November 2014 K-1



# Appendix K: Future Anticipated Baseline Constructio n: 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 T ypes .............................................. ..... 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 Pr ojections: 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-92 3. ................................................ ...................................... K-4 Table K-2. EIA-923 Biogenic Feedstock Consumption b y 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 Ener gy Generation (Short Tons) by Sector and Source in 2009 (Source: EIA-923). .................

................................................... ........................................... K-5 Figure K-2. Biogenic Feedstock Consumption by Elect ric Utilities in 2009 (Source: EIA-923). ......... K-6 Figure K-3. Total Biogenic Feedstock Consumption fo r 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 Consu mption at Electric and Industrial Plants and FASOM-GHG Region. .................................

................................................... ................................................... .... K-8 Figure K-5. Biogenic Feedstock Consumption Projecti ons by Sector. .................................... .................... K-12November 2014 K-2

Figure K-6. Terrestrial Carbon Flux from U.S. Fores t and Agricultural Sectors (Excluding Non-CO 2 Emissions, Emissions from Fossil Fuel Consumption, and Carbon Stored In Wood Products).

.................................................. ................................................... ................................................... .......................... K-16 Figure K-7. Cumulative Emissions Over Time by Bioma ss Scenario. ...................................... .................... K-17 Figure K-8. Emissions Intensity of Biomass Energy R elative to the Zero Biomass Scenario (Cumulative Additional Emissions Divided by Cumulat ive Biogenic Carbon from Additional Biogenic Feedstocks)...............................

................................................... ................................................... .. K-19 Figure K-9. Proportion of Biogenic Feedstock from W oody Sources by Scenario Relative to the Zero
Biomass Scenario. .................................

................................................... ................................................... ...... K-20 Figure K-10. Biogenic Feedstock Consumption Project ions by AEO Scenario Grouped by FASOMGHG Region. .......................................

................................................... ................................................... ............ K-24 Figure K-11. Biogenic Feedstock Consumption Project ions by FASOM-GHG Region Grouped by AEO
Scenario. .........................................

................................................... ................................................... ................ K-25 Figure K-12. Additional Emission per Ton of Biogeni c Feedstock Utilized. ............................. ................ K-26



## Introduction

This appendix describes a methodology for construct ing alternative future anticipated baseline scenarios that can be used to evaluate the potentia l net atmospheric contribution of biogenic carbon dioxide (CO 2 ) from increased consumption of biogenic feedstocks at stationary sources. The purpose of this analysis is to illustrate how lands cape CO 2 balances (emissions fluxes net of carbon sequestration in biogenic feedstocks and soils) cou ld respond to changes in land management associated with alternative biogenic feedstock dema nd projections, and how baseline formation can affect emissions projections estimates.

Biogenic feedstock consumption will likely grow ove r time as the demand for renewable electricity increases (driven in part by state renewable portfo lios, clean energy standards or other policies and incentives). Thus, it is important to consider anti cipated 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 bioge nic CO 2 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 condi tions, illustrating how baseline scenario projections can be sensitive to different macroecon omic inputs.

The first section of this appendix discusses how cu rrent biogenic feedstock consumption estimates are combined with regional energy market projection s to generate six alternative future anticipated baseline scenarios, representing alternative biogen ic feedstock demand trajectories. Next, the U.S. Forest and Agricultural Sector Optimization Model w ith Greenhouse Gases (FASOM-GHG) is used to simulate future biogenic CO 2 emissions fluxes estimated for each alternative ba seline scenario. These projected emissions trajectories are then com pared with a projected future with no biogenic feedstock consumption for electricity generation. T hen, results from the alternative future baseline scenarios are used to project cumulative landscape emissions associated with each baseline’s biogenic feedstock consumption. This appendix concl udes with a discussion of key findings,November 2014 K-3

uncertainties, and limitations. Two baseline scenar ios 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 CO 2 emissions from biogenic feedstock consumption at s tationary sources requires two primary pieces of information: current and anticipated future biogenic feedstock usage. The primary data sources used to estimate cu rrent facility-level biomass energy consumption are EIA-923 Annual Electric Utility dat a from December 2009. 1 This information serves as the basis for developing projections usin g 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 futu re biogenic feedstock consumption under five
alternative future scenarios. One of the outcomes f or 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 FAS OM-GHG model.



### 2.1. Estimates of Current Consumption

To arrive at the biogenic feedstock consumption est imates 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 b iogenic feedstock consumption at industrial, electricity, and commercial stationary sources. Ste p 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 code s 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 a nd longitude coordinates. These coordinates were then used to map the stationary so urces 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 th e 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 sec tors: electricity, industrial, and commercial. Electricity sector entities use biogenic feedstocks to generate electricity for an external electric g rid. Industrial sector entities, such as pulp and paper mills, use biomass for internal industrial production processes and electricity generation pur poses with residual bioelectricity sold back to the grid. Finally, commercial sector entities are p rimarily small-scale electric generators, burning

1 **The EIA-923 database is updated annually. The 2009 dataset is used for this analysis to represent sta**  **rting conditions for the “2010” simulation period in FASO**  **M-GHG.**November 2014 K-4

biomass to supply electricity to a single installat ion. A hospital with a boiler that co-fires biomass is a good example of a commercial plant. 2

In addition to categorizing biogenic feedstock dema nd by the electricity and industrial sectors, EIA923 further disaggregates consumption by specific b iogenic 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 silvicultura l 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 f eedstock consumption by biomass sources for
each of the three sectors with the industrial secto r further disaggregated to differentiate pulp and paper from other industrial entities. Electric util ities 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 an d wood solids that are forest-derived industrial by-products of pulp and paper production processes.

2 This analysis focuses on the major biomass consumi ng 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 Ener gy Generation (Short Tons) by Sector and Source in 2009 (Source: EIA-923).

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

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 par ts 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-derive d feedstocks receive a different treatment than forestry and agriculture-derived biogenic feedstock s (see Appendix N). Second, the FASOM-GHG model does not currently depict black liquor, an in dustrial 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 s olids (WDS); and other biomass solids (OBS). This subset of biomass represents forest and agricu ltural biomass and excludes any liquids or municipal solid waste used for electricity generati on; it thus accounts for approximately 37% of all biogenic feedstocks currently consumed for energy g eneration. 3

3 It is important to remember that because of the da ta filtering necessary for this specific study, the biogenic feedstock consumption projections provided in subse quent sections are lower than AEO or other bioenerg y

- 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 BiomassNovember 2014 K-6

Next, stationary sources that use these biogenic fe edstocks 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 an d the industrial sector. For the purposes of this study, the electricity sector dataset was created b y 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 s ource that does not have “industrial NAICS cogen” or “industrial NAICS non-cogen” as the secto r 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 a ppendices (Appendix L) are simulated.

Figures K-2 and K-3 provide a geographic depiction of biogenic feedstoc k consumption in short tons in the electricity and industrial sectors, res pectively, in 2009. Biomass consumption in the electricity sector is primarily confined to the Nor theast, Florida, California, and Minnesota. Within the industrial sector, biomass is consumed mostly i n the Southeast, Northeast, and Pacific Northwest, where much of the pulp and paper industr y is located

. These 2009 consumption rates are what determine the “Constant Biomass Consumptio n case” scenario discussed in subsequent

sections.

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

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

Figure K-3. Total Biogenic Feedstock Consumption fo r 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 th e stationary source level, but the geographic coordinates are not published. However

, EPA’s publicly available eGRID 2009 database uses E IA923 data and includes a common plant ID code to lin k the data sources as well as latitude and longitude coordinates for the stationary sources in cluded in this analysis. For industrial sector sources, only forest product and paper manufacturin g 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 North west 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 Consum ption at Electric and Industrial Plants and FASOM-GHG Region.

Table K-2 provides the results of the data processi ng exercise by sector and FASOM-GHG region in addition to percentages of agricultural versus nona gricultural biogenic feedstock categories consumed within the combined electricity and indust rial sectors. The majority of biogenic feedstocks used (once forest and agricultural bioma ss sources have been filtered out of the EIA-923 data)—96.7% of U.S. solid biomass feedstock consump tion—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 E IA-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”, “Dedi cated 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 cu rrent laws and regulations remain unchanged throughout the projections, unless explicitly chang ed for a policy scenario case (for instance, certain AEO scenarios include GHG mitigation polici es, including CO 2 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 b y 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 pro duct (GDP). According to the AEO website, the AEO Reference case “provides the basis for examinat ion 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 potentia l changes in energy policies.” In addition to the Reference case, EIA presents a number of other alte rnative 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 th is analysis. There are numerous AEO scenarios available from EIA, with deviations in economic gro wth assumptions, policy variables, and fuel prices (the AEO 2012 report included 29 total scena rios): 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 A EO scenarios, a fifth baseline scenario was developed in which 2009 biogenic feedstock consumpt ion levels are held constant. The Reference case is the baseline AEO (2012) scenario, which ass umes real GDP grows at a 2.4% average annual rate from 2008 to 2035, buoyed by a 1.5% per year g rowth in productivity in nonfarm businesses and 0.6% growth in non-farm employment. All other A EO baseline scenarios pivot off this Reference baseline scenario by changing specific as sumptions. The High Economic Growth baselineNovember 2014 K-10

assumes that real GDP grows by 3%, supported by pro ductivity growth of 2.4% and employment growth of 1.2%. The Low Economic Growth baseline as sumes that real GDP grows by 1.8%, supported by productivity growth of 1.5% and employ ment growth of 0.5%. The Low Renewable Energy Technology Cost baseline assumes annual leve lized cost for non-hydropower renewables is
10% lower than the Reference baseline in 2010 and d rops 35% by 2035 compared to Reference
baseline values.

To generate biogenic feedstock consumption projecti ons 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 Elec tricity Market Module (EMM) region in quadrillion British thermal unit (Btu). For industr ial 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 i n total renewable electricity generation by EMM region is used. Note that this assumes that the pro portion of biomass energy to total renewable energy would stay constant over time. Thus, it does not factor i n potential declining costs of alternative renewable energy technologies such as w ind, solar, or geothermal, and such declining costs could reduce the share of renewables coming f rom biogenic feedstocks.

These projections are multiplied by EIA-923 electri city 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 FA SOM-GHG regions using the eGRID latitude and longitude coordinates. This methodology provides a justifiable set of unique biogenic feedstock consumption projections calibrated to standard ener gy market projections.

2.1.1. AEO Baseline Scenario Projections

This section provides graphical representation of t he current and future alternate estimates of biogenic feedstock consumption derived above at the regional scale. The biogenic feedstock consumption baseline projections are shown in Figur e K-5. The results in this figure include both industrial and electricity sector biogenic feedstoc k consumption as well as the combined total. Of the five scenarios, the Low Renewable Energy Techno logy Cost case (in which renewable fuels are lower cost), exhibits the largest amount of biogeni c feedstock consumption in 2035, with growth accelerating after 2025. It is important to note th at 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 2 009 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 sh ifts on renewable demands without any endogenous changes in underlying technology.

Shifting focus to the individual sector graphs in F igure K-5, high demand growth is seen for the Low Renewable Technology Cost case in the electricity s ector after 2025. This growth occurs because large capacity exists for increases in renewable el ectricity 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 capacit y for fuel increase because many of theNovember 2014 K-11

industrial facilities included in this dataset alre ady use biogenic feedstocks as a primary fuel sourc e, such as pulp and paper mills.November 2014 K

- 12Figure 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. Regi onal 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 cons umption projections for each baseline scenario grouped by FASOM-GHG region. In general, the Low Re newable Technology Cost case represents the maximum growth path for any given FASOM-GHG reg ion. 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 baselin e scenarios. This variation is further seen in the Rocky Mountains (RM) and Pacific Southwest (PSW) re gions, where the high economic growth case yields the greatest amount of biogenic feedstock co nsumption for a number of years within the projection. This illustrates the regional variabili ty in biogenic feedstock consumption projections presented in the alternative future anticipated bas eline scenarios.

Figure K-11 presents the same data showing the biog enic 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 feeds tock consumption, whereas the Low Renewable Energy Technology Cost case shows the SE biogenic feedstock consumption expanding rapidly. This Low Renewable Technology Cost case sh ows there is large capacity potential for renewable energy expansion in the SE, given a reduc tion in the price of renewables generally, or policies that encourage renewable energy developmen t. 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 terrest rial carbon sequestration estimates produced
from the alternative baseline scenarios created in the previous section. The purpose of this analysis is to illustrate how landscape CO 2 balances (emissions flux net of carbon sequestrati on in biogenic feedstocks and soils) could respond to changes in l and management associated with alternative biogenic feedstock demand projections. This evaluat ion considers total soil and feedstock-related biogenic CO 2 emissions from agricultural and forestry land mana gement 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 base line 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 produ cer) 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 struct ure and key underlying datasets of the FASOMGHG model are described in the Supplemental Informa tion 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 gener ation on a regional basis. These regional constraints are all that vary across simul ation runs, and serve as the primary basis of comparison for examining GHG implications of cha nges in biomass energy demand.

• For biofuels production, all alternative baseline s cenarios assume that the RFS2 legislation binds and the mandated levels of biofuel are suppli ed. Feedstock-specific constraints are imposed and are based on the supplemental control c ase assumptions from the EPA Regulatory Impact Analysis of the RFS2. 5

• Other than biofuel feedstock restrictions, there ar e no constraints on feedstock choices for bioenergy across the alternative baseline scenarios , allowing the model to choose an optimal feedstock portfolio to achieve regional bio mass requirements.

• Agricultural productivity rates are linear growth r ates 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 assess ment (FIA) and other relevant forestry
sector datasets.

In addition to FASOM-GHG details included in Append ices J and L, further details regarding the model structure, regional detail, commodity represe ntation, and GHG accounting can be found in Beach et al. (2010).

The discussion of results below focuses on four bas eline scenarios. Two of the four AEO baseline
scenarios discussed above are used—the Reference ba seline and the Low Renewable Technology Cost baseline. The Low and High Economic Growth bas eline 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 baselin e 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 simulate d: one with constant 2009 biogenic feedstock consumption (as derived above—this scenario is refe rred to as the Constant baseline scenario throughout the remainder of this analysis) and anot her with no biogenic feedstock consumption at stationary sources (“Zero Biomass Consumption” scen ario). The difference between the Zero Biomass Consumption baseline scenario and each of t he other baseline scenarios (alternate AEObased and Constant scenario) indicates the addition al 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 Bi omass baseline scenarios is to respond directly to the SAB review of the previous accounting framew ork, 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 b ioenergy, is essential, as it is the crux of the

5 This includes growth in domestic (U.S.) biofuel pr oduction up to approximately 30 billion gallons in the 2020 simulation period (including 15 billion gallons of corn ethanol, 13.7 billion gallons of cellulosic et hanol, and 1.3 billion gallons of biodiesel, produced primarily fr om 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 dema nd for biomass... In general the Framework should provide a means to estimate the ef fect of stationary source biogenic feedstock demand, on the atmosphere, over time, com paring a scenario with the use of biogenic feedstocks to a counterfactual scenario wi thout the use of biogenic feedstocks.

With this in mind, baseline scenarios are construct ed so that business-as-usual (BAU) projections, which include anticipated growth in biogenic feedst ock 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 bi omass. Thus, this appendix considers a range of possible anticipated future baselines and alternati ve counterfactuals, allowing for a detailed assessment of potential biogenic emissions estimate s for illustrative purposes.

The following sections continue with more detail de scribing 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 CO 2 emissions are presented followed by cumulative net emissions, and finally additional emissions
relative to a counterfactual scenario in which no b iogenic feedstocks are consumed at stationary

sources.

3.1.1. Net Emissions Flux per Time Period

For each 5-year period, an annual emission or seque stration value was calculated using the FASOMGHG equations and parameters (presented in the supp lemental section at the end of Appendix L), then a total net emissions flux for that 5-year per iod was calculated by aggregating the individual fluxes. Figure K-6 illustrates projected CO 2 emissions flux trajectories across the different b aseline scenarios using atmospheric GHG accounting (a posit ive 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 chan ge 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 var y period-to-period, leading to high net emissions totals in some periods and net sequestrat ion 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 a re subtle, as the overall shape of these trajectories is similar. However, the absolute diff erence in annual emissions could be significant (for instance, this difference ranges 3–5 million t CO 2 e per year for the 2010 simulation period).November 2014 K-16

Furthermore that each biogenic feedstock consumptio n 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 scena rio, 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-CO 2 Emissions, Emissions from Fossil Fuel Consumption, and Carbon Stored In Wood Products).

3.1.2. Cumulative Net Emissions

Although the periodic net emissions flux trajectori es are similar over time, minor differences in annual fluxes can result in large differences in cu mulative emissions over time. Total emissions for each 5-year period are converted to a cumulative em issions total over the time horizon, displayed

in Figure K-7.

In 2035 (when biogenic feedstock demand peaks), cum ulative emissions for the AEO Reference baseline scenario are 22% higher than cumulative em issions for the Zero Biomass baseline. However, over time, projected cumulative emissions for the Zero Biomass baseline begin to converge with the alternative biogenic feedstock de mand baselines. There is little difference between the Zero and Constant Biogenic Feedstock ba seline scenarios toward the end of simulation horizon, and less than 7% difference across all sce narios. Thus, after immediate and medium-term emissions effects of increased biogenic feedstock d emand, 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 206 0Million TCO2 per year (U.S. Total)

AEO Low Renewable Energy Technology Cost Scenario

AEO Reference Case Scenario

Constant Biomass Consumption Case

Baseline With Zero BiomassNovember 2014 K-17

and the cumulative difference in emissions from Zer o Biomass to the alternative baselines begins to subside. One implication of these results is that t he 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 Bioma ss Scenario.

3.1.3. Additional Estimated Emissions

Additional biogenic CO 2 consumption and landscape-level emissions are calc ulated relative to the
Zero Biomass baseline scenario. This is done in ord er to capture all additional emissions effects of biomass consumption under an anticipated future bas eline projection relative to an alternative future in which there is no biomass consumption. Th us, all additional current and expected growth in biogenic feedstock consumption and the relative change in terrestrial CO 2 emissions are captured. This approach allows one to consider chan ges 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 feeds tock consumption relative to the Zero Biomass baseline scenario are computed (as this includes al l biogenic feedstock users). An emissions intensity is the ratio of net biogenic emissions (n et of emissions and landscape-level sequestration) to net biogenic CO 2 in biomass consumed for energy (tCO 2 equivalent emissions/ tCO 2 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 2060Million TCO2 per year (U.S. Total)

AEO Low Renewable Energy Technology Cost Scenario

AEO Reference Case Scenario

Constant Biomass Consumption Case

Baseline With Zero BiomassNovember 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 valu e of 0 implies no net emissions, meaning that biogenic CO 2 emissions from the use of biogenic feedstocks in e nergy production would be balanced with carbon uptake in the feedstock and on the land scape where the feedstock was produced. A value of 0.5 would imply that half of the biogenic CO 2 emissions are displaced by carbon uptake on the landscape. This metric provides insight into po tential emissions intensity of biogenic feedstock consumption by current and future stationary source s under these specific anticipated baseline scenarios and related parameters. This metric does not take into account stationary source process emissions, including combustion efficiencies, feeds tock losses during processing, or other possible components related to feedstock procurement or proc essing.

For the three alternative biogenic feedstock consum ption baseline scenarios (Constant, AEO Reference baseline, and AEO Low Renewable Technolog y 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 simulat ion horizon. Cumulative additional biogenic CO 2 from biogenic feedstock consumption is calculated b y converting annual biogenic feedstock consumption requirements for each scenario to a cum ulative value, and then converting to CO 2 equivalence (assuming that each dry ton of feedstoc k is 50% carbon). This cumulative additional emissions value is then divided by total CO 2 equivalence of biogenic feedstock consumption to derive the emissions intensity per unit of biogenic feedstock (Figure K-8).

In general, emissions intensity projections show th at biogenic CO 2 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 CO 2 consumption ranges 0.35–0.47 ton CO 2 e once the biogenic feedstock requirements are impo sed 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 R eference and AEO Low Renewable Technology Cost cases. Emissions intensity reaches values belo w 0 for the Constant Biomass case, indicating that cumulative biogenic CO 2 emissions are more than balanced by emissions chan ges on the landscape. This decline in emissions intensity is d riven by several factors:

1) A shift in land use/management early in the simulat ion horizon that increases tree carbon uptake over the long term (afforestation of croplan d 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 biomas s 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 Re lative to the Zero Biomass Scenario (Cumulative Additional Emissions Divided by Cumulat ive 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 2060t CO2emission / t biomass utilized

AEO Low Renewable Energy Technology Cost Scenario

AEO Reference Case Scenario

Constant Biomass Consumption CaseNovember 2014 K-20

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

In general, use of forest biogenic feedstocks for e nergy generation generates greater direct emissions and less energy output per-unit area than agricultural feedstocks or dedicated energy feedstocks, such as switchgrass (for more discussio n, 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 hi gher emissions per unit of biomass. However, this constraint is relaxed after the initial time p eriod, 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 bioge nic feedstocks are required for the AEO Reference and Low Renewable Technology Cost Baselin e scenarios, the portfolio shifts to a higher proportion of agricultural feedstocks, which decrea ses overall emissions intensity.

This decline is more pronounced for the Low Renewab le Technology Cost baseline as it adopts a much higher proportion of dedicated energy feedstoc ks 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 CaseNovember 2014 K-21



## Conclusion

This appendix developed biogenic feedstock demand p rojections with an initial (2009) value calibrated to observed consumption patterns at elec tricity and industrial sector stationary sources. Future projections were then calibrated to projecte d 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 r egions, representing regional biogenic feedstock requirements for simulation analysis.

Emissions trajectories across the alternative futur e baseline scenarios were compared to a scenario in which no agricultural and forestry biomass is co nsumed at stationary sources for energy generation. Results of the simulation analysis reve aled that emissions from biogenic feedstock consumption are not fully balanced by initial lands cape CO 2 uptake. However, over time, emissions intensity decreases, approaching or surpassing a ne t carbon balance for all alternative anticipated
future baseline scenarios assessed here.

In general, these results are consistent with previ ous studies that have shown that there are GHG consequences associated with biogenic feedstock pro duction, 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 associ ated with biogenic feedstock production and use are at least partially balanced by changes in s equestration on the landscape and that, over time (in this case decades), an increasing share of thes e carbon dioxide emissions is balanced at a national level and in most regions as well. When di saggregated 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 c auses 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, Con stant Biomass (existing sources in 2009), and AEO Reference. Appendix L develops feedstock- and r egion-specific demand shocks in addition to the AEO Reference case scenario. Emissions projecti ons from these feedstock case study scenarios are then compared to the three alternative baseline s 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 (Cons tant), 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 anticipate d baseline approach method and do not reflect EPA findings in the context of specific policies or pro grams. As with all modeling studies, there are a number of uncertainties present in the baseline sce nario assumptions and parameters adopted for this analysis. These uncertainties include future e nvironmental 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 pote ntial market and land use consequences of possible shifts in the demand for biogenic feedstoc ks at stationary sources. Furthermore, this study does not include full coverage of possible feedstoc ks 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. La tta, B.A. McCarl, B.C. Murray, S.K. Rose, E. White. 2010. Model Documentation for the Forest and Agricu lture Sector Optimization Model with Greenhouse Gases (FASOMGHG). p. 188.
Daigneault, A., B. Sohngen, & R. Sedjo. 2012. Econo mic 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 implication s 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 Biogen ic 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 prepa red by the Interagency Agricultural Projections Committee. Long-term Projections Report OCE-2009-1. U.S. Energy Information Administration. 2009. EIA-9 23 Electric Utility Data. http://www.eia.gov/electricity/data/eia923/ U.S. Energy Information Administration. 2012. Annua l Energy Outlook 2012. http://www.eia.gov/forecasts/aeo/pdf/0383(2012).pdf

. U.S. Environmental Protection Agency. 2012. Emissio ns and Generation Resource Integrated Database (E-GRID), Eight Edition. http://www.epa.go v/cleanenergy/energy-resources/egrid/
U.S. Environmental Protection Agency Assessment and Standards Division, Office of Transportation and Air Quality (OTAQ). 2010. Renewable Fuel Standa rd Program (RFS2) Regulatory Impact Analysis. Report No. EPA-420-R-10-006.November 2014 K-23



## Supplemental Data and Information

6.1. GraphicsNovember 2014 K

- 24Figure K-10. Biogenic Feedstock Consumption Projections by AEO Scenario Grouped by FASOM-GHG Region.01002003004005006007002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearCorn Belt (CB)024681012141618202010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearGreat Plains (GP)020004000600080001000012000140002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearLake States (LS)0200040006000800010000120002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearNortheast (NE)0501001502002503003504004505002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearPacific Northwest East (PNWE)02004006008001000120014002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearPacific Northwest West (PNWW)0200040006000800010000120002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearPacific Southwest (PSW)01002003004005006007002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearRocky Mountains (RM)05001000150020002500300035004000450050002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearSouth Central (SC)05000100001500020000250002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearSoutheast (SE)0501001502002503002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearSouthwest (SW)0100002000030000400005000060000700002010 2015 2020 2025 2030 2035

Thousands of Short Tons per YearUnited States Total (US)Constant Biomass Consumption CaseAEO Reference Case ScenarioAEO Low Renewable Energy Technology Cost ScenarioAEO High Economic Growth ScenarioAEO Low Economic Growth ScenarioNovember 2014 K-25

Figure K-11. Biogenic Feedstock Consumption Project ions 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 20351,000 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 20351,000 Dry Short Tons 

## AEO Reference Case Scenario

- 2,000 4,000 6,000 8,000 10,000 12,000

2010 2015 2020 2025 2030 20351,000 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 20351,000 Dry Short Tons 

## AEO Low Economic Growth

ScenarioNovember 2014 K

- 26Figure 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)-1.02.02010 2020 2030 2040 2050 2060

t CO 2 emission / t biomass utilizedCorn Belt (CB)(10.0)(5.0)-5.010.015.020.025.030.0201020202030204020502060

t CO 2 emission / t biomass utilizedGreat Plains (GP)(0.4)(0.2)-0.20.40.60.81.01.2201020202030204020502060

t CO 2 emission / t biomass utilizedLake States (LS)-0.10.20.30.40.50.60.70.80.9201020202030204020502060

t CO 2 emission / t biomass utilizedNortheast (NE)(1.0)(0.5)-0.51.01.52.0201020202030204020502060

t CO 2 emission / t biomass utilizedPacific Northwest East (PNWE)(0.5)-0.51.01.52.02010 2020 2030 2040 2050 2060

t CO 2 emission / t biomass utilizedPacific Northwest West (PNWW)(0.20)(0.15)(0.10)(0.05)-0.050.100.15201020202030204020502060

t CO 2 emission / t biomass utilizedPacific Southwest (PSW)(2.0)(1.5)(1.0)(0.5)-0.51.01.52.0201020202030204020502060

t CO 2 emission / t biomass utilizedRocky Mountains (RM)(1.0)(0.5)-0.51.01.52.02.5201020202030204020502060

t CO 2 emission / t biomass utilizedSouth Central (SC)(0.4)(0.3)(0.2)(0.1)-0.10.20.30.40.5201020202030204020502060

t CO 2 emission / t biomass utilizedSoutheast (SE)(0.2)-0.20.40.60.81.01.2201020202030204020502060

t CO 2 emission / t biomass utilizedSouthwest (SW)(0.1)-0.10.20.30.40.5201020202030204020502060

t CO 2 emission / t biomass utilizedUnited States Total (US)Constant Biomass Consumption CaseAEO Reference Case ScenarioAEO Low Renewable Energy Technology Cost Scenario