FAPRI Biofuels addendum 2

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Published on October 4, 2007

Author: Miguel

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Slide1:  First FAO Technical Consultation on Bioenergy and Food Security Working Draft for Discussion Only Addendum to FAPRI Report #06-07 www.fapri.missouri.edu William Meyers - MeyersW@missouri.edu Seth Meyer - MeyerSe@missouri.edu Stochastic Biofuel Modeling:  Stochastic Biofuel Modeling The FAPRI stochastic model of the U.S. agricultural sector is a non-spatial, partial equilibrium model covering markets for major grains (wheat, corn, rice, sorghum, barley, and oats), oilseeds and their derivatives, (soybeans, rapeseed, sunflowerseed, peanuts, and palm oil), cotton, sugar, beef, pork, poultry, and dairy products. Its structure is a simplification of the FAPRI deterministic modeling process with reduced form equations used to simulate trade in the rest of the world normally covered by international country and regional models, and aggregating U.S. domestic supply regions to a single national market. With these simplifications, the model still contains over 1,000 equations representing U.S. crop and livestock supply demand, trade and prices as well as sector aggregates such as government expenditures on farm programs, net farm income, agricultural land values and consumer food price indices. The model has also seen extensive development in the area of ethanol and biodiesel markets. The crops sector is modeled through behavioral equations representing crop acreage, domestic feed, food and industrial uses, stock holding and trade. Equivalently, the livestock sector is modeled through behavioral equations determined by animal numbers, meat and dairy product production, consumption, stock holding and trade. Equations in the biofuels module tie into industrial demands for grains and oilseeds, and behavioral equations determining ethanol and biodiesel production, consumption and trade in products and blends with other motor fuels. The model solves for the set of prices that brings annual supply and demand into balance in all markets. Ethanol production, denoted PROD in equation 4, is not determined directly but is the product of available productive capacity, CAP in equation 1, and capacity utilization rates, denoted CAPUTL in equation 2. Because of differing technologies, dry-mill and wet-mill processes are modeled separately. Given the multi-period nature of investment in biofuels production facilities, productive capacity is fixed in the current period and a function of historical average net returns, denoted NRT in equation 1. Given the relative youth of production facilities, there is a limited amount of capacity retirement in current equations. Capacity utilization is purely a function of current period net returns (see equation 2). Ethanol plant costs and returns are based upon USDA estimates. In the case of ethanol produced in a dry-mill process, net returns, denoted NRT, are a function of corn price, denoted CORNP; natural gas price, denoted NATP; the wholesale ethanol price, denoted WETHP; the co-product price of distillers’ dried grains prices, denoted DDGP; and other costs of conversion, denoted OVC (see equation 3). The demand for corn is then just a technical conversion of the quantity of ethanol produced CAPt = f( NRTt-1, NRTt-2, NRTt-3, NRT t-4, CAP t-10). (1) CAPUTLt = f( NRTt). (2) NRTt = f( CORNPt, NATPt, OVCt, DDGPt, WETHP). (3) PRODt = CAPt × CAPUTLt (4) With little historical data, the parameters cannot be econometrically estimated, so they are synthetic and represent the relationship of investment in capacity and naive expectations of net returns. Net returns in the previous period have smaller impacts on capacity than returns in periods t-3 and t-4. The small response in period t-1 reflects efforts to advance projects already under design or construction during higher prices, while larger earlier period responses reflect the time needed to design and gain approval for new facilities as well as construction time. Capacity utilization rates are synthetically specified in a logistic form, bounding utilization rates between 0% and 100% and varying the responsiveness to changes in price, depending on current utilization rates. By example, as shown in Figure 1, a sustained increase in ethanol prices would increase utilization rates in the current period, but in subsequent periods additional capacity would be built and utilization rates would return to their ‘natural’ rate. Additional production of ethanol from other grains besides corn and cellulosic based ethanol are included in production totals. Figure 1: Capacity and Utilization Behavior Stochastic Biofuel Modeling:  Stochastic Biofuel Modeling There are three key factors driving the changes in price volatility. First, the growing demand for maize reduces stocks and increases the price impacts of any supply or demand shock. Second, unlike the past when petroleum prices primarily affected commodities through input costs, they now have a major impact on the demand side through biofuel linkages (Figure 2). This linkage to volatile petroleum prices adds an important new source of price volatility. Finally, a structural change that can reduce price volatility is that a large new demand component (maize for ethanol production) is relatively price elastic. It is thereby increasing the aggregate demand elasticity for maize. It is an empirical question to determine the relative importance of each of these and the net result on price volatility. Our results so far indicate the net effect is an increase in price volatility, but it requires continual analysis as more information becomes available. Stronger demand for maize to produce ethanol has translated into higher prices for maize and other cereals in world commodity markets. Likewise, increased demand for vegetable oil to be used to convert to biodiesel will result in higher vegetable oil prices and increased supplies of oilseed meal. It is clear that increased biofuel production derived from agricultural commodities has far reaching implications across a variety of commodities. Changes in cereal and protein meal prices, in turn, have significant implications for livestock industries around the world. Biofuel market developments even affect the US federal budget and international trade negotiations, as higher crop prices translate into lower levels of government payments under existing US farm commodity programs. Figure 2: Corn Sector Impacts Stochastic Biofuel Modeling:  Stochastic Biofuel Modeling Retail ethanol demand is segmented by use and blend rates and tied to motor fuel use. Motor fuel use, denoted MFU in equation 5, is a function of the unleaded gasoline retail price, denoted UGRP; income, denoted INC; and a small substitution effect from the retail price of ethanol, denoted RETHP. The unleaded gasoline retail price, denoted UGRP, is a function of an exogenous petroleum price index, denoted PPIP, which is closely related to the price of crude oil. Motor fuel use and the retail unleaded gasoline price then impacts the demand for ethanol in various formulations. MFUt = f( UGRPt, RETHPt, INCt). (5) The segment of additive ethanol demand reflects the replacement of the oxygenate Methyl tertiary-butyl ether, denoted EADD, in select markets and is blended at 10% or less in motor fuels. Demand in this case is a function of motor fuel use and the wholesale ethanol price, which is adjusted for the blenders’ tax credit of $0.51 cents per gallon (ETTAX) of ethanol used in motor fuels. Ethanol additive demand also includes total ethanol disappearance with a negative sign, signifying that as ethanol disappearance increases, some of the oxygenate blends are replaced with blends of a higher ethanol inclusion rate. Demand for ethanol as an additive and a replacement for MTBE in regions with oxygenate mandates results in a relatively inelastic response to wholesale ethanol prices. For blends, use is broken into retail E10 and E85 blend market potential and penetration. Market potential for the two blends differ on the quantity of ethanol included with up to 10% inclusion for E10 and up to 85% inclusion for E85. The E10 blend can be used in the vast majority of motor vehicles on the road today. This makes the potential market, denoted E10MKT in equation 7, equal to 10% of motor fuel use with an adjustment for additive ethanol demand, denoted ADDAJ. The penetration of E10 into the market, denoted E10PEN in equation 8, is then specified as a percentage of the potential market and relative price responsiveness increases as the ratio of retail ethanol prices to retail gasoline prices falls below 0.75. Total ethanol demand in the E10 market, E10D, is then a product of the two (equation 9). The market potential for E85, denoted E85MKT in equation 10, differs as its use is limited to special ‘flex fuel’ vehicles. Therefore, the market is limited to the number of such vehicles on the road, which is expected to increase over time, denoted TREND, and may be accelerated or slowed depending upon the relative retail prices of ethanol and unleaded gasoline. E85 market penetration, denoted E85PEN in equation 11, and demand, denoted E85D in equation 12, is specified as a function of the same variables, but with different responsiveness than E10 penetration and demand. The acceptance and use of E85 accelerates as the ratio of retail ethanol to unleaded gasoline prices falls below 70%, but is very unresponsive at ratios above this point Demand is then the maximum of the aggregated demand across blends and uses or the mandated quantities under the Renewable Fuel Standard. EADDt = f(WETHPt, ETTAXt, MFUt). (6) E10MKTt = f(MFUt, ADDAJt). (7) E10PENt = f(RETHPt, UGRPt). (8) E10Dt = E10MKTt × E10PENt (9) E85MKTt = f(E85MKTt-1, TRENDt, RETHPt, UGRPt). (10) E85PENt = f(RETHPt, UGRPt). (11) E85Dt = E85MKTt × E85PENt (12) Ethanol stocks are specified as both speculative, a function of price, and pipeline, a function of wholesale ethanol prices. Net imports of ethanol are specified as a function of simulated world ethanol prices and domestic prices adjusted for the $0.54 import tariff, denoted ETTAF. Ethanol wholesale and retail prices are linked through a function which includes the blenders’ tax credit of $0.51 a gallon and a wedge taken from the wholesale to retail price spread of unleaded gasoline (see equation 13), and a identity closes the market on the wholesale level. RETHPt = f(WETHPt, UGRPt-UGWPt, ETTAXt). (13) Stochastic Biofuel Modeling:  Stochastic Biofuel Modeling The structure of the biodiesel equations is similar to ethanol, but more limited in scope given the size of the industry. The smaller scale of biodiesel production leads to some simplifications and demand is not segmented into use or blends, but explicitly accounts for the $1.00 tax credit to biodiesel blenders. Capacity and utilization in the biodiesel markets is determined by net returns as determined by the National Renewable Energy Laboratory. The market is driven largely by soybean oil as a feedstock but also includes rapeseed oil, and other oils and fats. Figure 3: Retail Ethanol Demand Curves by Inclusion Rate Demand by category in 2007, 2011 and 2016 The demand curves for each category of use are illustrated by tracing out the stochastic solutions for the years 2007, 2011 and 2016 (Figure 3). Ethanol as an additive (Eadd) is shown as highly inelastic as its use is mandated and moves with total motor fuel demand in specific geographic locations. Demand for ethanol as an additive bends backward at low prices as the low inclusion rate fuel is substituted with fuels mixed at a higher ethanol inclusion rate. When the ratio of ethanol prices to gasoline prices approaches the ratio of their energy content demand for E10 becomes much more elastic. The use of E10 becomes less responsive again at high levels of use as standard vehicles are limited to a maximum inclusion rate of 10%. Alternatively, ‘flex fuel’ vehicles capable of using up fuel mixed at up to an 85% ethanol inclusion rate. The point at which demand for E85 becomes more elastic is lower than that for E10 as its energy content is also lower (do to its higher ethanol content). In the short run, demand for E85 is limited by the number of ‘flex fuel’ vehicles in use which is a function of the past ratio of ethanol and gasoline prices, but becomes highly elastic in the long run when ethanol prices fall relative to gasoline prices. Ethanol Production and Grains Prices:  The average projected corn price across 500 outcomes for 2007-2016 is $124.20 per metric ton. High oil prices drive up ethanol production, increasing corn demand and corn production costs, both of which increase the price of corn. Weather, export demand, and other factors also affect corn prices. The average projected wheat price across 500 outcomes for 2007-2016 is $152.76 per metric ton. Rising corn prices resulting from increased ethanol production increases competition for wheat acreage in the corn belt and plains states, reducing acreage and increasing wheat prices. Wheat prices are also tied to corn prices through substitution in feed rations. The impact is smaller than the impact for corn. The average projected rice price across 500 outcomes for 2007-2016 is $186.85 per metric ton. Increased acreage competition and commodity prices drive up rice prices with increased ethanol production, reducing rice area and increasing rice prices. The price impacts for rice are smaller than for both corn and wheat due in part to geography and non-convertible capital equipment. Ethanol Production and Grains Prices Ethanol Production and Grains Exports:  Increased ethanol production means fewer exportable supplies of corn. The US dominates international trade in corn so changes at these levels have a significant corn trade impact. DDG exports with higher ethanol production offset little of the reduced corn exports. Wheat exports decline modestly with increased ethanol production as wheat acreage declines and prices increase. The US faces more competition for wheat exports than for corn, so global trade is less impacted. The US averaged 27.8 million metric tons of wheat exports in the first half of the decade. Rice exports decline with increased ethanol production due to smaller supplies and increased prices. The price response is smaller for rice than for wheat and corn, and this limits the response in exports. Ethanol Production and Grains Exports Ethanol, Biodiesel and Soybean Prices and Trade:  As with ethanol, higher petroleum prices are associated with higher biodiesel prices. The 10-year average biodiesel price at the plant across all 500 outcomes is $0.82 per liter. For both ethanol and biodiesel, these results assume extension of the federal tax credits that support producer biofuel prices and returns. Soybean oil prices rise with higher ethanol production due to both supply and demand effects. Acreage shifts to corn also push up the price for soybeans and its oil. DDG use in livestock rations replaces some soybean meal, pushing down soybean meal prices and pushing up soybean oil prices Increased ethanol production is associated with fewer soybean acres and smaller exportable supplies Higher soybean oil prices, related to higher oil and ethanol prices, reduces exportable supplies Ethanol, Biodiesel and Soybean Prices and Trade

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