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4 The Economics and Economic Effects of Biofuel Production T he supply of biofuels depends on the availability and price of feedstocks. As discussed in Chapter 3, a sufﬁcient quantity of cellulosic biomass could be produced in the United States to meet the Renewable Fuel Standard, as amended in the Energy Inde- pendence and Security Act (EISA) of 2007 (RFS2) mandate. However, buyers of biomass would have to offer a price that incentivizes suppliers to provide the requisite amount. For a cellulosic biomass market to be feasible, the price offered by suppliers would have to be equal to or lower than what buyers would be willing to pay and still make a proﬁt. The ﬁrst part of this chapter describes an economic analysis that estimates what the price of different types of biomass would need to be for producers to supply the bioenergy market and the cost of converting the biomass to fuel. After this examination of the economics of producing biofuels from cellulosic biomass, the chapter turns to look at the effects of biofuel production on related sectors of the U.S. economy. The newly emergent biofuel market intersects with established markets in agri- culture, forestry, and energy. The competition for feedstock created by increased production of biofuels could have substantial economic impacts on the prices of agricultural com- modities, food, feedstuffs, forest products, fossil fuel energy, and land values. Therefore, the second part of this chapter examines the price effects that biofuel policy can have on competing markets. Along with the prices of commodities, biofuel production will likely alter the availabil- ity of these products, which may change where they are produced and where they are de- manded. The third part of the chapter therefore examines the effects of biofuel production in the United States on the balance of trade. Effects on the imports and exports of grains, livestock, wood products and woody biomass, and petroleum are discussed. In addition to its interaction with commodity markets and trade, the biofuel industry also has economic effects related to federal spending. To make biofuels competitive in the energy market, the federal government supports biofuels through the RFS2 mandate and additional policy instruments discussed in Chapter 1. Tax credits and a tariff inﬂu- ence government revenue and expenditures. Support policies for biofuels also affect other 105
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106 RENEWABLE FUEL STANDARD government programs tailored to agricultural production, conservation, and human nutri- tion. The fourth section of this chapter reviews the federal and state policies that are related to biofuels or affected by biofuel policies and the observed and anticipated economic effects of biofuel-support policies on other government initiatives. The rationale for public support for these policies is also examined. Because of the costs that biofuel policies incur, alternative options have been proposed to achieve similar policy goals. The ﬁnal section provides an overview of alternatives that could possibly reduce or mitigate these costs while still encouraging biofuel production. It also examines how biofuel policy may interact with federal policy to reduce carbon emis- sions. Both policies have or would have reduction of greenhouse-gas (GHG) emissions as an objective. ESTIMATING THE POTENTIAL PRICE OF CELLULOSIC BIOMASS As of 2011, a functioning market for cellulosic biomass does not exist. Therefore, the committee chose to model possible prices based on production results found in published literature. This section explains the model, along with its assumptions and results, and es- timates the cost of converting biomass to liquid fuel. It was not feasible for the committee to model every possible conversion pathway and biofuel product in the duration of this study. Thus, biochemical conversion of biomass to ethanol was used as an illustration in this analysis. The ﬁrst part evaluates the production costs of various potential bioreﬁnery feedstocks, assuming constant bioreﬁnery processing costs. The second part analyzes the costs for various bioreﬁning technologies, assuming constant feedstock costs. Crop Residues and Dedicated Bioenergy Crops If a cellulosic feedstock market were in existence, the data on market outcomes would be collectable. For instance, the purchase price for feedstocks could be obtained by survey- ing bioreﬁneries, and the marginal costs of producing and delivering biomass feedstocks to a bioreﬁnery could be calculated based on observed production practices. Presumably, if the market is operating, the price the bioreﬁnery pays would be equal to or above the marginal cost of production and delivery. However, at the time this report was written, a commercial-scale cellulosic bioreﬁnery and feedstock supply system did not exist in the United States. As a consequence, industry values were not available to estimate or oth- erwise assess the biomass supplier’s marginal cost or supply curve and the bioreﬁnery’s derived demand for biomass. The Biofuel Breakeven model (BioBreak) was used to evaluate the costs and feasibil- ity of a local or regional cellulosic biomass market for a variety of potential feedstocks.1 BioBreak is a simple and ﬂexible long-run, breakeven model that represents the local or regional feedstock supply system and biofuel reﬁning process or bioreﬁnery. BioBreak calculates the maximum amount that a bioreﬁnery would be willing to pay for a dry ton of biomass delivered to the bioreﬁnery gate. This value, or willingness to pay (WTP), is a function of the price of ethanol, the conversion yield (gallons per dry ton of biomass) the 1 TheBioBreak model was originally developed as a research tool to estimate the bioreﬁnery’s long-run, break- even price for sufﬁcient biomass feedstock to supply a commercial-scale bioreﬁnery and the biomass supplier’s long-run, breakeven price for supplying sufﬁcient feedstock to operate such a bioreﬁnery at capacity. An earlier version of the model was used in the NAS-NAE-NRC report Liquid Transportation Fuels from Coal and Biomass: Technological Status, Costs, and Environmental Impacts (2009b).
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107 THE ECONOMICS AND ECONOMIC EFFECTS OF BIOFUEL PRODUCTION bioreﬁnery can expect with current technology, and the costs of processing the feedstock (Box 4-1). BioBreak also calculates the minimum value that a biomass feedstock producer would be willing to accept for a dry ton of biomass delivered to the bioreﬁnery. This value, or will- ingness to accept (WTA), depends on the biomass feedstock producer’s supply cost (that is, opportunity cost, production cost, and delivery cost) of supplying biomass in the long run (Box 4-2). A local or regional biofuel market for a speciﬁc feedstock will only exist or be sustained if the biofuel processor can obtain sufﬁcient feedstock and the feedstock produc- ers can deliver sufﬁcient feedstock at a market price that allows both parties to break even in the long run. For the analysis, BioBreak calculated the difference or “price gap” between the supplier WTA and the processor WTP for each feedstock scenario. If the price gap is zero or negative, a biomass market is feasible (Jiang and Swinton, 2009). If the price gap is positive, a biofuel market cannot be sustained under the assumed feedstock production and conversion technology. The BioBreak model is based on a number of assumptions. First, it assumes that the typical biomass feedstock producer minimizes costs and produces at the minimum point on the long-run average cost curve. Second, it assumes a yield distribution for biomass crops based on the expected mean yield and variation in yield within a region. Third, it assumes a transportation cost based on the average hauling distance for a circular capture region (that is, the biomass supply area) with a square road grid.2 Fourth, the model assumes that the bioreﬁnery has a 50-million gallon annual capacity. The model is ﬂexible and can be rescaled to consider other facility sizes. This scale is chosen because it is assumed to be the minimum scale necessary to be competitive in the ethanol market. A smaller scale will imply lower WTP. Fifth, the model assumes that each bioreﬁnery uses a single feedstock, and this feedstock is available without causing market disruptions (for example, changes in land rental prices) within the biomass capture region. Most bioreﬁneries will likely be built to use locally sourced material for input (Babcock et al., 2011; Miranowski et al., 2011), but to the extent that they source material from outside the capture region, the actual WTA will be higher than is estimated in the results presented in this chapter. Sixth, beyond solving for alternative oil price scenarios, the impact of energy price uncertainty on biofuel investment is not considered. If potential investors require a higher return because of future energy market uncertainty (that is, a risk premium), actual WTP will be lower and the price gap will be higher than the price gap estimates presented in this chapter. With energy market uncertainty, a price gap estimate below zero will satisfy the necessary condition for devel- opment of a feedstock market (that is, both biomass supplier and biomass processor will break even in the long run), but it may not be sufﬁcient to induce investment.3 2 Due to heterogeneity in nontransportation production costs within the capture region, BioBreak uses the aver- age distance rather than the capture region distance. Although the transportation cost per unit of biomass will be higher at the edge of the capture region, the supplier’s minimum willingness to accept will not necessarily be strictly increasing with distance due to heterogeneity in production and opportunity costs. Even with higher trans- portation costs, a biomass supplier at the edge of the capture region with low production costs may be willing to supply biomass at a lower price than a biomass supplier with relatively high production costs located close to the bioreﬁnery. BioBreak assumes that the average hauling distance within the capture region is representative of the location of the last unit of biomass purchased by the bioreﬁnery to meet the bioreﬁnery feedstock demand. Using the capture region distance would provide the correct estimate of the supplier’s willingness to accept if the last unit of biomass purchased by the bioreﬁnery is located at the edge of the capture region but would overestimate the supplier’s willingness to accept in all other cases. 3 For additional information on BioBreak model assumptions and limitations, refer to Appendix K and to Mi- ranowski and Rosburg (2010).
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113 THE ECONOMICS AND ECONOMIC EFFECTS OF BIOFUEL PRODUCTION rotation (CS).5 A 4-year corn stover-alfalfa rotation with 2 years of each crop (that is, CCAA) also was included. To account for regional variation in climate and agronomic character- istics, the WTP and WTA for switchgrass were evaluated in three regions: Midwest (MW), South-Central (SC), and Appalachia (App).6 Miscanthus was also evaluated in the Midwest and Appalachian regions, while corn stover and wheat straw were assumed to be pro- duced on cropland used for production in the Midwest and Paciﬁc Northwest7 regions, respectively. To account for the heterogeneity in Midwest land quality, perennial grasses (switchgrass and Miscanthus) on high quality (HQ) and low quality (LQ) Midwest crop- land were also considered. This is not an exhaustive list of potential feedstocks or of the potential variation in productivity across the United States, but it provides information on 13 combinations of the most widely discussed feedstocks in regions where they are likely to be produced. The 13 combinations evaluated were: corn stover (CS), stover-alfalfa, alfalfa, Midwest switchgrass (HQ), Midwest switchgrass (LQ), Appalachian switchgrass, South- Central switchgrass, Midwest Miscanthus (LQ), Midwest Miscanthus (HQ), Appalachian Miscanthus, wheat straw, short-rotation woody crops (SRWC), and forest residues. BioBreak derives a point estimate of WTA, WTP, and the price gap for a bioreﬁnery with a ﬁxed capacity and a local feedstock supply area. The point estimates are based on a number of assumptions and a number of parameter inputs. Since many of these param- eter inputs are uncertain, BioBreak uses Monte Carlo simulation to assess the implications of this uncertainty on the results.8 Monte Carlo simulation permits parameter variability, parameter correlation, and sensitivity testing not available in ﬁxed parameter analysis.9 For this analysis, distributional assumptions for each parameter were based on empirical data updated to 2007 values and veriﬁed with industry information when available.10 If appropriate data were insufﬁcient or not available, a distribution was constructed to ﬁt available data or a range of industry values was obtained. A sensitivity analysis was then performed to determine importance. Monte Carlo simulation with parameter distributional assumptions captures the range of variability found in the estimates in the literature, which were used in this analysis. Boxes 4-1 and 4-2 summarize the equations used to calculate the bioreﬁnery’s WTP and the biomass feedstock supplier’s WTA and the assumptions used in this committee’s analysis for the BioBreak model parameters. Appendix K provides further details about the assumptions for the feedstock supply costs. Summary tables of parameter assumptions used in the analysis are available in Appendix L, while Appendix M provides a review of the literature used to construct the parameter assumptions. 5 Compared to a corn-soybean rotation, corn from continuous corn production has a yield penalty but produces more stover over the course of the rotation. If the price of stover were sufﬁciently high, a farmer could ﬁnd it more proﬁtable to switch to continuous corn production because the additional stover revenue would more than offset the yield penalty (that is, opportunity cost). Whether this would occur in practice is in dispute. 6 Midwest includes North Dakota, South Dakota, Nebraska, Kansas, Iowa, Illinois, and Indiana. South-Central applies to Oklahoma, Texas, Arkansas, and Louisiana. Appalachian refers to Tennessee, Kentucky, North Carolina, Virginia, West Virginia, and Pennsylvania. 7 Washington, Idaho, and Oregon. 8 For the Monte Carlo simulations, BioBreak uses Oracle’s spreadsheet-based program Crystal Ball®. 9 See NAS-NAE-NRC (2009b) for an example of BioBreak applied in a ﬁxed parameter analysis. 10 Costs were updated using USDA-NASS agricultural prices from 1999-2007 (USDA-NASS, 2007a,b).
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115 THE ECONOMICS AND ECONOMIC EFFECTS OF BIOFUEL PRODUCTION TABLE 4-1 BioBreak Simulated Mean WTP, WTA, and Difference per Dry Ton Without Policy Incentives Price Gap Price Gap in in Dollars Dollars per per Gallon WTA-WTP Gallon of of Gasoline WTA WTP (per dry ton) Ethanol Equivalent Stover (CS) $92 $25 $67 $0.96 $1.43 Stover-Alfalfa $92 $26 $66 $0.94 $1.42 Alfalfa $118 $26 $92 $1.31 $1.97 Switchgrass (MW) $133 $26 $106 $1.51 $2.28 Switchgrass (MW LQ) $126 $27 $99 $1.41 $2.13 Switchgrass (App) $100 $26 $74 $1.06 $1.59 Switchgrass (SC) $98 $26 $72 $1.03 $1.53 Miscanthus (MW) $115 $26 $89 $1.27 $1.90 Miscanthus (MW LQ) $119 $27 $93 $1.33 $1.98 Miscanthus (App) $105 $27 $79 $1.13 $1.69 Wheat Straw $75 $27 $49 $0.70 $1.04 SRWC $89 $24 $65 $0.93 $1.39 Forest Residues $78 $24 $54 $0.77 $1.16 NOTE: Oil price is assumed to be $111 per barrel and conversion efﬁciency of biomass to fuel is assumed to be 70 gallons per dry ton. biofuels could lead investors to conclude the cellulosic mandate is not rigid (see Chapter 6 for further discussion of RINs). Without policy intervention, no feedstock market is feasible in economic terms in the baseline scenario. The price gap that would need to be closed to sustain a feedstock market ranges between $49 per dry ton for wheat straw to $106 per dry ton for switchgrass grown on high-quality land in the Midwest. Figure 4-2 provides a graphical depiction of the price gap for all 13 feedstock-rotation combinations (see also Box 4-3). The breakeven values and resulting price gaps depicted in Figure 4-2 are sensitive to assumptions and parameters used in the analysis. One key parameter in the BioBreak model is the price of oil (see Box 4-1). The price of oil drives the processor’s derived demand for feedstock given biomass conversion cost and inﬂuences biomass supply cost through production costs. An increase (decrease) in the price of oil increases (decreases) what the processor can pay per dry ton of each feedstock and break even in the long run. At the same time, an increase (decrease) in the oil price increases (decreases) harvest and transportation costs resulting in a higher (lower) biomass supplier long-run breakeven cost. Given the assumptions, the effect on the processor’s derived demand price from an oil price change dominates the effect on the biomass supply cost. Therefore, the price gap (WTA – WTP) decreases with higher oil prices and vice versa. The results in Table 4-1 and Figures 4-1 and 4-2 assume an oil price of $111 per barrel. At an oil price of $191 per barrel, the price gap is eliminated for several feedstocks, including stover (CS), switchgrass (App, SC), Miscanthus (App), wheat straw, SRWC, forest residue, and stover-alfalfa. Remaining feedstocks have a price gap between $5 and $23 per dry ton. Correspondingly, the price gap increases to between $110 and $168 per dry ton of biomass with an oil price of $52 per barrel. The breakeven price is also sensitive to the conversion rate of biomass to ethanol. The baseline results assume a conversion rate of 70 gallons per dry ton
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170 RENEWABLE FUEL STANDARD tonne for a resulting carbon price between $100 and $120 per tonne CO2 eq.33 The estimated carbon price is sensitive to technological progress, such as bioreﬁnery and fuel efﬁciency, and to the parameter values inﬂuencing breakeven values for the biomass processor and supplier, including the oil price, regional biomass productivity, and parameter variability. Interaction with Agricultural and Forestry Offsets Under a national carbon policy, biofuels would not be the only way to reduce carbon. Another means that could be encouraged is using agricultural land or forestry to supply carbon credits to carbon markets. These credits have come be to known as “offsets,” given that they are often assumed to be used to offset emissions of carbon dioxide from the energy sector. Offsets work by reducing GHG emissions from some activity in the agricultural or forestry sector or by increasing the carbon stored in soils or forest biomass. Examples of activities that potentially generate offsets include conversion of land to forests (afforesta- tion), extending timber rotations, increasing forest management, shifting from conventional tillage to no tillage, reducing methane (CH4) emissions from livestock operations, reducing fossil fuel use associated with agriculture, and reducing nitrogen oxide emissions from production agriculture (EPA, 2005). The amount of carbon offset by feedstocks differs with the type of crop, previous land uses, crop management, and agrichemical use. Environmental policies that encourage carbon offsets in agriculture or forestry will have important interactions with biofuel policies because they could inﬂuence the total amount of land devoted to forest or agricultural production. A number of studies have examined the implications of offsets for land use in the United States. One of the most im- portant effects relates to the potential for land-use change, and speciﬁcally for conversion of land into forests. A model by EPA (2005) suggested that up to 90 million acres of crop and pastureland could be converted to forestland if carbon prices are in the range of $15- $50 per tonne of CO2 eq. Results from Sohngen (2010) suggest similarly large changes in land use, around 100 million acres of new forestland by 2050 with carbon prices of $30 per tonne CO2 eq.34 These studies assume one of two types of carbon payment regimes, either a carbon rental regime that makes rental payments as carbon is stored in forests and a pay- ment for storage in wood products or a subsidy for storage and a tax for the net emission at harvest. Note that these two payment schemes are equivalent in present value terms for newly planted forests. In both these studies, the changes in land use described above are net of all underlying changes. New forests are derived from a combination of crop, pasture, and rangeland. Additional land-use changes outside the United States are captured by the global study by Sohngen (2010) but not by the EPA (2005) study. The scale of the changes in land use associated with carbon policies suggests that the overall value for land would increase dramatically if carbon policies were implemented. Such large shifts in land use occur with carbon offset policies because offsets in forestry are particularly valuable. Consider a typical acre of cropland in the Eastern Corn Belt. The accumulation of carbon if land is converted to mixed hardwoods could be as much as 4 tonnes CO2 eq per acre per year (Figure 4-24). Further, the carbon in the mixed hardwoods 33 Further,if fuel cell vehicle technology operating on pure ethanol (E100) is available by 2020, the carbon price would decrease to range between $54 to $68 per tonne CO2eq assuming a fuel economy of 44.3 mpgge for fuel cell vehicles and a conversion yield of 80 gallons per dry ton of feedstock. 34 Those model projections must be interpreted in the context of recent changes in land-use patterns in the United States. From 1982 to 2007, cultivated cropland declined by 70 million acres (from 375 million to 305 million acres), developed land increased by 40 million acres (from 71 million to 111 million acres), and there was little change in forestland, pastureland, rangeland, and noncultivated cropland (USDA, 2009).
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174 RENEWABLE FUEL STANDARD in the United States, and they could constitute 30 to 50 percent of the total carbon seques- tered in the next 30 years (EPA, 2005; Sohngen, 2010). Payments for these activities can be provided to owners with standing timber stocks in order to generate carbon offset credits. Since these incentives value the standing stock of timber, they would serve to increase the value of forests and make standing forests more competitive with other types of land uses, including feedstock production for biofuels. In other words, comprehensive carbon offset policies that pay for offsets through management and increasing timber rotation ages will increase the value of land and make the provision of tons of biomass for bioenergy markets more expensive. It is difﬁcult at this time to determine what the net effect of both carbon offsets and RFS2 would be on land use. Market studies of carbon offsets imply that additional land converts from livestock and crops to forests under most carbon price scenarios and that the returns to all types of land uses increase, though these studies do not account for the expansion of developed land. The recent EPA study on RFS2 suggests that the area of land used for dedicated bioenergy crops, such as switchgrass, increase, while forestland and rangeland decline. The combination of these results suggests that carbon offsets would compete with cellulosic biofuel production for the same land; thus, environmental policies that encourage carbon offsets could raise the costs of producing cellulosic biofuel feedstocks. Considered a different way, however, this also implies that carbon offsets could limit the negative exter- nalities associated with converting natural forests to dedicated bioenergy crops. An important caveat to this conclusion occurs if cellulosic biofuel feedstocks are in- creasingly derived from forest sawtimber and pulpwood supplies rather than residues. In this case, the demand for wood products would increase, timber prices would rise, and land returns in forestry would increase. Because long-term, sustained increases in timber prices raise timber rotation ages over time, and an increase in rotation age expands the supply of timber, in some cases, biofuel outputs and carbon offsets may be complementary. An additional possible response of markets to increasing biofuel demands is to shift land towards shorter rotation species. Shorter rotations can increase land values, but they do not necessarily increase timber supplies, as discussed in Box 4-6. The key way in which shorter rotations can increase timber supplies occurs if managers are able to manage them better to produce the desired outputs. For instance, there has been a long history of conver- sion of hardwoods to softwoods in the Southern United States. The key gain here has been an increase in value on the landscape as managers have been better able to control condi- tions on softwood plantations, and they have been able to obtain higher value output per acre with softwoods than hardwoods. However, net production of biomass on hardwoods is typically greater, but less of it is suitable for high-value market products on a per acre basis (Sohngen and Brown, 2006). CONCLUSION Because cellulosic biofuel is not yet commercially viable, the economics of this type of fuel and its economic effects on other commodities and government programs are specula- tive. However, with the data that are available and the present state of technology, cellulosic biofuel is not cost-competitive with fossil fuels without government support. Unless more subsidies are used, the RFS2 mandate is enforced rigidly, taxes on petroleum products are increased, or rapid technological advancements are made, cellulosic biofuel will not substantially affect other commodity markets, though it could have repercussions for the federal budget. If cellulosic biofuel becomes commercially viable, land prices will increase due to competition with other agricultural or forestry uses, though the extent of the increase
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175 THE ECONOMICS AND ECONOMIC EFFECTS OF BIOFUEL PRODUCTION due to biofuels will depend on the productivity of the land used for biomass production as well as demand for other uses of the land. Fossil fuel prices may decline slightly and imports will decrease, but this will also be inﬂuenced by improved fuel efﬁciency in the U.S. vehicle ﬂeet and the capacity of the U.S. ﬂeet to use biofuels. Because of its scarcity and its density, more woody biomass may be imported to meet the demand for biofuels and traditional uses. Corn-grain ethanol and, to a lesser extent, soybean biodiesel are closer to being com- petitive with fossil fuels, particularly when combined with the tax credit and encouraged by RFS2. They have contributed to upward price pressure on agricultural commodities, food, and livestock feed; however, they are just one factor among many, including the growing global population, crop failures in other countries, decline in the value of the U.S. dollar, and speculative activity in the marketplace. The greater use of DDGS in animal feed to some extent has muted the unfavorable effects on the livestock industry. If policies that were in place at the time this report was written are continued, it is extremely likely that meeting RFS2 will increase the federal budget, particularly in terms of subsidies spent on grants, loans, and loan guarantees to encourage cellulosic biofuel production and in terms of tax revenue forgone by the tax credits for blending biofuel with fossil fuels. To the extent that biofuel policy has raised food-related prices, it has af- fected federal spending in programs related to agriculture and food. Deciphering biofuels’ contribution to increases or decreases in these programs is difﬁcult, though, because of the number of variables. The effect of RFS2 on federal spending on conservation programs is uncertain. It remains to be seen whether biofuel feedstock production competes with acres or payments for CRP. REFERENCES Abbott, C. 2010. U.S. Biofuels Hurt if 2010 Tax Break Expires—Report. Available online at http://www.reuters. com/article/2010/03/09/biofuels-usa-taxbreak-idUSN0924548320100309. Accessed February 10, 2011. Abbott, P.C., C. Hurt, and W.E. Tyner. 2009. What’s Driving Food Prices? March 2009 Update. Oak Brook, IL: Farm Foundation. Abbott, P.C., C. Hurt, and W.E. Tyner. 2011. What’s Driving Food Prices in 2011? Oak Brook, IL: Farm Foundation. Aden, A., M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, B. Wallace, L. Montague, A. Slayton, and J. Lu- kas. 2002. Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover. Golden, CO: National Renewable Energy Laboratory. Anex, R.P., A. Aden, F.K. Kazi, J. Fortman, R.M. Swanson, M.M. Wright, J.A. Satrio, R.C. Brown, D.E. Daugaard, A. Platon, G. Kothandaraman, D.D. Hsu, and A. Dutta. 2010. Techno-economic comparison of biomass-to- transportation fuels via pyrolysis, gasiﬁcation, and biochemical pathways. Fuel 89(Supplement 1):S29-S35. Babcock, B., S. Marette, and D. Treguer. 2011. Opportunity for proﬁtable investments in cellulosic biofuels. Energy Policy 39:714-719. Babcock, B.A., and J.F. Fabiosa. 2011. The Impact of Ethanol and Ethanol Subsidies on Corn Prices: Revisiting His- tory. Ames: Iowa State University - Center for Agricultural and Rural Development. Babcock, B.A., K. Barr, and M. Carriquiry. 2010. Costs and Beneﬁts to Taxpayers, Consumers, and Producers from U.S. Ethanol Policies. Ames: Iowa State University - Center for Agricultural and Rural Development. Baffes, J., and T. Haniotis. 2010. Placing the 2006/08 Commodity Price Boom into Perspective. Washington, DC: The World Bank. Baier, S., M. Clements, C. Grifﬁths, and J. Ihrig. 2009. Biofuels Impact on Crop and Food Prices: Using an Interac- tive Spreadsheet. Washington, DC: Board of Governors of the Federal Reserve System. Banse, M., H. van Mejil, A. Tabeau, and G. Woltjer. 2008. Will EU biofuel policies affect global agricultural markets? European Review of Agricultural Economics 35(2):117-141. Becker, D., P. Jakes, D. Abbas, K.E. Halvorsen, P.J. Jakes, S.M. McCaffrey, and C. Moseley. 2009. Characterizing Lessons Learned from Federal Biomass Removal Projects. Boise, ID: Joint Fire Science Program.
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