November 2014 L-1 

Appendix L: Illustrative Forestry and Agriculture Case 

Studies Using a Future Anticipated Baseline 

Table of Contents 

1. Introduction ................................................................................................................................................................... L-2

2. Method and Parameters Used to Calculate the Illustrative BAFs Using the Future Anticipated 

Baseline Approach ...................................................................................................................................................... L-3

2.1. Case Study Methods .................................................................................................................................... L-4

2.2. Case Study Parameters .............................................................................................................................. L-5

3. Mapping and Interpreting Future Anticipated Baseline Data and Illustrative Results .................. L-6

3.1. FASOM-GHG Data Component Mapping to BAF Terms ................................................................ L-6

3.2. Potential Gross Emissions (PGE) ........................................................................................................... L-7

3.3. Net Growth (GROW) .................................................................................................................................... L-7

3.4. Total Net Change in Site Emissions (SITETNC) ............................................................................... L-7

3.5. Avoided Emissions (AVOIDEMIT) ......................................................................................................... L-8

3.6. Net Biogenic Emissions (NBE) ................................................................................................................ L-8

3.7. Biogenic Assessment Factor (BAF) ....................................................................................................... L-8

4. Guide to the Case Studies ......................................................................................................................................... L-8

4.1. Understanding the Illustrative Results ............................................................................................... L-8

4.2. Overview of the Illustrative Results ..................................................................................................... L-9

5. Case Study Details ..................................................................................................................................................... L-11

5.1. Southeast Roundwood ............................................................................................................................. L-11

5.1.1. Marginal Effects for the Southeast Roundwood Case Study ...................................... L-11

5.1.2. Average Effects for the Southeast Roundwood Case Study ........................................ L-13

5.2. Pacific Northwest (PNW) Logging Residues................................................................................... L-14

5.2.1. Marginal Effects for the PNW Logging Residue Case Study ....................................... L-14

5.2.2. Average Effects for the PNW Logging Residue Case Study ......................................... L-15

5.3. Corn Belt Corn Stover............................................................................................................................... L-17

5.3.1. Marginal Effects for the Corn Belt Corn Stover Case Study ........................................ L-17

5.3.2. Average Effects for the Corn Belt Corn Stover Case Study .......................................... L-18

6. Summary ....................................................................................................................................................................... L-19

7. References .................................................................................................................................................................... L-20

8. Supplemental Data and Information ................................................................................................................. L-20
November 2014 L-2 

8.1. Details on FASOM-GHG Carbon Accounting ................................................................................... L-20

8.1.1. FASOM-GHG Biogenic Feedstock Growth Functions .................................................... L-20

8.1.2. FASOM-GHG Functions Relating to Changes in Site Emissions................................. L-23

8.1.3. FASOM-GHG Functions Relating to Changes in Avoided Emissions ....................... L-27

9. References .................................................................................................................................................................... L-28

List of Tables 

Table L-1. FASOM-GHG Emissions Components Matched with BAF Equation Terms. ........................... L-6

Table L-2. Illustrative BAF Values for the Future Anticipated Baseline Case Studies: Cumulative 

Average Results from 2015–2060. ............................................................................................................. L-11

Table L-3. Southeast Roundwood Landscape Factor Results (Marginal User). ....................................... L-12

Table L-4. Southeast Roundwood Landscape Factor Results (Average User). ........................................ L-13

Table L-5. PNW Logging Residue Landscape Factor Results (Marginal User). ........................................ L-15

Table L-6. PNW Logging Residue Landscape Factor Results (Average User). ......................................... L-16

Table L-7. Corn Belt Corn Stover Landscape Factor Results (Marginal User).......................................... L-17

Table L-8. Corn Belt Corn Stover Landscape Factor Results (Average User). .......................................... L-18

Table L-9. FASOM-GHG Live and Dead Tree Biomass Equation Parameters. ........................................... L-21

Table L-10. FASOM-GHG Forest Floor Biomass Equation Parameters. ...................................................... L-24

Table L-11. FASOM-GHG Agricultural Soil Carbon Constants by Land Use and Region. ...................... L-25

Table L-12. FASOM-GHG Forest Soil Equation Parameters. ............................................................................ L-26

Table L-13. FASOM-GHG Forest Soil Carbon Constant by Region in Metric Tons of Carbon per Acre.

 ................................................................................................................................................................................... L-26

Table L-14. FASOM-GHG Soil Carbon Conversion Rates by Years Since Conversion and Region. ... L-27

Table L-15. FASOM-GHG Annual Coarse Woody Debris Decomposition Rates. ...................................... L-28

List of Figures 

Figure L-1. Comparison of Average Cumulative Landscape Factors in the Southeast Region for the 

AEO Reference Case Baseline and Roundwood Case Study Relative to the Zero Biomass 

Scenario. ................................................................................................................................................................ L-14

Figure L-2. Comparison of Average Cumulative Landscape Factors in the PNW Region for the AEO 

Reference Case and PNW Logging Residue Case Study Relative to the Zero Biomass 

Scenario. ................................................................................................................................................................ L-16

Figure L-3. Comparison of Average Cumulative Landscape Factors in the Corn Belt Region for the 

AEO Reference Case Baseline and Corn Stover Case Study Relative to the Zero Biomass 

Scenario. ................................................................................................................................................................ L-19

1. Introduction 

This appendix demonstrates the functionality of the future anticipated baseline approach through 

three illustrative region- and feedstock-specific case studies. These case studies use the baseline 

scenarios constructed in Appendix K as the basis for comparison of alternative biogenic feedstock 

production scenarios per specific feedstocks and specific regions. The application of the future 

anticipated baseline approach within these case study constructs allows for the calculation of 

illustrative values for the pertinent framework equation terms and ultimately generation of 
November 2014 L-3 

illustrative biogenic assessment factors (BAF) specific to the individual case study parameters and 

assumptions. 

Results show aggregate emissions estimates on a specific regional scale per case study. Results can 

be interpreted as the projected emissions intensity of specific biogenic feedstocks consumed at 

existing or anticipated stationary sources across multiple baseline projections of biogenic feedstock 

consumption. However, results do not reflect the net emissions contribution of a particular 

feedstock within a particular region but rather illustrate potential net biogenic emissions effects 

associated with increased consumption of a specific feedstock in a specific region under specific 

conditions. To maintain consistency with the reference point approach, region- and feedstockspecific simulation scenarios were developed to isolate the landscape-level carbon-based emissions 

fluxes related to a demand shift for an individual feedstock relative to the AEO Reference baseline 

(as presented in Appendix K). 

The three case studies are: 

• Roundwood in the Southeast (SE); 

• Corn stover in the Corn Belt (CB); and 

• Logging residues in the Pacific Northwest (PNW). 

This appendix first provides an overview of the methods used to construct the case study 

parameters, an explanation of the how landscape-level biogenic emissions fluxes are mapped to 

BAF equation terms, a discussion of how to interpret results (using different assessment methods), 

and a presentation of illustrative case study results and analysis. 

The values derived in this appendix are intended to illustrate the future anticipated baseline 

approach and do not reflect EPA findings in the context of specific policies or programs. As with all 

modeling studies, a number of uncertainties are present in the baseline assumptions and 

parameters adopted for this analysis. These uncertainties include historical input data, 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, 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. 

2. Method and Parameters Used to Calculate the Illustrative 

BAFs Using the Future Anticipated Baseline Approach 

The intertemporal optimization approach used in these illustrative case studies captures 

investment behavior under anticipated changes in feedstock demand; thus, land management 

responds in advance of an anticipated change. This approach allows for a depiction of land use 

investment/management over the long term, which provides an improved projection of landscapelevel biogenic CO2 emissions under anticipated changes in biogenic feedstock consumption than 

static (one-time) models or recursive dynamic models that do not react to future expectations. 
November 2014 L-4 

Ultimately, numerous assumptions and parameters can be varied to establish future anticipated 

baselines that differ from those presented here and in Appendix K. Furthermore, there are 

numerous possibilities for creating alternative feedstock scenarios relative to a future anticipated 

baseline. The primary goal of this appendix is to illustrate how the future anticipated baseline 

approach could be applied in practice to assess landscape-level emissions effects related to changes 

in demand for individual feedstocks. The secondary objectives of this appendix are to evaluate the 

potential direction and magnitude of biogenic CO2 emissions from increased regional feedstock 

consumption, using the modeling assumptions and baseline constructs as presented in Appendix K. 

2.1. Case Study Methods 

Each feedstock case study was developed using the same underlying methodology. Each feedstock 

scenario is evaluated relative to the three alternative baseline scenarios, as introduced in Appendix 

K (Zero Biomass, Constant Biomass [existing sources in 2009], and AEO Reference). For each 

feedstock case study, the regional emissions intensity of additional biogenic feedstock consumption 

(additional biogenic CO2 emissions divided by additional biogenic feedstock CO2) is computed, 

similar to the approach outlined in Appendix K and described in more detail below. 

Each of the case studies begins with regional biomass consumption trajectories from the AEO 

Reference baseline and then requires an additional 1 million short dry tons of specific biogenic 

feedstock consumption in the region under consideration. This additional biomass requirement is 

phased in linearly, beginning with 250,000 short dry tons in the 2015 simulation period, reaching 1 

million tons in 2030. The feedstock requirement is phased in over time under the conservative 

assumption that it could take time for a new facility or demand point to build up a steady supply 

source of one particular feedstock given regional market dynamics. The additional biomass 

requirement is then held constant for the remainder of the simulation horizon1 and must be met by 

the case study feedstock only. For the SE roundwood case, for example, the additional biomass 

requirement must come exclusively from hard and soft roundwood. This constraint is maintained 

throughout the simulation horizon to isolate the emissions effects of increased demand for a 

specific feedstock over the long term. 

Comparison of the 1 million ton increased feedstock consumption scenario to the AEO Reference 

baseline scenario can be interpreted as the marginal effect of a new source of consumption that is 

fueled by a single feedstock, relative to the AEO Reference anticipated baseline. This increased 

consumption could be thought of as the estimated marginal effect of the additional demand from a 

stationary source that is expected to consume approximately 1 million tons of biogenic CO2

annually for onsite energy generation over the long term. 

Comparison of the 1 million ton increased feedstock scenario to the Zero Biomass baseline scenario 

provides an estimate of the average biogenic CO2 emissions effect for all existing and planned 

biogenic feedstock consumption within a region (as defined by the eGRID/EIA dataset-derived 

2009 existing users and AEO Reference baseline anticipated new users) plus the additional 

 

1 The 2012 Annual Energy Outlook projections do not extend past 2030; thus, biomass consumption shock is held 

constant after this simulation period. 
November 2014 L-5 

feedstock-specific requirement from the case study. For this comparison, the feedstock scenarios 

were constructed in exactly the same way (same volumes, same feedstocks), with the anticipated 

baseline feedstock portfolio exactly matching the AEO Reference baseline simulation results over 

time. The difference here is that biogenic CO2 emissions intensity metrics are computed relative to 

the Zero Biomass simulation results. Thus, the numerator represents the net change in regional 

projected emissions (Feedstock Case Study − Zero Biomass), while the denominator represents 

regional biogenic CO2 consumption (AEO Reference baseline biomass plus additional feedstock 

requirement). 

Another possible method is the augmented average approach.2 Comparison of the 1 million ton 

increased feedstock scenario to the Constant Biomass baseline can be interpreted as the augmented

average biogenic CO2 emissions effect of planned expansion in biogenic feedstock consumption (as 

defined by the AEO Reference baseline) above the eGRID/EIA dataset-derived 2009 existing users, 

plus the additional feedstock specific requirement from the case study. Essentially, this is the same 

methodology as the comparison to the Zero Biomass baseline, but all calculations are relative to an 

anticipated baseline that holds biomass consumption fixed to observed levels in 2009. 

Illustrative equation term and estimated BAF results using the marginal and average methods 

above are presented for each case study. 

2.2. Case Study Parameters 

All feedstock case studies are simulated over an 80-year time horizon (2000–2080) to capture 

investment dynamics in the forestry sector over this period. The results are computed using key 

outputs for the 2010–2060 time horizon (in 5-year timesteps), which provides a 50-year time 

frame for evaluating land use and biogenic emissions changes (and avoids any terminal effects that 

may affect results in the last few time periods of results). Results generated within this overall time 

frame can be aggregated and evaluated in different ways (e.g., the BAF can be constructed for 10- to 

50-year time frames as desired), and the 50-year mark should not be interpreted as an EPA 

decision on applying time frames in the contexts of specific programs and policies. The spatial scale 

of these regional case studies is represented by the 11 primary agroforestry regions of FASOM-GHG. 

Additional FASOM-GHG modeling details are provided in the Supplemental Information section at 

the end of this document. 

Although FASOM-GHG offers full GHG accounting options (including N2O and CH4 emissions from 

crop and livestock operations), this study focuses on changes in landscape-level biogenic CO2 only 

(though a sensitivity evaluating the impact of including N2O is included in this analysis). This 

approach includes carbon in agricultural and forestry soils, and carbon stored in forest and 

agricultural biomass (additional details provided below). 

 

2

 Discussed here as a possible method, but this method was not employed to avoid further complexity, as many 

different methods could be discussed and employed using a future anticipated baseline approach. Therefore, the 

illustrative results tables do not include this category. 
November 2014 L-6 

3. Mapping and Interpreting Future Anticipated Baseline Data 

and Illustrative Results 

This section presents which FASOM-GHG data components are mapped to BAF equation terms as 

well as how BAF equation terms are calculated using these data over a specified simulation time 

horizon. Specifically, FASOM-GHG projections are used to derive representative values for regional 

net growth (GROW), total net carbon change on the feedstock production region (SITETNC), and 

avoided emissions from feedstock harvest or collection (AVOIDEMIT) in each simulation period. 

These terms are aggregated into a net biogenic emissions (NBE) term, which is used along with the 

total additional biogenic CO2 (calculated directly from the feedstock-specific biomass constraint) as 

a representative potential gross emissions (PGE) value to derive an estimated BAF. 

A major difference in the illustrative BAF terms generated with the retrospective reference point 

and the future anticipated baseline approach is that the equation terms (PGE, GROW, SITETNC, 

AVOIDEMIT, and NBE) as defined and applied within the future anticipated baseline approach do 

not represent the absolute emissions associated with the terms but rather the additional, or 

relative, emissions compared with an alternate potential future. 

3.1. FASOM-GHG Data Component Mapping to BAF Terms 

Deriving BAF equation term values from FASOM-GHG output data components involves aggregating 

the various emissions components into a single value. Table L-1 lists specific carbon-based GHG flux 

categories from FASOM-GHG simulations and the BAF equation term associated with each carbonbased GHG flux account. Note that non-CO2 emissions from crop and livestock management, carbon 

stored in wood products, and fossil fuel emissions from land management are not included in this 

analysis. 

Table L-1. FASOM-GHG Emissions Components Matched with BAF Equation Terms. 

FASOM-GHG Emissions Component 

Southeast 

Roundwood 

Pacific 

Northwest 

Logging 

Residues 

Corn Belt 

Corn Stover 

Agricultural LUC and Soil Management Carbon Flux SITETNC SITETNC SITETNC 

Logging Residue Decay Flux AVOIDEMIT AVOIDEMIT AVOIDEMIT 

Afforestation Harvest Flux GROW GROW SITETNC 

Afforestation Tree Carbon Flux GROW GROW SITETNC 

Existing Forest Harvest Flux GROW GROW SITETNC 

Existing Forest Tree Carbon Flux GROW GROW SITETNC 

Afforestation Litter and Understory Harvest Flux SITETNC SITETNC SITETNC 

Afforestation Soil Carbon Flux SITETNC SITETNC SITETNC 

Afforestation Litter and Understory Carbon Flux SITETNC SITETNC SITETNC 

Deforestation Soil Carbon Flux SITETNC SITETNC SITETNC 

Existing Forest Litter and Understory Carbon Flux SITETNC SITETNC SITETNC 

Existing Forest Litter and Understory Harvest Flux SITETNC SITETNC SITETNC 
November 2014 L-7 

FASOM-GHG Emissions Component 

Southeast 

Roundwood 

Pacific 

Northwest 

Logging 

Residues 

Corn Belt 

Corn Stover 

Logging Residue Carbon Flux SITETNC SITETNC SITETNC 

Existing Forest Soil Carbon Flux SITETNC SITETNC SITETNC 

Details about the underlying input data for the above FASOM-GHG elements and how they are 

calculated in the model are included in the Supplemental Information section in this appendix. 

3.2. Potential Gross Emissions (PGE) 

Because the BAF as calculated using the future anticipated baseline approach is a measure of 

emissions intensity, PGE, which also varies by time (t), is the estimated biogenic feedstock 

consumed (in terms of CO2) in the case study (“CS”) that is additional to the estimated biogenic 

feedstock consumed (in terms of CO2) in the alternate baseline (“AB”): 

 = 	 	
	_
”CS”,t − 	
	_
”AB”, (EQ. L.1) 

3.3. Net Growth (GROW) 

Similar to the retrospective reference point baseline approach, the future anticipated baseline 

treats GROW as the net landscape biogenic CO2 growth. The forest GROWTH i,”CS”,t is indexed by i to 

represent biogenic emissions fluxes contributing to GROW from Table L-1, representative scenario 

“CS” for the case study (or alternative baseline, “AB”), and t for the time period. The following 

equation represents the calculation of GROWt, where the i index specifically represents the 

individual biogenic CO2 fluxes from the SE roundwood column of Table L-1 labeled “GROW” 

(afforestation harvest flux, afforestation tree carbon flux, etc.) It should be noted that the fluxes 

considered in set i include both forest growth and removals, thus yielding a net growth value for 

each time period, t. Under this approach, each flux account is the difference between the simulated 

values for the feedstock case study scenario (“CS”) and the alternate baseline (“AB”) for all time 

periods. 


 = ∑
,””, − 
,””, (EQ. L.2)

3.4. Total Net Change in Site Emissions (SITETNC) 

SITETNC represents the difference in landscape-level biogenic CO2 emissions fluxes not directly 

related to the actual biogenic feedstock growth (for each time period in the simulation). This factor 

includes changes in carbon stored in soils, non-harvested biomass, and potentially other pools. The 

change in site carbon, SITEk,”CS”,t, is the sum of a set of k biogenic CO2 components from the SE 

roundwood column of Table L-1 labeled “SITETNC” for the case study scenario (“CS”) in time period 

t. The following equation illustrates how periodic SITETNC values are computed under the future 

anticipated baseline framework as the relative difference in emissions between the case study, “CS,” 

and an alternative baseline, “AB,” for each time period, t. 
November 2014 L-8 

	 = ∑	,””, − 	,””, (EQ. L.3) 

3.5. Avoided Emissions (AVOIDEMIT) 

A similar logic follows for AVOIDEMIT. AVOIDEMIT represents the avoidance of estimated biogenic 

emissions that could have occurred on the feedstock landscape without biogenic feedstock removal. 

In the context of the future anticipated baseline approach, AVOIDEMIT represents the relative 

difference in avoided biogenic emissions between scenarios. Each “AVOID” term in equation 4 

below represents the avoided biogenic emissions within a particular scenario. Letting AVOIDh,”CS”,t

represent the sum of the set of h biogenic CO2 components from the Southeast roundwood column 

of Table L-1 labeled AVOIDEMIT, for the case study scenario(“CS”) in time period t. The following 

equation illustrates how periodic AVOIDEMIT values are computed under the future anticipated 

baseline framework as the relative difference in emissions between the case study (“CS”) and an 

alternative baseline (“AB”) for each time period, t. 


		 = ∑
	,””, − 
	,””, (EQ. L.4) 

3.6. Net Biogenic Emissions (NBE) 

NBE represents the difference in biogenic landscape-level CO2 emissions (emissions from 

harvesting and using the biogenic feedstock) between scenarios (calculated as the sum of all 

landscape-level CO2 emissions). This is represented as: 

	 = 	 
 + 	 + 
		

 (EQ. L.5) 

3.7. Biogenic Assessment Factor (BAF) 

Thus, the biogenic assessment factor is ratio of the net biogenic emissions (NBEt) to the potential 

growth emissions (PGEt), or simply put: 

! =





" (EQ. L.6) 

4. Guide to the Case Studies 

4.1. Understanding the Illustrative Results 

The illustrative results provided below for the three case studies include positive and negative 

values. Positive values indicate a net flux of emissions (harvest or land use change emissions 

outweigh biogenic CO2 sequestration on the landscape), whereas negative values indicate net 

sequestration (biogenic CO2 sequestration on the landscape outweighs harvest or land use change 

emissions). However, determination of how and whether negative values would be applied in 

practice would depend on the policy or program being analyzed. 

BAF results can be illustrated in a variety of contexts, relative to different counterfactual scenarios: 

• Marginal and average user effects—As discussed in the Methods Section above, the 

average, augmented average, and marginal BAF results are a function of the comparison 
November 2014 L-9 

between the specified anticipated baseline scenario (Zero, Constant and AEO Reference) 

and the case study increased feedstock scenario. 

• Cumulative and per-period calculations and values—Per-period values, calculated using 

the formulas from the section above, illustrate BAF values specific to an individual point in 

time. By using intertemporal models, these periodic BAFs can vary widely from period to 

period as land management and forest harvest intervals adjust to the new biomass demand 

shock. This explains the variable nature of the periodic calculations. A 2015–2060 average 

is calculated to represent the average periodic BAF over the entire time frame of the 

analysis. Cumulative BAFs are calculated by taking the cumulative value of each term in the 

BAF equation over time. The cumulative value offers insight into potential anthropogenic 

biogenic carbon-based emissions effects over a specified future time horizon relative to the 

future anticipated baseline, whereas a single value at a point in time only offers insight into 

periodic deviations from the baseline. Calculation of the BAF using cumulative and average 

values can smooth out the fluctuations in equation terms per period and provide a more 

stable estimate of net biogenic emissions over time. 

Using the Zero versus the AEO or Constant Biomass baseline as the basis of analysis led to different 

BAF values. The Zero Biomass baseline comparison to the case study projection captures all 

anticipated biomass users, whereas the Constant Biomass comparison focuses on new users. 

Depending on the policy application of the framework, either of these approaches may be more 

appropriate. Also, the means for considering the results over time (averaged per-period BAFs 

versus cumulative) led to different BAF values. Per-period values, calculated using the formulas 

from the section above, illustrate BAF values specific to an individual point in time, which might be 

useful in some policy applications but not relevant for others. Given the nature of modeling 

methods employed, periodic BAFs can vary widely from period to period as land management and 

forest harvest intervals adjust to the new biomass demand shock. 

The supplemental data and information section provides the illustrative results and discussion for 

the various feedstock- and region-specific case studies. Data presented in this supplemental section 

include projected equation term values for each simulation period for emissions fluxes and 

cumulative emissions, using the average and marginal counterfactual approaches. 

4.2. Overview of the Illustrative Results 

Table L-2 provides illustrative values for NBE and the BAFs for each of the three case studies. These 

values are based on cumulative emissions totals for a simulation horizon that extends to 2060. In 

each case, NBE and BAF values are calculated relative to the Zero Biomass counterfactual scenario 

and, thus, represent an average regional BAF for all current and anticipated expansion in biogenic 

feedstock consumption from the additional 1 million dry ton feedstock demand shock. All BAF

equation terms presented in Table L-2 can be replicated based on the cumulative “average” value 

tables provided in the Supplemental Information section of this appendix, referencing the 2055–

2060 simulation period. 

Table L-2 presents estimated landscape attributes for each of the three case studies and concludes 

with two illustrative BAF values (with and without default process attributes P and L, which are 
November 2014 L-10 

assumed to be 1 and 1.1, respectively, for consistency with previous appendices). The third column 

of the table represents relative growth emissions, or the difference in cumulative forest carbon 

sequestration between the two scenarios. The fourth column represents relative removal (or 

harvest) emissions. Note that either of these columns could yield a positive or negative value, 

depending on the relative difference in these cumulative fluxes between the case study and Zero 

Biomass baseline scenario. For instance, a positive value in the relative removal column means that 

forest harvest emissions increase with the additional biogenic feedstock demand in the case study. 

Relative net growth (in the fifth column) is the sum of relative growth and relative removals. 

Dividing this absolute emissions change by the regional PGE term in the ninth column yields the 

GROW term for the NBE equation. Columns six and seven represent relative emissions changes for 

those fluxes captured by the SITETNC term (Table L-1 provides a list of all biogenic carbon-based 

fluxes included in AVOIDEMIT and SITETNC for this application). 

NBE (eighth column) is the sum of all relative landscape attributes in columns five through seven. In 

this particular application, PGE represents the total PGE for the region of assessment. This value 

represents cumulative additional consumption of biogenic feedstocks for energy generation (in 

million tCO2e) for the feedstock scenario over the future time horizon of assessment (2015–2059), 

and relative to the Zero Biomass case). Thus, this is a regional PGE term that could potentially be 

used to calculate the regional ratios for GROW, AVOIDEMIT, and SITETNC (depending on the policy 

program or context). 

The final columns represent proof-of-concept BAF values for the region and feedstock case study. 

The first BAF value does not adjust for process attributes P and L. Both the roundwood and logging 

residue case studies find a long-term cumulative BAF value that is very close to 0 or slightly 

negative in the Southeast roundwood case. The Corn Belt corn stover simulations result in a 

projected long-term cumulative BAF of 0.15, which suggests that 85% of PGE released during 

conversion at a stationary source would be reabsorbed by the landscape. 
November 2014 L-11 

Table L-2. Illustrative BAF Values for the Future Anticipated Baseline Case Studies: Cumulative 

Average Results from 2015–2060. 

 

Relative Growth & 

Removals Relative Carbon Fluxes Relative Total Carbon Flux & Biogenic Emissions 

Scenario Time 

Scale

Relative 

Growth 

Emissions 

(million 

tCO2e)

Relative 

Removals 

Emissions 

(million 

tCO2e)

Relative

Net 

Growth 

(GROW = 

Relative 

growth – 

relative 

removals) 

(million 

tCO2e) 

Relative 

Avoided 

Emissions 

(AVOIDEMIT) 

(million 

tCO2e)

Relative 

Net 

Landscape 

Emissions 

(SITE_TNC

) (million 

tCO2e)

Net 

Biogenic 

Emissions 

(NBE): 

Sum of all 

relative 

carbon 

fluxes 

(million 

tCO2e) 

Potential 

Gross 

Emissions 

(PGE): All 

Additional 

Biogenic 

Feedstock 

Consumption (million 

tCO2e) 

Assessment 

Factor (BAF)

(Ratio of 

relative total 

carbon flux 

to relative 

feedstock 

flux) 

Adjustment 

Factor (BAF) 

with 

ProcessBased 

Equation 

Terms P and 

L 

SE Roundwood 2015–

2060 

−37 17 −20 −0.6 −3 −24 672 −0.03 −0.03

PNW Logging 

Residues 

2015–

2060 

−14 16 2 0 4 7 155 0.04 0.04

CB Corn Stover 2015–

2060 

NA NA 0 0 16 16 108 0.15 0.16

5. Case Study Details 

5.1. Southeast Roundwood 

It is important to consider the regional effects of additional feedstock expansion given regional 

differences in forest species composition, management techniques, hardwood/softwood mixes, and 

forest products industry. For example, softwood plantation pine systems are common in the 

Southeast, and such plantations involve more intensive management but shorter rotations than 

typical hardwood stands in other regions such as the Northeast. Thus, high levels of emissions from 

biomass removals could occur more frequently on the landscape in the Southeast, but the carbon 

payback period could be shorter. 

5.1.1. Marginal Effects for the Southeast Roundwood Case Study 

Table L-3 displays average periodic and cumulative biogenic CO2 emissions results for the marginal 

estimated landscape factor calculations for three separate portions of the simulation horizon 

(2015–2029, 2015–2044, and 2015–2060).3 As noted previously, the marginal effect refers to a net 

change in regional landscape-level emissions and biogenic CO2 consumption for the feedstock case 

study relative to the AEO Reference baseline. Estimated per-period landscape factors vary over 

time for the marginal case, reflecting the cyclical nature of terrestrial CO2 fluxes from forest 

management, though this variation is smoothed by averaging over time. Initially, emissions 

intensity is negative and relatively large in magnitude, reflecting a net increase in carbon 

 

3

 Note that an estimated landscape factor has the same interpretation as the BAF without process attribute terms P

and L (presented in Equation 6 of this appendix). 
November 2014 L-12 

sequestration on the landscape driven by land-owner investment decisions (anticipatory planting) 

and harvest timing decisions in response to the anticipated long-term demand shift for roundwoodderived biomass. That is, landowners plant new trees and delay harvests in an effort to meet this 

long-term increase in demand. 

Furthermore, the Southeast region is a unique region with historically high levels of observed land 

use exchanges between agriculture and forestry (Wear and Gries, 2002; Milesi et al., 2003). This 

phenomenon is evident in the Southeast case study results, as afforestation and pasture-tocropland transitions occur in response to the added roundwood feedstock requirement leading to 

periodic fluctuations evident in the BAF equation terms and the estimated landscape factor itself. 

These land use changes can cause large periodic fluctuations in SITETNC emissions as new sources 

of carbon sequestration from afforested stands affect the projected terrestrial carbon balance (as 

seen in 2035 and 2040). In addition to land use change, differences in forest management 

techniques and shorter rotations in the Southeast relative to other regions lead to more variability 

in the GROW term as high levels of harvest emissions occur more frequently and forest carbon 

stocks recover more rapidly. 

Table L-3. Southeast Roundwood Landscape Factor Results (Marginal User). 

Case 

Study Term 

Emissions 

Projection 

Method 

Time Period 

2015–2030 2015–2045 2015–2060 

SE Roundwood 

Marginal User 

------ additional emissions (t CO2) from AEO Reference case baseline level ------

GROW 

Per Period 

-478 -444 -587 

SITETNC -129 -116 -118 

AVOIDEMIT 1.7 1.5 1.3 

PGE 917 1,375 1,528 

Estimated 

Landscape Factor 

-0.66 -0.41 -0.46 

Cumulative additional (t CO2) from AEO Reference baseline level 

GROW 

Cumulative 

-6,363 -12,505 -25,610 

SITETNC -2,920 -4,456 -6,299 

AVOIDEMIT 25 44.7 56.3 

PGE 13,750 41,250 68,750 

Estimated 

Landscape Factor 

-0.67 -0.41 -0.46 

Cumulative BAFs are smoother and less variable overall when compared with the periodic BAFs. 

However, the average of all periodic BAF values over the simulation period through 2060 is 

extremely close to the cumulative landscape factor. Thus, expanded roundwood consumption in the 

Southeast results in a net reduction in biogenic CO2 emissions relative to the AEO Reference 

baseline. 
November 2014 L-13 

5.1.2. Average Effects for the Southeast Roundwood Case Study 

Average effects are displayed in Table L-4. These results include the net change in biomass 

consumption and emissions for existing levels of consumption, planned expansion, and the 

additional case study feedstock requirement. Periodic landscape factors are more stable (less 

variable) under this approach than the marginal effects above, in part because the additional 

biogenic CO2 in the denominator includes the biomass consumption already projected to take place. 

Changes in the denominator are not overwhelmed by the large landscape-level emissions changes 

present in the numerator. Estimated landscape factors for the “average user” are positive at the 

beginning of the simulation horizon when the increase in biomass consumption has its greatest 

effect but decrease over time as landscape biogenic carbon balances recover. Like the “marginal” 

periodic landscape factors, average periodic landscape factors fluctuate over time and the average 

by 2060 is less than 0 at −0.07. 

Table L-4. Southeast Roundwood Landscape Factor Results (Average User). 

Case 

Study Term 

Emissions 

Projection 

Method 

Time Period 

2015–2029 2015–2044 2015–2060 

SE Roundwood 

Average User

------ additional emissions (t CO2) from Zero Biomass baseline level ------

GROW 

Per Period 

2,315 138 -769 

SITETNC 651 45 -155 

AVOIDEMIT -12.3 -9.4 -12.5 

PGE 12,378 13,670 14,069 

Estimated 

Landscape Factor 0.24 0.01 -0.07 

Cumulative additional emissions (t CO2) from Zero Biomass baseline level 

GROW 

Cumulative 

49,031 18,433 -20,308 

SITETNC 13,211 4,802 3,519 

AVOIDEMIT -228 -325.3 -604.8 

PGE 225,072 449,508 672,489 

Estimated 

Landscape Factor 0.28 0.05 -0.03 

Cumulative landscape factors end with a similar total in 2060 to the periodic average (−0.03). Note 

that this result differs from the previous “marginal” user landscape factor. An “average” value 

includes the landscape-level emissions effect of all biomass users (current, planned, and the 

additional roundwood consumption source), whereas the marginal case captures only the change in 

roundwood consumption (relative to all current and planned sources). The key difference here is 

that the marginal result is capturing land management changes early in the simulation horizon 

(afforestation, longer forest rotations) in anticipation of the long-term increase in roundwood 

demand. Much of the emissions effect of moving from zero biomass consumption to the feedstock 

case study is captured in the AEO Reference baseline, so the resulting change from AEO Reference 

to the roundwood feedstock case is only capturing the additional emissions and biomass 

consumption attributable to the additional roundwood demand source. Figure L-1 compares this 
November 2014 L-14 

case study’s cumulative average user trend BAFs with the average regional BAFs for the AEO 

Reference case baseline scenario presented in Appendix K (when comparing the AEO Reference 

case baseline to the Zero Biomass Baseline scenario). 

Figure L-1. Comparison of Average Cumulative Landscape Factors in the Southeast Region for the 

AEO Reference Case Baseline and Roundwood Case Study Relative to the Zero Biomass Scenario. 

5.2. Pacific Northwest (PNW) Logging Residues 

The PNW logging residues case study simulates a demand for additional feedstock that is met 

entirely from soft and hardwood logging residues. This case study helps illustrate the potential 

biogenic CO2 effects of increased demand for logging residues as a bioenergy feedstock. It is 

important to note that the FASOM-GHG model divides the PNW into western and eastern regions, 

reflecting differences in ecological, environmental, and production processes on either side of the 

Cascade Range in Oregon and Washington. This analysis only includes the western portion of the 

PNW where cool, relatively dry summers and mild, wet winters yield highly productive Douglas-fir, 

hemlock, and spruce forests. A full evaluation of the PNW region would require including the 

eastern portions of Oregon and Washington, which are primarily agricultural regions with limited 

market interaction with the area included in this assessment.

5.2.1. Marginal Effects for the PNW Logging Residue Case Study 

Table L-5 displays landscape factors for the PNW logging residue case study (marginal user case). 

Unlike the roundwood case studies previously examined, projected marginal BAFs are positive at 

the beginning of the analysis, quickly become negative through the near to medium term, and then 

become positive toward the end of the simulation horizon. This trend holds for both the periodic 

 (0.20)

 (0.10)

 -

 0.10

 0.20

 0.30

 0.40

 0.50

2015 2025 2035 2045 2055

Cumulative BAF

SE Roundwood to Zero Biomass AEO Reference to Zero Biomass
November 2014 L-15 

and cumulative marginal user calculations. Early in the simulation horizon, the model projects that 

the additional biomass requirement leads to increased forest harvest emissions. An increase in 

logging residue demand leads to a net increase in roundwood harvests for other products in order 

to meet the additional residue demand. Then, afforestation and forest management responses to 

the feedstock requirement lead to an increase in biogenic carbon sequestration (hence, large 

negative values for GROW), resulting in negative landscape factors from 2020–2040. Over the long 

term, however, this effect flips as harvest emissions outweigh growth in landscape-level biogenic 

carbon sequestration. 

The large emissions increase in GROW leads to high positive values for the periodic landscape 

factors (greater than 1) and flips the sign for the cumulative landscape factor by 2050. The average 

periodic landscape factor from 2015–2055 is 0.25, and the cumulative landscape factor in 2055 is 

slightly higher at 0.3. Thus, relative to the AEO Reference baseline, isolated expansion in logging 

residue consumption in the PNW would lead to a slight increase in biogenic CO2 emissions. 

Table L-5. PNW Logging Residue Landscape Factor Results (Marginal User). 

Case 

Study Term 

Emissions 

Projection 

Method 

Time Period 

2015–2030 2015–2045 2015–2060 

PNW Logging Residue 

Marginal User 

------ additional emissions (t CO2) from AEO Reference case baseline level ------

GROW 

Per Period 

-400 -141 261 

SITETNC -36 96 126 

AVOIDEMIT 1.0 0.3 -0.2 

PGE 917 1,375 1,528 

Estimated 

Landscape Factor -0.47 0.03 0.25 

Cumulative additional emissions (t CO2) from AEO Reference case baseline level 

GROW 

Cumulative 

-3,531 -1,748 14,197 

SITETNC 214 3,630 6,408 

AVOIDEMIT 15 8.5 -7.5 

PGE 13,750 41,250 68,750 

Estimated 

Landscape Factor -0.24 0.05 0.30 

5.2.2. Average Effects for the PNW Logging Residue Case Study 

For the PNW Logging Residue case, average user landscape factors, or the combined effects of 

current consumption, planned expansion, and the additional feedstock consumption source, trend 

toward 0 over time (Table L-6). Net emissions decrease rapidly initially due to additional tree 

planting and changes in forest management in response to the anticipated feedstock demand. 

Figure L-2 provides a comparison of cumulative landscape factor values for the AEO Reference and 

PNW logging residue scenarios, respectively, relative to the Zero Biomass case. The additional 

logging residue feedstock demand leads to a slight reduction in emissions intensity over the 

medium term due to anticipatory land management, but a slight increase in emissions over the long 
November 2014 L-16 

term due to sustained harvest emissions that increase with the demand for logging residues. The 

cumulative landscape factor is close to 0 (0.04) but positive in the 2050–2060 assessment period. 

Table L-6. PNW Logging Residue Landscape Factor Results (Average User). 

Case 

Study Term 

Emissions 

Projection 

Method 

Time Period 

2015–2030 2015–2045 2015–2060 

PNW Logging Res 

Average User 

------ additional emissions (t CO2) from Zero Biomass baseline level ------ 

GROW 

Per Period 

-80 -784 -195 

SITETNC -29 -33 39 

AVOIDEMIT 0.7 0.0 -0.2 

PGE 2,584 3,119 3,301 

Estimated 

Landscape Factor -0.04 -0.26 -0.05 

Cumulative additional emissions (t CO2) from Zero Biomass baseline level 

GROW 

Cumulative 

9,923 -12,398 2,355 

SITETNC 2,158 1,591 4,358 

AVOIDEMIT 9 -0.4 -8.5 

PGE 45,098 99,893 154,896 

Estimated 

Landscape Factor 0.27 -0.11 0.04 

Figure L-2. Comparison of Average Cumulative Landscape Factors in the PNW Region for the AEO 

Reference Case and PNW Logging Residue Case Study Relative to the Zero Biomass Scenario. 
November 2014 L-17 

5.3. Corn Belt Corn Stover 

The Corn Belt corn stover case study applies the same additional 1 million ton biomass shock over 

time to the Corn Belt region and requires this additional biomass demand to be met exclusively 

with corn residues from this region. However, there are some accounting differences between the 

corn stover case studies and the previous two roundwood scenarios. The GROW term defaults to 0 

for agricultural biomass sources in this methodology. The assumption is that, with annual crops, 

biogenic CO2 “growth” in this context equals what is harvested (removed) from the system for 

energy generation. However, because this effort seeks to also capture changes in landscape-level 

emissions, forest tree carbon and harvest emissions changes engendered by the increase in corn 

stover removal are included in the SITETNC term. 

One important point regarding the Corn Belt case study is that for each alternative future baseline, 

a significant amount of corn residue is projected to be harvested in the Corn Belt for producing 

cellulosic ethanol to meet the RFS2 advanced biofuel mandates (approximately 6.2 billion gallons). 

Thus, the additional biogenic feedstock constraint will pull from corn stover resources above and 

beyond what is used to produce cellulosic ethanol. 

5.3.1. Marginal Effects for the Corn Belt Corn Stover Case Study 

Table L-7 displays marginal BAF results for the Corn Belt corn stover case study. Unlike the 

roundwood scenarios, emissions fluxes are relatively stable over time. For the majority of the 

simulation horizon, periodic landscape factors are positive (but less than 1), which is driven by 

increased emissions from SITETNC carbon pools on the landscape. After 2015, the proportion of 

conventional tillage to no-till and conservation till stays relatively constant (thus, there are only 

minor biogenic soil carbon effects from increased residue harvesting). The majority of SITETNC

emissions are due to forest harvest fluxes and small levels of deforestation for crop production in 

response to the additional feedstock demand. 

Table L-7. Corn Belt Corn Stover Landscape Factor Results (Marginal User). 

Case 

Study Term 

Emissions 

Projection 

Method 

Time Period 

2015–2030 2015–2045 2015–2060 

CB Corn Stover 

Marginal User 

------ additional emissions (t CO2) from AEO Reference case baseline level ------ 

GROW 

Per Period 

0 0 0 

SITETNC 183 265 123 

AVOIDEMIT 0.0 0.1 0.0 

PGE 917 1,375 1,528 

Estimated 

Landscape Factor 0.20 0.19 0.08 

Cumulative additional emissions per ton of additional feedstock usage (t CO2) from AEO Reference 

case baseline level 

GROW 

Cumulat

ive 

0 0 0 

SITETNC 2,645 7,838 5,435 

AVOIDEMIT 0 1.7 2.0 
November 2014 L-18 

PGE 13,750 41,250 68,750 

Estimated 

Landscape Factor 0.19 0.19 0.08 

Cumulative BAF values are also relatively stable over time, ending up at 0.08. Thus, 40 years after 

the initial corn stover demand shock, only a small portion of biogenic CO2 emissions from additional 

corn stover removals are not balanced by landscape biogenic CO2 sequestration from land 

management changes. 

5.3.2. Average Effects for the Corn Belt Corn Stover Case Study 

Although the marginal effects in this case study are relatively stable, average effects fluctuate 

considerably over time in the Corn Belt region (Table L-8). The overall trend is similar to the 

alternative baseline Corn Belt regional results presented in Appendix K (AEO Reference relative to 

Zero Biomass) in that biogenic emissions are highly negative (high level of sequestration) in 2015 

and then increase over time (see Figure L-3). However, the additional corn stover requirement 

increases net biogenic CO2 emissions (hence the positive periodic flux values in the marginal case), 

which essentially shifts the BAF trajectory up for the majority of the simulation horizon. Note that 

the two BAF trajectories below converge over the long term, indicating a rise in land use change 

emissions in the AEO Reference baseline in the long term. 

In general, these results show that although biogenic CO2 emissions from corn stover biomass 

removals in the Corn Belt might be predominately offset by landscape-level CO2 accumulation, 

additional expansion of corn stover demand could increase the value of agricultural land relative to 

other uses, which could drive land use change and increase net emissions (especially if the land is 

converted to agricultural use from forestry). However, even with the resulting emissions effects, 

biogenic CO2 emissions from corn stover consumption in this scenario are almost fully offset by 

landscape-level CO2 changes in this case study scenario. 

Table L-8. Corn Belt Corn Stover Landscape Factor Results (Average User). 

Case 

Study Term 

Emissions 

Projection 

Method 

Time Period 

2015–2030 2015–2045 2015–2060 

CB Corn Stover 

Average User 

------ additional emissions (t CO2) from Zero Biomass baseline levels ------ 

GROW 

Per Period 

0 0 0 

SITETNC -3,047 -2,064 433 

AVOIDEMIT -2.4 -2.1 -0.9 

PGE 1,611 2,149 2,337 

Estimated 

Landscape Factor -1.89 -0.96 0.81 
November 2014 L-19 

Case 

Study Term 

Emissions 

Projection 

Method 

Time Period 

2015–2030 2015–2045 2015–2060 

Cumulative additional emissions (t CO2) from Zero Biomass baseline level 

GROW 

Cumulative 

0 0 0 

SITETNC 48,791 -65,015 16,425 

AVOIDEMIT -37 -64.2 -42.4 

PGE 26,771 67,069 107,770 

Estimated 

Landscape Factor -1.82 -0.97 0.15 

Figure L-3. Comparison of Average Cumulative Landscape Factors in the Corn Belt Region for the 

AEO Reference Case Baseline and Corn Stover Case Study Relative to the Zero Biomass Scenario. 

6. Summary 

The illustrative BAF values presented in this appendix do not reflect any specific policies or 

programs; rather they are estimated outcomes based on the baseline and scenario constructs, as 

well as the assumptions and parameters in the modeling system. The goal of this exercise is to 

illustrate the functionality of the future anticipated baseline approach and to provide insights into 

potential effects of biogenic feedstock production and consumption, the possible directionality of 

results, investor/market behavior, and magnitude of additionality (per the given specific 

assumptions and modeling system). There are different temporal and spatial scales that could be 

used, and choices pertaining to these factors can impact results. 

 (8.00)

 (7.00)

 (6.00)

 (5.00)

 (4.00)

 (3.00)

 (2.00)

 (1.00)

 -

 1.00

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055

Cumulative BAF

AEO Reference to Zero Biomass CB Corn Stover to Zero Biomass
November 2014 L-20 

Ultimately, the illustrative case studies and estimated values in this appendix are meant to 

demonstrate the flexibility of the framework as well as the importance of decisions made in terms 

of how results are to be calculated. Therefore, decisions about time, space, data aggregation, etc., all 

should be specific to the policy or program to which the framework is applied. 

Appendix M provides an overview and discussion of illustrative case study results as well as 

sensitivities derived from both the retrospective reference point and future anticipated baseline 

applications. In that appendix, the future anticipated results reflect the comparison of the 1 million

ton increased feedstock scenario to the Zero Biomass scenario to provide an estimate of the average 

biogenic CO2 emissions effect for all existing and planned biogenic feedstock consumption at 

national and regional scales and also applies the cumulative calculation method. 

7. References 

Milesi, C., Elvidge, C. D., Nemani, R. R., & Running, S. W. (2003). Assessing the impact of urban land

development on net primary productivity in the southeastern United States. Remote Sensing of 

Environment 86(3):401-410. 

Wear, D. N. and Greis, J.G. (2002). The Southern Forest Resource Assessment. 

http://www.srs.fs.usda.gov/sustain/report/pdf/sfra_summary_e.pdf

8. Supplemental Data and Information 

8.1. Details on FASOM-GHG Carbon Accounting 

FASOM-GHG incorporates detailed accounting for GHGs emitted from and sequestered by forestry 

and agricultural activities and land use change in the United States, including the dynamics of 

carbon sequestration in forests, soils, and wood products. In addition, the model tracks GHG 

emission reductions in other sectors caused by mitigation actions in the forest and agricultural 

sectors. In addition to CO2, FASOM-GHG’s accounting also includes CH4, and N2O. In this section, we 

provide additional information on the CO2 accounting functions and parameters used within the

model. 

To compare landscape-level emissions across baselines, the following CO2 flux categories are 

aggregated to yield a total net emissions flux. This aggregation is calculated for every time step in 

the simulation horizon (5-year time steps). Then, annual averages are evaluated for different 

portions in the simulated horizon to highlight the importance of temporal dynamics. 

8.1.1. FASOM-GHG Biogenic Feedstock Growth Functions 

For FASOM-GHG output, the GROW term focuses primarily on forest growth in the context of longer 

rotation woody biomass (i.e., roundwood). Short rotation woody crops such as hybrid poplar and 

willow do occur over time frames longer than a year and would typically be produced in plantations 

(which would achieve a steady state of CO2 flux; thus, growth would be in balance with removals). 

The agricultural feedstocks complete an entire growth/harvest/combustion cycle entirely within a 
November 2014 L-21 

year (and thus any CO2 sequestration in the feedstock would also be balanced by its removal and 

use). In FASOM-GHG the net forest carbon growth from period to period equated to GROW in the 

BAF equation would be best represented with the change in tree carbon over that same time period 

less any removals. This could be evaluated both regionally and nationally. Note that this net change 

in tree carbon would include both growth of trees that did not get harvested as well as a loss 

associated with the trees that did get harvested (removals). 

FASOM-GHG tree carbon calculation is based on two primary sources: timber yields and a set of 

factors that convert those yields to carbon. With the exception of the Pacific Northwest-West 

(PNWW) region, the timber yields come from the ATLAS model (Mills and Adams, 2007) as used in 

the national 2005 RPA Assessment Update (Haynes et al., 2007). In the PNWW the yields are based 

on FIA plots “grown” using the Forest Vegetation Simulator (FVS) and then averaged over strata. 

The yields include options for partial harvesting regimes of one or more thinning entries only in the 

PNWW, Southeast (SE), and South central (SC) regions. The conversion of yields for all management 

regimes including those that involve partial harvests to carbon are based on Smith et al. (2006). 

The growing stock volumes (VA) from the FASOM-GHG yield tables are in thousands of cubic feet 

per acre and therefore must be converted to cubic meters per hectare (VH) for use in the carbon 

equations. The volumes per acre are converted using the following equation:4

#$ =

%&&&∙#(

%).+

(EQ. L.7) 

To convert these volumes to carbon for the regions and forest types in Smith et al. (2007) were 

mapped to FASOM-GHG regions, and forest types and weighted averages of the parameters were 

calculated based on acreages from FIA. In addition to the basic Smith et al. (2007) equations (the 

1605b tables), the FASOM-GHG parameters include tree carbon and young stand adjustment from 

an update by Jim Smith in 2007. The L1, L2, and L3 parameters5 displayed in Table L-9 are for the 

live tree mass equation, the D2 and D3 parameters are for the dead tree mass equation, and the C1

parameter is used to “ramp up” the mass in young stands (because they may have no growing stock 

volume). 

Table L-9. FASOM-GHG Live and Dead Tree Biomass Equation Parameters. 

Region and Forest Type 

Carbon Equation Parameters 

L1 L2 L3 D2 D3 C1 

CB 

 SOFT 14.434 2.937 0.804 1.754 0.397 8.74 

 HARD 29.651 2.493 0.861 2.996 0.266 9.89 

LS 

 SOFT 14.434 2.937 0.804 1.754 0.397 8.74 

 HARD 29.651 2.493 0.861 2.996 0.266 9.89 

 

4

 Note that this equation is different from the 2008 FASOM-GHG documentation Section 13.2.1.1 where 

adjustments are made to the growing stock volumes up to total volumes as the model has been updated. 

5

 Note that these are the values we use after the weighted average from FIA process. They therefore do not match the 

1605b values exactly. 
November 2014 L-22 

Region and Forest Type Carbon Equation Parameters 

L1 L2 L3 D2 D3 C1 

NE 

 SOFT 35.372 2.062 0.85 4.056 0.233 9.941 

 HARD 31.51 2.598 0.843 3.108 0.266 8.79 

RM 

 SOFT 11.082 2.836 0.776 2.543 0.402 9.749 

 HARD 11.082 2.836 0.776 2.543 0.402 9.749 

PSW 

 SOFT 33.524 2.022 0.852 3.099 0.12 35.277 

 HARD 20.852 2.632 0.836 3.211 0.343 9.889 

PNWW 

 DOUG_FIR 31.823 1.102 0.949 5.691 0.336 6.1 

 OTH_SWDS 17.599 1.822 0.881 1.847 0.554 7.081 

 HARD 20.852 2.632 0.836 3.211 0.343 9.889 

PNWE 

 SOFT 33.524 2.022 0.852 3.099 0.12 35.277 

 HARD 20.852 2.632 0.836 3.211 0.343 9.889 
November 2014 L-23 

Region and Forest Type Carbon Equation Parameters 

L1 L2 L3 D2 D3 C1 

SC 

 NAT_PINE 37.244 1.553 0.846 1.203 0.271 5.743 

 OAK_PINE 30.637 2.734 0.798 1.133 0.337 5.986 

 PLNT_PINE 30.652 1.899 0.815 1 0.138 4.107 

 SOFT 37.244 1.553 0.846 1.203 0.271 5.743 

 BOT_HARD 25.128 4.691 0.741 4.056 0.137 7.986 

 HARD 25.128 4.691 0.741 4.056 0.137 7.986 

 UP_HARD 46.794 1.964 0.876 2.396 0.186 9.381 

SE 

 NAT_PINE 34.818 1.242 0.892 1 0.324 4.91 

 OAK_PINE 21.645 2.626 0.811 1 0.351 4.351 

 PLNT_PINE 34.148 1.157 0.908 1 0.265 4.8 

 SOFT 34.818 1.242 0.892 1 0.324 4.91 

 BOT_HARD 22.811 3.978 0.756 1.747 0.337 5.498 

 HARD 22.811 3.978 0.756 1.747 0.337 5.498 

 UP_HARD 28.976 3.213 0.803 2.256 0.257 6.108 

Tree carbon is calculated as the sum of live mass (Clive): 

,-./0 = 1% − 02

3450

,% 6

7 8% + 8 ∙ #$

89 (EQ. L.8) 

And dead mass (Cdead): 

,:04: = 1% − 02

3450

,% 6

7 ; ∙ #$

;9 (EQ. L.9) 

And converted to tree carbon per acre (Ctree) based on half of the mass being the carbon content: 

,<=00 =

>,-./0?,:04:@

&.A∙.)B% (EQ. L.10) 

8.1.2. FASOM-GHG Functions Relating to Changes in Site Emissions 

The SITETNC term represents the feedstock production site-level difference in the net CO2 flux to 

the atmosphere when biogenic feedstocks are used for bioenergy compared with a previous 

use/activity considering both emissions and sequestration changes (e.g., in the case of land use 

change or residue removal). In FASOM-GHG it may be difficult to differentiate between forest 

organic soil changes and forest litter and understory changes resulting from harvest residual 

removal. FASOM-GHG has stable soil carbon estimates for each of the major land use classifications 

(cropland, pasture, afforestation, and forest). Upon land use change there is a linear transition 

between the prior soil carbon level and that of the new use. The change in these soil carbon 

accounts resulting from additional biomass utilization can be evaluated by simply taking the 

difference between scenarios. The litter and understory carbon is based on a forest floor equation 

along with estimates of understory and coarse woody debris. Unlike tree carbon, these values 
November 2014 L-24 

adapted from Smith et al. (2007) are based solely on forest age, region, and forest type. The 

parameters for the equation are provided by Table L-10.

Table L-10. FASOM-GHG Forest Floor Biomass Equation Parameters. 

Region and Forest Type Forest Floor Carbon Parameters 

A B C D und cwd 

CB 

 SOFT 42 57.6 23.9 13.9 2.1 13.8 

 HARD 44.7 59.5 28.9 13.2 2.4 10.8 

LS 

 SOFT 42 57.6 23.9 13.9 2.1 13.8 

 HARD 44.7 59.5 28.9 13.2 2.4 10.8 

NE 

 SOFT 42 57.6 23.9 13.9 2.6 12.2 

 HARD 44.7 59.5 28.9 13.2 2.2 11.2 

RM 

 SOFT 42 57.6 23.9 13.9 5.7 12.6 

 HARD 44.7 59.5 28.9 13.2 9.2 26.7 

PSW 

 SOFT 42 57.6 23.9 13.9 4.9 12.8 

 HARD 44.7 59.5 28.9 13.2 2.8 11.5 

PNWW 

 DOUG_FIR 87.5 116.7 27.5 16 2 11.9 

 OTH_SWDS 87.5 116.7 27.5 16 3.2 15.4 

 HARD 44.7 59.5 28.9 13.2 4.5 3.9 

PNWE 

 SOFT 87.5 116.7 27.5 16 3 14.8 

 HARD 44.7 59.5 28.9 13.2 4.5 3.9 

SC 

 NAT_PINE 20.4 27.1 12.2 3.8 5.9 18.6 

 OAK_PINE 20.4 27.1 12.2 3.8 4.4 17.3 

 PLNT_PINE 20.4 27.1 12.2 3.8 5.9 18.6 

 SOFT 20.4 27.1 12.2 3.8 5.9 18.6 

 BOT_HARD 15.4 40.9 8.2 3.5 2.2 15.7 

 HARD 15.4 40.9 8.2 3.5 2.2 15.7 

 UP_HARD 15.4 40.9 8.2 3.5 3.7 15 

SE 

 NAT_PINE 20.4 27.1 12.2 3.8 6.8 23.9 

 OAK_PINE 20.4 27.1 12.2 3.8 4.4 17.3 

 PLNT_PINE 20.4 27.1 12.2 3.8 6.8 23.9 

 SOFT 20.4 27.1 12.2 3.8 6.8 23.9 

 BOT_HARD 15.4 40.9 8.2 3.5 2.2 21.8 

 HARD 15.4 40.9 8.2 3.5 2.2 21.8 

 UP_HARD 15.4 40.9 8.2 3.5 4.4 24.3 
November 2014 L-25 

The litter, understory, and coarse woody debris (U) is then calculated as: 

C =

2

(∙450

DE4506?,∙032450

;

6

.)B%

+

>CF:?,G:@∙8∙&.A

%&&∙.)B%

(EQ. L.11) 

If it is the first rotation (afforestation), the C and D terms are dropped, giving: 

C =

2

(∙450

DE4506

.)B%

+

>CF:?,G:@∙8∙&.A

%&&∙.)B%

(EQ. L.12) 

In addition to the litter, understory, and coarse woody debris carbon pools, FASOM-GHG tracks soil 

carbon. The approach used is adapted from earlier work by Birdsey (1996a), which had fixed 

forestland carbon values in all regions except the South6 that varied by region, while Smith et al. 

(2006) have all carbon in forest soils assumed to be constant over time but varied by region and 

forest type. Birdsey (1996a) also has soil carbon estimates for land that has been converted from 

both crop and pasture to forest that rises from an initial value that differs for crop or pasture land

to a steady state clos but not equal to, the forestland steady-state soil carbon values. To keep soil 

carbon values consistent across land use types (crop, pasture, and forest), FASOM-GHG does not use 

any of the Birdsey (1996a) or Smith et al. (2006) values but rather a loosely based approximation of 

their values and trends. 

To begin, the FASOM-GHG uses century-based crop and pasture soil values that are constant in each 

region. Table L-11 provides those values. 

Table L-11. FASOM-GHG Agricultural Soil Carbon Constants by Land Use and Region. 

Region 

FASOM-GHG Agricultural Land Use 

Cropland Cropland_Pasture Rangeland Forest_Pasture Pasture 

CB 15.373 18.751 18.751 18.751 18.751 

GP 6.872 11.295 11.295 11.295 11.295 

LS 9.946 13.619 13.619 13.619 13.619 

NE 7.242 11.649 11.649 11.649 11.649 

RM 5.463 7.955 7.955 7.955 7.955 

PSW 10.554 15.862 15.862 15.862 15.862 

PNWW 14.832 23.029 

PNWE 6.665 9.216 9.216 9.216 9.216 

SC 15.415 20.12 20.12 20.12 20.12 

SE 2.791 4.828 4.828 4.828 4.828 

SW 6.471 11.882 11.882 11.882 11.882 

 

6

 Birdsey (1996a) had minor variation (<10%) in soil carbon for southern forest over the life of a stand. 
November 2014 L-26 

A regression analysis of the Appendix 3 afforestation soil carbon values (Birdsey, 1996b) as a 

quadratic function of forest stand age after pasture reversion was estimated, yielding the following 

functional form: 

,HI.- = .F< + < ∙ 450 + < ∙ 450(EQ. L.13) 

The parameter estimates are provided in Table L-12 (values in thousand pounds of carbon per 

acres, not tons). 

Table L-12. FASOM-GHG Forest Soil Equation Parameters. 

Region 

Soil Carbon Parameters 

int t t2 

SE 44.964 0.626 −0.00337 

SC 44.017 0.61 −0.00322 

NE 93.884 1.159 −0.00461 

LS 75.803 0.938 −0.00383 

CB 48.509 0.586 −0.0023 

GP 46.655 0.6 −0.00266 

RM 46.655 0.6 −0.00266 

PNWW 56.686 0.696 −0.00287 

PNWE 56.686 0.696 −0.00287 

PSW 56.686 0.696 −0.00287 

SW 56.686 0.696 −0.00287 

The forest soil constants are determined as the maximum soil carbon value achieved when the 

FASOM-GHG minimum harvest ages for each region, owner, forest type, site class, and management 

intensity are used in the Csoil equation. The values obtained and used for the regional forest soil 

constants are provided in Table L-13. 

Table L-13. FASOM-GHG Forest Soil Carbon Constant by Region in Metric Tons of Carbon per 

Acre. 

Region Forest 

CB 20.561 

LS 30.853 

NE 40.044 

RM 19.931 

PSW 24.912 

PNWW 22.994 

PNWE 24.031 

SC 14.303 

SE 14.617 
November 2014 L-27 

Upon conversion from an agricultural use to forest (afforestation), there is a period of soil 

adjustment from the prior land use fixed soil amount to the new land use fixed soil amount. The 

adjustment is based on the parameters from Table L-14 using time since conversion as the age. This 

yields the following conversion values. 

Table L-14. FASOM-GHG Soil Carbon Conversion Rates by Years Since Conversion and Region. 

Years Since 

Land 

Conversion 

Region 

NE CB SC SE LS PSW PNWW PNWE RM 

0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

5 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 

10 0.05 0.05 0.04 0.04 0.05 0.05 0.05 0.05 0.05 

15 0.09 0.09 0.09 0.09 0.09 0.08 0.09 0.09 0.09 

20 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 

25 0.22 0.22 0.23 0.23 0.22 0.22 0.22 0.22 0.22 

30 0.30 0.31 0.32 0.32 0.31 0.30 0.30 0.30 0.30 

35 0.39 0.39 0.40 0.40 0.39 0.39 0.39 0.39 0.39 

40 0.47 0.47 0.49 0.49 0.47 0.47 0.47 0.47 0.47 

45 0.56 0.56 0.57 0.58 0.56 0.55 0.56 0.56 0.56 

50 0.63 0.63 0.66 0.66 0.63 0.63 0.63 0.63 0.63 

55 0.70 0.70 0.73 0.73 0.70 0.70 0.70 0.70 0.70 

60 0.76 0.76 0.79 0.79 0.76 0.76 0.76 0.76 0.76 

65 0.82 0.82 0.85 0.85 0.82 0.81 0.82 0.82 0.82 

70 0.86 0.86 0.89 0.89 0.86 0.85 0.86 0.86 0.86 

75 0.90 0.90 0.93 0.93 0.90 0.90 0.90 0.90 0.90 

80 0.92 0.92 0.96 0.96 0.92 0.92 0.92 0.92 0.92 

85 0.95 0.95 0.98 0.98 0.95 0.94 0.95 0.95 0.95 

90 0.96 0.96 1.00 1.00 0.96 0.96 0.96 0.96 0.96 

95 0.97 0.97 1.00 1.00 0.97 0.97 0.98 0.97 0.97 

100 0.98 0.98 1.00 1.00 0.98 0.98 0.98 0.98 0.98 

Using the conversion rates from Table L-14 and the fixed soil carbon amounts from Tables L-12 and 

L-13 the carbon flux (∆Csoil) associated with a land movement from pasture (Cpastsoil) to forest 

(Cforsoil) using the soil carbon conversion rate, St, in year t would be calculated using the following 

equation: 

∆KLM = >N% − 

@ 2KLM

OLP − KLM

QRK6 (EQ. L.14) 

8.1.3. FASOM-GHG Functions Relating to Changes in Avoided Emissions 

In addition to the litter, understory, and coarse woody debris discussed above, FASOM-GHG also 

accounts for unused fuelwood and logging residues. These are assumed to be different from the 

coarse wood debris in that unused fuelwood and logging residues can be either used or left to 

decompose onsite based on region and forest type. Specific decomposition rates from Turner et al. 
November 2014 L-28 

(1993) and Turner et al. (1995) are applied. Table L-15 gives the FASOM-GHG coarse woody debris 

decomposition rates. 

Table L-15. FASOM-GHG Annual Coarse Woody Debris Decomposition Rates. 

Forest 

Type 

FASOM-GHG Region 

CB LS NE RM PSW PNWW PNWE SC SE 

Softwood 0.048 0.048 0.053 0.02 0.023 0.027 0.027 0.057 0.057 

Hardwood 0.084 0.084 0.069 0.082 0.082 0.082 0.082 0.082 0.082 

9. References 

Birdsey, R.A. 1996a. “Carbon Storage for Major Forest Types and Regions in the Conterminous 

United States.” In Forests and Global Change. Volume II: Forest management opportunities for 

mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson (eds). Washington, DC: 

American Forests: 1-25. 

Birdsey, R.A. 1996b. “Regional estimates of timber volume and forest carbon for fully stocked 

timberland, average management after cropland or pasture reversion to forest.” In Forests and 

Global Change. Volume II: Forest management opportunities for mitigating carbon emissions, 

Chapter 1, D. Hair and Neil R. Sampson (eds). Washington, DC: American Forests: 309-333. 

Haynes, R., Adams, D., Alig,R., Ince, P., Mills, J., and Zhou, X. 2007. The 2005 RPA timber assessment

update. Gen. Tech. Rep. PNW-GTR-699. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 212 p.

Mills, J., and D. Adams. “Timber Inventory and Management—ATLAS.” In Resource and Market 

Projections for Forest Policy Development, edited by D. Adams and R. Haynes, 14:229–63. 

Managing Forest Ecosystems. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-

4020-6309-1_6. 

Smith, J.E., Heath, L.S., and Nichols, M.C.. 2007. U.S. forest carbon calculation tool: Forestland carbon 

stocks and net annual stock change. Gen. Tech. Rep. Gen. Tech. Rep. NRS-13. USDA Forest 

Service, Northern Research Station, Newtown Square, PA. [1 CD]. 

Smith, J.E.; Heath, L.S., Skog, K., Birdsey, R.A., 2006. Methods for calculating forest ecosystem and 

harvested carbon with standard estimates for forest types of the United States. Gen. Tech. Rep. 

NE-343. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern 

Research Station. 216 p. 

Turner, D.P., Koerper, G.J., Harmon, M.E. and Lee, J.J. 1995. A forest carbon budget for the United 

States: Current flux and projections to the year 2040. Tellus 47B:232-239.

Turner, D.P., Lee, J.L., Koerper, G.J., and Barker, J.R., 1993. The forest sector carbon budget of the

United States: carbon pools and flux under alternative policy options. EPA/600/3-93/093. 

Corvallis, OR: U.S. Environmental Protection Agency, Environmental Research Laboratory.