Visualizing the Effects of Parameter
Variability on Comparative LCA
Results
Céline Alexandre, Elisabeth van Overbeke, Maxime Dupriez,
Johan Lhotellier and Bernard De Caevel
Abstract Bar charts and other usual ways of presenting LCA results depict one
average or typical situation, lacking to represent the diversity of individual cases
and the uncertainties associated to input data or modelling assumptions. This paper
presents ways of visualizing variable results in comparative LCA. The main concept is to perform at once calculations representing this variability. Based on Monte
Carlo analysis, the approach is enabled by the LCA software RangeLCA, developed by RDC Environment. Results of all simulations can be plotted in function of
one or two influential parameters. A clear and complete view can hence be obtained
as well as more reliable conclusions. Instead of answering the question “Is a system
better than another one in specific cases?”, the presented graphs help LCA studies
to answer a much broader question: “In which range of situations is a system better
than another?”.
1 Introduction
In most LCAs, some parameters are better characterized by variable values than by
fixed “typical” values. The reasons for the variability can be of two types: uncertainty and diversity of situations. The uncertainty can be either systematic (e.g.
linked to imprecision of the measuring instrument), stochastic (e.g. fluctuation of a
parameter with time) or epistemic (e.g. modelling required in the absence of
measurement) [1]. The diversity of potential situations within the studied system
also leads to parameter variability, e.g. the transport to various customers is best
modelled by a range of distances or a waste can be either incinerated or landfilled.
The way the input data variability influences the results can be analysed. The
uncertainty analysis is the “systematic procedure to quantify the uncertainty
introduced in the results of a life cycle inventory analysis due to the cumulative
effects of model imprecision, input uncertainty and data variability” [2]. It is
C. Alexandre Á E. van Overbeke (&) Á M. Dupriez Á J. Lhotellier Á B. De Caevel
RDC Environment, 1160 Brussels, Belgium
e-mail: elisabeth.vanoverbeke@rdcenvironment.be
© The Author(s) 2018
E. Benetto et al. (eds.), Designing Sustainable Technologies,
Products and Policies, https://doi.org/10.1007/978-3-319-66981-6_38
343
Variability on Comparative LCA
Results
Céline Alexandre, Elisabeth van Overbeke, Maxime Dupriez,
Johan Lhotellier and Bernard De Caevel
Abstract Bar charts and other usual ways of presenting LCA results depict one
average or typical situation, lacking to represent the diversity of individual cases
and the uncertainties associated to input data or modelling assumptions. This paper
presents ways of visualizing variable results in comparative LCA. The main concept is to perform at once calculations representing this variability. Based on Monte
Carlo analysis, the approach is enabled by the LCA software RangeLCA, developed by RDC Environment. Results of all simulations can be plotted in function of
one or two influential parameters. A clear and complete view can hence be obtained
as well as more reliable conclusions. Instead of answering the question “Is a system
better than another one in specific cases?”, the presented graphs help LCA studies
to answer a much broader question: “In which range of situations is a system better
than another?”.
1 Introduction
In most LCAs, some parameters are better characterized by variable values than by
fixed “typical” values. The reasons for the variability can be of two types: uncertainty and diversity of situations. The uncertainty can be either systematic (e.g.
linked to imprecision of the measuring instrument), stochastic (e.g. fluctuation of a
parameter with time) or epistemic (e.g. modelling required in the absence of
measurement) [1]. The diversity of potential situations within the studied system
also leads to parameter variability, e.g. the transport to various customers is best
modelled by a range of distances or a waste can be either incinerated or landfilled.
The way the input data variability influences the results can be analysed. The
uncertainty analysis is the “systematic procedure to quantify the uncertainty
introduced in the results of a life cycle inventory analysis due to the cumulative
effects of model imprecision, input uncertainty and data variability” [2]. It is
C. Alexandre Á E. van Overbeke (&) Á M. Dupriez Á J. Lhotellier Á B. De Caevel
RDC Environment, 1160 Brussels, Belgium
e-mail: elisabeth.vanoverbeke@rdcenvironment.be
© The Author(s) 2018
E. Benetto et al. (eds.), Designing Sustainable Technologies,
Products and Policies, https://doi.org/10.1007/978-3-319-66981-6_38
343