Advertisement

The International Journal of Life Cycle Assessment

, Volume 24, Issue 12, pp 2238–2254 | Cite as

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

  • Laure PatouillardEmail author
  • Pierre Collet
  • Pascal Lesage
  • Pablo Tirado Seco
  • Cécile Bulle
  • Manuele Margni
UNCERTAINTIES IN LCA

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.

Keywords

Data collection Economic sector Global sensitivity analysis Prioritization Regionalization 

Notes

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.

Supplementary material

11367_2019_1635_MOESM1_ESM.pdf (589 kb)
ESM 1 (PDF 588 kb)

References

  1. Baitz M, Albrecht S, Brauner E et al (2012) LCA’s theory and practice: like ebony and ivory living in perfect harmony? Int J Life Cycle Assess 18:5–13Google Scholar
  2. Borgonovo E, Plischke E (2016) Sensitivity analysis: a review of recent advances. Eur J Oper Res 248:869–887Google Scholar
  3. Boulay A, Bulle C, Bayart J-B et al (2011) Regional characterization of freshwater use in LCA: modeling direct impacts on human health. Environ Sci Technol 45:8948–8957Google Scholar
  4. Boulay AM, Bare J, Benini L, Berger M, Lathuillière MJ, Manzardo A, Margni M, Motoshita M, Núñez M, Pastor AV, Ridoutt B, Oki T, Worbe S, Pfister S (2018) The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE). Int J Life Cycle Assess 23:368–378Google Scholar
  5. Bulle C, Margni M, Patouillard L, Boulay AM, Bourgault G, de Bruille V, Cao V, Hauschild M, Henderson A, Humbert S, Kashef-Haghighi S, Kounina A, Laurent A, Levasseur A, Liard G, Rosenbaum RK, Roy PO, Shaked S, Fantke P, Jolliet O (2019) IMPACT World+: a globally regionalized life cycle impact assessment method. Int J Life Cycle Assess.  https://doi.org/10.1007/s11367-019-01583-0 Google Scholar
  6. Chaudhary A, Verones F, de Baan L, Hellweg S (2015) Quantifying land use impacts on biodiversity: combining species-area models and vulnerability indicators. Environ Sci Technol 49:9987–9995Google Scholar
  7. Clavreul J, Guyonnet D, Tonini D, Christensen TH (2013) Stochastic and epistemic uncertainty propagation in LCA. Int J Life Cycle Assess 18:1393–1403Google Scholar
  8. Collet P, Lardon L, Steyer J-P, Hélias A (2014) How to take time into account in the inventory step: a selective introduction based on sensitivity analysis. Int J Life Cycle Assess 19:320–330Google Scholar
  9. Gregory JR, Noshadravan A, Olivetti EA, Kirchain RE (2016) A methodology for robust comparative life cycle assessments incorporating uncertainty. Environ Sci Technol 50:6397–6405Google Scholar
  10. Groen EA, Heijungs R (2017) Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk? Environ Impact Assess Rev 62:98–109Google Scholar
  11. Groen EA, Heijungs R, Bokkers EAM, de Boer IJM (2014) Methods for uncertainty propagation in life cycle assessment. Environ Model Softw 62:316–325Google Scholar
  12. Groen EA, Bokkers EAM, Heijungs R, de Boer IJM (2017) Methods for global sensitivity analysis in life cycle assessment. Int J Life Cycle Assess 22:1125–1137Google Scholar
  13. Heijungs R (1996) Identification of key issues for further investigation in improving the reliability of life-cycle assessments. J Clean Prod 4:159–166Google Scholar
  14. Heijungs R (2010) Sensitivity coefficients for matrix-based LCA. Int J Life Cycle Assess 15:511–520Google Scholar
  15. Helmes RJK, Huijbregts MAJ, Henderson AD, Jolliet O (2012) Spatially explicit fate factors of phosphorous emissions to freshwater at the global scale. Int J Life Cycle Assess 17:646–654Google Scholar
  16. Henriksson PJG, Heijungs R, Dao HM, Phan LT, de Snoo GR, Guinée JB (2015) Product carbon footprints and their uncertainties in comparative decision contexts. PLoS One 10:1–11.  https://doi.org/10.1371/journal.pone.0121221 CrossRefGoogle Scholar
  17. Hernández-Padilla F, Margni M, Noyola A, Guereca-Hernandez L, Bulle C (2017) Assessing wastewater treatment in Latin America and the Caribbean: enhancing life cycle assessment interpretation by regionalization and impact assessment sensibility. J Clean Prod 142:2140–2153Google Scholar
  18. Herrmann IT, Hauschild MZ, Sohn MD, McKone TE (2014) Confronting uncertainty in life cycle assessment used for decision support. J Ind Ecol 18:366–379Google Scholar
  19. Huijbregts MAJ (1998) Uncertainty in LCA LCA methodology application of uncertainty and variability in LCA part I: a general framework for the analysis of uncertainty and variability in life cycle assessment. Int J Life Cycle Assess 3:273–280Google Scholar
  20. Huijbregts M, Norris G, Bretz R (2001) Framework for modelling data uncertainty in life cycle inventories. Int J Life Cycle Assess 6:127–132Google Scholar
  21. Igos E, Meyer R, Benetto E et al (2015) Uncertainty and sensitivity analyses in LCA: a review and application to noise characterization. In: SETAC Europe 25th Annual MeetingGoogle Scholar
  22. Imbeault-Tétreault H (2010) Propagation analytique de l’incertitude à travers le calcul matriciel d’une analyse du cycle de vieGoogle Scholar
  23. International Organization for Standardization (ISO) (2006a) ISO14040:2006 Environmental management-life cycle assessment-principles and frameworkGoogle Scholar
  24. International Organization for Standardization (ISO) (2006b) ISO14044:2006 Environmental management—life cycle assessment—requirements and guidelinesGoogle Scholar
  25. Iooss B, Lemaître P (2015) A review on global sensitivity analysis methods. In: Dellino G., Meloni C. (eds) Uncertainty management in simulation-optimization of complex systems. Operations Research/Computer Science Interfaces Series, vol 59. Springer, Boston, MA, pp 101–122Google Scholar
  26. Jacques J, Lavergne C, Devictor N (2006) Sensitivity analysis in presence of model uncertainty and correlated inputs. Reliab Eng Syst Saf 91:1126–1134Google Scholar
  27. Laurin L, Amor B, Bachmann TM, Bare J, Koffler C, Genest S, Preiss P, Pierce J, Satterfield B, Vigon B (2016) Life cycle assessment capacity roadmap (section 1): decision-making support using LCA. Int J Life Cycle Assess 21:443–447Google Scholar
  28. Lesage P, Samson R (2016) The Quebec life cycle inventory database project. Int J Life Cycle Assess 21:1282–1289Google Scholar
  29. Lesage P, Mutel C, Schenker U, Margni M (2018) Uncertainty analysis in LCA using precalculated aggregated datasets. Int J Life Cycle Assess 23:2248–2265Google Scholar
  30. Most T (2012) Variance-based sensitivity analysis in the presence of correlated input variables. Conference: 5th International Conference on Reliable Engineering Computing, At Brno, Czech RepublicGoogle Scholar
  31. Muller S, Lesage P, Ciroth A, Mutel C, Weidema BP, Samson R (2016) The application of the pedigree approach to the distributions foreseen in ecoinvent v3. Int J Life Cycle Assess 21:1327–1337Google Scholar
  32. Mutel C (2017) Brightway: an open source framework for life cycle assessment. J Open Source Softw 2:doi:  https://doi.org/10.21105/joss.00236 Google Scholar
  33. Mutel CL, Hellweg S (2009) Regionalized life cycle assessment: computational methodology and application to inventory databases. Environ Sci Technol 43:5797–5803Google Scholar
  34. Mutel CL, Pfister S, Hellweg S (2012) GIS-based regionalized life cycle assessment: how big is small enough? Methodology and case study of electricity generation. Environ Sci Technol 46:1096–1103Google Scholar
  35. Mutel CL, de Baan L, Hellweg S (2013) Two-step sensitivity testing of parametrized and regionalized life cycle assessments: methodology and case study. Environ Sci Technol 47:5660–5667Google Scholar
  36. Padey P, Girard R, le Boulch D, Blanc I (2013) From LCAs to simplified models: a generic methodology applied to wind power electricity. Environ Sci Technol 47:1231–1238Google Scholar
  37. Page EB (1963) Ordered hypotheses for multiple treatments: a significance test for linear ranks. J Am Stat Assoc 58:216–230Google Scholar
  38. Patouillard L, Bulle C, Margni M (2016) Ready-to-use and advanced methodologies to prioritise the regionalisation effort in LCA. Mater Tech 104:105Google Scholar
  39. Patouillard L, Bulle C, Querleu C, Maxime D, Osset P, Margni M (2018) Critical review and practical recommendations to integrate the spatial dimension into life cycle assessment. J Clean Prod 177:398–412Google Scholar
  40. Pfister S, Scherer L (2015) Uncertainty analysis of the environmental sustainability of biofuels. Energy Sustain Soc 5:1–12Google Scholar
  41. Pfister S, Koehler A, Hellweg S (2009) Assessing the environmental impacts of freshwater consumption in LCA. Environ Sci Technol 43:4098–4104Google Scholar
  42. Plouffe G, Bulle C, Deschênes L (2015) Assessing the variability of the bioavailable fraction of zinc at the global scale using geochemical modeling and soil archetypes. Int J Life Cycle Assess 20:527–540Google Scholar
  43. Potting J, Hauschild M (2006) Spatial differentiation in life cycle impact assessment: a decade of method development to increase the environmental realism of LCIA. Int J Life Cycle Assess 11:11–13Google Scholar
  44. Refsgaard JC, van der Sluijs JP, Højberg AL, Vanrolleghem PA (2007) Uncertainty in the environmental modelling process—a framework and guidance. Environ Model Softw 22:1543–1556Google Scholar
  45. Reinhard J, Mutel CL, Wernet G, Zah R, Hilty LM (2016) Contribution-based prioritization of LCI database improvements: method design, demonstration, and evaluation. Environ Model Softw 86:204–218Google Scholar
  46. Riggs LS (1989) Numerical approach for generating beta random variates. J Comput Civ Eng 3:183–191Google Scholar
  47. Ross S, Evans D (2002) Excluding site-specific data from the LCA inventory: how this affects life cycle impact assessment. Int J Life Cycle Assess 7:141–150Google Scholar
  48. Roy P-O, Deschênes L, Margni M (2013) Uncertainty and spatial variability in characterization factors for aquatic acidification at the global scale. Int J Life Cycle Assess 19:882–890Google Scholar
  49. Sakai S, Yokoyama K (2002) Formulation of sensitivity analysis in life cycle assessment using a perturbation method. Clean Techn Environ Policy 4:72–78Google Scholar
  50. Saltelli A (2017) Sensitivity analysis. Numbers policy Pract Probl QuantifGoogle Scholar
  51. Saltelli A, Sobol’ IM (1995) About the use of rank transformation in sensitivity analysis of model output. Reliab Eng Syst Saf 50:225–239Google Scholar
  52. Saltelli A, Tarantola S (2002) On the relative importance of input factors in mathematical models. J Am Stat Assoc 97:702–709Google Scholar
  53. Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181:259–270Google Scholar
  54. Sobol IM (1993) Sensitivity estimates for nonlinear mathematical models. Math Model Comput Exp 1:407–414Google Scholar
  55. Turconi R, Boldrin A, Astrup T (2013) Life cycle assessment (LCA) of electricity generation technologies: overview, comparability and limitations. Renew Sust Energ Rev 28:555–565Google Scholar
  56. Udo de Haes HA, Jolliet O, Finnveden G et al (1999) Best available practice regarding impact categories and category indicators in life cycle impact assessment. Int J Life Cycle Assess 4:66–74Google Scholar
  57. United Nations Statistics Division (2008) ISIC Rev.4 - Classifications Registry. In: 2008. https://unstats.un.org/unsd/cr/registry/isic-4.asp. Accessed 27 Feb 2018
  58. Vigon BW, Tolle DA, Cornaby BW et al (1993) Life-cycle assessment: inventory guidelines and principles, EPA/600/R-. Washington, DC20460Google Scholar
  59. Wardekker JA, van der Sluijs JP, Janssen PHM, Kloprogge P, Petersen AC (2008) Uncertainty communication in environmental assessments: views from the Dutch science-policy interface. Environ Sci Pol 11:627–641.  https://doi.org/10.1016/j.envsci.2008.05.005 CrossRefGoogle Scholar
  60. Wei W, Larrey-Lassalle P, Faure T, Dumoulin N, Roux P, Mathias JD (2015) How to conduct a proper sensitivity analysis in life cycle assessment: taking into account correlations within LCI data and interactions within the LCA calculation model. Environ Sci Technol 49:377–385Google Scholar
  61. Weidema BP, Wesnæs MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4:167–174Google Scholar
  62. Wender BA, Prado V, Fantke P, Ravikumar D, Seager TP (2018) Sensitivity-based research prioritization through stochastic characterization modeling. Int J Life Cycle Assess 23:324–332Google Scholar
  63. Wernet G, Bauer C, Steubing B, Reinhard J, Moreno-Ruiz E, Weidema B (2016) The ecoinvent database version 3 (part I): overview and methodology. Int J Life Cycle Assess 21:1218–1230Google Scholar
  64. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80Google Scholar
  65. Xu C, Gertner GZ (2008) Uncertainty and sensitivity analysis for models with correlated parameters. Reliab Eng Syst Saf 93:1563–1573Google Scholar
  66. Yang Y, Tao M, Suh S (2018) Geographic variability of agriculture requires sector-specific uncertainty characterization. Int J Life Cycle Assess 23:1581–1589Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CIRAIG, Polytechnique MontréalMontrealCanada
  2. 2.IFP Energies nouvellesRueil-MalmaisonFrance
  3. 3.UMR 0210 INRA-AgroParisTech Economie publique, INRAThiverval-GrignonFrance
  4. 4.CIRAIG, ESG UQAMMontréalCanada

Personalised recommendations