Page 126 - Materials Chemistry, Second Edition

SOURCING LIFE CYCLE INVENTORY DATA 109

problem in multi-product systems) is an inherent part of consequential LCA
studies. Ultimately, choosing between an attributional or a consequential LCA
is decided by the defined goal of the study. The choice will also influence sys-
tem boundaries related to how allocation is conducted as well as other meth-
odological choices, such as the definition of functional unit and the choice of
life cycle impact assessment (LCIA) methods (Finnveden 2009).
The decision to use marginal data can be significant for modeling systems
that include activities such as electricity generation, land use, etc. or other
areas in which a change in the balance between supply and demand for a good
or service can have a far-reaching impact. For example, Searchinger et al. found
an attributional analysis of US corn-based ethanol resulted in a 20% decrease
in greenhouse gas emissions compared to conventional gasoline. However,
in a consequential analysis to account for policy-driven increases in output,
they predicted a 47% increase in emissions compared to gasoline, due to land
use changes induced by higher prices of corn, soybeans and other grains from
anticipated additional demand for corn starch for ethanol production.
A consequential LCA is conceptually complex because it includes addi-
tional, economic concepts such as marginal production costs, elasticity of sup-
ply and demand, etc. Consequential LCA depends on descriptions of economic
relationships embedded in models. It generally attempts to reflect economic
relationships by extrapolating historical trends in prices, consumption and
outputs. Some of the models are also much less transparent than the linear and
static model of attributional LCA. Their results can also be very sensitive to the
built-in assumptions. All these add to the risk that inadequate assumptions or
other errors significantly affect the final LCA results. To reduce this risk, it is
important to ensure that the various results regarding different consequences
can be explained using credible arguments.
It is possible that the inventory results of a consequential LCA will be nega-
tive, if the change in the level of production causes a reduction in emissions
greater than the emissions from the production of the product. This does not
mean that the absolute emissions from the production of the product are nega-
tive, but that the production of the product will cause a reduction in emissions
elsewhere in the system.
In the end, both approaches are legitimate and fulfill different needs (Ekvall
et al 2005). The distinction between attributional and consequential LCA is one
example of how choices in the Goal and Scope Definition of an LCA should
influence methodological and data choices for the LCI and LCIA phases.

5.3 Types of LCI Data

Clearly, defining the required data sources and types prior to data collection
helps to reduce costs and the time required to collect the data. Whenever possi-
ble, it is best to get well-characterized industry data for production processes.
Manufacturing processes can change over time by becoming more efficient,
adopting newer technology, incorporating changes to emissions standards,