Data representativeness in LCA: A framework for the systematic assessment of data quality relative to technology characteristics

Trine Henriksen*, Thomas Fruergaard Astrup, Anders Damgaard

*Corresponding author for this work

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A shortcoming in current data quality assessment schemes is that the data quality information is not used systematically to identify the critical data in a life cycle inventory (LCI) model. In addition, existing criteria employed to evaluate representativeness lack relevance to the specific context of a study. A novel framework is proposed herein for the evaluation of the representativeness of LCI data, including an analysis of the importance of the data and a modification of quality criteria based on unit process characteristics. Temporal characteristics are analyzed by identifying the technology shift, because data generated before this time are considered outdated. Geographical and technological characteristics are analyzed by defining a “related area” and a “related technology,” which is done by identifying a number of relevant geographical and technical factors, and then comparing the collected data with these factors. The framework was illustrated in a case study on household waste incineration in Denmark. The results demonstrated the applicability of the method in practice, and they provided data quality criteria unique to waste incineration unit processes, for example, different time intervals to evaluate temporal representativeness. However, the proposed method is time demanding, and thus sector‐level characteristic analyses are feasible instead of the user having to do the analyses.
Original languageEnglish
JournalJournal of Industrial Ecology
Issue number1
Pages (from-to)51-66
Publication statusPublished - 2021


  • data quality assessment
  • global sensitivity analysis
  • industrial ecology
  • life cycle inventory
  • technology characteristics
  • waste incineration


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