Pfister S.,ETH Zurich |
Pfister S.,Aveny GmbH |
Vionnet S.,Quantis International |
Levova T.,Ecoinvent Center |
Humbert S.,Quantis International
International Journal of Life Cycle Assessment | Year: 2015
Purpose: Water footprinting and the assessment of water use in life cycle assessment have become of major interest in sustainability assessments. Various initiatives for combining water resource issues with consumption of products and services have been initiated in the last decade. However, comprehensive databases fulfilling the requirements for addressing these issues have been lacking and are necessary to facilitate efficient and consistent assessments of products and services. To this purpose, ecoinvent focused on integrating appropriate water use data into version 3, since previously water use data has been inconsistently reported and some essential flows were missing. This paper describes the structure of the water use data in ecoinvent, how the data has been compiled and the way it can be used for water footprinting. Methods: The main changes required for proper assessment of water use are the addition of environmental and product flows in order to allow a water balance over each process. This is in accordance with the strict paradigm in ecoinvent 3 to focus on mass balances, which requires the inclusion of water contents of all products (also for e.g. waste water flows), as well as emissions of water to soil, air and various water bodies. Water inputs from air (e.g. rainwater harvesting) is introduced but is not yet used by any activity. Results and discussion: Ecoinvent version 3.1 consistently includes the relevant flows to address water use in life cycle assessment (LCA) and calculate water footprints on the product level for most processes including uncertainty information. Although some problems regarding data quality and spatial resolution remain, this is an important step forward and can limit efforts for detailed data collection to the most sensitive processes in the product system. With the combination of data on water use and emissions to water for each process, concentration and corresponding water classes can also be calculated and assessed with existing impact assessment methods. Conclusions: This comprehensive collection of water use data on the process level facilitates the proper assessment of water use within an LCA and water footprints beyond agricultural production. Especially in LCA, but also in tools for eco-design and specific water footprint, this data is essential and leads to a cost-efficient way of assessing consumption choices and product design decisions with full transparency. It enhances the effectiveness of investing in data collection by performing sensitivity analyses using ecoinvent data to identify the most relevant flows and processes. © 2015 Springer-Verlag Berlin Heidelberg
Koellner T.,University of Bayreuth |
De Baan L.,ETH Zurich |
Beck T.,University of Stuttgart |
Brandao M.,European Commission - Joint Research Center Ispra |
And 7 more authors.
International Journal of Life Cycle Assessment | Year: 2013
Purpose: To assess the diverse environmental impacts of land use, a standardization of quantifying land use elementary flows is needed in life cycle assessment (LCA). The purpose of this paper is to propose how to standardize the land use classification and how to regionalize land use elementary flows. Materials and methods: In life cycle inventories, land occupation and transformation are elementary flows providing relevant information on the type and location of land use for land use impact assessment. To find a suitable land use classification system for LCA, existing global land cover classification systems and global approaches to define biogeographical regions are reviewed. Results and discussion: A new multi-level classification of land use is presented. It consists of four levels of detail ranging from very general global land cover classes to more refined categories and very specific categories indicating land use intensities. Regionalization is built on five levels, first distinguishing between terrestrial, freshwater, and marine biomes and further specifying climatic regions, specific biomes, ecoregions and finally indicating the exact geo-referenced information of land use. Current land use inventories and impact assessment methods do not always match and hinder a comprehensive assessment of land use impact. A standardized definition of land use types and geographic location helps to overcome this gap and provides the opportunity to test the optimal resolution of land cover types and regionalization for each impact pathway. Conclusions and recommendation: The presented approach provides the necessary flexibility to providers of inventories and developers of impact assessment methods. To simplify inventories and impact assessment methods of land use, we need to find archetypical situations across impact pathways, land use types and regions, and aggregate inventory entries and methods accordingly. © 2012 Springer-Verlag.
Osses de Eicker M.,Empa - Swiss Federal Laboratories for Materials Science and Technology |
Hischier R.,Empa - Swiss Federal Laboratories for Materials Science and Technology |
Hischier R.,Ecoinvent Center |
Kulay L.A.,Centro Universitario Senac |
And 3 more authors.
Environmental Impact Assessment Review | Year: 2010
This study evaluated how applicable European Life Cycle Inventory (LCI) data are to assessing the environmental impacts of the life cycle of Brazilian triple superphosphate (TSP). The LCI data used for the comparison were local Brazilian LCI data, European LCI data in its original version from the ecoinvent database and a modified version of the European LCI data, which had been adapted to better account for the Brazilian situation. We compared the three established datasets at the level of the inventory as well as for their environmental impacts, i.e. at the level of Life Cycle Environmental Assessment (LCIA). The analysis showed that the European LCIs (both the original and the modified ones) considered a broader spectrum of background processes and environmental flows (inputs and outputs). Nevertheless, TSP production had in all three cases similar values for the consumption of the main raw materials. The LCIA results obtained for the datasets showed important differences as well. Therefore we concluded that the European data in general lead to much higher environmental impacts than the Brazilian data. The differences between the LCIA results obtained with the Brazilian and the European data can be basically explained by the methodological differences underlying the data. The small differences at the LCI level for selected inputs and outputs between the Brazilian and the European LCIs from ecoinvent indicate that the latter can be regarded as applicable for characterizing the Brazilian TSP. © 2009 Elsevier Inc. All rights reserved.
Ciroth A.,GreenDelta GmbH |
Muller S.,Ecole Polytechnique de Montreal |
Weidema B.,Ecoinvent Center |
Weidema B.,University of Aalborg |
Lesage P.,Ecole Polytechnique de Montreal
International Journal of Life Cycle Assessment | Year: 2013
Purpose: 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.). Methods: 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. Results and discussion: 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. Conclusions and perspectives: 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. © 2013 Springer-Verlag Berlin Heidelberg.
Wernet G.,Ecoinvent Center |
Hellweg S.,ETH Zurich |
Hungerbuhler K.,ETH Zurich
International Journal of Life Cycle Assessment | Year: 2012
Purpose Mixtures of organic chemicals are a part of virtually all life cycles, but LCI data exist for only relatively few chemicals. Thus, estimation methods are required. However, these are often either very time-consuming or deliver results of low quality. This article compares existing and new methods in two scenarios and recommends a tiered approach of different methods for an efficient estimation of the production impacts of chemical mixtures. Methods Four approaches to estimate impacts of a large number of chemicals are compared in this article: extrapolation from existing data, substitution with generic datasets on chemicals, molecular structure-based models (MSMs, in this case the Finechem tool), and using process-based estimation methods. Two scenarios were analyzed as case studies: soft PVC plastic and a tobacco flavor, a mixture of 20 chemicals. Results Process models have the potential to deliver the best estimations, as existing information on production processes can be integrated. However, their estimation quality suffers when such data are not available and they are time-consuming to apply, which is problematic when estimating large numbers of chemicals. Extrapolation from known to unknown components and use of generic datasets are generally not recommended. In both case studies, these two approaches significantly underestimated the impacts of the chemicals compared to the process models. MSMs were generally able to estimate impacts on the same level as the more complex process models. A tiered approach using MSMs to determine the relevance of individual components in mixtures and applying process models to the most relevant components offered a simpler and faster estimation process while delivering results on the level of most process models. Conclusions The application of the tiered combination of MSMs and process models allows LCA practitioners a relatively fast and simple estimation of the LCIA results of chemicals, even for mixtures with a large number of components. Such mixtures previously presented a problem, as the application of process models for all components was very timeconsuming, while the existing, simple approaches were shown to be inadequate in this study. We recommend the tiered approach as a significant improvement over previous approaches for estimating LCA results of chemical mixtures. © Springer-Verlag 2012.