Although it is not yet current practice in life cycle assessment, it is recommended that impact assessment methods be accompanied by their uncertainty data to better guide the decision maker. This work uses the best available information to assess uncertainty of the AWARE model for water scarcity and corresponding sensitivities of input parameters. An uncertainty estimate for the AWARE characterization factors (CFs) is provided via (1) arrays (5000 values per CF) with statistics, (2) dispersion analysis, and (3) distribution best fit and parameters. Results show that uncertainty, represented by the dispersion of the values, varies significantly around the world and tends to be more important in regions of higher scarcity and low in most regions around the world (area based) in terms of absolute spread. Globally, values of 18.8 and 66.28 are found for the spread, represented by the interpercentile range (95%) and interquartile range (25–75%), respectively. The lognormal distribution shows the best fit for most regions around the world and could be used as a default distribution. Two parameters come out as influential: actual water availability (because of precipitation uncertainty) and the global hydrological model itself (because of the variability of results obtained from different models). When compared with uncertainty associated with spatio-temporal variability, uncertainties found in this work are generally lower, and hence improving resolution in water scarcity assessments (to monthly and watershed levels) should remain the priority. Finally, required data for software integration of AWARE uncertainty are provided. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges .

Files contain 5000 samples of AWARE characterization factors, as well as sampled independent data used in their calculations and selected intermediate results. AWARE is a consensus-based method development to assess water use in LCA. It was developed by the WULCA UNEP/SETAC working group. Its characterization factors represent the relative Available WAter REmaining per area in a watershed, after the demand of humans and aquatic ecosystems has been met. It assesses the potential of water deprivation, to either humans or ecosystems, building on the assumption that the less water remaining available per area, the more likely another user will be deprived. The code used to generate the samples can be found here: https://github.com/PascalLesage/aware_cf_calculator/ Samples were updated from v1.0 in 2020 to include model uncertainty associated with the choice of WaterGap as the global hydrological model (GHM). The following datasets are supplied: 1) AWARE_characterization_factor_samples.zip Actual characterization factors resulting from the Monte Carlo Simulation. Contains 4 zip files: * monthly_cf.zip: contains 116,484 arrays of 5000 monthly characterization factor samples for each of 9707 watershed and for each month, in csv format. Names are cf__.csv, where is the watershed id and is the first three letters of the month ('jan', 'feb', etc.). * average_agri_cf.zip: contains 9707 arrays of 5000 annual average, agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_agri_.csv. * average_non_agri_cf.zip: contains 9707 arrays of 5000 annual average, non-agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_non_agri_.csv. * average_unknown_cf.zip: contains 9707 arrays of 5000 annual average, unspecified use, characterization factor samples for each watershed, in csv format. Names are cf_average_unknown_.csv.. 2) AWARE_base_data.xlsx Excel file with the deterministic data, per watershed and per month, for each of the independent variables used in the calculation of AWARE characterization factors. Specifically, it includes: Monthly irrigation Description: irrigation water, per month, per basin Unit: m3/month Location in Excel doc: Irrigation File name once imported: irrigation.pickle table shape: (11050, 12) Non-irrigation hwc: electricity, domestic, livestock, manufacturing Description: non-irrigation uses of water Unit: m3/year Location in Excel doc: hwc_non_irrigation File name once imported: electricity.pickle, domestic.pickle, livestock.pickle, manufacturing.pickle table shape: 3 x (11050,) avail_delta Description: Difference between "pristine" natural availability reported in PastorXNatAvail and natural availability calculated from "Actual availability as received from WaterGap - after human consumption" (Avail!W:AH) plus HWC. This should be added to calculated water availability to get the water availability used for the calculation of EWR Unit: m3/month Location in Excel doc: avail_delta File name once imported: avail_delta.pickle table shape: (11050, 12) avail_net Description: Actual availability as received from WaterGap - after human consumption Unit: m3/month Location in Excel doc: avail_net File name once imported: avail_net.pickle table shape: (11050, 12) pastor Description: fraction of PRISTINE water availability that should be reserved for environment Unit: unitless Location in Excel doc: pastor File name once imported: pastor.pickle table shape: (11050, 12) area Description: area Unit: m2 Location in Excel doc: area File name once imported: area.pickle table shape: (11050,) It also includes: * information (k values) on the distributions used for each variable (uncertainty tab) * information (k values) on the model uncertainty (model uncertainty tab) * two filters used to exclude watersheds that are either in Greenland (polar filter) or without data from the Pastor et al. (2014) method (122 cells), representing small coastal cells with no direct overlap (pastor filter). (filters tab) 3) independent_variable_samples.zip Samples for each of the independent variables used in the calculation of characterization factors. Only random variables are contained. For all watershed or watershed-months without samples, the Monte Carlo simulation used the deterministic values found in the AWARE_base_data.xlsx file. The files are in csv format. The first column contains the watershed id (BAS34S_ID) if the data is annual or the (BAS34S_ID, month) for data with a monthly resolution. the other 5000 columns contain the sampled data. The names of the files are . 4) intermediate_variables.zip Contains results of intermediate calculations, used in the calculation of characterization factors. The zip file contains 3 zip files: * AMD_world_over_AMD_i.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the ratio between the AMD (Availability Minus Demand) for the watershed-month and AMD_glo, the world weighted AMD average. Format is csv. * AMD_world.zip: contains one array of 5000 calculated values of the world average AMD. Format is csv. * HWC.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the total Human Water Consumption. Format is csv. 5) watershedBAS34S_ID.zip Contains the GIS files to link the watershed ids (BAS34S_ID) to actual spatial data.

The Circular Economy (CE) movement is inspiring new governmental policies along with company strategies. This led to the emergence of a plethora of indicators to quantify the "circularity" of individual companies or products. Approaches behind these indicators builds mainly on two implicit assumptions. The first is that closing material loops at product level leads to improvements in material efficiency for the economy as a whole. The second assumption is that maximizing material circularity contributes to mitigate environmental impacts. We test these two assumptions at different scales with a case study on the circularity of PET in the USA market. The Material Circularity Indicator (MCI) reveals that closing the material loops at the product level increases material circularity in one brand and in the USA plastic bottle market but not in the USA PET market as a whole. Life Cycle Assessment (LCA) results reveal that increasing closed loop recycling of PET bottles is environmentally beneficial from product-level assessment scope. When expanding the scope to the whole PET market, recycling PET into film, fiber and sheet industrial sectors results being more material efficient and environmental preferable, unless the postconsumer reclamation rate is significantly improved. Thus, we demonstrate that adopting a systemic approach for CE assessment is essential ; instead of looking at one particular product and seeking the best circular case with respect to a specific material content, we suggest to looking at the whole set of products served by the specific material, and to seek the best material market-wide circular case.

In the early building design stage, there are numerous uncertainties due to the lack of information on materials and processes. Designers therefore cannot quantify the environmental impacts of buildings in order to evaluate the environmental performance of their designs early on. In this paper, life cycle assessment (LCA) and building information modeling (BIM) are carried out in the early and detailed building design stages. The method is applied to a residential building in Québec, Canada. The BIM is conducted with Revit, and the LCA with openLCA. To prepare the Revit outputs as the appropriate inputs of the LCA model, a functional database was developed. It includes all building assemblies, layers and possible materials commonly found in residential buildings in Québec. The ecoinvent database was used to source of life cycle inventory (LCI) data for each material. To manage information uncertainty in the early design stage, a probability function was assigned to each material. At the detailed design stage, all material types and quantities were specified in BIM file, which was used in the LCA study. The environmental impacts of the building stages and assemblies were calculated to determine the best building assembly options from an environmental perspective—a process that could guide building designers in the environmental assessments of their designs, making it possible to select more sustainable materials for each assembly and thus reduce the environmental impacts of the building.

Purpose Regionalization in life cycle assessment (LCA) aims to increase the representativeness of LCA results and reduce the uncertainty due to spatial variability. It may refer to adapting processes to better account for regional technological specificities ( inventory regionalization ) or adding of spatial information to the elementary flows ( inventory spatialization ) which allow using more regionalized characterization factors. However, developing and integrating regionalization requires additional efforts for LCA practitioners and database developers that must be prioritized. Methods We propose a stepwise methodology for LCA practitioners to prioritize data collection for regionalization based on global sensitivity analysis (GSA) using Sobol indices. It involves several GSA to select the impact categories (ICs) that require further inventory data collection (IC ranking), prioritize between inventory regionalization and inventory spatialization (LCA phase ranking), and target specific data to collect. Then we propose a method to derive sector-specific recommendations using statistical tests to prioritize inventory regionalization versus spatialization and the ICs on which to focus inventory data collection. These recommendations are meant to help LCA practitioners and database developers define their strategy for regional data collection by focusing on data that have the highest potential to reduce the uncertainty of the results. Results and discussion The applicability of the methodology is illustrated through three case studies using the ecoinvent v3 database and the regionalized impact methodology IMPACT World+: one on prioritizing data collection in a single biofuel product system and two meta-analyses of all product systems in two distinct economic sectors (biofuel production and land passenger transport). Recommendations for regionalization can be derived for an economic sector and appear to be different from one economic sector to another. GSA seems to be more relevant to prioritize regionalization efforts than an impact contribution analysis (ICA) approach often used to prioritize data collection in LCA. However, further improvements, such as accounting for spatial correlations and better computational times for GSA, are required to implement it in LCA. Conclusions We recommend using the methodology based on GSA to efficiently prioritize regionalization efforts between ICs and between inventory regionalization and inventory spatialization. We proved that the implementation of IC ranking and LCA phase ranking is computationally feasible and therefore invite current LCA software providers to unlock this new horizon in LCA interpretation. We also invite to expand the meta-analysis to all sectors in an LCA database.

PurposeProduct systems use the same unit process models to represent distinct but similar activities. This notably applies to activities in cyclic dependency relationships (or feedback loops) that are required an infinite number of times in a product system. The study aims to test the sensitivity of uncertainty results on the assumption made concerning these different instances of the same activities. The default assumption assumes homogeneous production, and the same parameter values are sampled for all instances (e.g., there is one truck). The alternative assumption is that every instance is distinct, and parameter values are independently sampled for different instances of unit processes (e.g., there are infinitely many trucks). Intuitively, sampling the same values for each instance of a unit process should result in more uncertain results.MethodsThe results of uncertainty analyses carried out under either assumption are compared. To simulate models where each instance of a unit process is independent, we convert network models to acyclic LCI models (tree models). This is done three times: (1) for a very simple product system, to explain the methodology; (2) for a sample product system from the ecoinvent database, for illustrative purposes; and (3) forthousands of product systems from ecoinvent databases.Results and discussionThe uncertainty of network models is indeed greater than that of corresponding tree models. This is shown mathematically for the analytical approximation method to uncertainty propagation and is observed for Monte Carlo simulations with very large numbers of iterations. However, the magnitude of the difference in indicators of dispersion is, for the ecoinvent product systems, often less than a factor of 1.5. In few extreme cases, indicators of dispersion are different by a factor of 4. Monte Carlo simulations with smaller numbers of iterations sometimes give the opposite result.ConclusionsGiven the small magnitude of the difference, we believe that breaking away from the default approach is generally not warranted. Indeed, (1) the alternative approach is not more robust, (2) the current default approach is conservative, and (3) there are more pressing challenges for the LCA community to meet. This being said, the study focused on ecoinvent, which should normally be used as a background database. The difference in dispersion between the two approaches may be important in some contexts, and calculating the uncertainty of tree models as a sensitivity analysis could be useful.

Triticale (X TriticosecaleWittmack) is a non-food energy crop with potential as a biorefinery feedstock. In addition to technical, economical, and commercial risks, it is of critical importance that environmental issues be considered in the decision-making process regarding the development of the triticale-based biorefinery. In this study, life cycle assessment (LCA) has been used for this purpose. To facilitate overall decision-making including economic and other metrics, the number of environmental indicators should be minimized, and yet at the same time, these indicators should be representative, comprehensive, and easy to interpret. To identify such a set of environmental indicators from LCA results, a multi-criteria decision-making (MCDM) panel study was carried out and an external set of normalization factors used to assist in this decision-making context. The influence of eight technology choices on the environmental impacts resulting from the production of ethanol, polylactic acid (PLA), and thermoplastic starch (TPS) blend was assessed. Moreover, the environmental benefits of improved triticale yield and its ability to grow on marginal land were assessed. Although the complete spectrum of environmental impact categories was evaluated, the MCDM panel selected four criteria to be brought forward to an overall decision-making panel. The greenhouse gas (GHG) emissions metric was judged as the most important, followed by non-renewable resource depletion, cropland occupation, and human health. Moreover, it was shown that certain technology choices such as ultra-filtration and cogeneration significantly influence the environmental impacts of the triticale biorefinery. (c) 2018 Society of Chemical Industry and John Wiley & Sons, Ltd

Some LCA software tools use precalculated aggregated datasets because they make LCA calculations much quicker. However, these datasets pose problems for uncertainty analysis. Even when aggregated dataset parameters are expressed as probability distributions, each dataset is sampled independently. This paper explores why independent sampling is incorrect and proposes two techniques to account for dependence in uncertainty analysis. The first is based on an analytical approach, while the other uses precalculated results sampled dependently.

Whole Building life cycle assessment (LCA) calculations are increasingly done using building information modeling (BIM) data exports, but some challenges need to be overcome. BIM models lack data for a whole building LCA analysis. To counter this lack of detailed information, manual inputs are often required when using a static BIM model and cannot easily consider recalculations over the duration of the project. This paper presents a method to automatically perform LCA calculations early, at the first level of a BIM model's development (i.e. the LOD100 level), and to allow for easier updates of the calculation throughout the evolution of the BIM model. To achieve this goal, a novel data layer and format is proposed. This data layer fills the information gap between extracted BIM data and existing LCA data provided by common LCA databases such as ecoinvent.

Producing a product, providing a service, or making a decision that affects the way products are produced or consumed has repercussions on an intricate and wide range of activities. Life cycle assessment (LCA) aims to give a measure of the environmental potential impacts associated with a given product, service, or decision by accounting for the impacts of these webs of activities. This article describes the process of creating a model of the web of activities that allows determining its potential environmental impacts. This model consists of unit processes representing a specific activity and that are linked together through intermediate flows describing products' exchanges between the unit processes. The result of these linked unit processes consists of the product system. Unit processes in the product system exchanges flows with the nature that are named elementary flows. Because modeling each single unit process and its associated flows each time an LCA is performed will be time and data consuming, the LCA community developed life cycle inventory (LCI) databases and LCA tools. LCI databases store unit processes, their exchanges, the quantification of these exchanges and the way unit processes are linked. LCA tools are softwares that allow modeling the whole LCA: from the product system modeling to the potential environmental impacts calculation. Main characteristics of LCI databases and LCA tools as well as some examples are provided in this article.

Life cycle inventory data have multiple sources of uncertainty. These data uncertainties are often modeled using probability density functions, and in the ecoinvent database the lognormal distribution is used by default to model exchange uncertainty values. The aim of this article is to systematically measure the effect of this default distribution by changing from the lognormal to several other distribution functions and examining how this change affects the uncertainty of life cycle assessment results. Using the ecoinvent 2.2 inventory database, data uncertainty distributions are switched from the lognormal distribution to the normal, triangular, and gamma distributions. The effect of the distribution switching is assessed for both impact assessment results of individual products system, as well as comparisons between product systems. Impact assessment results are generated using 5,000 Monte Carlo iterations for each product system, using the Intergovernmental Panel on Climate Change (IPCC) 2001 (100-year time frame) method. When comparing the lognormal distribution to the alternative default distributions, the difference in the resulting median and standard deviation values range from slight to significant, depending on the distributions used by default. However, the switch shows practically no effect on product system comparisons. Yet, impact assessment results are sensitive to how the data uncertainties are defined. In this article, we followed what we believe to be ecoinvent standard practice and preserved the “most representative” value. Practitioners should recognize that the most representative value can depart from the average of a probability distribution. Consistent default distribution choices are necessary when performing product system comparisons.

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.).

Data 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.

Manufacturing companies are under pressure from consumers and legislation to reduce their environmental impacts. In some sectors where competition is particularly fierce, the ability to offer a product with a lighter environmental impact than the competition can be useful in significantly increasing market share. The forest industry, which harvests and processes wood, a renewable resource, also aims at being part of this trend towards transparency. Life cycle assessment (LCA) is often used to quantify the environmental footprint of harvested wood products (HWP). Based on a primary data inventory of four years of activity, this study presents an LCA of the portfolio of an innovative forest products manufacturer. The functional unit of that assessment is a cubic meter. A sensitive analysis on an economic allocation was also conducted. Because of loops in the studied system and flow conservation constraint, results of the portfolio LCA was verified using an organizational footprint assessment. From the material flow and the half-life of products, a bottom-up accounting method is suggested for integrating HWP in national greenhouse gas (GHG) inventories.

Life 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.

Because the potential impacts of emissions and extractions can be sensitive to timing, the temporal aggregation of life cycle inventory (LCI) data has often been cited as a limitation in life cycle assessment (LCA). Until now, examples of temporal emission and extraction distributions were restricted to the foreground processes of product systems. The objective of this paper is to evaluate the relevance of considering the temporal distribution of the background system inventory.

Christoph Koffler & Jon Dettling & Cashion East & Matthias Finkbeiner & Sergio F. Galeano & Roland Geyer & Mark J. Goedkoop & Troy R. Hawkins & Connie D. Hensler & Arpad Horvath & Sebastien Humbert & Scott M. Kaufman & Amy E. Landis & Lise Laurin & Pascal Lesage & Manuele Margni & Ken Martchek & H. Scott Matthews & Jamie K. Meil & Gregory Norris & Rita C. Schenk & Thomas P. Seager & Maureen Sertich & Greg Thoma & Casey Wagner

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 Commission's 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.