LCA Methodology: Uncertainties in LCA

The International Journal of Life Cycle Assessment

, Volume 9, Issue 4, pp 216-226

First online:

Uncertainty calculation in life cycle assessments

A combined model of simulation and approximation
  • Andreas CirothAffiliated withGreenDelta TC GmbH Email author 
  • , Günter FleischerAffiliated withTU Berlin, Institute of Environmental Technology
  • , Jörg SteinbachAffiliated withTU Berlin, Institute of Process and Plant Technology (IPAT)

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Goal and Background

Uncertainty is commonly not taken into account in LCA studies, which downgrades their usability for decision support. One often stated reason is a lack of method. The aim of this paper is to develop a method for calculating the uncertainty propagation in LCAs in a fast and reliable manner.


The method is developed in a model that reflects the calculation of an LCA. For calculating the uncertainty, the model combines approximation formulas and Monte Carlo Simulation. It is based on virtual data that distinguishes true values and random errors or uncertainty, and that hence allows one to compare the performance of error propagation formulas and simulation results. The model is developed for a linear chain of processes, but extensions for covering also branched and looped product systems are made and described.


The paper proposes a combined use of approximation formulas and Monte Carlo simulation for calculating uncertainty in LCAs, developed primarily for the sequential approach. During the calculation, a parameter observation controls the performance of the approximation formulas. Quantitative threshold values are given in the paper. The combination thus transcends drawbacks of simulation and approximation.

Conclusions and Outlook

The uncertainty question is a true jigsaw puzzle for LCAs and the method presented in this paper may serve as one piece in solving it. It may thus foster a sound use of uncertainty assessment in LCAs. Analysing a proper management of the input uncertainty, taking into account suitable sampling and estimation techniques; using the approach for real case studies, implementing it in LCA software for automatically applying the proposed combined uncertainty model and, on the other hand, investigating about how people do decide, and should decide, when their decision relies on explicitly uncertain LCA outcomes-these all are neighbouring puzzle pieces inviting to further work.


Approximation formula error propagation life cycle inventory (LCI) life cycle impact assessment (LCIA) models Monte Carlo Simulation quantitative thresholds uncertainties