Abstract

The University of Tennessee’s (UT) Department of Agricultural and Resource Economics models supply chains for both liquid and electricity generating technologies currently in use and/or forthcoming for the bio/renewable energy industry using the input–output model IMPLAN®. The approach for ethanol, biodiesel, and other liquid fuels includes the establishment and production of the feedstock, transportation of the feedstock to the plant gate, and the one-time investment as well as annual operating of the facility that converts the feedstock to a biofuel. This modeling approach may also include the preprocessing and storage of feedstocks at depots. Labor/salary requirements and renewable identification number (RIN) values and credits attributable to the conversion facility, along with land-use changes for growing the feedstock are also included in the supply chain analyses. The investment and annual operating of renewable energy technologies for electricity generation for wind, solar, and digesters are modeled as well. Recent modeling emphasis has centered on the supply chain for liquid fuels using the Bureau of Economic Analysis’s 179 economic trading areas as modeling regions. These various data layers necessary to estimate the economic impact are contained in UT’s renewable energy economic analysis layers (REEAL) modeling system. This analysis provides an example scenario to demonstrate REEAL’s modeling capabilities. The conversion technology modeled is a gasification Fischer–Tropsch biorefinery with feedstock input of 495,000 metric tons per year of forest residue transported to a logging road that is less than one mile in distance. The biorefinery is expected to produce sustainable aviation fuel (SAF), diesel, and naphtha. An estimated one million tons of forest residue are required at fifty percent moisture content. Based on a technical economic assessment (TEA) developed by the Aviation Sustainability Center (ASCENT) and the quantity of hardwood residues available in the Central Appalachian region, three biorefineries could be sited each utilizing 495,000 dry metric tons per year. Each biorefinery could produce 47.5 million liters of SAF, 40.3 million liters of diesel, and 23.6 million liters of naphtha. Annual gross revenues for fuel required for the biorefineries to break even are estimated at $193.7 million per biorefinery. Break-even plant gate fuel prices when assuming RINs and 12.2 percent return on investment are $1.12 per liter for SAF, $1.15 per liter for diesel, and $0.97 per liter for naphtha. Based on IMPLAN, an input–output model, and an investment of $1.7 billion, the estimated economic annual impact to the Central Appalachian region if the three biorefineries are sited is over a half a billion dollars. Leakages occur as investment dollars leaving the region based on the regions local purchase coefficients (i.e., LPPs), which totals $500 million. This results in an estimated $2.67 billion in economic activity with a multiplier of 1.7, or for every million dollars spent, an additional $0.7 million in economic activity is generated in the regional economy. Gross regional product is estimated at $1.28 billion and employment of nearly 1,200 jobs are created during the construction period of the biorefineries, which results in $700 million in labor income with multiplier effects. Economic activity for the feedstock operations (harvesting and chipping) is estimated at slightly more than $16.8 million resulting in an additional $30 million in the economic impact. The stumpage and additional profit occurring from the harvest of the forest residues result in $40 million directly into the pockets of the resource and logging operation owners. Their subsequent expenditures resulted in a total economic activity increase of $71.4 million. These operations result in creating an estimated 103 direct jobs for a total of 195 with multiplier effects. Direct feedstock transportation expenditures of more than $36.7 million provide an estimated increase in economic activity of almost $68 million accounting for the multiplier effects.

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