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Prioritizing regionalization efforts in life cycle assessment through global sensitivity analysis: a sector meta-analysis based on ecoinvent v3

  • UNCERTAINTIES IN LCA
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Abstract

Purpose

Regionalization in life cycle assessment (LCA) aims to increase the representativeness of LCA results and reduce the uncertainty due to spatial variability. It may refer to adapting processes to better account for regional technological specificities (inventory regionalization) or adding of spatial information to the elementary flows (inventory spatialization) which allow using more regionalized characterization factors. However, developing and integrating regionalization requires additional efforts for LCA practitioners and database developers that must be prioritized.

Methods

We propose a stepwise methodology for LCA practitioners to prioritize data collection for regionalization based on global sensitivity analysis (GSA) using Sobol indices. It involves several GSA to select the impact categories (ICs) that require further inventory data collection (IC ranking), prioritize between inventory regionalization and inventory spatialization (LCA phase ranking), and target specific data to collect. Then we propose a method to derive sector-specific recommendations using statistical tests to prioritize inventory regionalization versus spatialization and the ICs on which to focus inventory data collection. These recommendations are meant to help LCA practitioners and database developers define their strategy for regional data collection by focusing on data that have the highest potential to reduce the uncertainty of the results.

Results and discussion

The applicability of the methodology is illustrated through three case studies using the ecoinvent v3 database and the regionalized impact methodology IMPACT World+: one on prioritizing data collection in a single biofuel product system and two meta-analyses of all product systems in two distinct economic sectors (biofuel production and land passenger transport). Recommendations for regionalization can be derived for an economic sector and appear to be different from one economic sector to another. GSA seems to be more relevant to prioritize regionalization efforts than an impact contribution analysis (ICA) approach often used to prioritize data collection in LCA. However, further improvements, such as accounting for spatial correlations and better computational times for GSA, are required to implement it in LCA.

Conclusions

We recommend using the methodology based on GSA to efficiently prioritize regionalization efforts between ICs and between inventory regionalization and inventory spatialization. We proved that the implementation of IC ranking and LCA phase ranking is computationally feasible and therefore invite current LCA software providers to unlock this new horizon in LCA interpretation. We also invite to expand the meta-analysis to all sectors in an LCA database.

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Acknowledgements

We acknowledge the financial and technical support of IFP Energies nouvelles and of the industrial partners of the International Chair in Life Cycle Assessment (a research unit of the CIRAIG): Arcelor-Mittal, Hydro-Québec, LVMH, Michelin, Nestlé, Solvay, Optel Vision, Total, Umicore.

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Authors and Affiliations

  1. CIRAIG, Polytechnique Montréal, P.O. Box 6079, Montreal, QC, H3C 3A7, Canada

    Laure Patouillard, Pascal Lesage, Pablo Tirado Seco & Manuele Margni

  2. IFP Energies nouvelles, 1-4 avenue de Bois-Préau, 92852, Rueil-Malmaison, France

    Laure Patouillard & Pierre Collet

  3. UMR 0210 INRA-AgroParisTech Economie publique, INRA, Thiverval-Grignon, France

    Laure Patouillard

  4. CIRAIG, ESG UQAM, C.P.8888, succ. Centre ville, Montréal, QC, H3C 3P8, Canada

    Cécile Bulle

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  1. Laure Patouillard
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  2. Pierre Collet
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  3. Pascal Lesage
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Correspondence to Laure Patouillard.

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Patouillard, L., Collet, P., Lesage, P. et al. Prioritizing regionalization efforts in life cycle assessment through global sensitivity analysis: a sector meta-analysis based on ecoinvent v3. Int J Life Cycle Assess 24, 2238–2254 (2019). https://doi.org/10.1007/s11367-019-01635-5

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