Estimation of the size of error introduced into consequential models by using attributional background datasets



A systematic comparison is made of attributional and consequential results for the same products using the same unit process database, thus isolating the effect of the two system models. An analysis of this nature has only recently been made possible due to the ecoinvent database version 3 providing an access to both unallocated and unlinked unit process datasets as well as both attributional and consequential models based on these datasets. The analysis is therefore limited to the system models provided by ecoinvent.


For both system models, the analysis was made on the life cycle inventory analysis (LCIA) results as published by ecoinvent (692 impact categories from different methods, for 11,650 product/activity combinations). The comparison was made on the absolute difference relative to the smallest absolute value.

Results and discussion

The comparison provides quantified results showing that the consequential modelling provides large differences in results when the unconstrained (marginal) suppliers have much more/less impact than the average, when analysing the by-products, and when analysing determining products from activities with important amounts of other coproducts.


The analysis confirms that for consequential studies, attributional background datasets are not appropriate as a substitute for consequential background. The overall error will of course depend on the extent to which attributional modelling is used as part of the overall system model. While the identified causes of differences between the attributional and consequential models are of general nature, the identified sizes of the errors are specific to the way the two models are implemented in ecoinvent.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. Andrae ASG (2014) Method based on market changes for improvement of comparative attributional life cycle assessments. Int J Life Cycle Assess 20(2):263–275

    Article  Google Scholar 

  2. Consequential-LCA (2015) Multiple determining products from joint production. Last updated: 2015–10-27.

  3. Curran MA, Mann M, Norris G (2005) The international workshop on electricity data for life cycle inventories. J Clean Prod 13(8):853–862

    Article  Google Scholar 

  4. Ecoinvent Centre (2013) ecoinvent data v3. Swiss Centre for Life Cycle Inventories, St. Gallen. Available from

  5. Plevin RJ, Delucchi MA, Creutzig F (2014) Using attributional life cycle assessment to estimate climate-change mitigation benefits misleads policy makers. J Ind Ecol 18(1):73–83

    Article  Google Scholar 

  6. Sonnemann G, Vigon B (eds) (2011) Global guidance principles for life cycle assessment databases. UNEP/SETAC Life Cycle Initiative, Paris/Pensacola

  7. Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Reinhard J, Vadenbo CO, Wernet G (2013) Overview and methodology. Data quality guideline for the ecoinvent database version 3. Ecoinvent Report 1(v3). St. Gallen: The ecoinvent Centre

  8. Weidema BP, Ekvall T, Heijungs R (2009) Consequential LCA. In: Guidelines for applications of deepened and broadened LCA. Deliverable D18 of work package 5 of the CALCAS project.

Download references

Author information



Corresponding author

Correspondence to Bo P. Weidema.

Additional information

Responsible editor: Shabbir Gheewala

Electronic supplementary material


(DOCX 17 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Weidema, B.P. Estimation of the size of error introduced into consequential models by using attributional background datasets. Int J Life Cycle Assess 22, 1241–1246 (2017).

Download citation


  • Attributional modelling
  • Comparison
  • Consequential modelling
  • Coproducts
  • Decision support
  • Marginal suppliers