Agriculture’s Potential Regional Economic Contributions to the United States Economy When Supplying Feedstock to the Bio-Economy

Abstract

The economic impact of obtaining biomass could become significant to U.S. rural economies via the establishment of a bioeconomy. In 2023, the Bioenergy Technologies Office (BETO) and Oak Ridge National Laboratory provided a road map to obtain over a billion tons of biomass for conversion to bioenergy and other products. Using information from this roadmap, this study estimates the potential positive and negative economic impacts that occur because of land use change, along with increased technological advances. This is achieved by using the input–output model, IMPLAN, and impacting 179 Bureau of Economic Analysis regions in the conterminous United States. Biomass included in the analysis comprises dedicated energy crops, crop residues, and forest residues. The analysis found that managing pastures more intensively could result in releasing land to produce dedicated energy crops on 30.8 million hectares, resulting in the production of 361 million metric tons of biomass. This, coupled with crop residues from barley, corn, oats, sorghum, and wheat (162 million metric tons), plus forest residues (41 million metric tons), provide 564 million dry metric tons of biomass. Assuming the price for biomass in 2023 dollars was USD 77 per dry metric-ton, this additional production results in an economic benefit for the nation of USD 619 billion, an increase from the Business As Is scenario (Baseline) of almost USD 100 billion per year, assuming a mature biomass industry. An additional 700,000 jobs are required to grow, harvest/collect, and transport the biomass material from the land.

1. Introduction

In 2021, the federal government launched the Sustainable Aviation Fuel (SAF) Grand Challenge. Methods for conversion of biomass exist that can create SAF and other fuels and chemicals from a few different biochemical and thermochemical platforms or pathways, including catalytic fast pyrolysis and hydrotreating [1], gasification, Fischer–Tropsch synthesis, and hydrothermal liquefaction to mention a few [2]. This program was designed to seek methods for cost reduction, increased sustainability and expanded use of SAF [3]. Prior to this declaration, several national programs provided motivations to adopt sustainable means of producing energy from biomass. One of the efforts was designed to identify the amount, cost, and location of potential sources of biomass. In 2005 [4], 2011 [5], 2016 [6,7], and 2023 [8], the U.S. Department of Energy and U.S. Department of Agriculture jointly commissioned studies that identified the potential supply of biomass. The Billion-Ton 2023 (BT23) analysis shows a U.S. biomass production capacity of 0.7 to 1.7 billion dry tons of biomass per year depending on the scenario and price. The U.S. Department of Agriculture led a study that developed a plan to advance the formation of a bioeconomy in the United States [9]. In this report, the authors indicate that new sources of revenue for U.S. farmers and foresters located in rural areas of the country would materialize. However, the study did not address the potential benefits and costs that might occur if a bioeconomy were to emerge, nor did it address where those benefits and costs might occur. This manuscript uses information from the billion-ton analysis and identifies the potential positive and negative economic impacts that might occur because of land use change and potential increased agronomic and engineering technological advances through the production of a bioeconomy based on biomass feedstock.

According to G. Moore’s 1975 article, economic indicators such as production, prices, income, employment and investment levels can be used to measure the progress of an economy [10]. In this study, economic impacts are measured using changes in three coincidence indicators—Total Industrial Output (TIO), Employment, and Gross Regional Product (GRP). TIO is the annual economic activity for industry or value of production in that year; employment represents total wage and salary employees, as well as self-employed jobs in a region, for both full- and part-time workers; and GRP includes employee compensation, proprietary income, other property-type income, and taxes on production and imports [11]. GRP is known as the most important economic indicator as it combines the annual monetary value of all that is produced in an economy [12]. These macroeconomic indicators have been used by economists to reflect the welfare of a region and the impact on that region if an event occurred. Since economic impacts are likely to be regional in nature because of the bulky characteristics of biomass, this is achieved for the 179 Bureau of Economic Analysis regions in the conterminous United States.

These impact indicators are generated using input–output models. The foundation of input–output models is the interdependence between sectors or industries within a region’s economy. The analysis estimates the impacts of economic events on a region including the ripple effect through the region’s economy. All three indicators, TIO, employment, and GRP, used in this analysis are outputs of an input–output analysis. There are numerous studies that use these as indicators for a single event.

Economic activity is generated over each stage of a supply chain through the transactions that occur as products are formed, distributed, and sold. In this analysis, transactions are made to generate the product, biomass. Each type of biomass has a separate set of transactions. The Billion-Ton Report has identified quantities of biomass and specified costs and prices at the county level. Trade is not restricted to the county level, and in this analysis market areas are used to estimate the economic impacts of supplying biomass. There are numerous annual impacts that can occur to the agricultural sector because of projected changes in crop acreage, crop prices, and government payments, plus the growing of dedicated energy crops, the collection of crop and logging residues, and harvest of pulpwood-class trees. Transportation of the energy feedstocks needs to occur as well. Means of conversion to energy are not defined in the Billion Ton Report, so incorporation of the transactions that would occur as biomass is used to create energy sources are estimated, assuming transportation occurs from the field or landing to a facility of some type. The analysis begins and stops with agricultural production impacts, both positive and negative, plus their impact on the region’s economy.

2. Materials and Methods

The goal in this manuscript is to provide estimates of the potential economic impacts of the biomass portion of the Billion-Ton Report conducted in 2023 (BT23). The boundaries for this project were determined by Oak Ridge National Laboratory (ORNL) and include the estimated transactions of obtaining, collecting, and getting the material ready for transport to a biomass facility. The analysis is conducted at the 179 Bureau of Economic Analysis (BEA) regions level (Figure 1), consistent with the analytical tool called Renewable Energy Economic Analysis Layers or REEAL and is limited to 48 conterminous states [13]. REEAL will use information from the BT23 analysis that contains quantity and cost by biomass type at the county level. These data are aggregated to the BEA level by biomass type. Biomass types examined include the following:

• Residues from cropland—barley straw, corn stover, oats straw, sorghum biomass, and wheat straw;
• Residues from pine and hardwood timberlands;
• Young trees with a diameter breast height (dbh) less than 10 or 11 inches depending on tree species;
• Dedicated Energy crops—energy cane, miscanthus, poplar, sweet sorghum, switchgrass, and willows.

Numerous scenarios were developed for the BT23 analysis. For this analysis, data from two of the scenarios will be used. The Mature-Market Medium scenario (MED070) with a biomass price of USD 77.14 per dry metric ton USD (USD 70 per dry ton), along with a wood residue price of USD 44.08 per dry metric ton USD (USD 40 per dry ton) are compared to the results of the no biomass “Baseline” scenario (MED000) (Table 1). Each scenario will use information from the final simulation year, 2041. In the MED070 Scenario, an annual one percent increase in dedicated energy crop yields and improvements in harvest technology from 50% (current level) to 90% is assumed. Data from the two land-based models used in BT23, POLYSYS and ForSEAM, provided estimates on the magnitude of biomass production and land use shifts.

Table 1. Scenarios evaluated.

Item	Scenario Name and (Description)
	Baseline (No Biomass)	MED070 (With Biomass)
Price of Biomass	USD 0.00 per dry metric ton	USD 77.14 per dry metric ton 1
Year Simulation Analysis Results are Based	2041	2041
Energy Crop Harvest Technology	50% efficiency	90% efficiency
Traditional Crop Yield Technology	USDA Baseline Extended	USDA Baseline Extended
Dedicated Energy Crop Technology	Annual Increase = 0 percent	Annual Increase = 1 percent

¹ The price was set at USD 77.14 per dry metric ton harvested/collected and ready for transport except for forest residues where the price was set to USD 44.08 per dry ton. Sources: [7,14].

Table 1. Scenarios evaluated.

Item	Scenario Name and (Description)
	Baseline (No Biomass)	MED070 (With Biomass)
Price of Biomass	USD 0.00 per dry metric ton	USD 77.14 per dry metric ton 1
Year Simulation Analysis Results are Based	2041	2041
Energy Crop Harvest Technology	50% efficiency	90% efficiency
Traditional Crop Yield Technology	USDA Baseline Extended	USDA Baseline Extended
Dedicated Energy Crop Technology	Annual Increase = 0 percent	Annual Increase = 1 percent

¹ The price was set at USD 77.14 per dry metric ton harvested/collected and ready for transport except for forest residues where the price was set to USD 44.08 per dry ton. Sources: [7,14].

Figure 1. Bureau of Economic Analysis economic areas for input–output analysis modeling. Source: [15].

Figure 1. Bureau of Economic Analysis economic areas for input–output analysis modeling. Source: [15].

Indicators of changes in economic activity that reflect potential economic impacts used in this analysis (e.g., economic activity, employment, and gross regional product) are at the BEA and national levels. These indicators are frequently used by others when determining positive and negative economic impacts on a region given the introduction of an event to that region (Appendix A 

Table A1. Synthesis of Prior Studies that examine the Economic Impacts of Bio-based Projects.

Citation	Models Used	Description and Findings
[37]	IMPLAN	Sustainable conversion of new amounts of woody biomass to power, after considering accessibility 8 thresholds and extrapolated to potential impacts in the U.S. economy, could translate into new economic contributions in the range of USUSD 5.50 billion to USUSD 21.98 billion per year
[38]	(MaxEnt) and an I-O model of Pennsylvania based on IMPLAN	65 industrial sites were identified. A case study was conducted for forest biomass to pellet fuel in the U.S. Mid-Atlantic region. The socio-economic impacts assessment indicates that the one-year construction of a medium-size pellet fuel facility (75,000 dry tons/year) could create 127 jobs, USD 8.78 million of labor income, while the operation could create 202 jobs, USD 10.52 million of labor income, USD 14.66 million of value added, and USD 33.61 million of output in total per year for the state-level economy. Used industry output, value added, employment, and labor income variables from the I–O model.
[39]	IMPLAN	Our input–output model utilizing the IMPLAN software requires data on (1) annual available almond biomass residue; (2) almond acreage in the studied counties; (3) biomass to biochar conversion rates; (4) biochar production costs; and (5) possible biochar selling prices. Examine employment and labor income impacts.
[40]	Comparison of JEDI with IMPLAN	This paper calculates the impact on job, income and output creation of a new solar power plant in an input–output framework.
[41]	IMPLAN	Used Employment, Labor Income, Total Value Added and output as economic indicators. For example, 100 percent distribution of available woody biomass for bio-oil facilities would generate 5932 full- and part-time jobs with USUSD 700 million of economic output.
[42]	IMPLAN	Study objectives were to describe the main economic impacts of developing bioenergy and to specifically quantify the economic impacts on Mississippi’s economy of logging residue recovery, electricity generation from woody biomass, and construction and operation of a biofuel facility. Variables used to measure the impact include output, value added, employment, and income.
[43]	IMPLAN and JEDI	A 147 MW wind farm near Weatherford generated an estimated USUSD 27 million in local spending and created 188 jobs during the construction phase. Once operational, the wind farm supports an estimated 13 jobs directly at the wind farm, including technicians and management. Additional estimates show that USUSD 1.7 million continues to be spent annually in the local economy, with over USUSD 600,000 in additional property tax revenue and almost USUSD 400,000 in direct land lease payments to landowners. The model estimates that the combined direct and induced impact annually is over USUSD 25 million.
[44]	IMPLAN	The estimated impacts of residue procurement and electricity production on the region’s employment, value added, and output for selected counties in East Texas. The impact assessment was based on current power generation and distribution systems as of 2007 with fossil fuel replacement with woody biomass in power generation. Value-added impact of USD 216M or almost 60% of the total current value-added generated by the logging industry would be added to the economy. Residue procurement would account for less than one-third of the total impacts on value added and output.
[45]	IMPLAN	24 counties make up the hardwood fiber shed for a recently closed pulp mill. Three alternatives were examined in revitalizing the impacted economy by using the surplus fiber and creating new opportunities for the displaced workforce. Results from a multiregional input–output analysis revealed spillover economic opportunities beyond the impacted areas.

). The values are expressed in 2023 dollars and reflect the economic relationships within the economy that existed in 2019 (reflecting a 2019 picture of the economy and its structure may over- or underestimate the economic impacts of what might occur under today’s structure since the interrelationships between different sectors of the economy may have changed in a specified BEA). Furthermore, the analysis only includes biomass that derives from removal operations and does not include waste such as sawdust, tallow, waste grease and oil, etc.

Figure 2a contains a schematic regarding the estimation of Impact Coefficients. These coefficients are defined for each event. The events are defined by the POLYSYS and ForSEAM solutions shown in Figure 2b. Information is taken from the BT23 solutions and based on those solutions; events are determined. In addition, for each event located in the BEA, quantities, costs, income and land area are determined.

Economic Impact Factors are estimated based on the POLYSYS and ForSEAM’s solutions for the prespecified scenario (Baseline of MED070). These, when combined with the Impact Coefficients, provide estimates of the Event Impacts (Figure 2c). The Event Impacts are then aggregated to provide BEA or national economic indicator estimates for total output, employment, value added as well as national and state/local taxes.

2.1. IMPLAN

IMPLAN 3 is a regional economic analysis software application that is designed to estimate the impact or ripple effect (specifically backward linkages) of a given economic activity within a specific geographic area through its Input–Output and Social Accounting Matrix model [11,16]. An input–output model is characterized by several assumptions: (1) constant return to scale, (2) no supply constraints or resource constraints, (3) fixed input and output structure, (4) delineated time, and (5) captured backward linkages. The use of IMPLAN data is identified by IMPLAN^® model, 2019 data, using inputs provided by the user and IMPLAN Group LLC, IMPLAN System, 16,905 Northcross Dr., Suite 120, Huntersville, NC, USA (studies, results, and reports that rely on IMPLAN data or applications are limited by the researcher’s assumptions and not endorsed or verified by IMPLAN Group, LLC unless otherwise stated by a representative of IMPLAN; however, the model and its structure are copyrighted [17]. A client “may use, display, reproduce and publish such output in analyses, reports, presentations, and publications, provided that client includes a notation in such output”).

In addition to providing descriptive measures of the economy (e.g., total industry output (economic activity or the value of all sales), employment, and value added, the model also provides information on federal/state/local taxes for 546 industries based on the U.S. Department of Census’s North American Industry Classification System (NAICS) in each BEA) (total industry output is defined as the annual dollar value of goods and services that an industry produces. Employment represents total wage and salary employees, as well as self-employed jobs in a region, for both full- and part-time workers. Labor income consists of employee compensation and proprietor income. Total value added is defined as all income to workers paid by employers (employee compensation); self-employed income (proprietor income); interests, rents, royalties, dividends, and profit payments; and excise and sales taxes paid by individuals to businesses. State/local taxes are comprised of sales tax, property taxes, motor vehicle licenses taxes, and other taxes). Changes in these four measures will be used as indicators of the impact on the region’s economy that a scenario will have when compared to a baseline. The metrics IMPLAN provides are expressed in 2023 dollars. The employment indicator conveys the number of full- and part-time jobs supported. IMPLAN^® (Version 3.0 using basic data for 2019) county data from the model are aggregated to BEA economic areas and then converted to BEA input–output models. Once the economic model is formed, an event is modeled, and the events’ impacts are estimated.

The impacts operate on the assumption that as consumers and institutions increase their expenditure, demand increases for products made by local industries who in turn make new purchases from other local industries and so forth. The model provides the response of the entire BEA economy to a set of changes in the production of biomass energy feedstocks. As purchases are made from outside the BEA, a leakage occurs and that ends the response. While the impact of that leakage does not occur within the BEA of interest, it may in fact impact other parts of the United States. This impact occurs because of InterBEA commerce and is measured using the national (48 state) model.

The analysis is achieved by using Analysis by Parts (ABP) methodology [18]. ABP is conducted by splitting the payments for inputs of an event into the industries that receive them. In this analysis, the costs of production budgets are used to provide estimates of the potential payments that would occur for a specific event. The total impact is the aggregation of all the parts over the backward-linked supply chain. In addition, labor impacts and the impacts of changes in proprietor income are also incorporated. The estimates resulting from the event provide economic impact coefficients.

To generate these economic impact coefficients, 36 different sets of events are evaluated in each of the 180 IMPLAN models representing BEA’s regions or the conterminous nation. These 7560 simulations provided economic impact coefficients for Total Industrial Output; Employment; Employee Compensation; Proprietors’ Income; Indirect Business Taxes; Other Property Type Income; Total Value Added; Labor Income; and Federal State/Local Taxes by BEA region and for the nation. The economic impact coefficients are generated for each of the BEA models and the conterminous nation model assuming the impact occurs if 100,000 acres of the event occurs for the traditional crops, dedicated energy crops, and crop residues. For the forest, labor, and proprietor events, the impact coefficients are generated based on USD 1,000,000 in transactions; and transportation impact coefficients are based on USD 10,000,000 in expenditures.

The impact coefficients’ factors are formed by summing up acreage, cost, or proprietor income by BEA and using the factor divisor of 40,468.56 hectares, USD 1,000,000, or USD 10,000,000. These factors serve as the impact multiplier and, when combined with the economic indicator coefficients, provide a product representing the economic impacts of one of 42 sets of economic impacts at either a BEA or national level. The difference between the national level of impacts and the sum of impacts identified at the BEA level is assumed to occur because of the InterBEA commerce.

For instance, in the Baseline solution, there is an estimated 35,751,000 hectares of corn planted nationally in the year 2041. The economic impact factor for the analysis would be 883.445 for the national impact and ranges from 0 to 77.517 based on planted hectares for each of the 1–179 BEAs in the analysis. Nationally, it is estimated that 35,751,000 hectares create USD 104.4 million in output and support 828 jobs. Therefore, using the economic impact factor, it is estimated that USD 92.2 billion in economic activity and 732 thousand jobs are supported because of corn production in the conterminous United States. Conducting this process for each BEA and summing up the impacts indicate that USD 41.9 billion occurs within the BEA where the acreage is planted and USD 50.3 billion occurs because of InterBEA commerce. Jobs supported are similarly treated and 399 thousand jobs are supported within the BEAs where the corn is planted, and 333 thousand additional jobs are supported within the United States.

2.2. The Events

The events and the scenario in which they are included are presented in

Table 2. Events used in the analysis by scenario.

Event Category	Different Events Identified	Baseline	MED070
TRADITIONAL CROPS	9—Barley, Corn, Cotton, Hay, Oats, Rice, Sorghum, Soybean, and Wheat	Yes	Yes
DEDICATED ENERGY CROPS	7—Energy Cane, Miscanthus, Hybrid Poplar, Sweet Sorghum, Switchgrass, Willows, and Intensified Pasture	No	Yes
CROP RESIDUES	5—Barley Straw, Corn Stover, Oat Straw, Sorghum Residue, and Wheat Straw	No	Yes
FOREST PRODUCTS	6—Forest Residue (Machinery, Labor, and Proprietor Income) and Young Trees (Machinery, Labor, and Proprietor Income)	No	Yes
TRANSPORTATION	5—Transportation for Traditional Crops, Herbaceous Dedicated Energy Crops, Woody Dedicated Energy Crops, Crop Residues, and Forest Products	Partial—only Traditional Crops	Yes
PROPRIETOR INCOME	4—Proprietor Income for Traditional Crops, Dedicated Energy Crops, Crop Residues, and Forest Products	Partial—only Traditional Crops	Yes

. The 36 events are categorized into six distinct categories—Traditional Crops, Dedicated Energy Crops, Crop Residues, Forest Products, Transportation, and Proprietor Income. The following pages provide estimates of the transactions required to make the event a reality and include costs of production and per-acre yields for the traditional crops, dedicated energy crops, and crop residues. The national price required or assumed minus these costs provides proprietor income estimates. Transportation of the biomass and traditional commodities is based on the current literature and data found in secondary sources.

2.2.1. Cost of Feedstock Production

To identify initial transactions and the impact of the industries, costs of production are required. These costs are typically in simplified budget form and are measured based on a unit of production. The costs of producing the feedstock are derived from three sources, the BT23 study, an agricultural and forest model POLYSYS [19], and ForSEAM [20] and are expressed in 2019 dollars. For perennial dedicated energy herbaceous and tree crops, an establishment cost is estimated and treated as an investment in the development of feedstock. These investment costs are annuitized and only the annuitized costs are used. All the crops have annual maintenance and harvest/collection costs.

Traditional Crops

The per-acre costs require labor, seed, fuel, lubricants, repairs, fertilizers, herbicide, insecticide, other chemicals, irrigation, housing, insurance, interest, depreciation, and any other costs. These costs were assigned to IMPLAN industries as indicated in

Table 3. Crosswalk between budget item and the impacted IMPLAN industry.

Budget Cost Item	Impacted IMPLAN Industry Sector
Depreciation	(456) Accounting, Tax Preparation, Bookkeeping, & Payroll Services
Fertilizer K	(19) Support Activities for Agriculture & Forestry
Fertilizer N	(19) Support Activities for Agriculture & Forestry
Fertilizer P	(19) Support Activities for Agriculture & Forestry
Fuel	(408) Retail—Gasoline Stores
Herbicide	(19) Support Activities for Agriculture & Forestry
Housing	(448) Tenant-Occupied Housing
Insecticide	(19) Support Activities for Agriculture & Forestry
Insurance	(444) Insurance Carriers, except Direct Life
Interest	(439) Non-depository Credit Intermediation & Related Activities
Irrigation	(49) Water, Sewage, & Other Systems
Labor	(19) Support Activities for Agriculture & Forestry
Lubricants	(408) Retail—Gasoline Stores
Other Chemical	(19) Support Activities for Agriculture & Forestry
Other Costs	(19) Support Activities for Agriculture & Forestry
Other Fertilizer	(19) Support Activities for Agriculture & Forestry
Repairs	(515) Commercial & Industrial Machinery & Equipment Repair & Maintenance
Seed	(405) Retail—Building Material & Garden Equipment & Supplies Stores
Labor	Proprietor Income
Pasture	(19) Support Activities for Ag/Forestry
Transportation	(417) Truck Transportation
Logging	(16) Commercial Logging
Chipping	(408) Retail—Gasoline Stores and (515) Commercial & Industrial Machinery & Equipment Repair & Maintenance
Harvest	(19) Support Activities for Ag/Forestry; (408) Retail—Gasoline Stores; (439) Nondepository Credit Intermediation & Related Activities; (444) Insurance Carriers, except Direct Life; (456) Accounting, Tax Preparation, Bookkeeping, & Payroll Services; (515) Commercial & Industrial Machinery & Eq Repair & Maintenance; and Labor—Employee Compensation

.

Figure 3 contains information on these costs for the following traditional feedstocks: (1) Feed grains—barley, corn, oats, and sorghum; (2) Food grains—wheat and rice; (3) Oilseeds—soybeans; and (4) Fiber—cotton. The costs of production in 2023 dollars per acre range from USD 550 per hectare for oats to over USD 2500 per hectare for rice (Appendix A 

Table A2. A summary of the average cost of crop production in 2023 USD used in POLYSYS (USD/hectare).

Item/Crop Number ( )	Corn (1)	Sorghum (2)	Oats (3)	Barley (4)	Wheat (5)
LABOR	102.01	64.52	49.77	58.07	51.27
SEED	171.22	29.11	52.66	56.34	59.33
FUEL	159.68	90.76	81.99	88.78	93.92
LUBRICANTS	4.82	13.29	12.31	13.32	14.09
REPAIRS	136.95	65.21	51.92	60.29	59.87
FERTILIZER N	115.79	53.89	87.57	94.00	77.42
FERTILIZER P	27.38	2.92	23.33	15.86	33.16
FERTILIZER K	17.77	1.61	19.50	7.34	21.60
OTHER FERTILIZER	46.46	39.46	17.74	40.70	34.87
HERBICIDE	119.77	63.78	3.98	12.40	17.17
INSECTICIDE	63.21	2.97	1.33	2.50	1.06
OTHER CHEMICAL	0.00	0.00	0.00	0.00	4.79
IRRIGATION	0.00	0.00	0.00	0.00	0.00
OTHER COSTS	2.87	0.00	0.00	0.00	0.57
HOUSING	22.17	8.06	5.81	7.39	6.82
INSURANCE	28.96	18.24	16.41	18.01	17.57
INTEREST	82.26	66.13	58.84	65.41	64.10
DEPRECIATION	165.29	80.38	67.01	76.18	78.63
TOTAL	1266.59	600.32	550.16	616.60	636.25
Item/Crop Number ( )	Soybeans (6)	Cotton (7)	Rice (8)	Hay (12)
LABOR	76.48	127.68	196.37	217.23
SEED	146.76	218.02	209.99	28.94
FUEL	108.33	174.43	316.02	314.34
LUBRICANTS	16.26	26.17	47.39	10.25
REPAIRS	76.97	157.23	283.11	172.31
FERTILIZER N	2.42	75.22	221.90	11.42
FERTILIZER P	33.29	38.75	11.91	31.14
FERTILIZER K	50.56	29.68	41.96	52.61
OTHER FERTILIZER	59.43	47.35	5.91	27.45
HERBICIDE	48.28	88.59	178.51	73.69
INSECTICIDE	0.00	147.32	99.46	25.75
OTHER CHEMICAL	0.12	33.38	3.73	0.37
IRRIGATION	0.00	0.00	0.00	0.00
OTHER COSTS	0.00	330.18	143.89	0.00
HOUSING	9.14	16.56	15.99	24.09
INSURANCE	21.82	34.22	94.54	42.18
INTEREST	79.39	122.84	334.88	82.06
DEPRECIATION	95.63	136.82	357.54	165.14
TOTAL	824.89	1804.44	2563.10	1278.97

).

Dedicated Energy Crops

Dedicated energy crops are either perennials or annuals. In this analysis, sweet sorghum must be established each year on cropland while the other five dedicated energy crops can be planted on permanent pasture or cropland. The costs of production for these crops are displayed in Figure 4 with the numerical values displayed in Appendix A 

Table A3. Cost of Production Estimates Used in POLYSYS for Dedicated Energy Crops: Switchgrass, Miscanthus, Sweet Sorghum, Energy Cane, Poplar, and Willow in 2023 USD (USD/hectare).

Item	Switchgrass	Miscanthus	Sweet Sorghum	Energy Cane	Poplar	Willow
LABOR	5.04	14.18	48.09	17.05	9.24	7.36
SEED	16.65	46.97	62.20	219.40	37.19	121.58
FUEL	2.64	7.04	35.29	12.90	5.91	5.39
LUBRICANTS	0.40	1.06	5.31	1.93	0.89	0.82
REPAIRS	5.88	15.52	41.64	14.16	7.22	7.83
N FERTILIZER	0.00	0.00	447.68	0.00	35.24	23.33
P FERTILIZER	6.89	18.06	151.50	17.59	0.00	0.00
K FERTILIZER	9.02	27.16	294.65	12.01	9.02	0.00
OTHER FERTILIZER	9.66	23.35	95.23	21.77	24.59	0.00
HERBICIDE	19.37	51.10	67.78	257.24	21.99	14.97
INSECTICIDE	0.00	0.00	0.00	0.00	4.97	0.00
OTHER CHEMICALS	0.00	0.00	0.00	0.00	0.00	0.00
NUTRIENT REPLACEMENT	238.11	215.11	0.00	40.45	0.00	0.00
OTHER COSTS	0.00	0.00	0.00	81.77	36.89	0.00
HOUSING	1.09	2.92	7.07	2.77	1.33	0.96
INSURANCE	0.37	0.99	2.35	0.94	0.44	0.32
INTEREST	1.46	4.00	4.70	2.40	1.58	15.35
DEPRECIATION	5.19	13.71	36.18	14.88	6.10	5.66
HARVEST COST	149.05	187.31	0.00	405.99	344.09	401.82
AVERAGE PER-ACRE COST	470.83	628.49	1299.65	1123.24	546.70	605.38

. Crops included in the analysis include herbaceous crops including energy cane, miscanthus, switchgrass, and sweet sorghum and tree crops grown in short rotations such as hybrid poplar and willow. The annuitized per-acre costs range from USD 471 per hectare for switchgrass to over USD 1300 per hectare for sweet sorghum.

If energy crops are planted on current pastureland rather than cropland, intensified management of some pastureland is required so that the grazing material level is maintained. Each acre of permanent or cropland pastureland converted to dedicated energy crops results in an intensification of some of the remaining pastureland. This intensification increases the cost of dedicated energy crops. In POLYSYS, the cost of converting pasture to intensified pasture is set at USD 124/hectare, evenly split between fencing and water, plus USD 37 per hectare per year in increased management costs [19]. For the eastern non-arid regions of the United States, 0.6 hectares of pasture need to be intensified for every 0.4 hectare of purpose-grown energy crop. This is equivalent to a 67% increase in stocking rates under management-intensive grazing. In arid Western regions, a little over one hectare of pasture is needed to maintain current levels of grazing. This is equivalent to a 40% increase in the stocking rate. The initial investments in fencing and water need replacement over time. Agricultural fencing lasts 7 years based on IRS depreciation schedules, with other agricultural land improvements lasting longer, typically 15 years [21]. The annual replacement expenditure is estimated at USD 12.92 per hectare per year USD (USD 8.82 per hectare for fencing and USD 4.13 per hectare for water) plus the USD 37/hectare management cost or a USD 50 cost per hectare for intensified pasture. Sector 19 in IMPLAN is used to provide the factor coefficients in the analysis. IMPLAN Sector 19 includes both fencing, water, and agricultural employment costs. The number of pastureland acres in the baseline is 116.5 million hectares split between cropland (3.4%) and permanent pasture (96.6%).

Crop Residues

The costs of collecting crop residues are defined in the POLYSYS data. The costs incorporate the actual per hectare transactions that occur when the residue is baled and made ready for transport. The costs also include the replacement required for nutrients removed from the land because of residue removal. The costs do not include those activities involved in growing the residue as those costs are traditional crop costs. The residues collected include barley straw, corn stover, oat straw, sorghum residue, and wheat straw. Using POLYSYS output files, estimates of residue collection costs are estimated by averaging the per-acre costs for harvest and nutrient replacement across the two scenarios (

Table 4. Collection costs of crop residues.

Residue Type	Collection Costs	Nutrient Costs	Proprietor Income	Average Yield
	USD per hectare			Metric Tons/hectare
Corn Stover	77.10	215.35	166.97	6.5
Sorghum Residue	119.77	149.08	38.28	4.3
Oat Straw	75.94	104.67	35.04	2.9
Barley Straw	84.83	106.45	28.00	3.1
Wheat Straw	93.13	97.26	28.32	3.1

).

These costs subtracted from a biomass price of USD 71.74 per metric ton (in 2041USD US dollars) provided an estimated proprietor income value. This value is aggregated to the BEA region level over all types of crop residues and divided by USD 1,000,000 to estimate the proprietor income for crop residues factor.

Forest Products

Two types of forest products are used as energy feedstock—residues formed after meeting traditional wood product harvest, and young trees that do not have a diameter breast height (dbh) that meets the sawtimber definition in ForSEAM. The costs of harvesting the forest products (residues and whole young trees) are included in the ORNL file used to create the downscaled dataset. The stumpage costs were available at the POLYSYS region level and assumed to remain at that level for each county within the POLYSYS region for both residues (average of USD 5.96 per metric ton) and whole tree (average of USD 36.38 per metric ton). The sum of harvest cost and stumpage cost multiplied by tons harvested provided an estimate of the cost of the forest feedstock. The proprietor’s income is estimated by taking the difference between the biomass price received and expenses. The aggregated BEA harvest estimate is divided into two different components, logging (65%) and labor (35%). Forest processing wastes from hardwoods, softwoods, and mixed forests were not included in this analysis.

Transportation Cost

Estimating transportation cost impacts is problematic. To get precise estimates, knowledge of the available infrastructure and the methods (for example, truck, train, or barge) used to transport commodities is needed before impact on the economy caused by transactions made to transport energy feedstocks can be calculated. This information is not provided in the BT23 analysis. An attempt to deliver an estimate can still be made, however. The location of the land from where the biomass is grown is unknown, except for the county, and the estimation of where the biomass needs to be taken is also unknown. In this analysis, an arbitrary assumption that a ton of biomass travels within an 80 km maximum distance is made; therefore, the average travel distance is 59.5 km assuming the biomass is equally dense throughout the 12,800 square kilometer area and using a north-to-south and east-to-west road network [22,23].

To provide an estimate of potential transactions, an estimate of costs on a per-ton-kilometer basis is required. This cost times distance (an average of 59.5 metric ton-kilometers) divided by the load weight (

Table 5. Trucking cost of biomass feedstock.

Feedstock	Capital Cost for Dump Truck or Semi-Truck with Flatbed Trailer or Hopper a	Weight /Load b	Distance		Transportation Cost	Transportation Cost
	USD	Dry Metric Ton	km	USD/Metric Ton	USD/Metric Ton-Kilometer
Wood chips	164,900	21.7 b	59.5	14.69	0.25
Baled material	169,155	14.2 b	59.5	22.73	0.38
Traditional Crops	123,810	25.8 c	59.5	9.68 c	0.16

^a [28,29,30] ^b Private communication with Sam Jackson, Genera, Knoxville, TN., 2018 for information on wood chips and baled material weights.; ^c [27]

) provides an estimated transportation cost. The load weight depends on the truck capacity, and this varies for individual feedstock types. The metric ton-trip provides an estimate of the transactions required. Transportation costs are based on a USD 0.93 per liter diesel fuel price [24], 8% interest rate [25], speed of 80.5 kilometers per hour, operator wage rate of USD 29.10 per hour, and a driver wage rate of USD 26.12 per hour [26]. The metric ton-kilometer transportation costs are estimated at USD 0.25 for chipped material, USD 0.38 for baled herbaceous material, USD 0.38 for bailed cotton, and USD 0.16 for grain, wheat, and oilseed crops. The analysis assumes that woody biomass material is chipped, and herbaceous biomass material is baled. Traditional crops except for hay are transported via trucks in the form that they currently take and are based on quarterly grain truck rates [27]. Hay is assumed to be used on the farm and therefore is not transported.

Proprietor Income

Proprietor income impacts come from all segments of the agricultural/forest economy. In some cases, this value is positive and in some it is negative. The segments providing estimates in this analysis include the traditional or conventional cropping systems, the dedicated energy crops, and the crop and forest residues. These estimates aggregated to BEA and to the conterminous U.S. and divided by USD 1,000,000 provide the factor used to estimate the impact these “profit” values might have on the economy if the recipient dispersed the money in the region of analysis.

2.3. Data from BT23 Economic Modeling Efforts

To conduct the ABP, information on what transactions would occur is required. These transactions are assumed to be reflected in the costs used in the economic modeling efforts that the BT23 analysis relies on. The models used include POLYSYS and ForSEAM.

2.3.1. Output from POLYSYS

Oak Ridge National Laboratory’s POLYSYS model is a dynamic partial equilibrium displacement model of the US agricultural sector [31]. The POLYSYS model, in the BT23 analysis, served as the agricultural model for estimating feedstock supply potential of biomass. Traditional crops presently considered in POLYSYS include corn, grain sorghum, oats, barley, wheat, soybeans, cotton, rice, and hay. In addition, POLYSYS incorporates energy crops including short rotation woody crops—poplar and willow; herbaceous perennials—energy cane, miscanthus, sweet sorghum, and switchgrass; and crop residues—corn stover, barley straw, oat straw, sorghum residue, and wheat straw. In addition to cropland, the POLYSYS simulation tracks three distinct types of pasturelands—cropland pasture, permanent pasture, and intensified pasture.

POLYSYS’s analysis is anchored to USDA’s published baseline projections for the agriculture sector [32] from 2021 and extended to 2041. Changes in agricultural land use, based on cropland allocation decisions made by individual farmers, are primarily driven by the expected productivity of land, crop production costs, other resource availability, the expected economic returns to crops, and domestic and world market demand. County-level crop land area (2021) is used as an initial point of departure for projections. The model is national in nature and the four scenarios are based on technology change impacts. As indicated, the Baseline Scenario portrays agriculture with no additional biomass demand. Traditional crop demands are traced through time from 2022 through 2041. In the biomass scenario, POLYSYS is run with a biomass price set to USD 70 a ton for energy biomass feedstock, biomass variety yields are assumed to increase by one percent per year, and harvest efficiency is assumed to increase from 50 percent to 90 percent over the simulation period (see Table 1).

Currently, the supply side land allocation of all crops within POLYSYS is solved for each county independently. There are 3109 counties in the 48 conterminous United States. Outputs of land use, production, costs of production, and net returns are available for all crops in all counties. The model generates eight output files, and this analysis uses information from the ‘production’, ‘quantity’, ‘residue’, and ‘national’ files. Production of all crops is summed to the national level where econometric demand equations distribute supplies to commodity demands under market-clearing assumptions. The system of simultaneous equations estimates equilibrium prices and demands to clear markets for each year. The prices determined one year are used in the following year in land allocation decisions. The model iterates year to year, and in the billion-ton analysis it simulates iteratively for 20 years from 2021 to 2041. In setting up POLYSYS for the analysis, the baseline is forced to replicate the 10-year USDA baseline extended another 10 years through projecting the USDA baseline yield increase, export, and population growth assumptions.

2.3.2. Output from ForSEAM

The Forest Sustainable and Economic Analysis Model (ForSEAM) is a programming model that can be used to estimate forestland production over time, and its capacity to produce not only traditional forest products but also products of woody biomass to meet energy feedstock demand. It minimizes cost subject to a set of demands and forest inventory. Once solved, ForSEAM generates information on the quantity and types of woody biomass that can be available as energy feedstock with respect to certain marginal costs at the 305 Agricultural Statistical Districts (ASD) level of the conterminous United States (Figure 5).

ForSEAM is divided into three major sections including supply, demand, and sustainability. The supply component includes general timber production activities at the Crop Reporting District (CRD) level. Each region has a set of production activities defined by the U.S. Forest Service [33]. These production activities include sawtimber, young trees, and energy feedstock and are characterized as woody biomass [20]. Two sources of energy feedstock are considered in the model: (1) logging residue generated from sawtimber and pulpwood harvest activities, and (2) removal of whole young trees and un-merchantable trees. Production costs for traditional forest activities and for biomass in the model include stumpage, harvest, and transporting the material to the forest landing. Whole trees are transported to the forest landing where they are bucked and delimbed. The logs are then assumed to be used for meeting traditional sawmill, papermill, chip mill, and veneer demands, and the remaining material is available as energy feedstock. Young trees along with non-merchantable trees are also available as an energy feedstock. Biomass is assumed to be chipped at the landing and placed in a truck ready to be hauled to a bioenergy facility. Transportation costs are therefore not included in the ForSEAM model as the precise location of bioenergy facilities is unknown. The demand component is based on U.S. Forest Service Scenarios with estimates developed for the 2020 Resources Planning Act analysis. The sustainability component ensures that harvest in each region does not exceed annual growth, that existing roads are within a half mile of the timber tracts considered, and that current year forest attributes reflect previous year’s conventional wood product harvests and woody biomass energy feedstock removals. Dynamic tracking of forest growth is incorporated into the analysis [20]. The solution data used in the BT23 analysis was downscaled to the county level. Information on the feedstock supply available was uploaded from ORNL’s bioenergy KDF assuming a USD 70/dry ton price for young trees and USD 40 per dry ton for residues.

3. Results

3.1. Baseline Scenario Results

The 2041 results for the Baseline scenario indicate there are 122.1 million hectares dedicated to the production of traditional crops with a projected 35.75 million hectares planted in corn, 35.53 million hectares planted in soybeans, 20.92 million hectares planted in hay and 19.20 million hectares planted in wheat. The remaining hectares are planted in barley (0.94 million), oats (1.00 million), rice (1.05 million), and sorghum (2.51 million) (Figure 6). Note, as expected, there are no dedicated energy crops planted, and there is no crop residue collected. The simulation indicates that there are 4 million hectares in cropland pasture and 112.13 million hectares in permanent pasture.

To acquire this level of production and meet demand for barley, corn, oats, sorghum, soybeans, and wheat, average national commodity prices are projected to be at USD 248, USD 177, USD 230, USD 169, USD 393, and USD 225 per metric ton respectively, as well as USD 1808 per metric ton for cotton, USD 190 per metric ton for hay, and USD 359 per metric ton for rice (

Table 6. Annual Estimated Economic Impact of the Baseline Scenario, 2041.

Traditional Crop	Traditional Crop Price	Direct	Indirect	Induced	Total
	USD/metric ton	Million USDs
BARLEY	248.48	312	86	141	539
CORN	177.16	22,980	6776	12,169	41,924
COTTON	1807.79	6613	1453	3501	11,566
HAY	190.44	16,719	5351	8533	30,603
OATS	230.11	308	92	160	560
RICE	359.35	1533	443	737	2714
SORGHUM	168.50	893	229	403	1525
SOYBEAN	393.16	14,806	4820	7588	27,213
WHEAT	225.24	6784	1895	3190	11,869
TRANSPORTATION	NA	46,342	22,072	20,008	88,423
PROPRIETOR INCOME	NA	38,385	0	30,933	69,317
SUBTOTAL	ND	155,675	43,217	87,361	286,254
INTRABEA COMMERCE	NA	37,530	52,636	143,592	233,759
TOTAL	ND	193,205	95,854	230,953	520,012

NA indicates the estimate is not available and ND indicates the estimate is not determined.

). Crop producers in the Baseline scenario would generate USD 520 billion in economic activity. Through USD 155.7 billion in direct expenditure within the BEA where production occurs, USD 286 billion in economic activity is generated revealing a multiplier of 1.84. In addition, IntraBEA commerce generates USD 233.8 billion. Much of that impact is generated through salaries and profit expenditures outside the BEA but in the United States. Almost USD 42 billion of economic activity within BEA boundaries is generated through the input purchases required by corn production and USD 27 billion by soybean production.

Accompanying the magnitude of economic activity via crop production is employment. It is estimated that over 3.2 million full and part-time jobs are supported (

Table 7. Added number of annual jobs created through the production of traditional crops by event.

Event	Direct	Indirect	Induced	Total
	Number of full- and part-time jobs
BARLEY	4130	515	934	5579
CORN	283,200	38,449	77,337	398,986
COTTON	108,548	8403	23,712	140,662
HAY	220,663	31,005	54,315	305,983
OATS	4069	522	1014	5605
RICE	19,341	2621	4823	26,785
SORGHUM	11,338	1388	2693	15,418
SOYBEAN	167,915	27,671	48,407	243,994
WHEAT	86,842	11,120	20,726	118,688
TRANSPORTATION	282,680	126,751	128,964	541,516
PROPRIETOR INCOME	0	0	196,787	196,787
SUBTOTAL	1,188,727	248,444	559,712	2,000,003
INTRABEA COMMERCE	391,381	177,935	667,197	1,233,919
TOTAL	1,580,108	426,379	1,226,909	3,233,922

). Crop transportation supports an estimated 541.5 thousand jobs. Both corn and hay production support over 300,000 jobs, with soybeans needing 244 thousand jobs. The economic impacts extend beyond economic activity and employment. Federal, state, and local governments also benefit through their tax structures. It is estimated that the traditional crops in 2041 will annually bring in USD 38.7 billion in federal and USD 26 billion in state and local taxes or USD 64.7 billion in total (

Table 8. Federal, state, and local taxes generated in the Baseline scenario.

Event Category	Federal	State/Local	Total
	Billion USDs
Traditional Crops	11,289	8359	19,647
Energy Crops	0	0	0
Residue	0	0	0
Forest Feedstock	0	0	0
Transportation	6248	4283	10,531
Proprietor	2119	1878	3997
Subtotal	19,655	14,520	34,175
IntraBEA Commerce	19,082	11,474	30,556
US Total	38,738	25,993	64,731

). An estimated USD 19.6 billion in taxes is generated through on-farm activities, transportation of the commodities produced generates about USD 10 billion, and proprietor income will add USD 4 billion in total taxes. In addition, there is USD 30.5 billion generated through IntraBEA commerce.

3.2. MED070 Biomass Scenario Results

For the MED070 Scenario by 2041, 142.31 million hectares are planted either in traditional or dedicated energy crops. Of the 142.31 million hectares, 275.5 million are in traditional crops with a projected 33.57 million planted in corn, 31.69 million planted in soybeans, 20.16 million in hay and 16.09 million hectares planted in wheat. The remaining hectares are planted in barley (0.96 million hectares), oats (0.95 million hectares), rice (1.00 million hectares), and sorghum (2.09 million hectares) (Figure 7). In this scenario, dedicated energy crops are planted in energy cane of 23.6 thousand hectares, miscanthus (6.69 million hectares), poplar (438 thousand hectares), sweet sorghum (119 thousand hectares), switchgrass (20.15 million hectares) and willow (3.41 million hectares). Of the total biomass supplied, energy crops supply 50.4 percent, with crop residues and forest products supplying 46.83 percent and 2.7 percent, respectively, to meet biomass demands. Over 61.14 million hectares supply 564.4 million metric tons of biomass. Switchgrass and corn stover supply 62% of the biomass (

Table 9. Aggregated land area and potential biomass supply for MED070 scenario in year 2041.

Biomass Category and Biomass Event	Area Supplying Biomass	Biomass Quantity	Accumulated by Biomass Type	Total Biomass Production
	Hectares	Dry metric tons
Dedicated Energy Crops:
Energy Cane	23,550	505,317	361,205,055	564,360,433
Miscanthus	6,689,879	99,985,196
Poplars	438,039	4,882,940
Biomass Sorghum	119,014	3,176,675
Switchgrass	20,154,485	209,050,292
Willows	3,405,308	43,604,636
Dedicated Energy Crop Land Area Total	30,830,275
Crop Residues:		477,920
Barley Straw	152,969	144,530,629	162,306,374
Corn Stover	23,019,372	9662
Oat Straw	3116	1,243,812
Sorghum Residue	275,871	16,044,351
Wheat Straw	5,184,093	477,920
Crop Residue Acreage Total	28,635,422
Forest Products:
Forest Residues	947,438	14,936,106	40,849,004
Whole Tree Feedstock	729,084	25,912,897
Forest Product Acreage Total	1,676,522
Totals	61,142,219	564,360,433

).

Almost 38 percent of the biomass produced comes from corn stover, 33 percent is from growing and harvesting switchgrass, and 10.9 percent from miscanthus (Figure 8). Total production of biomass in the MED070 scenario (564 billion metric tons) is spread across the United States (Figure 9). The largest biomass-producing areas are in the southern states, eastern Great Plains, and North Central regions of the country. Almost every BEA region in the Southern States of Oklahoma and Texas produces 2.5 million metric tons or more. Arkansas, Mississippi, Tennessee, Louisiana, and Illinois are also large producers.

To plant 30.8 million hectares in dedicated energy crops, most of which are perennials, permanent pasturelands and cropland pastures are converted to dedicated energy crops and 35.3 million hectares are intensified. To maintain roughage needs for the nation’s livestock, rotational grazing practices incorporating paddock fencing and watering facilities are required. This conversion takes place at an average rate of 1.76 million hectares per year. Once the conversion occurs, the simulation indicates that there are 0.81 million hectares in cropland pasture and 59.76 million hectares in permanent pasture. As expected, pasture intensification occurs in the locations where biomass is produced.

To meet the levels of production in 2041, average national commodity prices for barley, corn, oats, sorghum, soybeans, and wheat are projected to be at USD 272.36, USD 183.85, USD 204.80, USD 183.06, USD 420.71, and USD 258.68 per metric ton, respectively, as well as USD 959 per metric ton for cotton, USD 190.42 per metric ton for hay, and USD 374 per metric ton for rice. Producing both traditional crops and biomass generates USD 619.17 billion from direct expenditures of USD 230 billion within the United States (

Table 10. Annual projected economic activity resulting from mature traditional crop and biomass production, MED070 scenario, year 2041.

Traditional Crop/Biomass Supply Event	Crop Price	Direct	Indirect	Induced	Total
	USD/metric ton	Million USDs
Barley	272.36	318	90	146	553
Corn	183.85	21,416	6384	11,411	39,211
Cotton	959.01	4995	1137	2643	8775
Hay	190.42	16,588	5308	8471	30,367
Oats	204.8	289	87	151	527
Rice	373.68	1460	427	706	2592
Sorghum	183.06	746	191	336	1273
Soybean	420.71	13,313	4333	6829	24,474
Wheat	258.68	5697	1589	2729	10,015
Subtotal Traditional Crops		64,822	19,546	33,421	117,789
Barley Straw	41.88	17	2	11	29
Corn Stover	60.41	3526	499	2820	6844
Oat Straw	43.21	1	0	0	1
Sorghum Residue	41.51	38	4	23	65
Wheat Straw	41.48	495	62	325	881
Subtotal Crop Residues		4076	566	3179	7821
Residues from Forest Operations	44.09	427	30	297	754
Whole Tree Harvest for Feedstock	72.62	805	177	541	1522
Subtotal Forest Products		1232	206	838	2276
Energy Cane	72.62	10	2	6	18
Miscanthus	72.62	1410	252	889	2551
Poplar	72.62	53	12	32	97
Sweet Sorghum	72.62	77	13	46	137
Switchgrass	72.62	4084	642	2552	7277
Willow	72.62	290	82	138	510
Subtotal Dedicated Energy Crops	NA	5924	1003	3663	10,589
Traditional Plus Biomass Production	ND	76,053	21,321	41,101	138,475
Transportation	NA	56,548	27,062	24,460	108,070
Proprietor Income	NA	66,970	0	53,328	120,298
Subtotal Allocated to BEAs	ND	199,572	91,025	201,091	491,688
IntraBEA Commerce	NA	30,390	16,723	80,378	127,490
Total	ND	229,961	107,748	281,469	619,178

NA indicates the information is not available and ND indicates the information is not defined.

).

Crop production and residue collection from cropland and forest land generates USD 138.5 billion, transportation generates USD 108.1 billion and USD 120.3 billion in economic activity is generated through the sale of biomass and traditional crops (proprietor income). More than 3.9 million part- and full-time jobs are supported by the economic activity generated (

Table 11. Number of annual jobs created through the production of traditional crops and Biomass by event.

Event	Direct	Indirect	Induced	Total
	Number of full- and part-time jobs
Commodity Production	1,145,816	131,818	283,459	1,561,093
Transportation	282,680	126,751	128,964	541,516
Proprietor Income	0	0	196,787	196,787
Subtotal	1,428,496	258,569	609,210	2,296,275
IntraBEA Commerce	487,496	217,236	886,428	1,591,160
Total	1,915,993	475,805	1,495,638	3,887,435

). These jobs are split, with 2.3 million working in producing the crops within the BEAs and 1.6 million in IntraBEA commerce.

As in the Baseline scenario, the economic impacts extend beyond economic activity and employment as Federal, state, and local governments benefit through their tax structures, also. It is estimated that on-farm tax generation in 2041 will bring in USD 21.6 billion federally and USD 14.1 billion in state and local taxes or USD 35.6 billion in total taxes from traditional crops (

Table 12. Federal, state, and local taxes generated in the MED070 Scenario.

Event Category	BEA Allocated Taxes	Interstate Commerce	Generated in USA
	Million USD
Federal Taxes:
Traditional	10,310	11,263	21,573
Energy Crops	1250	902	2152
Crop Residues	854	1849	2703
Timberland	142	121	263
Transportation	7627	5823	13,450
Proprietor Income	3631	3463	7095
Subtotal	23,815	23,421	47,235
State/Local Taxes:
Traditional	7740	6374	14,115
Energy Crops	525	631	1156
Crop Residues	418	941	1359
Timberland	88	73	161
Transportation	5144	4046	9191
Proprietor Income	3125	2120	5245
Subtotal	17,040	14,186	31,227
Total Taxes:
Traditional	18,050	17,637	35,687
Energy Crops	1775	1534	3309
Crop Residues	1272	2790	4062
Timberland	229	194	424
Transportation	12,772	9869	22,641
Proprietor Income	6756	5584	12,340
Total	40,855	37,607	78,462

). In addition, the growing and harvesting of crop residues and dedicated energy crops generate another USD 7.3 billion. Transportation and proprietor income events add another USD 35 billion. Total taxes generated annually are estimated to be USD 78.5 billion.

4. Discussion

4.1. A Comparison on the Economic Benefits of Biomass

To estimate the economic benefits of biomass, the Baseline scenario impacts are compared to the MED070 scenario (

Table 13. Comparisons of major event indicators and economic impact variables comparing the MED070 scenario with the Baseline 2041 solutions and incorporating 2023 production levels.

Indicators from Event Categories	Units	2023 Production a	MED070 Scenario	Baseline	Difference
		Million units
Traditional:
Land use	Hectares	123.8	111.5	122.1	−10.6
Commodity Value	USD	172,777	180,454	170,692	9761.3
Transportation Cost b	USD	5624	6113	6247	−134.3
Proprietor Value	USD	Undefined	50,173	36,496	13,677.0
Quantity	Metric Tons	685.1	741	761	−20.2
Pastureland:
Cropland Pasture	Hectares	0.0	0.8	4.0	−3.2
Permanent Pasture	Hectares	0.0	59.8	112.1	−52.4
Intensified Pasture	Hectares	Undefined	35.3	0.0	35.3
Energy Crops:
Land use	Hectares	0	30.8	0	30.8
Commodity Value	USD	Undefined	172,777	0	172,777.2
Transportation Cost	USD	Undefined	782	0	782.0
Proprietor Value	USD	Undefined	9126	0	9126.0
Quantity	Metric Tons	0	755	0	755.2
Crop Residues:
Land use c	Hectares	0	28.6	0	28.6
Commodity Value	USD	Undefined	11,787	0	11,787.3
Transportation Cost	USD	Undefined	369	0	368.9
Proprietor Value	USD	Undefined	4115	0	4115.0
Quantity	Metric Tons	0	179	0	178.9
Forest Products:
Land use	Hectares	0	1.7	0	1.7
Commodity Value	USD	Undefined	2658	0	2658.1
Transportation Cost	USD	Undefined	60	0	60.0
Proprietor Value	USD	Undefined	261	0	260.7
Quantity	Metric Tons	USD0	45	0	45.0
Total:
Land use	Hectares	124	173	122	51
Commodity Value	USD	Undefined	367,676	170,692	196,984
Transportation Cost	USD	Undefined	7324	6247	1077
Proprietor Value	USD	Undefined	63,675	36,496	27,179
Quantity	Metric Tons	0	979	0	979
		Full Impact Measures
Economic Activity	USD	Unknown	619,178	520,012	99,166.1
Employment Impacts	Jobs	Unknown	3.89	3.23	0.7
Gross Regional Product	USD	Unknown	379,273	315,437	63,836
Tax Collections	USD	Unknown	75,845	64,731	11,114

^a Data derived from [34,35]. ^b This is a calculated value based on the assumption that all commodities travel an average of 37 miles. ^c This land use is also supplying traditional products, and those commodities are included in the traditional land use data.

). This analysis not only compares land use, gross commodity value, transportation cost, proprietor values by major event category for traditional crops, pastureland, energy crops, crop residue, and forest products, along with overall economic activity, employment, gross regional product and tax collections, but also 2023 production levels of traditional crops in the United States.

Economic activity generated within the United States increases by almost USD 100 billion if a mature biomass industry is developed. This increase in economic activity is accompanied by an additional 700,000 additional part- and full-time jobs. The increase in jobs and economic activity increases the Gross Regional Demand by USD 63.8 billion.

These estimates are not uniform across the United States. As seen in Figure 7 and Figure 8, the production impacts that occur in various parts of the United States differ. Further, it is unknown where a sizable portion of these impacts will occur because of trade between BEAs within the United States. As Figure 9 indicates, the decreases in traditional crop plantings occur primarily in the central part of the United States from Dakota to Texas with smaller decreases in acreage in the eastern part of the United States. The blue areas indicate a slight increase of less than a hectare. Land-use shifts have occurred throughout the United States with most regions having a decrease in traditional crops because of dedicated energy crop production.

Changes in economic activity and number of jobs supported also reflect increases when developing a mature biomass industry. Changes in land use are projected when comparing the MED070 scenario to the Baseline. Since Baseline has a price set of USD 0.00 there is little reason to produce any residue; therefore, the maps also reveal quantity of total production by BEA in the MED070 scenario. Figure 10a provides a picture of where dedicated crops are projected to be grown; Figure 10b,c provide the locations where forest products and crop residues are collected, respectively.

For biomass to be worth more than use as a combustible, it must be converted to fuel, chemicals, or other valued commodities. Although the authors have experience in estimating these types of supply chain economic impacts in other studies (e.g., [13,20]), the transportation of the biomass to the farm gate to the plant gate, including conversion, are not included in this BT23 analysis.

4.2. Supply Chain Areas

For instance, the biomass to liquid fuel pathways might incorporate the following:

• Biomass—growing and harvesting of dedicated energy crops, collection of crop residues and forest residues and other forest products and transportation of biomass 59 km;
• Transportation—movement from land to facility beyond 59 km;
• Pre-processing—densification, size reduction, and repackaging;
• Transportation—movement from preprocessing to conversion facility;
• Conversion—conversion of biomass to products such as fuel or chemicals, government loans and subsidies;
• Transportation—transportation to users;
• Purchase—use of product, government subsidies.

As in the biomass stage, there are multiple components that are likely to take place in each step of the biomass industrial pathways. Each step will result in supporting jobs and result in economic activity. In addition, governmental support measures for this industry, such as through renewable information numbers (RINs) [36] and production subsidies, are not included in the analysis. The biomass stage has the potential to support significant additional economic activity by providing the feedstock for new fuels, chemicals, and other commodities. The biomass stage would create significant economic opportunities and would bolster the potential job outlook in the nation’s rural areas. Federal tax collections would increase as well as state and local tax collections.

The preprocessing stage would also likely be located close to the biomass supply, and further increases in economic activity and jobs would take place most likely in rural areas. It is difficult to predict where conversion of biomass to products will take place as this would depend both on the raw material used in the conversion processes and the purchasers of the products.

5. Conclusions

I–O models are common tools for measuring the economic impact of a change in the economy. Based on assumptions made in this analysis, the scenarios considered here demonstrate a better understanding of the potential outcomes if a mature bioeconomy were to surface. The analysis provides a perspective on the potential economic impacts that are likely to occur as a region shifts towards a bioeconomy. This shift has the potential to ensure the economic survival of rural communities. The nation’s rural towns will have additional jobs to offer to their residents. New industrial growth will occur and areas having significant quantities of biomass will benefit. The economic benefits inherent in GRP and jobs will be widespread and occur throughout the nation as seen by the annual increase resulting from an increase in TIO of over USD 99 billion, jobs of 700,000, and GRP of nearly USD 68 billion when comparing the MED070 to the Baseline Scenario.

Input–output models have limitations themselves. First and foremost, I–O models are linear, and inputs consumed vary directly with output. The interdependences between sectors of the economy are fixed and therefore the model assumes a fixed production technology and constant returns to scale. There is no input substitution nor supply constraints. New sectors could locate in regions where they are not currently present, altering the current interdependences. Finally, only backward linkages occur, so only upstream effects are measured. Input–output models do not examine the impacts of downstream effects. The impacts in this analysis do not include Stages 3 through 7 of the potential supply chain indicated above. These stages depend on how the biomass will be utilized to create fuel, chemicals, etc. which are downstream events that are not defined in the BT23 analysis. Nor do these findings reflect the impacts of additional investment that would occur as the industry moves from its current state to a mature condition.

Emerging bioenergy and bioproduct industries utilizing crop and forest residues and energy crops could offer new market opportunities for farmers. Due to the low energy density of biomass feedstocks, transportation costs are high; thus, the initial development of these industries will hinge on the local availability of sufficient, cost-competitive biomass feedstocks. Farmers are likely to be reluctant to plant dedicated energy crops without the assurance of a market. This issue will require local, state, and federal programs to develop the markets. The nation’s Experiment Stations will need to demonstrate the process in planting, maintaining, harvesting, and handling the biomass of dedicated energy crops. Planting, harvesting, and handling equipment will need to be improved.

The economic impacts of a mature biomass industry will extend far beyond the impacts presented in this paper. This manuscript measures the impacts of the backward linkages of growing, harvesting/collecting, and transporting from the land to an unknown point 59 kilometers in distance. The manuscript does not include the forward linkages that will result, nor any negative impacts caused by replacement of final commodities using biomass as a feedstock.