ENERGY AND THE U.S. ECONOMY

Resource /Energy Economics and Policy,

Ethanol subsidies, Who gets the benefits?

Agricultural and Food Policy, Resource /Energy Economics and Policy,

Ethanol Policy Analysis - What Have We Learned So Far?

Resource /Energy Economics and Policy, Q48, Q42,

DEVELOPMENT OF VARIABLE ETHANOL SUBSIDY AND COMPARISON WITH THE FIXED SUBSIDY

The federal government currently subsidizes ethanol with a fixed payment of $.51/gallon of ethanol blended with gasoline. Ethanol profitability is closely linked to the prices of corn and ethanol. The purpose of this paper was to develop a variable subsidy based on corn and ethanol prices and then to compare that variable subsidy with the fixed subsidy. This analysis proceeded in several distinct steps: • First, we estimated ethanol profitability over a wide range of ethanol, corn, and distillers grains prices. • This data was used in a regression analysis to estimate the ethanol profitability from the set of corn and ethanol prices. The regression coefficients became the basis for the variable subsidy. • A version of the subsidy that used gasoline prices instead of ethanol prices was also developed. • Administratively, it would be burdensome to have a subsidy that changed every month, so we implemented both variable subsidies using quarterly data. • We then compared the average annual government cost and monthly private profitability using historical data and assuming the variable subsidy and the$0.51 fixed subsidy was applied. When using historic gasoline and corn prices from the last ten years, the variable rate subsidy cost the government nearly 40% less than the flat rate subsidy. Profit received by producers on average is a little less; however, producer’s risk is lower with the variable subsidy than the flat rate subsidy.Ethanol, variable subsidy, energy policy, ethanol economics

Land Use Implications of Biofuel Production in the Presence of Idled Cropland and Crop Yield Improvement: Analytical and Numerical General Equilibrium Analyses

Labor and Human Capital,

Comparison of a Fixed and Variable Corn Ethanol Subsidy

Agricultural and Food Policy, Crop Production/Industries, Q48,

ECONOMIC AND TECHNICAL ANALYSIS OF ETHANOL DRY MILLING: MODEL DESCRIPTION

Ethanol, the common name for ethyl alcohol, is fuel grade alcohol that is predominately produced through the fermentation of simple carbohydrates by yeasts. In the United States, the carbohydrate feedstock most commonly used in the commercial production of ethanol is yellow dent corn (YDC). The use of ethanol in combustion engines emits less greenhouse gasses than its petroleum equivalent, and it is widely hoped that the increased substitution of petroleum by ethanol will reduce US dependence on imported oil and decrease greenhouse gas emissions. Production of ethanol within the United States is expected to double, from 3.4 billion gallons in 2004, to about seven billion gallons in the next five years. Two processes currently being utilized to produce ethanol from YDC are dry milling and wet milling. The wet mill process is more versatile than the dry mill process in that it produces a greater variety of products; starch, corn syrup, ethanol, Splenda, etc., which allows for the wet mill to better react to market conditions. However, the costs of construction and operation of a wet mill are much greater than those of a dry mill. If ethanol is the target product, then it can be produced at a lower cost and more efficiently in a dry mill plant than in a wet mill plant, under current economic conditions. Of the more than 70 US ethanol plants currently in production, only a few are of the wet mill variety. A descriptive engineering spreadsheet model (DM model) was developed to model the dry mill ethanol production process. This model was created to better understand the economics of the ethanol dry mill production process and how the profitability of dry mill plants is affected under different conditions. It was also developed to determine the economic and environmental costs and benefits of utilizing new and different technologies in the dry mill process. Specifically, this model was constructed to conduct an economic analysis for novel processes of obtaining greater alcohol yields in the dry mill process by conducting a secondary fermentation of sugars converted from lignocellulosics found in the dry mill co-product, distiller’s grains. This research is being conducted at Purdue University, Michigan State, Iowa State, USDA, and NCAUR under a grant from the US Department of Energy. The DM model is more technically precise, and more transparent, than other models of the dry mill process that have been constructed for similar purposes. The Tiffany and Eidman model (TE model) uses broad generalities of the dry mill process, based on the current state of production, to approximate the sensitivities of the process to changes in variables. The TE model parameters were well researched, but the model suffers from several drawbacks. The main limitations of this model are that the results are very sensitive to the input values chosen by the user. Unlike the DM model, complex manipulations, such as determining the effect of new technologies would require accurate parameter estimates using the TE model. The McAloon model [11].uses highly technical engineering software (ASPEN) that acts essentially as a “black box” in the dry mill production process. This very exact model does not allow for a more general examination of the dry mill process. Changes in the production process would necessitate precise engineering plans. Similar to the TE and McAloon models, the DM model is a spreadsheet model, but unlike the McAloon model it is completely self-contained. The DM model is a feed backward model, input requirements (corn, enzymes, chemicals, utilities, etc) are calculated based on the user entered values for annual production and process parameters. The mass flow rates, in pounds per hour were then calculated and used in estimating the size, in dimension or power, of each major piece of equipment. The cost associated with each piece of major equipment was then calculated as an exponential function of its corresponding size. Total capital costs associated with a dry mill plant were then estimated using the percentage of equipment costs method [13]. It was found that the DM model estimates of the total capital costs associated with medium to large dry mill plants (those with the capacity to produce between 10 and 100 million gallons of ethanol a year) were within 5% of total fixed costs estimated by BBI [2]. Operating costs were compared with the 2002 USDA survey results and also found to be very close [15]. A companion document, “Economic and Technical Analysis of Dry Milling: Model User’s Manual,” staff paper no 06-05, explains how the model is used to conduct analysis of dry milling alternatives.Ethanol, DDGS, Dry Milling, Biochemical Process Engineering, Economic Modeling, Financing, Fermentation Process Modeling

Cellulosic Biofuels Analysis: Economic Analysis of Alternative Technologies

The passage of U.S. laws mandating and subsidizing advanced cellulosic biofuels may spur the development of a commercial cellulosic biofuels industry. However, a cellulosic industry will only develop if the overall economics including government incentives render investment in the sector attractive to private investors.This study compares the profitability of three biofuel production types: grain based ethanol, cellulosic biochemical ethanol, and cellulosic thermochemical biofuels. In order to compare the current profitability of each of the production types, the Biofuels Comparison Model (BCM) was developed. The BCM is a spreadsheet model that estimates the net present value (NPV) for each production type given input and output prices, technical, and financial assumptions. The BCM can be updated to reflect the current profitability through embedded web price links. The study finds that grain, biochemical, and thermochemical production types are all currently unprofitable when subsidies and mandates are ignored. However, the grain based ethanol process is predicted to be the most profitable (lowest loss) compared to the cellulosic biofuels. When the 2008 Farm Bill subsidies are added to the BCM, all three production types are projected to be profitable. With the addition of the different subsidies, the cellulosic biofuels are estimated to have higher NPV’s than grain based ethanol. When compared on an energy equivalent basis, the estimated cost of producing grain ethanol is $114/bbl. crude oil equivalent, biochemical ethanol$141/bbl., and thermochemical gasoline $108/bbl.biofuels, cellulosic biofuels, corn ethanol, biofuel economics

IMPROVING IRRIGATION WATER ALLOCATION EFFICIENCY USING ALTERNATIVE POLICY OPTIONS IN EGYPT

This study provides an empirical perspective on alternative irrigation policies for allocating limited water to agricultural production in Egypt. Positive mathematical programming is used for model calibration. Three policy options for Egypt are tested: water pricing, water complementary input factor taxes, and output taxes. The results of the research show that: 1) water pricing needs to be much higher than the recovery cost in order to be effective in limiting irrigation water use; 2) at a higher tax rate, fertilizer and energy taxes are effective in reducing the irrigation water demand while maintaining adequate welfare levels; 3) a pesticide tax is less effective than fertilizer or energy taxes; and 4) an output tax on sugar cane and rice would decrease irrigation water demand substantially while allocating land to other crops which are less water intensive and have higher market values.Resource /Energy Economics and Policy,