Sixty billion gallons by 2030: economic and agricultural impacts of ethanol and biodiesel expansion.

Use of bioenergy feedstocks to produce transportation fuels could not only help reduce reliance on foreign oil, but could also provide significant environmental benefits and invigorate rural economies. Agriculture is well positioned as a feedstock source because the fuels can be utilized with current engine technologies and are compatible with the current distribution infrastructure. The anticipated commercialization of cellulose-to-ethanol technology will enable fuels to be derived from a diverse portfolio of feedstocks from numerous regions of the country.

The Energy Policy Act of 2005 establishes a renewable fuel requirement for the nation, mandating 7.5 billion gallons of renewable fuels by 2012. A more sweeping renewable fuels standard was proposed as part of The Biofuels Security Act of 2007 (sponsored by Senator Tom Harkin and co-sponsored by Senators Lugar, Biden, Dorgan, and Obama) (Harkin, et al., 2007). The Governors' Ethanol Coalition has recommended replacing at least 25% of petroleum used as transportation fuels by the year 2025 (Governor's Ethanol Coalition 2006).

The objective of this study is to project the U.S. agricultural sector and economic impacts of increasing ethanol and biodiesel production beyond the levels specified in the recently enacted renewable fuel standard. The levels of ethanol production analyzed are 10, 30, and 60 billion gallons of ethanol annually by 2010, 2020, and 2030, respectively. Sensitivity to the timing of commercialization of cellulose-to-ethanol technology and impacts of associated corn to ethanol industry adjustments is projected. Impacts of producing 1 billion gallons of biodiesel production by 2012 and 1.6 billion gallons by 2030 are also projected.

Prior Research

Several studies have addressed various aspects of bioenergy production and contribution toward renewable energy (USDA-OCE 2002; Urbanchuk 2001; Shapouri, Duffield, and Wang 2002; Sheehan 2002; Walsh et al. 2003; McLaughlin et al. 2002). Previous economic modeling evaluating agriculture feedstocks for energy has been conducted in the context of carbon displacement potential (McCarl and Schneider 2000). Adjustment costs incurred in the short run for implementing new technologies and/or policies are not considered by these models (Schneider 2000). The potential regional economic impacts of converting corn stover to ethanol have been projected using IMPLAN (English, Menard, and De La Torre Ugarte 2000).

Methodology

Key methodological steps to conducting the analysis include: definition of biofuels goals, selection of representative conversion technologies, collection of associated cost information, definition of key assumptions, update and expansion of POLYSYS (a dynamic agricultural sector model), development of a program to integrate POLYSYS results into IMPLAN (PII), modification of IMPLAN (economic input output model) to accommodate biomass feedstock production and biofuels conversion industries, and scenarios development (De La Torre Ugarte et al. 2006).

The conversion technologies include ethanol from shelled corn, cellulosic residues (stover, switchgrass, and wheat straw), food residues, wood residues (forest residues, mill wastes, fuel treatment and forestland thinnings), and biodiesel from soybeans and from yellow grease/tallow. Representative facility output, feedstock use, and associated costs are developed based on prior studies (McAloon et al. 2000; personal correspondence from V. Eidman 2006; Aden et al. 2002; BBI International 2002; English, Jensen, and Menard 2002; Fortenberry 2005). Conversion coefficients of cellulose to ethanol are increased linearly for stover, straw, and dedicated energy crops from 2015 to 2030. Conversion coefficients of feedstocks to corn grain ethanol and biodiesel are assumed to increase through 2019 and thereafter remain steady.

Several key study assumptions are required in addition to setting biofuels targets and selecting conversion technologies. Cellulose-to-ethanol technology is assumed to be commercially available in 2012. Switchgrass serves as a proxy for dedicated energy crops, with yields increasing over time that range from 1.5 to 5%, depending on region. No-till adoption increases from 20 to 55%. The available land base includes 307 million acres of cropland in major crops plus hay and 56.2 million acres of cropland in pasture. Maximum percents of Distiller's Dry Grains (DDG's) in feed rations are assumed at 30% for beef and 10% for dairy, hogs, and broilers.

The targeted biofuels production levels plus data on conversion costs for agricultural and forest feedstocks are introduced into POLYSYS to estimate the quantity and type of energy to be produced from agriculture, as well as the price, net farm income, and other agricultural sector impacts (De La Torre Ugarte and Ray 2000). In regions where dedicated energy crops are determined to be profitable, pastureland has the potential to be converted to dedicated energy and other crops. Loss of forage production is replaced with new hay production. The livestock module in POLYSYS is an integrated version of the Economic Research Service's econometric livestock model. To project the potential of dedicated energy crops to provide feedstocks, enterprise budgets and yields for switchgrass are incorporated into POLYSYS. Production is assumed to be suitable on 368 million acres.

To evaluate the potential of crop residues to provide feedstocks to the bioproduct markets, POLYSYS includes corn stover and wheat straw response curves that estimate stover and straw quantities as a function of corn and wheat grain yields, as well as stover and straw production costs as a function of yields of removable residue. Estimated response curves are obtained through the Oak Ridge National Laboratory (Walsh et al. 2003). Residues needed to keep erosion at less than or equal to the tolerable soil loss level are incorporated. Total quantities of corn stover and wheat straw that can be collected in each county are estimated for each tillage and dominant crop rotation scenario. The costs of collecting corn stover and wheat straw include baling, staging, and replacing nutrients.

The cost of transporting biomass feedstocks from the farm gate to the production facilities is added to conversion costs. An iterative process is used until the corresponding price of biomass is equal to current iteration biomass price. Once the iterative process has converged and equivalent costs of ethanol production exist, the model has determined the optimal market level of feedstock quantities. The price at which these wood residues feedstocks come into use is determined by regional harvesting costs plus transportation costs. Distiller's dry grains from ethanol production and soybean meal from biodiesel production are integrated within the model to evaluate how their quantities and prices affect the final market equilibrium.

For biodiesel, beef and poultry wastes are modeled as a function of beef and poultry cash receipts, respectively. Yellow grease from food waste is a function of population. Soybean meal byproduct from crushing enters into the POLYSYS soybean product module where prices are endogenously determined.

An interface program, the POLYSYS/ IMPLAN Integrator (PII), developed at The University of Tennessee, takes POLYSYS projections of acreage, price, change in government programs, and cost output and automates changes to IMPLAN data bases (English, Menard, Wilson, and De La Torre Ugarte 2004). PII adds an energy crop sector to IMPLAN based on information supplied by POLYSYS. A renewable energy sector is added to each state's IMPLAN model, and the operating impacts from the renewable energy sector are estimated. IMPLAN employs a regional social accounting system to generate a set of balanced economic/social accounts and multipliers (MIG 1999). The model estimates total industry output, employment, and value added for over 500 industries.

Results under three scenarios are compared with a baseline scenario called USDAExt, which is an extension of the 2006 USDA baseline. The first scenario projects the impacts of attaining the targets assuming the cellulose-to-ethanol technology would be commercially available by 2012 (ETH60). In this scenario, use of corn grain is kept at near production capacity of plants, even with introduction of cellulose-to-ethanol technology. The second scenario allows the corn grain-to-ethanol industry to adjust as cellulose-to-ethanol technology becomes commercially available in 2012 (ETH60CA). In the third scenario (ETH60CACD), the cellulose-to-ethanol technology would be delayed until 2015, and the corn grain based ethanol industry is allowed to adjust in response to cellulose-to-ethanol technology introduction.

Results