Page 175 - Materials Chemistry, Second Edition

160 A. Bjørn et al.

9.7.5 Assumptions for Each Life Cycle Stage

Due to lack of information and budget constraints, it is common to make several
assumptions when constructing an LCI model. For example, data originally planned
to be collected in medium or high specificity may end up being collected in low
specificity. Thereby assumptions need to be made on what low-quality data can best
represent the actual data. For example, should a wastewater treatment process in
Vietnam, for which data could not be obtained, be approximated by a process in
Thailand, possibly correcting for the Vietnamese electricity mix, or should it rather
be approximated by an average process for the entire South East Asian region? All
assumptions made during the construction of the LCI model should be transparently
documented. We recommend that major assumptions are indicated, when
describing the data collection and modelling of each individual life cycle stage, to
facilitate cross-comparison with the documentation of metadata. Major assumptions
may also be included directly in the table containing metadata. References to the
sensitivity analysis should be given for assumptions whose influence on LCIA
results are tested by the creation and analysis of sensitivity scenarios (see next
subsection). We also recommend that a list of all assumptions, minor and major, be
placed in an ‘Appendix’.

9.7.6 Documentation of Data Collected for Uncertainty
and Sensitivity Analysis

For sensitivity analyses, the LCA report must state which parameters are analysed
and whether this is done by calculating normalised sensitivity coefficients (for
parameters of a continuous nature) or by the construction of sensitivity scenarios
(for parameters of a discrete nature). In the former case, the perturbed values for
each parameter must be documented and the basis of these explained (e.g. reported
min/max-values, 2.5/97.5 percentiles, or an arbitrary value, such as ±10%). In the
latter case, the sensitivity scenarios should be documented and references to the
assumptions they are based on made (see previous subsection).
For uncertainty analyses, the best practice is to use statistical distributions of
parameter values as input to Monte Carlo analysis (see Sect. 9.6), in which case the
distributions (e.g. uniform, normal or log-normal) and statistical parameters (e.g.
standard deviation) must be documented for each parameter value covered in the
uncertainty analysis. If, due to lack of such data, the Pedigree approach is taken, the
underlying uncertainty factors and calculated geometric standard deviation for
process must be documented. An example was given earlier in Table 9.6.