Bias in normalization: Causes, consequences, detection and remedies

Heijungs, Reinout; Guinée, Jeroen B.; Kleijn, René; Rovers, Vera

doi:10.1065/lca2006.07.260

Cited by 83 publications

(34 citation statements)

References 8 publications

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“…Previous studies comparing LCIA methods shed light on gaps in characterization factors and explain how that may influence the outcome of results (Huijbregts et al 2003;Cavalett et al 2012;Castellani et al 2016). For instance, the issue of characterization of impacts of metals has been an issue in impact assessment that leads to an overestimation of ecotoxicity impacts as seen in CML-IA (Ligthart et al 2004;Heijungs et al 2007). Given the consistency of results over diverse applications, it is unlikely this bias is due to an overestimation at inventory level in the product system (as newer inventory libraries become available, this type of evaluation could help point out biases early on).…”

Section: Discussionmentioning

confidence: 99%

“…Externally normalized results show the relative significance of category indicators and thus can help guide improvement efforts within a single product system (Heijungs et al 2007). Note the emphasis in single product system as these results should not be used to make comparisons across impact categories and multiple product systems.…”

Section: External Normalizationmentioning

confidence: 99%

“…To compare between product systems and across impact categories, externally normalized results must be weighted, which provides the basis for aggregation to a single score (Heijungs et al 2007;Lautier et al 2010;Prado et al 2012). This practice, although included in the ISO guidelines and the Handbook on LCA, can have misleading recommendations as it is subject to the same biases of external normalization (Prado et al 2012;Prado-Lopez et al 2014).…”

Section: External Normalizationmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: External Normalizationmentioning

confidence: 99%

Section: External Normalizationmentioning

confidence: 99%

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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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“…human and eco-toxicities). Particularly biased marine ecotoxic results have been observed (Heijungs et al, 2007). Limitation in the characterization factors for toxicity-related impact categories have been discussed in previous studies (Huijbregts, 1999b.…”

Section: Montreal Protocolmentioning

confidence: 99%

“…However, as pointed out by Heijungs et al(2007), biased normalization profiles can be delivered due to the limitations in the coverage of elemental flows for the product system and for the reference system. A large degree of bias may occur in normalized indicator results for impact categories which are not well-established (e.g.…”

Section: Montreal Protocolmentioning

confidence: 99%

Life Cycle Assessment (LCA) of Light-Weight Eco-composites

Guo¹

2012

Springer Theses

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LCA of Light-weight Eco-composites Statement of originality 2 Statement of originalityI hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person.Contributions made to the research of this thesis by others, with whom I have worked with are explicitly acknowledged. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others with data collection and modification of process-oriented model DNDC is acknowledged. Miao GuoSep 2010 LCA of Light-weight Eco-compositesAbstract 3 AbstractThe environmental profiles of novel wheat based foam materials were investigated in this thesis using Life Cycle Assessment (LCA) methods. The LCAs were developed using primary data collected from industrial sources combined with new laboratory experiments supplemented with secondary data from publicly available sources.Laboratory research was conducted to obtain important missing data on WBFs for the LCA modelling, including physico-chemical parameters, biodegradability and energy recovery under anaerobic digestion conditions.Contribution analysis suggested that the emissions evolved from the wheat agroecosystem and PVOH production, together with the energy and infrastructure involved in WBF production were the major contributors to the environmental burdens of the WBF life cycle in most impact categories. The atmospheric emissions resulting from WBF degradation at the end-of-life also emerged as another important contributor to environmental impact. Amongst the diverse ‗end-of-life' scenarios examined, AD and home composting were suggested to be the optimum choices for WBF waste treatment. This research discusses two N 2 O modelling approaches and presents a method to expand the system boundary by integrating the process-oriented model DNDC for field emissions into the LCA. Sensitivity analysis suggests that the environmental profiles of agricultural products are influenced substantially by the system boundary definition. LCA of Light-weight Eco-compositesAbstract 4 Furthermore, it suggests that the ‗general rule' in LCA practice by applying an empirical model or a default emission factor (EF) could deliver unreliable LCA findings.This study also evaluated the sensitivity of the LCA results to methodology and data variations and quantified the uncertainties in the LCA outcomes arising from uncertainty in the inventory and data variability. This has led to an increase in confidence in the LCA findings. At the same time it indicates the areas where improvements in data or methods are needed in order for robust conclusions to be drawn and unbiased information to be delivered e.g. the methodological rigidity of characterization models, the IPCC Tier 1 EF uncertainty range. LCA of Light-weight Eco-compositesAcknowledgements 5

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Wirkungsabschätzung

Klöpffer

Grahl²

2009

Ökobilanz (LCA)

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Bias in normalization: Causes, consequences, detection and remedies

Cited by 83 publications

References 8 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

Life Cycle Assessment (LCA) of Light-Weight Eco-composites

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