Sensitivity to weighting in life cycle impact assessment (LCIA)

The International Journal of Life Cycle Assessment volume 25, pages2393–2406(2020)Cite this article

Abstract

Purpose

Weighting in life cycle assessment (LCA) incorporates stakeholder preferences in the decision-making process of comparative LCAs. Research efforts on this topic are concerned with deriving weights according to different principles, but few studies have evaluated the relationship between normalization and weights and their effect on single scores. We evaluate the sensitivity of aggregation methods to weights in different life cycle impact assessment (LCIA) methods to provide insight on the receptiveness of single score results to value systems.

Methods

Sensitivity to weights in two LCIA methods is assessed by exploring weight spaces stochastically and evaluating the rank of alternatives via the Rank Acceptability Index (RAI). We assess two aggregation methods: a weighted sum based on externally normalized scores and a method of internal normalization based on outranking across CML-IA and ReCipE midpoint impact assessment. The RAI represents the likelihood that an alternative occupies a certain rank given all possible weight spaces, and it can be used to compare the sensitivity of final ranks to weight values in each aggregation method and LCIA. Evaluation is based on a case study of a comparative LCA of five PV technologies whose inventory is readily available in Ecoinvent.

Results and discussion

Influence of weights in single scores depend on the scaling/normalization step more than the value of the weight itself. In each LCIA, aggregated results from a weighted sum with external normalization references show a higher weight insensitivity in RAI than outranking-based aggregation because in the former, results are driven by a few dominant impact categories due to the normalization procedure. Differences in sensitivity are caused by the notable variety (up two orders of magnitude) in the scales of normalized values for the weighted sum with external normalization and intrinsic properties of the methods including compensation and a lack of accounting for mutual differences.

Conclusions

Contrary to the belief that the choice of weights is decisive in aggregation of LCIA results, in this case study, it is shown that the normalization step has the greatest influence in the results. This point holds for EU and World references in ReCiPe and CML-IA alike. Aggregation consisting of outranking generates rank orderings with a more balanced contribution of impact categories and sensitivity to weights’ values as opposed to weighted sum approaches that rely on external normalization references.

Recommendations

Practitioners aiming to include stakeholder values in single scores for LCIA should be aware of how the weights are treated in the aggregation method as to ensure proper representation of values.

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Notes

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    Due to constraints related to modeling metals on longer time horizons in multi-media models, MAET is often excluded from baseline categories (Heijungs et al. 2004)

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Funding

Marco Cinelli declares that this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 743553.

Author information

Affiliations

  1. School of Management, Los Andes University, Bogotá, Colombia

    Valentina Prado

  2. EarthShift Global LLC, 37 Route 236, Suite 112, Kittery, ME, 03904, USA

    Valentina Prado

  3. Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA

    Thomas P. Seager

  4. Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Marco Cinelli

  5. Department of Industrial Ecology, Institute of Environmental Sciences (CML), Leiden University, Einsteinweg 2, 2333, Leiden, CC, The Netherlands

    Sterre F. Ter Haar, Reinout Heijungs & Jeroen Guinée

  6. School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Dwarakanath Ravikumar

  7. Department of Econometrics and Operations Research, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands

    Reinout Heijungs

Authors
  1. Valentina Prado
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  2. Marco Cinelli
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  3. Sterre F. Ter Haar
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  4. Dwarakanath Ravikumar
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  5. Reinout Heijungs
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  6. Jeroen Guinée
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Correspondence to Valentina Prado.

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Prado, V., Cinelli, M., Ter Haar, S.F. et al. Sensitivity to weighting in life cycle impact assessment (LCIA). Int J Life Cycle Assess 25, 2393–2406 (2020). https://doi.org/10.1007/s11367-019-01718-3

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Keywords

  • Aggregation in LCA
  • LCIA (Life Cycle Impact Assessment)
  • PV technologies
  • Weighting in LCA