November 2014 K-1 

Appendix K: Future Anticipated Baseline Construction: 

Methodology and Results 

Table of Contents 

Introduction ................................................................................................................................................................. K-2

Methodology for Projecting U.S. Biogenic Feedstock Consumption Scenarios ................................ K-3

2.1. Estimates of Current Consumption........................................................................................................... K-3

2.1.1. Step 1: Querying EIA-923 Feedstock Consumption Data ....................................................... K-3

2.1.2. Step 2: Filtering the Data for FASOM-GHG Biogenic Types ................................................... K-5

2.1.3. Step 3: Mapping EIA-923 Data to FASOM-GHG Regions ........................................................ K-7

2.1. Estimates of Future Consumption ............................................................................................................. K-8

2.1.1. AEO Baseline Scenario Projections .............................................................................................. K-10

Biogenic Feedstock Consumption Baseline Scenario Projections: Results and Analysis .......... K-13

3.1. FASOM-GHG Simulation Results ............................................................................................................. K-15

3.1.1. Net Emissions Flux per Time Period ........................................................................................... K-15

3.1.2. Cumulative Net Emissions ............................................................................................................... K-16

3.1.3. Additional Estimated Emissions.................................................................................................... K-17

Conclusion .................................................................................................................................................................. K-21

References .................................................................................................................................................................. K-22

Supplemental Data and Information............................................................................................................... K-23

6.1. Graphics ............................................................................................................................................................ K-23

List of Tables 

Table K-1. Description of Biomass Sources in EIA-923. ...................................................................................... K-4 

Table K-2. EIA-923 Biogenic Feedstock Consumption by Sector and FASOM-GHG Region as well as 

Regional Proportion Derived from Woody Biomass (EIA-923, 2009). ......................................... K-9

List of Figures 

Figure K-1. Biogenic Feedstock Consumption for Energy Generation (Short Tons) by Sector and 

Source in 2009 (Source: EIA-923). ............................................................................................................... K-5

Figure K-2. Biogenic Feedstock Consumption by Electric Utilities in 2009 (Source: EIA-923). ......... K-6

Figure K-3. Total Biogenic Feedstock Consumption for Electricity Generation at Stationary Sources 

in the Industrial Sector in 2009 (Source: EIA-923). .............................................................................. K-7

Figure K-4. Map of EIA-923 Biogenic Feedstock Consumption at Electric and Industrial Plants and 

FASOM-GHG Region. ........................................................................................................................................... K-8

Figure K-5. Biogenic Feedstock Consumption Projections by Sector. ........................................................ K-12
November 2014 K-2 

Figure K-6. Terrestrial Carbon Flux from U.S. Forest and Agricultural Sectors (Excluding Non-CO2

Emissions, Emissions from Fossil Fuel Consumption, and Carbon Stored In Wood Products).

 .................................................................................................................................................................................. K-16

Figure K-7. Cumulative Emissions Over Time by Biomass Scenario. .......................................................... K-17

Figure K-8. Emissions Intensity of Biomass Energy Relative to the Zero Biomass Scenario 

(Cumulative Additional Emissions Divided by Cumulative Biogenic Carbon from Additional 

Biogenic Feedstocks)....................................................................................................................................... K-19

Figure K-9. Proportion of Biogenic Feedstock from Woody Sources by Scenario Relative to the Zero 

Biomass Scenario. ............................................................................................................................................. K-20

Figure K-10. Biogenic Feedstock Consumption Projections by AEO Scenario Grouped by FASOMGHG Region. ......................................................................................................................................................... K-24

Figure K-11. Biogenic Feedstock Consumption Projections by FASOM-GHG Region Grouped by AEO 

Scenario. ............................................................................................................................................................... K-25

Figure K-12. Additional Emission per Ton of Biogenic Feedstock Utilized. ............................................. K-26

Introduction 

This appendix describes a methodology for constructing alternative future anticipated baseline 

scenarios that can be used to evaluate the potential net atmospheric contribution of biogenic 

carbon dioxide (CO2) from increased consumption of biogenic feedstocks at stationary sources. The 

purpose of this analysis is to illustrate how landscape CO2 balances (emissions fluxes net of carbon 

sequestration in biogenic feedstocks and soils) could respond to changes in land management 

associated with alternative biogenic feedstock demand projections, and how baseline formation can 

affect emissions projections estimates. 

Biogenic feedstock consumption will likely grow over time as the demand for renewable electricity 

increases (driven in part by state renewable portfolios, clean energy standards or other policies and 

incentives). Thus, it is important to consider anticipated growth in stationary source biogenic 

feedstock demand in addition to current consumption levels. Using a compilation of different 

energy sector datasets as inputs to a dynamic land use model, several potential future anticipated 

baseline scenarios are constructed to project biogenic CO2 emissions from the U.S. forest and 

agricultural sectors as well as emissions intensity values for biogenic feedstock consumption for 

electricity generation at stationary sources. These potential future baseline scenario projections are

developed to show a range of potential future conditions, illustrating how baseline scenario 

projections can be sensitive to different macroeconomic inputs. 

The first section of this appendix discusses how current biogenic feedstock consumption estimates 

are combined with regional energy market projections to generate six alternative future anticipated 

baseline scenarios, representing alternative biogenic feedstock demand trajectories. Next, the U.S. 

Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOM-GHG) is used to 

simulate future biogenic CO2 emissions fluxes estimated for each alternative baseline scenario. 

These projected emissions trajectories are then compared with a projected future with no biogenic 

feedstock consumption for electricity generation. Then, results from the alternative future baseline 

scenarios are used to project cumulative landscape emissions associated with each baseline’s 

biogenic feedstock consumption. This appendix concludes with a discussion of key findings, 
November 2014 K-3 

uncertainties, and limitations. Two baseline scenarios from this appendix are then used for further 

application and analysis in the future anticipated baseline case studies (Appendix L). 

Methodology for Projecting U.S. Biogenic Feedstock 

Consumption Scenarios 

A prospective analysis of CO2 emissions from biogenic feedstock consumption at stationary sources 

requires two primary pieces of information: current and anticipated future biogenic feedstock 

usage. The primary data sources used to estimate current facility-level biomass energy 

consumption are EIA-923 Annual Electric Utility data from December 2009.1 This information 

serves as the basis for developing projections using data derived from the Energy Information 

Agency’s (EIA’s) Annual Energy Outlook (AEO) models from 2012. This section provides the 

methodology and presents estimated current and future biogenic feedstock consumption under five 

alternative future scenarios. One of the outcomes for this section is a table representing current 

biogenic feedstock consumption for both forest- and agriculture-derived feedstock types delineated 

according to regions appropriate for use in the FASOM-GHG model. 

2.1. Estimates of Current Consumption 

To arrive at the biogenic feedstock consumption estimates used for this analysis, three basic steps 

were required. In Step 1, the December 2009 version of the Form EIA-923 survey representing 

current facility-level data was queried for total biogenic feedstock consumption at industrial, 

electricity, and commercial stationary sources. Step 2 involved filtering the data to remove biogenic 

feedstocks such as black liquor and municipal solid waste, which are not included in the FASOMGHG model. Finally, in Step 3, common plant ID codes were obtained for each EIA-923 power 

generation unit by matching the units to EPA’s Emissions & Generation Resource 

Integrated Database (eGrid) 2009 database to obtain latitude and longitude coordinates. These 

coordinates were then used to map the stationary sources to the 11 primary FASOM-GHG 

agroforestry regions. 

2.1.1. Step 1: Querying EIA-923 Feedstock Consumption Data

EIA-923 contains detailed monthly and annual electric power data on electricity generation, fuel 

consumption, fossil fuel stocks, and receipts at the stationary source level (EIA, 2012). The dataset 

contains information on the feedstock type used as well as the different generation processes. 

Specifically, the data splits plants into three sectors: electricity, industrial, and commercial. 

Electricity sector entities use biogenic feedstocks to generate electricity for an external electric grid. 

Industrial sector entities, such as pulp and paper mills, use biomass for internal industrial 

production processes and electricity generation purposes with residual bioelectricity sold back to 

the grid. Finally, commercial sector entities are primarily small-scale electric generators, burning 

 

1

 The EIA-923 database is updated annually. The 2009 dataset is used for this analysis to represent starting 

conditions for the “2010” simulation period in FASOM-GHG. 
November 2014 K-4 

biomass to supply electricity to a single installation. A hospital with a boiler that co-fires biomass is 

a good example of a commercial plant.2

In addition to categorizing biogenic feedstock demand by the electricity and industrial sectors, EIA923 further disaggregates consumption by specific biogenic feedstock sources, as shown in Table K1. Biomass-derived energy can come from a multitude of feedstocks, including raw biomass 

sources, waste streams, by-products of silvicultural practices and/or agricultural cultivation, or byproducts of industrial processes. 

Table K-1. Description of Biomass Sources in EIA-923. 

EIA 923 Code Biomass Description

Solid 

Renewable 

Fuels 

AB Agricultural crop by-products/straw/energy crops

MSB Municipal solid waste—biogenic component 

OBS Other biomass solids 

WDS Wood/wood waste solids (paper pellets, railroad ties, wood chips, etc.) 

Liquid 

Renewable 

Fuels 

OBL Other biomass liquids 

BLQ Black liquor 

SLW Sludge waste 

WDL Wood waste liquids excluding black liquor 

Figure K-1 provides estimates of current biogenic feedstock consumption by biomass sources for 

each of the three sectors with the industrial sector further disaggregated to differentiate pulp and 

paper from other industrial entities. Electric utilities consumed the most biomass in 2009, more 

than 40 million short tons, with the largest share coming from wood solids and municipal solid 

waste. Pulp and paper mills are the second largest consumers of biomass for energy, with the 

majority of consumption coming from black liquor and wood solids that are forest-derived 

industrial by-products of pulp and paper production processes. 

 

2

 This analysis focuses on the major biomass consuming industries, thus, commercial facilities such as hospitals and 

schools are dropped from the underlying dataset and this analysis. 
November 2014 K-5 

Figure K-1. Biogenic Feedstock Consumption for Energy Generation (Short Tons) by Sector and 

Source in 2009 (Source: EIA-923). 

2.1.2. Step 2: Filtering the Data for FASOM-GHG Biogenic Types 

With the EIA-923 biogenic feedstock consumption for 2009 identified, it is now necessary to 

process the data so that these can be used as part of the FASOM-GHG modeling approach. This 

means that biogenic feedstocks handled in other parts of the framework and those not included in 

FASOM-GHG, as well as coinciding stationary sources, must be filtered out of the dataset. 

First, municipal solid waste and other waste-derived feedstocks receive a different treatment than 

forestry and agriculture-derived biogenic feedstocks (see Appendix N). Second, the FASOM-GHG 

model does not currently depict black liquor, an industrial processing byproduct from pulp and 

paper milling. Thus, for the purposes of this study, the estimates of current biogenic feedstock 

consumption are restricted to the following EIA-923 biomass types: agricultural crop by-products, 

straw, and energy crops (AB); wood and wood waste solids (WDS); and other biomass solids (OBS). 

This subset of biomass represents forest and agricultural biomass and excludes any liquids or 

municipal solid waste used for electricity generation; it thus accounts for approximately 37% of all 

biogenic feedstocks currently consumed for energy generation.3

 

3

 It is important to remember that because of the data filtering necessary for this specific study, the biogenic 

feedstock consumption projections provided in subsequent sections are lower than AEO or other bioenergy 

 -

 10,000,000

 20,000,000

 30,000,000

 40,000,000

 50,000,000

 60,000,000

 70,000,000

 80,000,000

 90,000,000

 100,000,000

Electricity Sector Industrial Sector

(Forest Products and

Pulp/Paper)

Industrial Sector

(Other)

Short Tons

Wood Solids

Sludge Waste

Other Biomass Solids

Municipal Solid Waste

(Non-Biogenic)

Municipal Solid Waste

(Biogenic)

Black Liquor

Agricultural Biomass
November 2014 K-6 

Next, stationary sources that use these biogenic feedstocks that have been filtered out of the dataset

are removed, eliminating all of the sources in the commercial sector, and resulting in two remaining 

power generation sectors: the electricity sector and the industrial sector. For the purposes of this 

study, the electricity sector dataset was created by excluding any stationary source that does not 

have “electric utility” as the sector name or does not have the appropriate NAICS code 22. The 

industrial sector dataset excludes any stationary source that does not have “industrial NAICS 

cogen” or “industrial NAICS non-cogen” as the sector name. This 2009 feedstock consumption 

estimate represents the base-level value from which all projections presented for the future 

anticipated baseline approach in this and related appendices (Appendix L) are simulated. 

Figures K-2 and K-3 provide a geographic depiction of biogenic feedstock consumption in short 

tons in the electricity and industrial sectors, respectively, in 2009. Biomass consumption in the 

electricity sector is primarily confined to the Northeast, Florida, California, and Minnesota. Within 

the industrial sector, biomass is consumed mostly in the Southeast, Northeast, and Pacific 

Northwest, where much of the pulp and paper industry is located. These 2009 consumption rates 

are what determine the “Constant Biomass Consumption case” scenario discussed in subsequent 

sections.

Figure K-2. Biogenic Feedstock Consumption by Electric Utilities in 2009 (Source: EIA-923). 

 

projections that would include municipal solid waste and other important biogenic feedstock types. 
November 2014 K-7 

Figure K-3. Total Biogenic Feedstock Consumption for Electricity Generation at Stationary Sources 

in the Industrial Sector in 2009 (Source: EIA-923).

2.1.3. Step 3: Mapping EIA-923 Data to FASOM-GHG Regions 

Once the biogenic feedstock types and sectors were filtered, EIA 923 data were merged with 

FASOM-GHG regions. EIA-923 data are collected at the stationary source level, but the geographic 

coordinates are not published. However, EPA’s publicly available eGRID 2009 database uses EIA923 data and includes a common plant ID code to link the data sources as well as latitude and 

longitude coordinates for the stationary sources included in this analysis. For industrial sector 

sources, only forest product and paper manufacturing facilities are included. Figure K-4 displays 

the EIA-923 biogenic feedstock consumption data at the eGRID stationary source locations overlaid 

with a map of the FASOM-GHG regions.4

 

4 Region Definitions for Figure K-4: CB = Corn Belt; GP = Great Plains; LS = Lake States; NE = Northeast; 

PNWE = Pacific Northwest East; PNWW = Pacific Northwest West; PSW = Pacific Southwest; RM = Rocky 

Mountains; SE = Southeast; SC = South Central; SW = Southwest
November 2014 K-8 

Figure K-4. Map of EIA-923 Biogenic Feedstock Consumption at Electric and Industrial Plants and 

FASOM-GHG Region.

Table K-2 provides the results of the data processing exercise by sector and FASOM-GHG region in 

addition to percentages of agricultural versus nonagricultural biogenic feedstock categories 

consumed within the combined electricity and industrial sectors. The majority of biogenic 

feedstocks used (once forest and agricultural biomass sources have been filtered out of the EIA-923 

data)—96.7% of U.S. solid biomass feedstock consumption—originates from nonagricultural 

sources. Thus, forest-derived biomass in solid and liquid form makes up the majority of biomass 

consumption at stationary sources in the filtered EIA-923 dataset. Forest biomass includes the 

categories “Roundwood” and “Logging Residues,” plus a set of “Forest Derived Industrial Products 

or Processing By-products,” as defined in Appendix D. Agricultural feedstocks include the 

categories “Conventional Agricultural Crops”, “Dedicated Energy Crops,” and “Agricultural Crop 

Residues,” which are also defined and discussed in Appendix D. 

2.1.Estimates of Future Consumption 

EIA’s AEO focuses on factors that influence the U.S. energy system in the long run (energy demand, 

supply, and prices). AEO projections assume that current laws and regulations remain unchanged 

throughout the projections, unless explicitly changed for a policy scenario case (for instance, 

certain AEO scenarios include GHG mitigation policies, including CO2 emissions allowance fees). 

These laws and regulations include mandatory state renewable or clean energy standards, which 

are applied to the underlying model used to produce the AEO (the National Energy Modeling 

System) to the extent possible. 
November 2014 K-9 

Table K-2. EIA-923 Biogenic Feedstock Consumption by Sector and FASOM-GHG Region as well as 

Regional Proportion Derived from Woody Biomass (EIA-923, 2009). 

FASOM Region 

Electric Fuel Consumed (short tons) Percentage 

Derived from 

Woody 

Biomass 

Nonelectric Stationary 

Sources 

Electric Stationary 

Sources 

Total 

CB 192,185 73,292 265,477 95% 

GP 3,477 4,308 7,785 0% 

LS 525,588 3,151,535 3,677,123 99% 

NE 470,644 5,336,594 5,807,238 100% 

PNWE 8,437 290,853 299,290 100% 

PNWW 437,863 326,206 764,069 100% 

PSW 312,445 3,629,679 3,942,124 95% 

RM 91,502 229,078 320,580 100% 

SC 2,170,840 363,043 2,533,883 96% 

SE 1,739,475 2,556,641 4,296,116 92% 

SW 162,325 0 162,325 0% 

U.S. Total 6,114,781 15,961,229 22,076,010 97% 

The AEO projections also include key macroeconomic factors that significantly influence the energy 

market, including population and gross domestic product (GDP). According to the AEO website, the 

AEO Reference case “provides the basis for examination and discussion of energy production, 

consumption, technology, and market trends and the direction they may take in the future. It also 

serves as a starting point for analysis of potential changes in energy policies.” In addition to the 

Reference case, EIA presents a number of other alternative cases to illustrate uncertainties 

associated with the Reference case projections. 

The same exercise is done in this study. To account for uncertainty in future anticipated biogenic 

feedstock consumption, multiple anticipated future baseline scenarios are developed that calibrate 

directly to AEO 2012 scenarios (discussed below). The 2012 AEO projections used in this report are 

carried out until 2035, and all biogenic feedstock consumption beyond this period is held constant 

in FASOM-GHG simulation periods beyond 2035. 

AEO scenario projections are used to build biogenic feedstock consumption trajectories off of the 

2009 feedstock consumption values calculated for this analysis. There are numerous AEO scenarios 

available from EIA, with deviations in economic growth assumptions, policy variables, and fuel 

prices (the AEO 2012 report included 29 total scenarios): the discussion here focuses on the 

following four baseline scenarios: Reference, High Economic Growth, Low Economic Growth, and 

Low Renewable Technology Cost. In addition to the AEO scenarios, a fifth baseline scenario was 

developed in which 2009 biogenic feedstock consumption levels are held constant. The Reference 

case is the baseline AEO (2012) scenario, which assumes real GDP grows at a 2.4% average annual 

rate from 2008 to 2035, buoyed by a 1.5% per year growth in productivity in nonfarm businesses 

and 0.6% growth in non-farm employment. All other AEO baseline scenarios pivot off this 

Reference baseline scenario by changing specific assumptions. The High Economic Growth baseline 
November 2014 K-10 

assumes that real GDP grows by 3%, supported by productivity growth of 2.4% and employment 

growth of 1.2%. The Low Economic Growth baseline assumes that real GDP grows by 1.8%, 

supported by productivity growth of 1.5% and employment growth of 0.5%. The Low Renewable 

Energy Technology Cost baseline assumes annual levelized cost for non-hydropower renewables is 

10% lower than the Reference baseline in 2010 and drops 35% by 2035 compared to Reference 

baseline values. 

To generate biogenic feedstock consumption projections for each AEO baseline scenario, index 

variables were created using 2009 as the base year; these values reflect the rate of growth in 

projected renewable electricity consumption by Electricity Market Module (EMM) region in 

quadrillion British thermal unit (Btu). For industrial sector stationary sources, growth rates are 

equal to the change in industrial sector renewable energy consumption by EMM region. For 

electricity sector stationary sources, the change in total renewable electricity generation by EMM 

region is used. Note that this assumes that the proportion of biomass energy to total renewable 

energy would stay constant over time. Thus, it does not factor in potential declining costs of 

alternative renewable energy technologies such as wind, solar, or geothermal, and such declining 

costs could reduce the share of renewables coming from biogenic feedstocks. 

These projections are multiplied by EIA-923 electricity consumption data (i.e. total 2009 biomass 

consumption for each facility) to produce facility-level biogenic feedstock projections from 2009 to 

2035. This facility-level data is then mapped to FASOM-GHG regions using the eGRID latitude and 

longitude coordinates. This methodology provides a justifiable set of unique biogenic feedstock 

consumption projections calibrated to standard energy market projections. 

2.1.1. AEO Baseline Scenario Projections 

This section provides graphical representation of the current and future alternate estimates of 

biogenic feedstock consumption derived above at the regional scale. The biogenic feedstock 

consumption baseline projections are shown in Figure K-5. The results in this figure include both 

industrial and electricity sector biogenic feedstock consumption as well as the combined total. Of 

the five scenarios, the Low Renewable Energy Technology Cost case (in which renewable fuels are 

lower cost), exhibits the largest amount of biogenic feedstock consumption in 2035, with growth 

accelerating after 2025. It is important to note that in this case biogenic feedstock consumption 

growth is driven by growth in renewables generally. Because these projections are derived from 

2009 numbers, the renewable portfolio is fixed in 2009 and is not allowed to change. The High and 

Low Economic Growth cases represent potential upper and lower bounds for the AEO Reference 

baseline scenario because they include exogenous shifts on renewable demands without any 

endogenous changes in underlying technology. 

Shifting focus to the individual sector graphs in Figure K-5, high demand growth is seen for the Low 

Renewable Technology Cost case in the electricity sector after 2025. This growth occurs because 

large capacity exists for increases in renewable electricity generation in that sector. This 

exponential rise is contrasted with the relatively modest rise seen for the industrial sector graph. 

The industrial sector does not have as much capacity for fuel increase because many of the 
November 2014 K-11 

industrial facilities included in this dataset already use biogenic feedstocks as a primary fuel source, 

such as pulp and paper mills. 
November 2014

K-12

Figure K-5. Biogenic Feedstock Consumption Projections by Sector. 
November 2014 K-13 

Breaking down the scenario projections by FASOM-GHG regions allows insights into the regional 

distribution of growth in biomass consumption. Regional baseline scenario projections are 

provided in two groups of graphs at the end of this appendix. Figure K-10 (located in the addendum 

of this appendix) shows the biogenic feedstock consumption projections for each baseline scenario 

grouped by FASOM-GHG region. In general, the Low Renewable Technology Cost case represents 

the maximum growth path for any given FASOM-GHG region. However, the magnitude of the effect 

of this growth rate varies greatly between regions, with large growth in the Southeast (SE) and 

Lake States (LS) regions, relative to other baseline scenarios. This variation is further seen in the 

Rocky Mountains (RM) and Pacific Southwest (PSW) regions, where the high economic growth case 

yields the greatest amount of biogenic feedstock consumption for a number of years within the 

projection. This illustrates the regional variability in biogenic feedstock consumption projections 

presented in the alternative future anticipated baseline scenarios.

Figure K-11 presents the same data showing the biogenic feedstock consumption projections for 

each FASOM-GHG region grouped by AEO scenario. The top four consumers of biogenic feedstocks 

for all the cases are the PSW, Northeast (NE), LS, and SE. However, for the Reference case and High 

Economic Growth case, the PSW has the highest feedstock consumption, whereas the Low 

Renewable Energy Technology Cost case shows the SE biogenic feedstock consumption expanding 

rapidly. This Low Renewable Technology Cost case shows there is large capacity potential for 

renewable energy expansion in the SE, given a reduction in the price of renewables generally, or 

policies that encourage renewable energy development. With few existing state energy policies 

requiring or incentivizing renewable energy in the SE, much of the additional renewable capacity 

potential exists in these states. 

Biogenic Feedstock Consumption Baseline Scenario 

Projections: Results and Analysis 

This section analyzes the GHG emissions and terrestrial carbon sequestration estimates produced 

from the alternative baseline scenarios created in the previous section. The purpose of this analysis 

is to illustrate how landscape CO2 balances (emissions flux net of carbon sequestration in biogenic 

feedstocks and soils) could respond to changes in land management associated with alternative 

biogenic feedstock demand projections. This evaluation considers total soil and feedstock-related 

biogenic CO2 emissions from agricultural and forestry land management decisions across multiple 

future anticipated baselines. A detailed discussion of the emissions fluxes evaluated in this analysis

is found in Appendix L. Results illustrate how baseline scenario formation can have a large impact 

on emissions projections. 

FASOM-GHG is used to simulate market equilibrium in the forest and agricultural sectors by 

maximizing net economic surplus (consumer and producer) over 17 5-year periods (2000–2080), 

along with a terminal period valuation. Several key assumptions made for this baseline scenario 

formulation are highlighted below (the basic structure and key underlying datasets of the FASOMGHG model are described in the Supplemental Information section of the future anticipated 

baseline case studies appendix, Appendix L): 
November 2014 K-14 

• For the alternative baseline scenarios, constraints are imposed requiring that a specific 

volume of biomass be consumed for electricity generation on a regional basis. These 

regional constraints are all that vary across simulation runs, and serve as the primary basis 

of comparison for examining GHG implications of changes in biomass energy demand. 

• For biofuels production, all alternative baseline scenarios assume that the RFS2 legislation 

binds and the mandated levels of biofuel are supplied. Feedstock-specific constraints are 

imposed and are based on the supplemental control case assumptions from the EPA 

Regulatory Impact Analysis of the RFS2.5 

• Other than biofuel feedstock restrictions, there are no constraints on feedstock choices for 

bioenergy across the alternative baseline scenarios, allowing the model to choose an 

optimal feedstock portfolio to achieve regional biomass requirements. 

• Agricultural productivity rates are linear growth rates in agricultural productivity growth 

and demand growth; these parameters are calibrated to USDA (2009) projections of yield 

and demand growth for key commodities forestry data (yields, species mix, etc.) are from 

USFS, calibrated to the forest inventory and assessment (FIA) and other relevant forestry 

sector datasets. 

In addition to FASOM-GHG details included in Appendices J and L, further details regarding the 

model structure, regional detail, commodity representation, and GHG accounting can be found in 

Beach et al. (2010). 

The discussion of results below focuses on four baseline scenarios. Two of the four AEO baseline 

scenarios discussed above are used—the Reference baseline and the Low Renewable Technology 

Cost baseline. The Low and High Economic Growth baseline scenarios were not simulated in 

FASOM-GHG as these do not deviate greatly from the AEO Reference case scenario. The AEO 

Reference and Low Renewable Technology Cost baseline scenarios provide a reasonable range of 

potential biomass energy expansion. In addition to these AEO-based baseline scenarios, two other 

counterfactual baseline scenarios are also simulated: one with constant 2009 biogenic feedstock 

consumption (as derived above—this scenario is referred to as the Constant baseline scenario 

throughout the remainder of this analysis) and another with no biogenic feedstock consumption at 

stationary sources (“Zero Biomass Consumption” scenario). The difference between the Zero 

Biomass Consumption baseline scenario and each of the other baseline scenarios (alternate AEObased and Constant scenario) indicates the additional calculated emissions associated with that 

level of biogenic feedstock consumption compared to no biogenic feedstock consumption. The 

purpose of simulating both the Constant and Zero Biomass baseline scenarios is to respond directly 

to the SAB review of the previous accounting framework, which noted (Swackhamer and Khanna, 

2011): 

Estimating additionality, i.e., the extent to which forest stocks would have been growing or 

declining over time in the absence of harvest for bioenergy, is essential, as it is the crux of the 

 

5

 This includes growth in domestic (U.S.) biofuel production up to approximately 30 billion gallons in the 2020 

simulation period (including 15 billion gallons of corn ethanol, 13.7 billion gallons of cellulosic ethanol, and 1.3 

billion gallons of biodiesel, produced primarily from soybean oil). 
November 2014 K-15 

question at hand. To do so requires an anticipated baseline approach... [the] Framework 

would need to model a “business as usual” scenario along some time scale and compare that 

carbon trajectory with a scenario of increased demand for biomass... In general the 

Framework should provide a means to estimate the effect of stationary source biogenic 

feedstock demand, on the atmosphere, over time, comparing a scenario with the use of 

biogenic feedstocks to a counterfactual scenario without the use of biogenic feedstocks. 

With this in mind, baseline scenarios are constructed so that business-as-usual (BAU) projections, 

which include anticipated growth in biogenic feedstock consumption, can be compared relative to 

alternative baseline scenarios that include no new growth in biogenic feedstock demand (the 

Constant scenario), and no future consumption of biomass. Thus, this appendix considers a range of 

possible anticipated future baselines and alternative counterfactuals, allowing for a detailed 

assessment of potential biogenic emissions estimates for illustrative purposes. 

The following sections continue with more detail describing the baseline scenario results, with a 

brief look at periodic net emissions and cumulative net emissions for each of the scenarios. Finally, 

the model results for additional emissions in each AEO baseline scenario are presented and 

discussed at both the national and regional levels. 

3.1. FASOM-GHG Simulation Results 

This section presents and compares results for the various baseline scenarios. First net CO2

emissions are presented followed by cumulative net emissions, and finally additional emissions 

relative to a counterfactual scenario in which no biogenic feedstocks are consumed at stationary 

sources. 

3.1.1. Net Emissions Flux per Time Period 

For each 5-year period, an annual emission or sequestration value was calculated using the FASOMGHG equations and parameters (presented in the supplemental section at the end of Appendix L), 

then a total net emissions flux for that 5-year period was calculated by aggregating the individual 

fluxes. Figure K-6 illustrates projected CO2 emissions flux trajectories across the different baseline 

scenarios using atmospheric GHG accounting (a positive value represents net emissions, while a 

negative value represents net carbon sequestration on the landscape). Note that the difference 

between scenarios is not as significant as the change in net emissions between periods (i.e., over 

time). The cyclical shape of these trajectories is driven by periodic shifts in forest management; 

harvest emissions and forest biomass growth can vary period-to-period, leading to high net 

emissions totals in some periods and net sequestration in others. Land use change emissions can 

also contribute to this cycle. Assumptions related to management practices for the various 

feedstocks considered are described in Appendix H. Periods with high agricultural land use 

conversion (such as pasture or forest conversion to cropland) can result in increased emissions, 

while afforestation can increase terrestrial carbon uptake. 

Differences between baseline scenario projections are subtle, as the overall shape of these 

trajectories is similar. However, the absolute difference in annual emissions could be significant 

(for instance, this difference ranges 3–5 million tCO2e per year for the 2010 simulation period). 
November 2014 K-16 

Furthermore that each biogenic feedstock consumption scenario results in an immediate increase 

in emissions in the 2010 time step, coinciding with the first year of the biogenic feedstock demand 

shock applied to each alternative baseline scenario. Thus, biomass demand initially increases 

emissions relative to the Zero Biomass demand scenario, driven by changes in management in 

response to the new feedstock demand. 

Figure K-6. Terrestrial Carbon Flux from U.S. Forest and Agricultural Sectors (Excluding Non-CO2

Emissions, Emissions from Fossil Fuel Consumption, and Carbon Stored In Wood Products). 

3.1.2. Cumulative Net Emissions 

Although the periodic net emissions flux trajectories are similar over time, minor differences in 

annual fluxes can result in large differences in cumulative emissions over time. Total emissions for 

each 5-year period are converted to a cumulative emissions total over the time horizon, displayed 

in Figure K-7. 

In 2035 (when biogenic feedstock demand peaks), cumulative emissions for the AEO Reference 

baseline scenario are 22% higher than cumulative emissions for the Zero Biomass baseline. 

However, over time, projected cumulative emissions for the Zero Biomass baseline begin to 

converge with the alternative biogenic feedstock demand baselines. There is little difference 

between the Zero and Constant Biogenic Feedstock baseline scenarios toward the end of simulation 

horizon, and less than 7% difference across all scenarios. Thus, after immediate and medium-term 

emissions effects of increased biogenic feedstock demand, physical carbon stocks begin to recover 

-10

-5

0

5

10

15

20

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060

Million TCO2 per year (U.S. Total)

AEO Low Renewable Energy Technology Cost Scenario

AEO Reference Case Scenario

Constant Biomass Consumption Case

Baseline With Zero Biomass
November 2014 K-17 

and the cumulative difference in emissions from Zero Biomass to the alternative baselines begins to 

subside. One implication of these results is that the choice of time scale is important and can have a

large impact on the cumulative emissions difference between scenarios (i.e., with a shorter 

timeframe used for this illustration, the observed convergence would not occur). 

Figure K-7. Cumulative Emissions Over Time by Biomass Scenario. 

3.1.3. Additional Estimated Emissions 

Additional biogenic CO2 consumption and landscape-level emissions are calculated relative to the 

Zero Biomass baseline scenario. This is done in order to capture all additional emissions effects of 

biomass consumption under an anticipated future baseline projection relative to an alternative 

future in which there is no biomass consumption. Thus, all additional current and expected growth 

in biogenic feedstock consumption and the relative change in terrestrial CO2 emissions are 

captured. This approach allows one to consider changes in emissions for relative changes in future 

biomass demand (captured by the Constant [no growth in consumption], AEO Reference, and AEO 

Low Renewable Technology Cost Baselines). 

Emissions intensities for additional biogenic feedstock consumption relative to the Zero Biomass 

baseline scenario are computed (as this includes all biogenic feedstock users). An emissions 

intensity is the ratio of net biogenic emissions (net of emissions and landscape-level sequestration) 

to net biogenic CO2 in biomass consumed for energy (tCO2 equivalent emissions/ tCO2 equivalent in 

consumed biogenic feedstocks).In this context, emissions intensity represents the ratio of 

0

100

200

300

400

500

600

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060

Million TCO2 per year (U.S. Total)

AEO Low Renewable Energy Technology Cost Scenario

AEO Reference Case Scenario

Constant Biomass Consumption Case

Baseline With Zero Biomass
November 2014 K-18 

emissions relative to a unit of biogenic feedstock used for energy (i.e., the portion of biogenic 

feedstock carbon emitted to the atmosphere). A value of 0 implies no net emissions, meaning that 

biogenic CO2 emissions from the use of biogenic feedstocks in energy production would be balanced 

with carbon uptake in the feedstock and on the landscape where the feedstock was produced. A 

value of 0.5 would imply that half of the biogenic CO2 emissions are displaced by carbon uptake on 

the landscape. This metric provides insight into potential emissions intensity of biogenic feedstock 

consumption by current and future stationary sources under these specific anticipated baseline 

scenarios and related parameters. This metric does not take into account stationary source process 

emissions, including combustion efficiencies, feedstock losses during processing, or other possible 

components related to feedstock procurement or processing. 

For the three alternative biogenic feedstock consumption baseline scenarios (Constant, AEO 

Reference baseline, and AEO Low Renewable Technology Cost Baseline), cumulative additional 

emissions are calculated as the difference from the Zero Biomass baseline scenario and each 

alternative scenario for each period of the simulation horizon. Cumulative additional biogenic CO2

from biogenic feedstock consumption is calculated by converting annual biogenic feedstock 

consumption requirements for each scenario to a cumulative value, and then converting to CO2

equivalence (assuming that each dry ton of feedstock is 50% carbon). This cumulative additional 

emissions value is then divided by total CO2 equivalence of biogenic feedstock consumption to 

derive the emissions intensity per unit of biogenic feedstock (Figure K-8). 

In general, emissions intensity projections show that biogenic CO2 emissions are not entirely 

displaced by terrestrial carbon sequestration early in the simulation horizon. When compared with 

the Zero Biomass baseline, additional emissions per-unit of additional biogenic CO2 consumption 

ranges 0.35–0.47 ton CO2e once the biogenic feedstock requirements are imposed in 2010. Note, 

however, that emissions intensity declines steadily over time for each baseline scenario, 

approaching a net carbon balance of 0 for the AEO Reference and AEO Low Renewable Technology 

Cost cases. Emissions intensity reaches values below 0 for the Constant Biomass case, indicating 

that cumulative biogenic CO2 emissions are more than balanced by emissions changes on the 

landscape. This decline in emissions intensity is driven by several factors: 

1) A shift in land use/management early in the simulation horizon that increases tree carbon 

uptake over the long term (afforestation of cropland and pastureland); 

2) Declining market effects of initial biomass demand shocks; 

3) Improved agricultural productivity over time due to exogenous yield growth assumptions 

and endogenous yield growth responses to the biomass requirements (including regional 

crop mix changes); and 

4) A shift in biogenic feedstock composition (as seen in Figure K-9). 
November 2014 K-19 

Figure K-8. Emissions Intensity of Biomass Energy Relative to the Zero Biomass Scenario 

(Cumulative Additional Emissions Divided by Cumulative Biogenic Carbon from Additional 

Biogenic Feedstocks). 

(0.10)

-

0.10 

0.20 

0.30 

0.40 

0.50 

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060

t CO2 emission / t biomass utilized

AEO Low Renewable Energy Technology Cost Scenario

AEO Reference Case Scenario

Constant Biomass Consumption Case
November 2014 K-20 

Figure K-9. Proportion of Biogenic Feedstock from Woody Sources by Scenario Relative to the 

Zero Biomass Scenario. 

In general, use of forest biogenic feedstocks for energy generation generates greater direct 

emissions and less energy output per-unit area than agricultural feedstocks or dedicated energy 

feedstocks, such as switchgrass (for more discussion, see Latta et al., 2013). Initially, the overall 

feedstock portfolio is required to be approximately 96% derived from forestry feedstocks to match 

observed biomass consumption patterns, hence the higher emissions per unit of biomass. However, 

this constraint is relaxed after the initial time period, giving the model flexibility to choose a 

biogenic feedstock portfolio mix that minimizes the costs of meeting the total biogenic feedstock 

requirement. Over time, as greater amounts of biogenic feedstocks are required for the AEO 

Reference and Low Renewable Technology Cost Baseline scenarios, the portfolio shifts to a higher 

proportion of agricultural feedstocks, which decreases overall emissions intensity. 

This decline is more pronounced for the Low Renewable Technology Cost baseline as it adopts a 

much higher proportion of dedicated energy feedstocks to meet total biogenic feedstock demand. 

Although the Constant Biomass scenario continues to consume a relatively high proportion of 

forest-derived feedstocks, total biogenic feedstock requirements do not increase from the base

year, so land management, markets, and terrestrial carbon stocks adjust more rapidly, causing 

emissions to fall. 

40%

50%

60%

70%

80%

90%

100%

2010 2020 2030 2040 2050 2060

% of forest sourced biomass

AEO Low Renewable Energy Technology Cost Scenario

AEO Reference Case Scenario

Constant Biomass Consumption Case
November 2014 K-21 

Conclusion 

This appendix developed biogenic feedstock demand projections with an initial (2009) value 

calibrated to observed consumption patterns at electricity and industrial sector stationary sources. 

Future projections were then calibrated to projected growth rates in renewable energy demand (by 

sector and region) from the AEO (2012). A range of anticipated future baseline scenarios were 

created, representing a range of possible biogenic feedstock consumption futures. These 

projections were mapped to FASOM-GHG agroforestry regions, representing regional biogenic 

feedstock requirements for simulation analysis. 

Emissions trajectories across the alternative future baseline scenarios were compared to a scenario 

in which no agricultural and forestry biomass is consumed at stationary sources for energy 

generation. Results of the simulation analysis revealed that emissions from biogenic feedstock 

consumption are not fully balanced by initial landscape CO2 uptake. However, over time, emissions 

intensity decreases, approaching or surpassing a net carbon balance for all alternative anticipated 

future baseline scenarios assessed here. 

In general, these results are consistent with previous studies that have shown that there are GHG 

consequences associated with biogenic feedstock production, especially immediately following an 

increase in biogenic feedstock demand (Latta, et al., 2013; Daigneault et al., 2012). However, this 

analysis shows that carbon dioxide emissions associated with biogenic feedstock production and 

use are at least partially balanced by changes in sequestration on the landscape and that, over time 

(in this case decades), an increasing share of these carbon dioxide emissions is balanced at a 

national level and in most regions as well. When disaggregated regionally, emissions intensity 

trajectories also approach net carbon balances for most FASOM-GHG regions, with a few clear 

exceptions (CB, PSW). For the CB and PSW, land use change early in the simulation horizon 

(afforestation) leads to net sequestration, which causes negative emissions intensity in the near 

term. 

Three of the future anticipated baseline scenarios presented in this appendix are utilized within the 

case study appendix (Appendix L): Zero Biomass, Constant Biomass (existing sources in 2009), and 

AEO Reference. Appendix L develops feedstock- and region-specific demand shocks in addition to 

the AEO Reference case scenario. Emissions projections from these feedstock case study scenarios 

are then compared to the three alternative baselines above to evaluate the emissions effect of a 

marginal increase in consumption of one feedstock (comparison to AEO Reference), an average 

effect relative to current consumption levels (Constant), and an average effect relative to no 

biogenic feedstock consumption (Zero). 

The baselines and estimated values derived in this appendix and in Appendix L are intended to 

illustrate the functionality of a future anticipated baseline approach method and do not reflect EPA 

findings in the context of specific policies or programs. As with all modeling studies, there are a 

number of uncertainties present in the baseline scenario assumptions and parameters adopted for 

this analysis. These uncertainties include future environmental conditions and the biophysical 

emissions accounting parameters, future economic or policy conditions, and technological growth 

(both for agricultural/forestry feedstock yield and commodity-processing technologies). However, 
November 2014 K-22 

model projections provide key insight into the potential market and land use consequences of 

possible shifts in the demand for biogenic feedstocks at stationary sources. Furthermore, this study 

does not include full coverage of possible feedstocks from agricultural and forestry production 

processes. Most notably, FASOM-GHG does not include production of black liquor as an industrial 

processing by-product of pulp and paper production. 

References 

Beach, R.H., D.A. Adams, R. Alig, J. Baker, G.S. Latta, B.A. McCarl, B.C. Murray, S.K. Rose, E. White.

2010. Model Documentation for the Forest and Agriculture Sector Optimization Model with 

Greenhouse Gases (FASOMGHG). p. 188. 

Daigneault, A., B. Sohngen, & R. Sedjo. 2012. Economic approach to assess the forest carbon 

implications of biomass energy. Environmental science & technology, 46(11):5664-5671. 

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 US forest and agricultural biomass 

electricity expansion. Journal of Forest Economics, 19(4):361-383. 

Swackhamer, D., M. Khanna, M., SAB Review of EPA’s Accounting Framework for Biogenic CO2 

Emissions from Stationary Sources. EPA-SAB-12-011. September 28, 2011 

U.S. Department of Agriculture. Office of the Chief Economist, World Agricultural Outlook Board. 

USDA Agricultural Projections to 2018. Report prepared by the Interagency Agricultural 

Projections Committee. Long-term Projections Report OCE-2009-1. 

U.S. Energy Information Administration. 2009. EIA-923 Electric Utility Data. 

http://www.eia.gov/electricity/data/eia923/ 

U.S. Energy Information Administration. 2012. Annual Energy Outlook 2012. 

http://www.eia.gov/forecasts/aeo/pdf/0383(2012).pdf. 

U.S. Environmental Protection Agency. 2012. Emissions and Generation Resource Integrated 

Database (E-GRID), Eight Edition. http://www.epa.gov/cleanenergy/energy-resources/egrid/ 

U.S. Environmental Protection Agency Assessment and Standards Division, Office of Transportation 

and Air Quality (OTAQ). 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact 

Analysis. Report No. EPA-420-R-10-006. 

 
November 2014 K-23 

Supplemental Data and Information 

6.1. Graphics 

 
November 2014

K-24Figure K-10. Biogenic Feedstock Consumption Projections by AEO Scenario Grouped by FASOM-GHG Region. 

0

100200300400500600700

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Corn Belt (CB)

02468

101214161820

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Great Plains (GP)

0

2000400060008000

100001200014000

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Lake States (LS)

0

2000400060008000

1000012000

2010 

Thousands of Short Tons per Year

0

50

100150200250300350400450500

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Pacific Northwest East (PNWE)

0

200400600800

100012001400

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Pacific Northwest West (PNWW)

0

2000400060008000

1000012000

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Pacific Southwest (PSW)

0

100200300400500600700

2010 2

Thousands of Short Tons per Year

0

500

100015002000250030003500400045005000

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

South Central (SC)

0

5000

10000150002000025000

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Southeast (SE)

0

50

100150200250300

2010 2015 2020 2025 2030 2035

Thousands of Short Tons per Year

Southwest (SW)

0

10000200003000040000500006000070000

2010 

Thousands of Short Tons per Year

U

Constant Biomass Consumption CaseAEO Reference Case ScenarioAEO Low Renewable Energy Technology Cost ScenarioAEO High Economic Growth ScenarioAEO Low Economic Growth Scenario
November 2014 K-25 

Figure K-11. Biogenic Feedstock Consumption Projections by FASOM-GHG Region Grouped by 

AEO Scenario. 

 -

 2,000

 4,000

 6,000

 8,000

 10,000

 12,000

 14,000

 16,000

 18,000

 20,000

2010 2015 2020 2025 2030 2035

1,000 Dry Short Tons

AEO Low Renewable Energy 

Technology Cost Scenario

 -

 2,000

 4,000

 6,000

 8,000

 10,000

 12,000

2010 2015 2020 2025 2030 2035

1,000 Dry Short Tons

AEO Reference Case Scenario

 -

 2,000

 4,000

 6,000

 8,000

 10,000

 12,000

2010 2015 2020 2025 2030 2035

1,000 Dry Short Tons

AEO High Economic Growth 

Scenario

 -

 2,000

 4,000

 6,000

 8,000

 10,000

 12,000

2010 2015 2020 2025 2030 2035

1,000 Dry Short Tons

AEO Low Economic Growth 

Scenario
November 2014

K-26

Figure K-12. Additional Emission per Ton of Biogenic Feedstock Utilized. 

(8.0)(7.0)(6.0)(5.0)(4.0)(3.0)(2.0)(1.0)- 1.0 

2.0 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

Corn Belt (CB)

(10.0)

(5.0)- 5.0 

10.0 15.0 20.0 25.0 30.0 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

Great Plains (GP)

(0.4)(0.2)- 0.2 

0.4 0.6 0.8 1.0 1.2 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

Lake States (LS) - 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 

2010 2020 

t CO

2 emission / t biomass utilized

North

(1.0)(0.5)- 0.5 

1.0 1.5 2.0 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

Pacific Northwest East (PNWE)

(0.5)- 0.5 

1.0 1.5 2.0 

t CO 2010 2020 2030 2040 2050 2060

2 emission / t biomass utilized

Pacific Northwest West (PNWW)

(0.20)(0.15)(0.10)(0.05) - 0.05 

0.10 0.15 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

Pacific Southwest (PSW)

(2.0)(1.5)(1.0)(0.5)- 0.5 

1.0 1.5 2.0 

2010 2020 

t CO

2 emission / t biomass utilized

Rocky Mo

(1.0)(0.5)- 0.5 

1.0 1.5 2.0 2.5 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

South Central (SC)

(0.4)(0.3)(0.2)(0.1)- 0.1 

0.2 0.3 0.4 0.5 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

Southeast (SE)

(0.2)- 0.2 

0.4 0.6 0.8 1.0 1.2 

2010 2020 2030 2040 2050 2060

t CO

2 emission / t biomass utilized

Southwest (SW)

(0.1)- 0.1 

0.2 0.3 0.4 0.5 

2010 2020 

t CO

2 emission / t biomass utilized

United Sta

Constant Biomass Consumption CaseAEO Reference Case ScenarioAEO Low Renewable Energy Technology Cost Scenario