Pascal Lesage

Considering time in LCA: dynamic LCA and its application to global warming impact assessments.

The lack of temporal information is an important limitation of life cycle assessment (LCA). A dynamic LCA approach is proposed to improve the accuracy of LCA by addressing the inconsistency of temporal assessment. This approach consists of first computing a dynamic life cycle inventory (LCI), considering the temporal profile of emissions. Then, time-dependent characterization factors are calculated to assess the dynamic LCI in real-time impact scores for any given time horizon. Although generally applicable to any impact category, this approach is developed here for global warming, based on the radiative forcing concept. This case study demonstrates that the use of global warming potentials for a given time horizon to characterize greenhouse gas emissions leads to an inconsistency between the time frame chosen for the analysis and the time period covered by the LCA results. Dynamic LCA is applied to the US EPA LCA on renewable fuels, which compares the life cycle greenhouse gas emissions of different biofuels with fossil fuels including land-use change emissions. The comparison of the results obtained with both traditional and dynamic LCA approaches shows that the difference can be important enough to change the conclusions on whether or not a biofuel meets some given global warming reduction targets.

Biogenic Carbon and Temporary Storage Addressed with Dynamic Life Cycle Assessment

A growing tendency in policy making and carbon footprint estimation gives value to temporary carbon storage in biomass products or to delayed greenhouse gas (GHG) emissions. Some life cycle‐based methods, such as the British publicly available specification (PAS) 2050 or the recently published European Commissions International Reference Life Cycle Data System (ILCD) Handbook, address this issue. This article shows the importance of consistent consideration of biogenic carbon and timing of GHG emissions in life cycle assessment (LCA) and carbon footprint analysis. We use a fictitious case study assessing the life cycle of a wooden chair for four end‐of‐life scenarios to compare different approaches: traditional LCA with and without consideration of biogenic carbon, the PAS 2050 and ILCD Handbook methods, and a dynamic LCA approach. Reliable results require accounting for the timing of every GHG emission, including biogenic carbon flows, as soon as a benefit is given for temporarily storing carbon or delaying GHG emissions. The conclusions of a comparative LCA can change depending on the time horizon chosen for the analysis. The dynamic LCA approach allows for a consistent assessment of the impact, through time, of all GHG emissions (positive) and sequestration (negative). The dynamic LCA is also a valuable approach for decision makers who have to understand the sensitivity of the conclusions to the chosen time horizon.

Empirically based uncertainty factors for the pedigree matrix in ecoinvent

PurposeEcoinvent 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.).MethodsUncertainty 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.Results and discussionThe 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.Conclusions and perspectivesAn 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.

Life Cycle Attribute Assessment

Practitioners of life cycle assessment (LCA) have recently turned their attention to social issues in the supply chain. The United Nations life cycle initiatives social LCA task force has completed its guidelines for social life cycle assessment of products, and awareness of managing upstream corporate social responsibility (CSR) issues has risen due to the growing popularity of LCA. This article explores one approach to assessing social issues in the supply chainlife cycle attribute assessment (LCAA). The approach was originally proposed by Gregory Norris in 2006, and we present here a case study. LCAA builds on the theoretical structure of environmental LCA to construct a supply chain model. Instead of calculating quantitative impacts, however, it asks the question What percentage of my supply chain has attribute X?- X may represent a certification from a CSR body or a self-defined attribute, such as is locally produced.- We believe LCAA may serve as an aid to discussions of how current and popular CSR indicators may be integrated into a supply chain model. The case study demonstrates the structure of LCAA, which is very similar to that of traditional environmental LCA. A labor hours data set was developed as a satellite matrix to determine number of worker hours in a greenhouse tomato supply. Data from the Quebec tomato producer were used to analyze how the company performed on eight sample LCAA indicators, and conclusions were drawn about where the company should focus CSR efforts.

Assessing temporary carbon sequestration and storage projects through land use, land-use change and forestry: comparison of dynamic life cycle assessment with ton-year approaches

In order to properly assess the climate impact of temporary carbon sequestration and storage projects through land-use, land-use change and forestry (LULUCF), it is important to consider their temporal aspect. Dynamic life cycle assessment (dynamic LCA) was developed to account for time while assessing the potential impact of life cycle greenhouse gases (GHG) emissions. In this paper, the dynamic LCA approach is applied to a temporary carbon sequestration project through afforestation, and the results are compared with those of the two principal ton-year approaches: the Moura-Costa and the Lashof methods. The dynamic LCA covers different scenarios, which are distinguished by the assumptions regarding what happens at the end of the sequestration period. In order to ascertain the degree of compensation of an emission through a LULUCF project, the ratio of the cumulative impact of the project to the cumulative impact of a baseline GHG emission is calculated over time. This ratio tends to 1 when assuming that, after the end of the sequestration project period, the forest is maintained indefinitely. Conversely, the ratio tends to much lower values in scenarios where part of the carbon is released back to the atmosphere due to e.g. fire or forest exploitation. The comparison of dynamic LCA with the ton-year approaches shows that it is a more flexible approach as it allows the consideration of every life cycle stage of the project and it gives decision makers the opportunity to test the sensitivity of the results to the choice of different time horizons.

The application of the pedigree approach to the distributions foreseen in ecoinvent v3

PurposeData used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004). 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.MethodsA 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.Results and discussionIn 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.ConclusionsProviding 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.

Comparative life cycle assessment of two landfill technologies for the treatment of municipal solid waste.

Goal and ScopeThe potential environmental impacts associated with two landfill technologies for the treatment of municipal solid waste (MSW), the engineered landfill and the bioreactor landfill, were assessed using the life cycle assessment (LCA) tool. The system boundaries were expanded to include an external energy production function since the landfill gas collected from the bioreactor landfill can be energetically valorized into either electricity or heat; the functional unit was then defined as the stabilization of 600 000 tonnes of MSW and the production of 2.56x108 MJ of electricity and 7.81x108 MJ of heat.MethodsOnly the life cycle stages that presented differences between the two compared options were considered in the study. The four life cycle stages considered in the study cover the landfill cell construction, the daily and closure operations, the leachate and landfill gas associated emissions and the external energy production. The temporal boundary corresponded to the stabilization of the waste and was represented by the time to produce 95% of the calculated landfill gas volume. The potential impacts were evaluated using the EDIP97 method, stopping after the characterization step.Results and DiscussionThe inventory phase of the LCA showed that the engineered landfill uses 26% more natural resources and generates 81% more solid wastes throughout its life cycle than the bioreactor landfill. The evaluated impacts, essentially associated with the external energy production and the landfill gas related emissions, are on average 91% higher for the engineered landfill, since for this option 1) no energy is recovered from the landfill gas and 2) more landfill gas is released untreated after the end of the post-closure monitoring period. The valorization of the landfill gas to electricity or heat showed similar environmental profiles (1% more raw materials and 7% more solid waste for the heat option but 13% more impacts for the electricity option).Conclusion and RecommendationsThe methodological choices made during this study, e.g. simplification of the systems by the exclusion of the identical life cycle stages, limit the use of the results to the comparison of the two considered options. The validity of this comparison could however be improved if the systems were placed in the larger context of municipal solid waste management and include activities such as recycling, composting and incineration.

Systematic disaggregation: a hybrid LCI computation algorithm enhancing interpretation phase in LCA

PurposeBefore the advent of large databases, practitioners often lacked data for calculating life cycle inventories, but the actual computation was a straightforward task. Now that databases represent supply chains including feedback loops and several thousand unit processes and emissions, more formalized calculation methods are necessary. Two methods are widely used: sequential method and matrix inversion. They both exhibit different advantages and drawbacks. The present paper proposes a hybrid algorithm combining the advantages of both methods while minimizing their inconveniences.MethodsSequential algorithm requires a form of cutoff criteria, as the supply chains are of infinite length in the presence of feedback loops. The proposed implementation allows the detailing of individual paths until their upstream contribution falls below a user-defined disaggregation criteria, while also allowing the total impact scores of all paths to be stored and considered. The output is then structured to facilitate consultation and re-aggregation, enhancing the work of practitioners in the interpretation phase of LCA. The algorithm is a variation on structural path analysis and accumulative structural path analysis. It is computationally efficient and uses a reporting threshold criterion based on multiple impact categories.ResultsAlthough the algorithm leads to a more voluminous inventory than matrix inversion, it produces detailed, useful information on the particular instances of processes responsible for the impacts. An average laptop can compute the results within seconds. This algorithm has the potential to improve the interpretation phase of LCA. More specifically, selective replacement of values (characterization factors, input from technosphere, or emission intensities) in parts of the process tree can be applied without affecting the rest of the system.ConclusionsLCA software would benefit from the inclusion of the algorithm presented in this paper. It produces additional information on the structure of the supply chain and the impacts of its constituents, which would be available for a more in-depth interpretation by practitioners. Its potential for understanding the propagation of uncertainty and acceleration of Monte Carlo assessment should also be investigated.

The Quebec Life Cycle Inventory Database Project

PurposeLife cycle assessment (LCA) in Quebec (Canada) is increasingly important. Yet, studies often still need to rely on foreign life cycle inventory (LCI) data. The Quebec government invested in the creation of a Quebec LCI database. The approach is to work as an ecoinvent “National Database Initiative” (NDI), whereby the Quebec database initiative uses and contributes to the ecoinvent database. The paper clarifies the relationship between ecoinvent and the Quebec NDI and provides details on prioritization and data collection.MethodsThe first steps were to select a partner database provider and to work out the modalities of the partnership. The main criterion for partner selection was database transparency, i.e., availability of unit process data (gate-to-gate), necessary for database adaptation. This and other criteria, such as free access to external reviewers, conservation of dataset copyright, seamless embedding of datasets, and overall database sophistication, pointed to ecoinvent. Once started, the NDI project proceeded as follows: (1) data collection was prioritized based on several criteria; (2) some datasets were “recontextualized,” i.e., existing datasets were duplicated and relocated in Quebec and linked to datasets representing regional suppliers, where relevant; (3) new datasets were created; and (4) Canadian environmentally extended supply-use tables were created for the ecoinvent IO repository.Results and discussionPrioritization identified 500 candidate datasets for recontextualization, based on the relative importance of relative contribution of direct electricity consumption to cradle-to-gate impacts, and 12 key sectors from which about 450 data adaptation or collection projects were singled out. Data collection and private sector solicitation are underway. Private sector participation is highly variable. A number of communication tools have been elaborated and a solicitation team formed to palliate this obstacle. The new ecoinvent database protocol (Weidema et al. 2011) increases the amount of information that is required to create a dataset, which can lengthen or, in extreme cases, impede dataset creation. However, this new information is required for the new database functionalities (e.g., providing multiple system models based on the same unit process data and regionalized LCA).ConclusionsBeing an NDI is advantageous for the Quebec LCI database project on multiple levels. By conserving dataset copyright, the NDI remains free to spawn or support other LCI databases. Embedding datasets in ecoinvent enables the generation of LCI results from “day 1.” The costs of IT infrastructure and data review are null. For these reasons, and because every NDI improves the global representativity of ecoinvent, we recommend other regional or national database projects work as NDIs.

Giving a scientific basis for uncertainty factors used in global life cycle inventory databases: an algorithm to update factors using new information

PurposeLife cycle inventory (LCI) databases provide generic data on exchange values associated with unit processes. The “ecoinvent” LCI database estimates the uncertainty of all exchange values through the application of the so-called pedigree approach. In the first release of the database, the used uncertainty factors were based on experts’ judgments. In 2013, Ciroth et al. derived empirically based factors. These, however, assumed that the same uncertainty factors could be used for all industrial sectors and fell short of providing basic uncertainty factors. The work presented here aims to overcome these limitations.MethodsThe proposed methodological framework is based on the assessment of more than 60 data sources (23,200 data points) and the use of Bayesian inference. Using Bayesian inference allows an update of uncertainty factors by systematically combining experts’ judgments and other information we already have about the uncertainty factors with new data.Results and discussionThe implementation of the methodology over the data sources results in the definition of new uncertainty factors for all additional uncertainty indicators and for some specific industrial sectors. It also results in the definition of some basic uncertainty factors. In general, the factors obtained are higher than the ones obtained in previous work, which suggests that the experts had initially underestimated uncertainty. Furthermore, the presented methodology can be applied to update uncertainty factors as new data become available.ConclusionsIn practice, these uncertainty factors can systematically be incorporated in LCI databases as estimates of exchange value uncertainty where more formal uncertainty information is not available. The use of Bayesian inference is applied here to update uncertainty factors but can also be used in other life cycle assessment developments in order to improve experts’ judgments or to update parameter values when new data can be accessed.