Regional agricultural and forestry cellulosic and oil/fat biomass feedstock supplies are derived from the Forest and Agriculture Sector Optimization Model with Greenhouse Gases (FASOM-GHG). With FASOM-GHG, we estimate biomass feedstock supply curves over time for potential energy applications within US-REGEN, which could be electricity generation, industrial, transportation, or fuel-conversion. FASOM-GHG endogenously and simultaneously models the potential demand for numerous agriculture and forestry biomass feedstock types, capturing competition, complementarities, and co-products, and accounting for food, feed, and wood products production opportunities and opportunity costs. It also evaluates environmental and economic implications for, among other things, land allocation, land management, net greenhouse gas changes, and commodity market implications. The resulting state and feedstock type annual delivered biomass supply curves (price and quantities) through 2050 are inputs to the US-REGEN fuels model for cost-effective allocation across potential economy-wide applications. Oil and fat feedstock supplies are estimated regionally, consistent with regional agricultural processing activities. Figure 1 illustrates available supply of agriculture and forestry cellulosic biomass feedstocks at varying biomass prices, broken out by state (states with the largest potential are labeled). Variation across states is significant, both in total supply and feedstock composition (see examples shown in Figure 2). The largest supplies are estimated to be in Missouri, Minnesota, New Mexico, Illinois, Iowa, and Texas. In general, feedstock supply is dominated by energy crops and increasingly so as prices rise; however, supplies of agriculture and forestry residues and logs are also substantial and increasing with price.
Forestry and Agricultural Biomass Supply Modeling
US-REGEN makes use of regional agricultural and forestry biomass supply feedstocks derived from the Forest and Agriculture Sector Optimization Model with Greenhouse Gases (FASOM-GHG). A detailed study of the modeling, results, and insights, including land-use, GHG emissions, and market effects is forthcoming. FASOM-GHG is a dynamic economic model of U.S. forestry and agricultural commodity production and markets, including land-use allocation and land management decisions. The model is unique in its detailed simultaneous and dynamic optimization of forestry and agricultural production and markets and has a long peer-review publication record (e.g., Latta et al., 2013; Beach et al., 2010; Baker et al., 2010; USEPA, 2005; McCarl and Schneider, 2001). The biomass supply modeling includes logistics associated with delivering biomass feedstocks for energy use (i.e., storage and transportation). The core model development team includes Texas A&M University, University of Idaho, and EPRI. This section provides an overview of the model's structure and a discussion of specific biomass feedstock modeling features.
FASOM-GHG is an economic model that solves for the market equilibrium prices and quantities that simulate inter-temporal market clearing in the U.S. forestry and agricultural sectors given a set of assumptions. It does this by following Samuelson and maximizing the net present value of what economists refer to as the sum of consumer and producer surplus. The model is used to simulate alternative futures and glean insights regarding the potential implications of different assumptions regarding future policy, technology, land productivity, etc.
To model long-run forest investment decisions and long-run policy proposals, model simulations have 5-year time steps and a time horizon of at least 50 years depending on the application. For this application, we model individual states, with some states further disaggregated, and regional forestry markets. Model results over time include projections of commodity prices and quantities, production input prices and quantities, export and import quantities, land-use allocation between agriculture and forestry and between specific crops as well as pasture, and land management system choices such as chemical inputs, irrigation, tillage, and crop type.
FASOM-GHG models primary and secondary forestry, agricultural, and wood products commodity production opportunities and their markets. Commodity supply and demand are calibrated to historic price and production data.
Land is allocated within each state and region to the combination of uses that yields the highest net present value. The productivity of land varies by use and region and is a function of biophysical conditions and growth characteristics, land management, and exogenous technological improvements.
The version of FASOM-GHG developed and utilized here includes a variety of conventional dedicated and residue agriculture and forestry biomass feedstocks (Table 1). Simultaneous modeling of the set of agriculture and forestry biomass feedstocks allows the model to capture competition and complementarities, as well as the value of co-production (e.g., oil supplies from agricultural processing). In US-REGEN, multiple feedstocks can be used for a bioenergy use and a single feedstock can be used for more than one use, as shown in Table 1. In particular, cellulosic feedstocks can be used as inputs to several alternative fuel conversion technologies, including liquids fuels, gas, and hydrogen, as described in Bioenergy Refining and Hydrogen Production.
The relative value of an individual feedstock is a function of direct costs (associated with harvesting, transportation, storage, and processing), the opportunity costs of alternatives, energy content, moisture content, energy prices, co-products (e.g., oil and livestock feed substitutes), and direct and net greenhouse gas benefits if valued. Table 2 shows the energy (higher heating value) and moisture content assumptions for each feedstock. Note that the delivered energy of the feedstocks is assumed to have undergone natural drying in the field/forest and during storage (i.e., not bone-dry, but not just harvested moisture content).
Because of the seasonal nature of the availability of some biomass feedstocks, we include off-site storage to ensure year-round availability. This is in addition to any short-term on-site storage that might be required at power plants or refineries, which is assumed to be included in the cost and performance specifications of individual bioenergy conversion technologies in US-REGEN.
Like storage, transportation of biomass is a critical component of the cost of biomass fuel per unit energy given the low energy density of biomass compared to fossil fuels. We model the costs of transporting biomass to state collection points. Off-site storage is assumed to occur at the collection points. In addition to proximity (collection to farm/field), which defines distance, transportation costs for each feedstock are determined by biomass energy yield per ton, spatial density of the biomass being used as a feedstock (defined as the proportion of the land area around a collection point with available biomass of the type being used as a feedstock), and the truck-hauling rate (load size and number of loads required for a given amount of energy). Within US-REGEN, all new energy conversion facilities using biomass feedstocks are assumed to be located at or near the collection point, thus there are no additional transportation costs (though there can be secondary energy distribution costs, e.g., transmission lines or pipelines).
|Conventional Ethanol||Conventional Biodiesel||Cellulosic Fuel Conversion||Combustion for Power/Heat|
|Starch & Sugar-Based Crops|
Barley, corn, oats, sugar, rice, sorghum, wheat
Barley, corn, oats, rice, sorghum, wheat residues
Hybrid poplar, miscanthus, switchgrass, willow
Hardwood, softwood residues
Bagasse, pulpwood milling, sweet sorghum pulp, lignin, manure
|Oils & Fats|
Soybean oil, canola oil, corn oil, tallow, lard
Average biomass transportation costs per ton to collection points are computed for each region based on an approach developed by French (1960) and described in McCarl et al. (2000) that assumes a gridded road system and considers biomass proximity to county centroids. Fixed cost, cost per mile, and load size assumptions vary across feedstocks—grain crops, crop residues, energy crops, processing residues, forestry residues, logs, and fats and oils.
GHG Abatement in Agriculture and Forestry
FASOM-GHG is also used to provide estimates of agriculture and forestry GHG abatement supply curves. We are also able to estimate biomass supply cost implications of GHG incentives for agriculture and forestry. A GHG abatement program that includes agriculture and forestry could provide incentives for changing land management practices, e.g., growing trees, modifying fertilizer use, reducing conventional tillage practices, and reducing livestock methane emissions. Placing a value on GHG reductions could also increase the cost of fossil fuel use and increase the value of bioenergy. In addition to the biomass supply estimates discussed above, we will be estimating biomass supplies for scenarios with land mitigation GHG incentives, as well as for alternative potential biomass GHG accounting approaches. This analysis will provide adjusted biomass supplies and complementary agriculture and forests GHG abatement supplies that can be used for GHG policy scenarios.
Waste Methane Supply
Methane (CH4) is a waste product of anaerobic decomposition in landfills and represents a potential renewable natural gas feedstock. US-REGEN estimates for waste methane supply are developed separately from the FASOM-GHG model. Landfill CH4 availability is modeled based on state level landfill activity and waste composition over time (See Figure 3), considering site characteristics, such as landfill size and age, as well as accounting for current CH4 utilization and abatement (see Rose et al, 2016). Landfill gas (LFG) supply (price and quantity) for RNG production is derived using the cost profile estimated by American Gas Foundation (2019), which adjusts for pre-conversion costs and, as noted, accounts for currently utilized LFG (see Figure 4).
EPRI is conducting further research under LCRI to refine state landfill waste CH4 RNG supply estimates by, among other things, updating landfill input and projected activity data, more explicitly accounting for collection, upgrading, conditioning, and gas system delivery/injection costs, and accounting for potential direct GHG mitigation of LFG. We are also developing waste RNG supply estimates for other waste sources, including CH4 from livestock manure, wastewater, and food waste, as well as municipal solid waste. Finally, note that gasification of cellulosic agriculture and forestry feedstocks is also an option for renewable natural gas supply in US-REGEN, as described in Bioenergy Refining.
Latta, G.S., J.S Baker, R.H. Beach, S.K. Rose, B.A McCarl (2013). A multi-sector intertemporal optimization approach to assess the GHG implications of U.S. forest and agricultural biomass electricity expansion. Journal of Forest Economics 19(4): 361-383. ↩︎
Beach, R., Adams, D., Alig, R., Baker, J., Latta, G., McCarl, B. A., Murray, B., Rose, S., & White, E. (2010). Model documentation for the Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG). U.S Environmental Protection Agency. ↩︎
Rose, SK, J Petrusa, L Davis (2016). Regional Non-CO2 Greenhouse Gas Abatement Potential in the United States to 2030. EPRI Report 3002009609. ↩︎
American Gas Foundation (2019). Renewable Sources of Natural Gas: Supply and Emissions Reduction Assessment, December. ↩︎