Page 43 - Materials Chemistry, Second Edition

Key Issues in Conducting Life Cycle Assessment 29

involve hundreds to thousands of decision-makers, unlike oil companies that have
a less hierarchical structure for decision-making (McKone et al. 2011).
Data gaps and uncertainties are typical to agricultural processes because field
measurements are difficult to obtain. Different feedstocks, types of soil, agricul-
tural practice, and climate conditions result in various emission levels so that it is
difficult to generalize the environmental performance of biofuels. For example, in
the debate around palm oil biodiesel, the emissions from soil related to the agri-
cultural process depend heavily on local circumstances, while the GHG benefits
over fossil fuels are global in nature. These emissions vary from very positive to
very negative. Such differences are problematic in the sense that they would offer
an uncertain basis for policy making (van der Voet et al. 2010).
As previously mentioned, there are three time periods examined to determine the
long-term consequences of agricultural activities. The period before agriculture is
highly uncertain since the history of when the transformation was taking place is
usually unknown. Similarly, the restoration period after the cessation of agriculture
activities is highly dynamic. In relation to restoration time, McLauchlan (2006)
mentioned that some systems may reach the condition of pre-agricultural time after
decades to millennia. From the above description, it is clear that periods before and
after agriculture are not easy to adopt in the assessment of land-use impact, mainly
due to lack of data availability to follow such a long-term soil quality dynamic.
Furthermore, topography, soil, and climate variability within a region prevent direct
scaling of LCA balances to geographical scales (Schmer et al. 2008).

3.5.2 Conversion Process Variability

Data gaps and uncertainties related to bioenergy technological routes, particularly
on an industrial scale, are not fully resolved. Many advance bioenergy processes
are still in a stage of development, and data will become more informative as
technologies are deployed. This fact makes LCA methodology difficult to apply
during the early phases of a major technology shift (McKone et al. 2011). This is
especially true for immature technologies where validation is presently not pos-
sible. In the case of second-generation bioethanol, for example, most of the LCA
studies use advanced process configurations that are still in developing stages and
no existing commercial scale can be referred to for validation. In this regard, there
is a risk of under- or over-estimating the real impacts of the current production
technology; Therefore, sensitivity analysis is necessary (Wiloso et al. 2012).
There are many technological routes which can be used to convert raw biomass
feedstock into bioenergy products. These different technologies all have a different
development status as illustrated in Fig. 5. For example, the production of heat by
direct combustion of biomass is historically practiced and still the leading bio-
energy application throughout the world. For a more energy efficient use, modern
and large-scale heat applications are often combined with electricity production
(combined heat and power) systems. The use of biomass residues for second-
generation biofuels production would significantly decrease the potential pressure