es502128k_si_001.pdf (432.72 kB)
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
journal contribution
posted on 2015-01-06, 00:00 authored by Wei Wei, Pyrene Larrey-Lassalle, Thierry Faure, Nicolas Dumoulin, Philippe Roux, Jean-Denis MathiasSensitivity analysis (SA) is a significant
tool for studying the
robustness of results and their sensitivity to uncertainty factors
in life cycle assessment (LCA). It highlights the most important set
of model parameters to determine whether data quality needs to be
improved, and to enhance interpretation of results. Interactions within
the LCA calculation model and correlations within Life Cycle Inventory
(LCI) input parameters are two main issues among the LCA calculation
process. Here we propose a methodology for conducting a proper SA
which takes into account the effects of these two issues. This study
first presents the SA in an uncorrelated case, comparing local and
independent global sensitivity analysis. Independent global sensitivity
analysis aims to analyze the variability of results because of the
variation of input parameters over the whole domain of uncertainty,
together with interactions among input parameters. We then apply a
dependent global sensitivity approach that makes minor modifications
to traditional Sobol indices to address the correlation issue. Finally,
we propose some guidelines for choosing the appropriate SA method
depending on the characteristics of the model and the goals of the
study. Our results clearly show that the choice of sensitivity methods
should be made according to the magnitude of uncertainty and the degree
of correlation.