Page 153 - Materials Chemistry, Second Edition

138 A. Bjørn et al.

intensify research related to yield increase, potentially leading to, say, a decrease in
area/fertiliser/pesticide use per produced unit of wheat.
Other types of ‘secondary consequences’ related not to prices of products but to
the time consumption of products can also be imagined for some products. For
example, a washing machine may lead to significant time savings for the user. The
question is then what this time will be used for. In some cases, what is gained in
terms of time savings by various household appliances is to some extent used on
other ‘time-consuming’ household appliances, such as TV or videogames. When
assessing a washing machine, it may therefore in some cases make sense to include
an increase in power consumption from the TV set, or something similar.
As may be obvious from the example above, identifying the secondary conse-
quences will in many cases be very difficult and associated with considerable
uncertainties. Furthermore, these effects are typically far from linear and when
certain thresholds are passed a complete shift of parts of the market can be the
consequence (e.g. the point where the production cost of wind power makes it fully
competitive in certain market segments).
Whenever these effects are considered in an LCA, it will often be advisable to
make several different scenarios where various realistic possibilities are addressed
in order to assess the potential variability of the results (see Sect. 9.6).
However, despite the problems of identifying these secondary consequences, it
is evident that if the goal of the assessment is to get as complete an overview of the
consequences of a decision, none of these should be omitted a priori, but should be
included if at all considered to be practically possible and important for the outcome
of the study.
This concludes the introduction and guide to consequential LCA. Readers are
invited to consult the Appendix for an example of how to use the 4-step guideline in
a case study of the consequences of increasing the supply of biodiesel from poultry
fat. As we hope to have demonstrated, consequential LCA is conceptually
appealing because it aims to address the consequences of a potential decision. After
all, why bother making an LCA study (or paying for one) if its outcomes are not
expected to have a consequence on the physical world? We also hope to have
demonstrated that the answers to the many questions that need to be addressed
throughout the 4-step guide are often associated with large uncertainties. Even
advanced economic models generally do a poor job at predicting concrete conse-
quences in markets following some sort of perturbation (consider how global
financial crises tend to take also financial analysts by surprise) and simplifying
assumptions have to be applied. These uncertainties are one reason why many LCA
practitioners prefer an attributional approach. Its use of average process data and
frequent use of allocation is theoretically difficult to defend when the goal of an
LCA study is to support decisions (i.e. study the consequence of decisions), which
is the case for Situation A and B studies in the terminology of ILCD (see Sect. 7.4).
Yet, attributional LCA does not suffer from uncertainties related to economic
modelling and is preferred by some LCA practitioners for this reason and con-
sidered to be ‘on average more correct than consequential LCA’. This is also part of
the reason why ILCD recommends an attributional approach even for goal situation