Page 170 - Materials Chemistry, Second Edition

9 Life Cycle Inventory Analysis 155

In practice, the number of flows and processes is normally huge, but no manual
work is typically required from the LCA practitioner as the LCA software can
calculate LCI results for a product system with one click of a mouse button.
Such LCI results are the basis for the subsequent life cycle impact assessment phase
(unless the goal of a study is to simply calculate the LCI results).

9.6 Data Needs for Uncertainty and Sensitivity Analysis

Uncertainty and sensitivity analysis is important for the interpretation of LCIA
results because they can inform the LCA practitioner on how robust the conclusions
of the study are and where future studies should focus to make results even more
robust. Chapter 11 is dedicated to these matters and details the theoretical back-
ground and the practical use of uncertainty and sensitivity analyses. The following
describes the data that needs to be collected during the inventory analysis as inputs
for uncertainty and sensitivity analyses.
Uncertainty analysis allows for the quantification of uncertainties of the final
result, as a consequence of the uncertainty of each parameter in the LCI model. To
enable an uncertainty analysis, the practitioner must, for quantitative parameters in
the foreground system, collect information on their statistical distribution
(e.g. normal, log-normal or uniform) and corresponding statistical parameter values
(e.g. mean and standard deviation for normally distributed parameters).
Sensitivity analysis allows for systematic identification of the parameters that
have the highest influence on the LCIA results. The influence of parameters on
results is calculated by changing them, one by one, and observing the changes in
results. These changes in parameters should reflect uncertainties about the actual
product system modelled. For quantitative parameters in the foreground system, the
practitioner should aim to collect minimum and maximum values, or a low and a
high percentile (e.g. 2.5th and 97.5th) when a parameter’s statistical distribution is
known (see above), in addition to the default value that is used in the LCI model.
For example, a specific farmer may on average apply 2 kg of a specific pesticide to
produce 1 tonne of potatoes, but this number may vary from 0.5 to 3 kg, depending
on weather conditions. For discrete parameters or assumptions in the foreground
system the practitioner should develop a number of sensitivity scenarios. For
example, a part of the product system may be located in a different country than
assumed in the LCI model and a sensitivity scenario would thus involve differences
in energy mix, waste treatment technologies, etc. Note that the data requirements
for sensitivity and uncertainty analysis overlap and data collection can therefore be
performed in parallel by the practitioner.
It often takes more time to collect sensitivity and uncertainty data for some
parameters in the foreground system than for others and it may not be necessary to
collect data for all processes, depending on the outcome of the first iteration of the
analysis. For example, if a process is found to contribute to less than 0.1% of total