economic conditions into consideration. Taking all those factors into consideration, realized supply is likely to be much lower than potential availability.
This chapter describes the estimated supply of cellulosic biomass made by different groups, including the researchers at the University of California, Davis, the U.S. Environmental Protection Agency (EPA), the Biomass Research and Development Initiative, and researchers at the University of Tennessee. Other factors, such as biotechnology, competition for biomass with other sectors, weather-related losses, and pests and diseases, which are typically not considered in projecting biomass supply, contribute to uncertainties in feedstock supply and are discussed at the end of this chapter.
Several studies attempted to predict the most likely locations for biomass production and corresponding siting of the biofuel biorefineries for regulatory and other planning purposes (BRDB, 2008; English et al., 2010; EPA, 2010; Parker et al., 2010b; USDA, 2010). Some studies principally identify the regional availability of bioenergy feedstocks that could be used for biofuel production, while others also identify likely biorefinery locations. The following sections describe some of the approaches and assumptions used in the modeling of potential feedstock supply and biorefinery locations and compare projected locations for biorefineries among studies and with some of the proposed locations of cellulosic biofuel refineries. A comparison of the assumptions related to the types and amounts of feedstocks and the conversion rate to energy is provided in Table 3-1.
National Biorefinery Siting Model
Approach and Assumptions
The National Biorefinery Siting Model (NBSM) was developed by researchers at the University of California, Davis (Parker et al., 2010a; Tittmann et al., 2010; Parker, 2011). It integrates geographically explicit biomass resource assessments, engineering and economic models of the conversion technologies, models for multimodal transportation of feedstock and fuels based on existing transportation networks, and a supply chain optimization model that locates and supplies a biorefinery based on inputs from the other models (Parker et al., 2010a). To identify the location of biorefineries, the model first maximizes the profitability of the entire national biofuel industry. The profit maximized is the sum of the profits for each individual feedstock supplier and fuel producer. Costs minimized in the model are those associated with feedstock procurement, transportation, conversion to fuel, and fuel transmission to distribution terminals. Fuel production and selling price determine industry revenue. Coproduct revenues are included.
NBSM used data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and Forest Service (USFS) provided by Skog et al. (2006, 2008) to project crop and woody biomass location and abundance and create spatially explicit estimates of biomass availability. NBSM constrained estimates for the supply of corn to be equal to the quantity needed to meet the RFS2 mandate of 15 billion gallons per year for conventional ethanol. Soybean and canola were assumed to be grown and used for biofuels. To limit the proportion of soybean dedicated to fuel production in the model, the use of soybean oil for biodiesel is limited to not more than 38 percent of all soy oil produced.