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Overview and recommendations for regionalized life cycle impact assessment

  • Chris Mutel
  • Xun Liao
  • Laure Patouillard
  • Jane Bare
  • Peter Fantke
  • Rolf Frischknecht
  • Michael Hauschild
  • Olivier Jolliet
  • Danielle Maia de Souza
  • Alexis Laurent
  • Stephan Pfister
  • Francesca Verones
LCIA OF IMPACTS ON HUMAN HEALTH AND ECOSYSTEMS

Abstract

Purpose

Regionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still face multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provide an overview of the status of regionalization in LCIA methods. We give guidance and recommendations to harmonize and support regionalization in LCIA for developers of LCIA methods, LCI databases, and LCA software.

Methods

A survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method’s spatial resolution and scale, the spatial resolution of input parameters, the choice of native spatial resolution and limitations, operationalization and alignment with life cycle inventory data, methods for spatial aggregation, the assessment of uncertainty from input parameters and model structure, and the variability due to spatial aggregation. Recommendations are formulated based on the survey results and extensive discussion by the authors.

Results and discussion

Survey results indicate that majority of regionalized LCIA models have global coverage. Native spatial resolutions are generally chosen based on the availability of global input data. Annual modeled or measured elementary flow quantities are mostly used for aggregating characterization factors (CFs) to larger spatial scales, although some use proxies, such as population counts. Aggregated CFs are mostly available at the country level. Although uncertainty due to input parameter, model structure, and spatial aggregation are available for some LCIA methods, they are rarely implemented for LCA studies. So far, there is no agreement if a finer native spatial resolution is the best way to reduce overall uncertainty. When spatially differentiated model CFs are not easily available, archetype models are sometimes developed.

Conclusions

Regionalized LCIA methods should be provided as a transparent and consistent set of data and metadata using standardized data formats. Regionalized CFs should include both uncertainty and variability. In addition to the native-scale CFs, aggregated CFs should always be provided and should be calculated as the weighted averages of constituent CFs using annual flow quantities as weights whenever available. This paper is an important step forward for increasing transparency, consistency, and robustness in the development and application of regionalized LCIA methods.

Keywords

Archetypes Impact assessment Regionalization Spatial differentiation Standardization Uncertainty Variability 

Notes

Disclaimer

The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the organizations to which they belong. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the UNEP/SETAC Life Cycle Initiative concerning the legal status of any country, territory, city, or area or of its authorities, or concerning delimitation of its frontiers or boundaries. Moreover, the views expressed do not necessarily represent the decision or the state policy of the UNEP/SETAC Life Cycle Initiative, nor does citing of trade names or commercial processes constitute endorsement. Although an EPA employee contributed to this article, the research presented was not performed or funded by EPA and was not subject to EPA’s quality system requirements. Consequently, the views, interpretations, and conclusions expressed in the article are solely those of the authors and do not necessarily reflect or represent EPA’s views or policies.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Chris Mutel
    • 1
  • Xun Liao
    • 2
    • 3
  • Laure Patouillard
    • 6
    • 4
    • 5
  • Jane Bare
    • 7
  • Peter Fantke
    • 8
  • Rolf Frischknecht
    • 9
  • Michael Hauschild
    • 8
  • Olivier Jolliet
    • 10
  • Danielle Maia de Souza
    • 12
    • 11
  • Alexis Laurent
    • 8
  • Stephan Pfister
    • 13
  • Francesca Verones
    • 14
  1. 1.Paul Scherrer InstituteVilligenSwitzerland
  2. 2.Industrial Process and Energy Systems EngineeringEcole Polytechnique Fédérale de LausanneSionSwitzerland
  3. 3.Quantis, EPFL Innovation Park (EIP-D)LausanneSwitzerland
  4. 4.CIRAIGPolytechnique MontréalMontréalCanada
  5. 5.IFP Energies nouvellesRueil-MalmaisonFrance
  6. 6.UMR 0210 INRA-AgroParisTech Economie publiqueThiverval-GrignonFrance
  7. 7.Office of Research and DevelopmentUS Environmental Protection AgencyCincinnatiUSA
  8. 8.Quantitative Sustainability Assessment Division, Department of Management EngineeringTechnical University of DenmarkKgs. LyngbyDenmark
  9. 9.TreezeUsterSwitzerland
  10. 10.Environmental Health Sciences, School of Public HealthUniversity of MichiganAnn ArborUSA
  11. 11.Department of Agricultural, Food and Nutritional ScienceUniversity of AlbertaEdmontonCanada
  12. 12.Département de Stratégie, Responsabilité Sociale et EnvironnementaleUniversité du Québec à MontréalMontrealCanada
  13. 13.Institute of Environmental EngineeringETH ZurichZurichSwitzerland
  14. 14.Industrial Ecology Programme, Department of Energy and Process EngineeringNorwegian University of Science and TechnologyTrondheimNorway

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