currently mostly performed by Monte Carlo analysis, which is available in several
commercial LCA software tools [1].
Sensitivity analysis is defined as a “systematic procedure for estimating the effects
of the choices made regarding methods and data on the outcome of a study” [2]. It
helps identifying the most influential parameters, for prioritizing data refining and
reinforcing conclusions. Sensitivity analyses are in current practice mainly carried out
by varying one parameter at a time or by studying alternative scenarios [1].
Wei et al. [3] propose some guidelines on how to perform sensitivity analysis
when some data and modelling parts are correlated. Groen et al. [4] discuss several
approaches to conduct a sensitivity analysis. They analyse their capacities and
drawbacks. They determined that the disadvantage of the method of elementary
effects (MEE), using ranges of values from upper to lower boundary of an input
parameter, is that “the results are not an estimation of the actual variance decomposition” but MEE can be used as a precursor to the more computationally
demanding sampling methods as regression. They see as a main drawback of the
standardized regression coefficients (using Monte Carlo sampling) that many runs
are needed to calculate the variance decomposition.
In fact, the advantages of using ranges of values for input parameter and Monte
Carlo sampling can be combined and the analysis is made easier to interpret by
using some fast calculation methods and visual interpretation.
This paper describes the use of Monte Carlo analysis for depicting a large range
of possible situations. Emphasis is put on presenting innovative ways of visualizing
result variability, which helps interpretation and decision making. The discussion is
based on a real case study.
2 Description of the Case Study
The selected case study aims at comparing two systems of packaging: a corrugated
box versus a reusable folding plastic crate. The functional unit is defined as: the
packaging, transport and delivery of goods in one case from the good manufacturer
to the distributor or repacker. It is assumed that the cases have similar dimensions in
both systems and contain the same number of goods.
For the purpose of this paper, results are only presented for the impact category
global warming potential (GWP, with biogenic carbon taken into account). The
study is divided into two parts: (i) the “average comparison” scenario, i.e. investigation of the impacts of two average cases, each representative of one system;
(ii) the sensitive analysis, i.e. calculation and interpretation of results covering a
large range of situations defined as possible for each system.
344
C. Alexandre et al.
commercial LCA software tools [1].
Sensitivity analysis is defined as a “systematic procedure for estimating the effects
of the choices made regarding methods and data on the outcome of a study” [2]. It
helps identifying the most influential parameters, for prioritizing data refining and
reinforcing conclusions. Sensitivity analyses are in current practice mainly carried out
by varying one parameter at a time or by studying alternative scenarios [1].
Wei et al. [3] propose some guidelines on how to perform sensitivity analysis
when some data and modelling parts are correlated. Groen et al. [4] discuss several
approaches to conduct a sensitivity analysis. They analyse their capacities and
drawbacks. They determined that the disadvantage of the method of elementary
effects (MEE), using ranges of values from upper to lower boundary of an input
parameter, is that “the results are not an estimation of the actual variance decomposition” but MEE can be used as a precursor to the more computationally
demanding sampling methods as regression. They see as a main drawback of the
standardized regression coefficients (using Monte Carlo sampling) that many runs
are needed to calculate the variance decomposition.
In fact, the advantages of using ranges of values for input parameter and Monte
Carlo sampling can be combined and the analysis is made easier to interpret by
using some fast calculation methods and visual interpretation.
This paper describes the use of Monte Carlo analysis for depicting a large range
of possible situations. Emphasis is put on presenting innovative ways of visualizing
result variability, which helps interpretation and decision making. The discussion is
based on a real case study.
2 Description of the Case Study
The selected case study aims at comparing two systems of packaging: a corrugated
box versus a reusable folding plastic crate. The functional unit is defined as: the
packaging, transport and delivery of goods in one case from the good manufacturer
to the distributor or repacker. It is assumed that the cases have similar dimensions in
both systems and contain the same number of goods.
For the purpose of this paper, results are only presented for the impact category
global warming potential (GWP, with biogenic carbon taken into account). The
study is divided into two parts: (i) the “average comparison” scenario, i.e. investigation of the impacts of two average cases, each representative of one system;
(ii) the sensitive analysis, i.e. calculation and interpretation of results covering a
large range of situations defined as possible for each system.
344
C. Alexandre et al.