2.3.1 Biomass cost estimates by feedstock type

Lignocellulosic biomass feedstocks employed by biorefineries can broadly be divided into two categories: dedicated energy crops and residues. Dedicated energy crops are crops grown specifically for use as biomass feedstocks in biorefineries. These are divided into two further categories: herbaceous energy crops and short-rotation woody crops. Herbaceous energy crops contain little to no woody material and are exemplified by grasses. Common examples include switchgrass, Miscanthus giganteus, and energy cane. Short-rotation woody crops are softwoods and hardwoods with short harvest rotations. Common examples include hybrid poplar and eucalyptus. Short-rotation woody crops have longer harvest rotations than most herbaceous crops but compensate for this by also producing higher yields by biomass weight.

Biomass residues are waste products from either urban or rural areas. Residues from urban areas include both municipal solid waste (MSW) and processing residues from factories and manufacturing centers utilizing biomass as an input. Residues from urban areas are characterized by high concentration and low costs due to the avoidance of tipping fees otherwise paid to waste haulers. The disadvantages to using urban residues as biorefinery feedstocks are their heterogeneous nature (for example, MSW frequently contains plastics, metals, and glass capable of damaging a biorefinery) and high values for nearby land, thereby increasing biorefinery costs in the form of either capital or transportation costs. Biomass residues from rural areas most commonly take the form of agricultural residues left on the field after a crop harvest, such as corn stover. These are spread out over a large area and require specialized collection equipment, resulting in higher costs as biorefinery feedstocks than urban residues. Agricultural residues have the advantages of being homogeneous and located near inexpensive land, allowing biorefineries employing them as feedstock to minimize both capital and transportation costs.

Two methods are employed for estimating biorefinery feedstock costs. The first is the use of field trials that account for detailed costs of feedstock production, collection, transportation, and mitigation of negative environmental effects (e. g., nutrient replacement necessitated by the removal of corn stover). Several studies employing field studies have calculated the cost of agricultural residues to be lower than the cost of dedicated energy crops; the delivered cost of stover is calculated to be in the range of $47/MT to $75/MT (Brechbill et al., 2011; Perlack and Turhollow, 2003; Petrolia, 2008) while that of switchgrass is calculated to be in the range of $80/MT to $96/MT (Brechbill et al., 2011). The disparity between the costs of agricultural residues and dedicated energy crops is due to the fact that residues do not require an accounting of production costs and opportunity costs, as they are produced during the normal course of crop production and just need to be collected and transported to the biorefinery. Dedicated energy crops must account for these costs in addition to production and opportunity costs.

The second method employed for estimating biorefinery feedstock costs is the use of economic models based on a combination of field trials, supply chain data, and macroeconomic prices. Two recent examples have been developed by researchers at North Carolina State University (Gonzalez et al., 2011) and the National Research Council (Committee on Economic and Environmental Impacts of Increasing Biofuels Production, 2011). In both cases the costs estimated by the economic models have been greater than those from field trials, with the delivered cost of switchgrass ranging from $94/MT to $108/MT and stover ranging from $96/MT to $101/MT.

The higher cost estimates from the economic model methodology relative to the field trial methodology can be attributed to the highly specific and localized nature of the latter. Field trials are commonly performed at the farm — or county-scale, which are then sometimes extrapolated to the state — scale. While this entails a high degree of accuracy on smaller scales, these results are not suitable for analyses at the regional or national scale. Economic models produce results at the regional or national scale and, while they do not have the levels of detail and accuracy found in field trials, they are more suitable for large-scale analyses.