Environmental Engineering Reference
In-Depth Information
11.1.6 S yStEm B oundariES
Defining system boundaries is part of an iterative process, in which a balance must be struck between
achieving study goals, analysis feasibility, and data availability (Keoleian and Spitzley 2006). In prac-
tice, system boundaries are defined with a cutoff rule—flows below a specified mass, energy amount,
or environmental relevance criteria are deemed negligible and ignored (Keoleian and Spitzley 2006).
One contributing factor to the differing NEVs in Figure 11.1 is the Pimentel and Patzek (2005)
study's inclusion of farm worker food and transportation energy. These parameters are gener-
ally considered to be outside system boundaries in other biofuel LCAs. Recently, biofuel studies
have controversially (Sylvester-Bradley 2008) examined the effects of indirect land-use change on
greenhouse gas (GHG) emissions. This issue is discussed in greater detail in Section 11.4. It should
be noted that the federal Renewable Fuel Standard, which mandates biofuel production levels for
the United States using life-cycle GHG reduction criteria, does include indirect land-use change.
11.1.7 m odEling
Life-cycle models are constructed according to system boundaries, allocation rules, data characteris-
tics, and assumptions. Several well-developed models were utilized in the life-cycle studies reviewed
in this chapter. Argonne National Energy Laboratory's peer-reviewed Greenhouse Gases, Regulated
Emissions, and Energy Use in Transportation (GREET) series 1 model is capable of simulating the well-
to-wheel fuel production pathways of more than 100 fuels (ANL 2009). GREET calculates an LCI of
energy consumption, GHG emissions, and criteria pollutant emissions based on user-specified scenarios.
DAYCENT, another well-developed model used in the following studies, is a daily time-step ver-
sion of the CENTURY dynamic soil model (Del Grosso 2009). It allows nitrous oxide emissions to
be estimated from cultivated farm fields on the basis of inputs that include climate and soil param-
eters. Models can be one of the greatest sources of uncertainty because they may represent systems
that are complex and not well understood.
11.1.8 l ifE -c yclE i nvEntory and i mpact a SSESSmEnt
The LCI is a quantification of the material, energy, and emissions inputs and outputs that pass
through the system boundary. The LCI is generated by simulating a scenario with the life-cycle
model. The ISO (ISO 1998, 2000) and the U.S. Environmental Protection Agency (EPA) (EPA
2008) offer guidance on preparing LCIs. To characterize the environmental and social effects of
LCI flows, life-cycle impact assessment is required (Keoleian and Spitzley 2006).
The purpose of impact assessment is to better understand how the modeled product or sys-
tem affects the surrounding environment. The impacts can be measured by indicators related to
resources, human health, and ecological health. This assessment depends on classification and char-
acterization of LCI results. For example, carbon dioxide (CO 2 ) and methane (CH 4 ) emissions can
be classified as causing global warming impacts and characterized in terms of kg CO 2 -equivalent
(CO 2 e) emissions by using their global warming potential values, available from Intergovernmental
Panel on Climate Change assessment reports (IPCC 2009).
11.2
BIoFuels lIFe-cycle enerGy and GhG emIssIons
11.2.1 i introduction
Over half of the world's fuel ethanol production is based in the United States, as shown in Figure 11.2,
and greater than 93% of this ethanol is derived from corn grain (EERE 2006). In 2007, nearly 23%
of the U.S. corn crop was used to supply the ethanol market (USDA 2009). The second-largest pro-
ducer of ethanol is Brazil, with most of their ethanol derived from sugarcane. Although biodiesel
production is an order of magnitude less than ethanol production in the United States (EIA 2009),
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