Environmental Engineering Reference
In-Depth Information
Process residues,
87
Grain-to-ethanol,
87
Crop residues, 446
Logging/other
residue, 64
Agriculture
Fuel treatments, 60
Forestry
Perrenial crops,
377
Urban wood
residues, 47
Wood processing
residues, 70
Pulping liquor, 74
Fuelwood, 52
FIGure 11.32
U.S. biomass production potential in million dry tons per year by 2030 from agricultural
and forestry sources. (From USDOE/USDA,
Biomass as a Feedstock for a Bioenergy and Bioproducts
Industry: The Technical Feasibility of a Billion-Ton Annual Supply
, 2005. U.S. Department of Energy/
U.S. Department of Agriculture, available at http://www1.eere.energy.gov/biomass/pdfs/final_billionton_
vision_report2.pdf)
with the impacts typically categorized as direct or indirect. Direct land-use impacts are those that
result from the actions of the biofuel supply chain. An example would be expansion of corn acreage
for ethanol production. Indirect land-use change is more difficult to measure because it is caused
by market forces and can act over vast distances. An example would be farmland expansion in
Brazil that reduces available rangeland for cattle and causes ranchers to clear rainforest, driven by
increased commodity crop prices in the United States. Some have suggested that indirect land-use
change should not be included in life-cycle calculations (Kim et al. 2009). When indirect land-use
change is modeled within LCA system boundaries, the increase in GHG emissions can be substan-
tial. Integration of land-use impacts into LCA is an active area of research (Kløverpris et al. 2008b).
More generally, indirect land-use change studies are examples of the recent shift from attributional
to consequential LCAs and require global economic models to understand complex ripple effects
(Sheehan 2009). Since this chapter was written many new studies have been conducted on the con-
troversial and complex topic of land use change. The following studies represent a few of the first
that brought major attention to this issue.
Searchinger et al. (2008) assessed the GHG emissions of corn ethanol, in comparison to petro-
leum, with a focus on the effects of indirect land-use change. Key assumptions were that increased
ethanol consumption in the United States would increase planting of replacement crops because total
crop demand is inelastic, and subsequently cropland would expand globally because doing so is cost-
effective, fast, and convertible lands are relatively abundant. This market-driven land clearing for agri-
culture would result in a large initial release of the carbon stored in soil and plants—a carbon debt—as
well as reduce the annual carbon sequestering capacity of the land, with the magnitude of both depen-
dent on the type of natural habit lost (e.g., forest, grassland, bog). Searchinger's results agree with
Wang et al. (2007) for corn ethanol when land-use change is not considered; GHG emissions would be
reduced by approximately 20% for the average corn ethanol case, as shown in Figure 11.16. However,
when land-use change is considered, life-cycle GHG emissions for corn ethanol are 93% higher those
of petroleum over the 30-year study period, according to Searchinger et al. (2008). Because the corn
ethanol production that is indirectly responsible for this cropland expansion would displace GHG-
intensive petroleum, the large initial carbon debt allocated to the corn ethanol will slowly be paid back
over the life of the project. Searchinger et al. (2008) calculated a payback period of 167 years—the
time required to negate the land-use change GHG emissions and return to a carbon-neutral state.
