Screening stochastic life cycle assessment inventory models

Kelly G. Canter, Dale J. Kennedy, Douglas Montgomery, J. Bert Keats, W. Matthew Carlyle

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


A screening methodology is presented that utilizes the linear structure within the deterministic life cycle inventory (LCI) model. The methodology ranks each input data element based upon the amount it contributes toward the final output. The identified data elements along with their position in the deterministic model are then sorted into descending order based upon their individual contributions. This enables practitioners and model users to identify those input data elements that contribute the most in the inventory stage. Percentages of the top ranked data elements are then selected, and their corresponding data quality index (DQI) value is upgraded in the stochastic LCI model. Monte Carlo computer simulations are obtained and used to compare the output variance of the original stochastic model with modified stochastic model. The methodology is applied to four real-world beverage delivery system LCA inventory models for verification. This research assists LCA practitioners by streamlining the conversion process when converting a deterministic LCI model to a stochastic model form. Model users and decision-makers can benefit from the reduction in output variance and the increase in ability to discriminate between product system alternatives.

Original languageEnglish (US)
Article numberBF02978906
Pages (from-to)18-26
Number of pages9
JournalInternational Journal of Life Cycle Assessment
Issue number1
StatePublished - 2002


  • Data quality indicator (DQI)
  • Input data quality
  • LCI
  • Life cycle inventory (LCI)
  • Monte carlo simulation
  • Ranking
  • Screening
  • Stochastic modeling
  • Streamlining

ASJC Scopus subject areas

  • General Environmental Science


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