It is important for the future of LCA, as one of the important techniques in environmental management, that LCA results become generally regarded as relevant, reliable, and uncontroversial.
To this end, LCAs must:
• be understood and perceived as a reasonable basis for decisions by the intended audience,
• be implemented into decision making and industrial practice without unnecessary controversy,
• communicate the reliability of their results in terms of uncertainty, based on an assessment of the data quality of the information used,
• be critically reviewed according to the ISO procedures at a high level of excellence.
Ecoinvent applies a method for estimation of default standard deviations for flow data from characteristics of these flows and the respective processes that are turned into uncertainty factors in a pedigree matrix, starting from qualitative assessments. The uncertainty factors are aggregated to the standard deviation. This approach allows calculating uncertainties for all flows in the ecoinvent database. In ecoinvent 2 the uncertainty factors were provided based on expert judgment, without (documented) empirical foundation. This paper presents (1) a procedure to obtain an empirical foundation for the uncertainty factors that are used in the pedigree approach and (2) a proposal for new uncertainty factors, received by applying the developed procedure. Both the factors and the procedure are a result of a first phase of an ecoinvent project to refine the pedigree matrix approach. A separate paper in the same edition, also the result of the aforementioned project, deals with extending the developed approach to other probability distributions than lognormal (Muller et al.).
Uncertainty is defined here simply as geometric standard deviation (GSD) of intermediate and elementary exchanges at the unit process level. This fits to the lognormal probability distribution that is assumed as default in ecoinvent 2, and helps to overcome scaling effects in the analysed data. In order to provide the required empirical basis, a broad portfolio of data sources is analysed; it is especially important to consider sources outside of the ecoinvent database to avoid circular reasoning. The ecoinvent pedigree matrix from version 2 is taken as a starting point, skipping the indicator “sample size” since it will not be used in ecoinvent 3. This leads to a pedigree matrix with five data quality indicators, each having five score values. The analysis is conducted as follows: for each matrix indicator and for each data source, indicator scores are set in relation to data sets, building groups of data sets that represent the different data quality indicator scores in the pedigree matrix. The uncertainty in each of the groups is calculated. The uncertainty obtained for the group with the ideal indicator score is set as a reference, and uncertainties for the other groups are set in relation to this reference uncertainty. The obtained ratio will be different from 1, it represents the unexplained uncertainty, additional uncertainty due to a lower data quality, and can be directly used as uncertainty factor candidates.
The developed approach was able to derive empirically based uncertainty factor candidates for the pedigree matrix in ecoinvent. Uncertainty factors were obtained for all data quality indicators and for almost all indicator scores in the matrix. The factors are the result of the first analysis of several data sources, further analyses and discussions should be used to strengthen their empirical basis. As a consequence, the provided uncertainty factors can change in future. Finally, a few of the qualitative score descriptions in the pedigree matrix left room for interpretation, making their application not ambiguous.
An empirical foundation for the uncertainty factors in the pedigree matrix overcomes one main argument against their use, which in turn strengthens the whole pedigree approach for quantitative uncertainty assessment in ecoinvent. This paper provides an approach to obtain an empirical basis for the uncertainty factors, and it provides also empirically based uncertainty factors, for indicator scores in the pedigree matrix. Basic uncertainty factors are not provided, it is recommended to use the factors from ecoinvent 2 for the time being. In the developed procedure, using GSD as the uncertainty measure is essential to overcome scaling effects; it should therefore also be used if the analysed data do not follow a lognormal distribution. As a consequence, uncertainty factors obtained as GSD ratios need to be translated to range estimators relevant for these other distributions. Formulas for this step are provided in a separate paper (Muller et al.). The work presented in this paper could be the starting point for a much broader study to provide a better basis for input uncertainty in LCA, not only in ecoinvent.
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Data used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format.
A new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach.
In order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited.
Providing formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.
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The impact assessment methods Eco-Indicator 99 (H), Stepwise2006, and ReCiPe2008 (H) are compared with respect to the relative and absolute importance that they assign to the different mid-point impact categories. The comparison is done by a common monetary valuation of the three endpoints that are common to the three methods: human well-being, nature, and resources. Land use, global warming, and respiratory inorganic pollutants together make up between 86% and 97% of the overall impacts compared in all three methods. The overall monetarized impacts amount to 30%, 28%, and 165% of the gross domestic product (GDP), respectively. Resource depletion, land use, and global warming explain 99.5% of the positive deviation of ReCiPe2008, relative to the other two methods. The main causes for these differences are investigated and discussed, pointing to possibly questionable calculations and assumptions, for example, regarding the nonsubstitutability of resources and the very long relaxation time for transformed forestland in the relatively new ReCiPe2008 method, which leads us to recommend users to be cautious and critical when interpreting the results. Sensitivity analysis is made for other cultural perspectives and normalization references.
Over the years, we have contributed to the ISO standardisation work in the following contexts:
Input–output tables (IOTs) are widely used in several types of analyses. Although born in an economic context, IOTs are increasingly used for the environmental impact assessment of product systems, e.g. in environmental policy analysis, and for several others such as the accounting of greenhouse gases.
However, the use in these contexts does not ensure the validity of the IOT as a consistent and robust multidisciplinary modeling tool in itself. It is in respect to certain basic requirements that IOTs should find their legitimacy. In this paper, we study their validity with respect to a well-established scientific law: the mass balance. Compliance with this basic balance is an important check for data consistency.
Following such a track, we focus specifically on monetary input–output tables and we reach the conclusion that IOTs can fail in respecting the basic balance laws whenever prices differ per purchaser. Therefore caution is needed because the estimations in terms of environmental pressures can be biased. The drawback lays in the use of homogeneous prices, which determines a discrepancy in physical units between what is used and what is asked for, within and between activities.
The role of a standard is to provide uniform rules for a procedure, so as to minimize or eliminate unnecessary variation in its performance, typically with the aim of reducing costs. The basic standard for performing life cycle assessment (LCA) was published by the International Organization for Standardization (ISO) as ISO 14040/41/42 in 1996 and reorganized, largely unchanged, into ISO 14040/44 in 2006.
In recent years, we have seen a proliferation of guidelines that interpret the basic ISO 14040/44 standards for LCA, either for a specific geography as, for example, the BPX 30-323 for France and the Product Environmental Footprint Guideline for the European Union, a specific sector with the so-called product category rules (PCRs) that seek to regulate the production and communication of LCA information for products within the product category, or a specific topic as in carbon or water footprints.
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Commonly, life cycle assessment (LCA), input-output analysis (IOA) and mass flow analysis (MFA) are seen as separate assessment tools each with specific application areas. Recent and ongoing EU 6th and 7th framework projects are creating and integrating several different national accounts enabling for a full integration of the above mentioned assessment tools. The following projects together have led to the creation of the, to date, most detailed and complete set of integrated model for LCA, IO analysis and MFA: FORWAST [1], EXIOPOL [2], CREEA [3] and DESIRE [4]. The integrated model, which is called the exiobase, is a global multi-regional hybrid IO database which is based on fully balanced monetary, mass and energy accounts (supply use tables). The database has several application areas for use as an assessment tool for policy development at different levels of scopes like product, corporate, project, program and policy impact assessment, at different levels of organization from individual company to government/ intergovernmental, and at different geographical scales from local to global.
