprovisioning functions for humans and regulating and maintenance services for the globe as a whole, following the ecosystem services framework. A fourth and fifth safeguard subject has been identified: recognizing the role Natural Resources for human welfare, either specifically as building block in supply chains of products and services as such, either with or without their functions beyond provisioning. But as these are far broader as they in principle should include characterization of mechanisms within the human industrial society, they are considered as subjects for an integrated sustainability assessment (LCSA: life cycle sustainability assessment), that is, incorporating social, economic and environmental issues.

Integrated assessment of environmental impact of Europe in 2010: data sources and extrapolation strategies for calculating normalisation factors

PurposeAssessing comprehensively the overall environmental impacts of a region remains a major challenge. Within life cycle assessment (LCA), this evaluation is performed calculating normalisation factors (NFs) at different scales. Normalisation represents an optional step of LCA according to ISO 14040/44 which may help in understanding the relative magnitude of the impact associated to a product when compared to a reference value. In order to enhance the robustness and comprehensiveness of NFs, this paper presents a methodology for building an extended domestic inventory of emission and resources extraction. The domestic inventory refers to emissions and extractions due to the processes located within a geographical region, Europe (EU 27), in 2010. A robust regional inventory is a fundamental element for supporting the calculation of global factors, often resulting form extrapolation and upscaling from regional ones.MethodsThe NFs for EU 27 in 2010 are based on extensive data collection and the application of extrapolation strategies for data gaps filling. The inventory is based on domestic emissions into air, water and soil and on resource extracted in EU, adopting a production-based approach. A hierarchy is developed for selection of data sources based on their robustness and quality. Data gap filling is based on several proxy indicators, specific for each impact category, capitalising existing statistics on pressure indicators (e.g. estimating ionising radiation emissions based on data of electricity production from nuclear power plants). To calculate NFs, the inventory is characterised using the International reference Life Cycle Data System (ILCD) Handbook (EC-JRC 2011a) midpoint indicators.Results and discussionThe resulting NFs present several added values compared to earlier normalisation exercises based on domestic inventories, namely more complete inventory, based on wide variety of sources; more comprehensive coverage of the flows within each impact category; overall evaluation of the robustness of the final figures; and robustness evaluation of the data sources. Contribution analysis shows that few flows (NOx, SOx, NH4, etc.) are driving the impacts of several impact categories, and the choice of the data sources is particularly crucial, as this may lead to differences in the NFs. A qualitative uncertainty assessment is reported for each impact category. Besides, in order to test the robustness of the NFs, a sensitivity analysis on key choices and assumptions has been advocated.Conclusion and outlookNFs may help identification of the relative magnitude of the impact. Nonetheless, several limitations still exist both at the inventory and at the impact assessment level, e.g., several inventory flows are not characterised as there is no characterisation factor available in current models. Those limitations should be clearly reported and understood by the users of normalisation factors in order to correctly interpret the results of their study as well as when regional NFs are used as basis for building global set of factor. The adoption of domestic NFs may, in fact, result in overestimating the relative magnitude of certain impacts, especially when those impacts are associated with traded goods from or outside the EU 27. Qualitative and quantitative assessment of uncertainties should be conducted from inventory to characterised results. Comprehensive testing is needed on the following: data sources, data mapping, regionalisation as well as models and system boundaries thereof. Strengths and limitations of the current study have implications also in other application contexts, as when indicators are needed to evaluate progress towards environmental policies goals. In fact, environmental impact indicators at regional scale often require data gap filling and estimation methodologies.

Characterization of raw materials based on supply risk indicators for Europe

PurposeThe concept of “resource criticality” has recently emerged as a policy priority and research subject, usually referred to the risk of supply disruption for mineral resources, due to economic and geopolitical reasons. Different methodologies for assessing critical raw materials (CRM) have been developed in governmental and research contexts, and the possibility of including the resource security aspect in life cycle assessment (LCA) has been claimed by different authors. The present paper aims at integrating CRM considerations in LCA in order to address socio-economic and strategic aspects related to resource use.MethodsIn this paper, we first explore how resource criticality could be interpreted, taking into account a wider perspective and a multidimensional concept. This includes the consideration of environmental and depletion aspects, in addition to the dominant interpretation, based on economic and geopolitical considerations. We then focus on the economic dimension of the resource criticality and propose the integration of this aspect in LCA through the use of characterization factors (CFs) based on the supply risk factors for Europe. Four different methodological options for resource security CFs are tested in the impact assessment of 50 processes and products. These options include the following: supply risk factors as such; two exponential functions of the supply risk factors, aimed at increasing the variability of the dataset, and the ratio between supply risk and production data, which reflect the size of the market, giving more importance to the materials used in small amounts in products and applications (like, e.g. specialty metals, that are often perceived as critical).Results and discussionThe results show how the impact depends on the supply risk factors or on the mass depending on the algorithm used. Even if there is no objective way to establish how much importance should be given to one aspect or the other, we conclude that the use of the factors “supply risk/production” results might better reflect CRM importance and therefore could be used in LCA for an assessment of resource security impact for EU. Applying an exponent to the supply risk factors, the values are spread on a wider range and it is possible to spot the CRM among the resources within an inventory.ConclusionsThe choice of the indicator is based rather on how important is the need of identifying a CRM in the inventory, e.g. in order to optimize CRM use, explore substitution options and enhance recovery in waste management.

Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

PurposeNormalisation 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.Materials and methodsFive 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).Results and discussionThe 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.ConclusionsThe 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).