Overview and recommendations for regionalized life cycle impact assessment.
- Mutel C Paul Scherrer Institute, 5232 PSI Villigen, Switzerland.
- Liao X Industrial Process and Energy Systems Engineering, Ecole Polytechnique Fédérale de Lausanne, EPFL Valais Wallis, Rue de l'Industrie 17, CH-1951 Sion, Switzerland.
- Patouillard L CIRAIG, Polytechnique Montréal, P.O. Box 6079, Montréal, Québec H3C 3A7, Canada.
- Bare J US Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA.
- Fantke P Quantitative Sustainability Assessment Division, Department of Management Engineering, Technical University of Denmark, Bygningstorvet 116B, 2800 Kgs. Lyngby, Denmark.
- Frischknecht R Treeze, 8610 Uster, Switzerland.
- Hauschild M Quantitative Sustainability Assessment Division, Department of Management Engineering, Technical University of Denmark, Bygningstorvet 116B, 2800 Kgs. Lyngby, Denmark.
- Jolliet O Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
- de Souza DM Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, T6G 2P5, AB, Canada.
- Laurent A Quantitative Sustainability Assessment Division, Department of Management Engineering, Technical University of Denmark, Bygningstorvet 116B, 2800 Kgs. Lyngby, Denmark.
- Pfister S Institute of Environmental Engineering, ETH Zurich, Switzerland.
- Verones F Industrial Ecology Programme, Department of Energy and Process Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
- 2020-10-30
Published in:
- The international journal of life cycle assessment. - 2019
archetypes
impact assessment
regionalization
spatial differentiation
standardization
uncertainty
variability
English
Purpose
Regionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still faces multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provides 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.
Method
A survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method spatial resolution and scale, the spatial resolution of input parameters, 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 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 modelled 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 models 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.
Regionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still faces multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provides 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.
Method
A survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method spatial resolution and scale, the spatial resolution of input parameters, 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 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 modelled 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 models 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.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1007/s11367-018-1539-4
- PMID 33122880
- Persistent URL
- https://sonar.ch/global/documents/286601
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