The ecoinvent Database: Overview and Methodological Framework (7 pp)

Frischknecht, Rolf; Jungbluth, Niels; Althaus, Hans‐Jörg; Doka, Gabor; Dones, Roberto; Heck, Thomas F.; Hellweg, Stefanie; Hischier, Roland; Nemecek, Thomas; Rebitzer, Gerald; Spielmann, Michael

doi:10.1065/lca2004.10.181.1

Cited by 1,020 publications

(771 citation statements)

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“…(Frischknecht et al 2004;Suh and Huppes 2005;Miller and Theis 2006;Dones et al 2007;Verbeeck and Hens 2010;Jungbluth et al 2012), calculating new midpoint characterization factors (Koellner and Scholz 2007;Pfister et al 2009;Van Zelm et al 2009;Gallego et al 2010;Saad et al 2011;Hauschild et al 2013), and improving end-point damage modeling (Hayashi et al 2006;Boulay et al 2011;Motoshita et al 2014). Less emphasis has been placed on normalization and weighting, which are optional practices in life cycle impact assessment (LCIA).…”

Section: Responsible Editor: Sangwon Suhmentioning

confidence: 99%

Interpretation of comparative LCAs: external normalization and a method of mutual differences

Prado

Wender

Seager

2017

Int J Life Cycle Assess

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Purpose Identification of environmentally preferable alternatives in a comparative life cycle assessment (LCA) can be challenging in the presence of multiple incommensurate indicators. To make the problem more manageable, some LCA practitioners apply external normalization to find those indicators that contribute the most to their respective environmental impact categories. However, in some cases, these results can be entirely driven by the normalization reference, rather than the comparative performance of the alternatives. This study evaluates the influence of normalization methods on interpretation of comparative LCA to facilitate the use of LCA in decision-driven applications and inform LCA practitioners of latent systematic biases. An alternative method based on significance of mutual differences is proposed instead. Methods This paper performs a systematic evaluation of external normalization and describes an alternative called the overlap area approach for the purpose of identifying relevant issues in a comparative LCA. The overlap area approach utilizes the probability distributions of characterized results to assess significant differences. This study evaluates the effects in three LCIA methods, through application of four comparative studies. For each application, we call attention to the category indicators highlighted by each interpretation approach. Results and discussion External normalization in the three LCIA methods suffers from a systematic bias that emphasizes the same impact categories regardless of the application. Consequently, comparative LCA studies that employ external normalization to guide a selection may result in recommendations dominated entirely by the normalization reference and insensitive to data uncertainty. Conversely, evaluation of mutual differences via the overlap area calls attention to the impact categories with the most significant differences between alternatives. The overlap area approach does not show a systematic bias across LCA applications because it does not depend on external references and it is sensitive to changes in uncertainty. Thus, decisions based on the overlap area approach will draw attention to tradeoffs between alternatives, highlight the role of stakeholder weights, and generate assessments that are responsive to uncertainty. Conclusions The solution to the issues of external normalization in comparative LCAs proposed in this study call for an entirely different algorithm capable of evaluating mutual differences and integrating uncertainty in the results.

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Section: Responsible Editor: Sangwon Suhmentioning

confidence: 99%

Interpretation of comparative LCAs: external normalization and a method of mutual differences

Prado

Wender

Seager

2017

Int J Life Cycle Assess

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“…Another common cut-off is the 181 exclusion of capital goods from inventories. Frischknecht et al (2007) have investigated the contribution of capital 182 goods to several environmental impacts for a range of products and services of the database ecoinvent v1.2. In their 183 study, capital goods contributed 6.6% of the acidification potential for incineration and 45% for sanitary landfills.…”

mentioning

confidence: 99%

Quantifying uncertainty in LCA-modelling of waste management systems

Clavreul¹,

Guyonnet²,

Christensen³

2012

Waste Management

266

152

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26 27Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste 28 management, various methods have been implemented but a systematic method for uncertainty analysis of waste-29 LCA studies is lacking. The objective of this paper is (1) to present the sources of uncertainty specifically inherent to 30 waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general 31 framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a 32 sequence of four steps combining the selected methods: (Step 1) a sensitivity analysis evaluating the sensitivities of 33 the results with respect to the input uncertainties, (Step 2) an uncertainty propagation providing appropriate tools for 34 representing uncertainties and calculating the overall uncertainty of the model results, (Step 3) an uncertainty 35 contribution analysis quantifying the contribution of each parameter uncertainty to the final uncertainty and (Step 4) 36 as a new approach, a combined sensitivity analysis providing a visualization of the shift in the ranking of different 37 options due to variations of selected key parameters. This tiered approach optimizes the resources available to LCA 38 practitioners by only propagating the most influential uncertainties. Waste management has during the last decade been subject to a range of life cycle assessment (LCA; described in 48 ISO, 2006) studies e.g. Damgaard et al. (2011, Lazarevic et al. (2010) and Pires et al. 49 (2011). The purposes of these studies have been to help quantifying, for example, where in the waste management 50 system the environmental loads and savings are taking place, which technologies are preferable under specific 51 conditions, or the balance between material and energy recovery. LCA-models specifically focusing on waste 52 management systems are available; see Gentil et al. (2010) for a review of the models. 53As for any LCA study, results are subject to uncertainty due to the combined effects of data variability, 54 erroneous measurements, wrong estimations, unrepresentative or missing data and modelling assumptions. 55Uncertainty is of two different natures: while epistemic uncertainty relates to an incomplete state of knowledge 56 (Hoffman and Hammonds, 1994), stochastic uncertainty originates from the inherent variability of the natural world. 57Such uncertainty can be spatial (e.g. when the farming practice of land receiving compost varies spatially) or 58 temporal (e.g. when the performance of a process varies with time). These two different natures of the uncertainty are 59 usually treated together and referred to by the term "uncertainty". 60 They found that stochastic modelling was the most frequently-used method to propagate uncertainties in LCA. This 75 method propagates probability distributions using random sampling like the Monte Carlo analysis. However, they 76 noted that many of the studies using such modelling...

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“…Linking process modeling to LCA allows for the systematic calculation of environmental impact when changing the process design. This life cycle inventory model uses data from the Ecoinvent database whenever possible (20). The inventory result is then used to calculate the life cycle's imbedded fossil energy and its environmental impact.…”

Section: Methodsmentioning

confidence: 99%

“…The two weighting methods are chosen because they are well-known methods among LCA practitioners and for the applicability of Eco-scarcity to Switzerland. Indices from the Ecoinvent database (20) for these three methods are used.…”

Section: Methodsmentioning

confidence: 99%

Hydrothermal Gasification of Waste Biomass: Process Design and Life Cycle Asessment

Luterbacher

Fröling

Vogel

et al. 2009

Environ. Sci. Technol.

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A process evaluation methodology is presented that incorporates flowsheet mass and energy balance modeling, heat and power integration, and life cycle assessment. Environmental impacts are determined by characterizing and weighting (using CO 2 equivalents, Eco-indicator 99, and Eco-scarcity) the flowsheet and inventory modeling results. The methodology is applied to a waste biomass to synthetic natural gas (SNG) conversion process involving a catalytic hydrothermal gasification step. Several scenarios are constructed for different Swiss biomass feedstocks and different scales depending on logistical choices: large-scale (155 MW SNG ) and small-scale (5.2 MW SNG ) scenarios for a manure feedstock and one scenario (35.6 MW SNG ) for a wood feedstock. Process modeling shows that 62% of the manure's lower heating value (LHV) is converted to SNG and 71% of wood's LHV is converted to SNG. Life cycle modeling shows that, for all processes, about 10% of fossil energy use is imbedded in the produced renewable SNG. Converting manure and replacing it, as a fertilizer, with the process mineral byproduct leads to reduced N 2 O emissions and an improved environmental performance such as global warming potential: -0.6 kg CO 2 eq. /MJ SNG vs -0.02 kg CO 2 eq. /MJ SNG for wood scenarios.

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The ecoinvent Database: Overview and Methodological Framework (7 pp)

Cited by 1,020 publications

References 15 publications

Interpretation of comparative LCAs: external normalization and a method of mutual differences

Interpretation of comparative LCAs: external normalization and a method of mutual differences

Quantifying uncertainty in LCA-modelling of waste management systems

Hydrothermal Gasification of Waste Biomass: Process Design and Life Cycle Asessment

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