November 2014 L-1



# Appendix L: Illustrative Forestry and Agriculture C ase Studies Using a Future Anticipated Baseline

Table of Contents
1. Introduction ......................................

................................................... ................................................... ....................... L-2
2. Method and Parameters Used to Calculate the Illustr ative BAF s 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 Baselin e 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 S tudy ...................................... L-11 5.1.2. Average Effects for the Southeast Roundwood Case St udy ........................................ L-13 5.2. Pacific Northwest (PNW) Logging Residues...........

................................................... ..................... L-14 5.2.1. Marginal Effects for the PNW Logging Residue Case S tudy ....................................... L-14 5.2.2. Average Effects for the PNW Logging Residue Case St udy ......................................... L-15 5.3. Corn Belt Corn Stover..............................

................................................... .............................................. L-17 5.3.1. Marginal Effects for the Corn Belt Corn Stover Case Study ........................................ L-1 7 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-20November 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 Emi ssions................................. 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 w ith BAF Equation Terms. ........................... L-6 Table L-2. Illustrative BAF Values for the Future Anticipated Baseline Case St udies: Cumulative Average Results from 2015–2060. ...................

................................................... ....................................... L-11 Table L-3. Southeast Roundwood Landscape Factor Res ults (Marginal User). ............................. .......... L-12 Table L-4. Southeast Roundwood Landscape Factor Res ults (Average User). .............................. .......... L-13 Table L-5. PNW Logging Residue Landscape Factor Res ults (Marginal User). ............................. ........... L-15 Table L-6. PNW Logging Residue Landscape Factor Res ults (Average User). .............................. ........... L-16 Table L-7. Corn Belt Corn Stover Landscape Factor R esults (Marginal User)............................. ............. L-17 Table L-8. Corn Belt Corn Stover Landscape Factor R esults (Average User). ............................ .............. L-18 Table L-9. FASOM-GHG Live and Dead Tree Biomass Equ ation Parameters. ................................. .......... L-21 Table L-10. FASOM-GHG Forest Floor Biomass Equation Parameters. ...................................... ................ L-24 Table L-11. FASOM-GHG Agricultural Soil Carbon Cons tants by Land Use and Region. ..................... . L-25 Table L-12. FASOM-GHG Forest Soil Equation Paramete rs. ............................................... ............................. L-26 Table L-13. FASOM-GHG Forest Soil Carbon Constant b y 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 De composition Rates. ................................ ...... L-28 

## List of Figures Figure L-1. Comparison of Average Cumulative Landsc ape Factors in the Southeast Region for the AEO Reference Case Baseline and Roundwood Case Stu dy Relative to the Zero Biomass Scenario. .........................................

................................................... ................................................... ................. L-14 Figure L-2. Comparison of Average Cumulative Landsc ape Factors in the PNW Region for the AEO Reference Case and PNW Logging Residue Case Study R elative to the Zero Biomass Scenario. .........................................

................................................... ................................................... ................. L-16 Figure L-3. Comparison of Average Cumulative Landsc ape Factors in the Corn Belt Region for the AEO Reference Case Baseline and Corn Stover Case St udy 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 c ase studies. These case studies use the baseline scenarios constructed in Appendix K as the basis fo r comparison of alternative biogenic feedstock production scenarios per specific feedstocks and sp ecific regions. The application of the future anticipated baseline approach within these case stu dy constructs allows for the calculation of illustrative values for the pertinent framework equ ation terms and ultimately generation ofNovember 2014 L-3

illustrative biogenic assessment factors ( BAF ) specific to the individual case study parameters and assumptions. Results show aggregate emissions estimates on a spe cific 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 m ultiple baseline projections of biogenic feedstock consumption. However, results do not reflect the ne t emissions contribution of a particular feedstock within a particular region but rather ill ustrate 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 refere nce point approach, region- and feedstockspecific simulation scenarios were developed to iso late 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 met hods used to construct the case study parameters, an explanation of the how landscape-lev el biogenic emissions fluxes are mapped to BAF equation terms, a discussion of how to interpret re sults (using different assessment methods), and a presentation of illustrative case study resul ts 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 con text of specific policies or programs. As with all modeling studies, a number of uncertainties are pre sent in the baseline assumptions and parameters adopted for this analysis. These uncerta inties include historical input data, future environmental conditions and the biophysical emissi ons accounting parameters, future economic or policy conditions, and technological growth (bot h for agricultural/forestry feedstock yield and commodity processing technologies). However, model projections provide key insight into the potential market and land use consequences of possi ble shifts in the demand for biogenic feedstocks at stationary sources. 

## 2. Method and Parameters Used to Calculate the Illustr ative BAF s Using the Future Anticipated Baseline Approach
The intertemporal optimization approach used in the se illustrative case studies captures investment behavior under anticipated changes in fe edstock 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 pro vides an improved projection of landscapelevel biogenic CO 2 emissions under anticipated changes in biogenic fe edstock consumption than static (one-time) models or recursive dynamic model s 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 fee dstock scenarios relative to a future anticipated baseline. The primary goal of this appendix is to i llustrate how the future anticipated baseline approach could be applied in practice to assess lan dscape-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 CO 2 emissions from increased regional feedstock consumption, using the modeling assumptions and bas eline constructs as presented in Appendix K.


### 2.1. Case Study Methods Each feedstock case study was developed using the s ame underlying methodology. Each feedstock scenario is evaluated relative to the three alterna tive 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 intens ity of additional biogenic feedstock consumption (additional biogenic CO 2 emissions divided by additional biogenic feedstock CO 2 ) is computed, similar to the approach outlined in Appendix K and described in more detail below. Each of the case studies begins with regional bioma ss 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 considera tion. This additional biomass requirement is phased in linearly, beginning with 250,000 short dr y 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 facili ty or demand point to build up a steady supply source of one particular feedstock given regional m arket dynamics. The additional biomass requirement is then held constant for the remainder of the simulation horizon 1 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 sof t roundwood. This constraint is maintained throughout the simulation horizon to isolate the em issions 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 R eference anticipated baseline. This increased consumption could be thought of as the estimated ma rginal effect of the additional demand from a stationary source that is expected to consume appro ximately 1 million tons of biogenic CO 2 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 CO 2 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 anti cipated new users) plus the additional

1 The 2012 Annual Energy Outlook projections do not e xtend 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 volu mes, same feedstocks), with the anticipated baseline feedstock portfolio exactly matching the A EO Reference baseline simulation results over time. The difference here is that biogenic CO 2 emissions intensity metrics are computed relative to the Zero Biomass simulation results. Thus, the nume rator represents the net change in regional projected emissions (Feedstock Case Study − Zero Bi omass), while the denominator represents regional biogenic CO 2 consumption (AEO Reference baseline biomass plus a dditional feedstock requirement). Another possible method is the augmented average approach. 2 Comparison of the 1 million ton increased feedstock scenario to the Constant Biomas s baseline can be interpreted as the augmented average biogenic CO 2 emissions effect of planned expansion in biogenic feedstock consumption (as defined by the AEO Reference baseline) above the eG RID/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 b aseline, 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 thi s period. The results are computed using key outputs for the 2010–2060 time horizon (in 5-year t imesteps), which provides a 50-year time frame for evaluating land use and biogenic emission s changes (and avoids any terminal effects that may affect results in the last few time periods of results). Results generated within this overall tim e 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 ma rk 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 th e 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 optio ns (including N 2 O and CH 4 emissions from crop and livestock operations), this study focuses on changes in landscape-level biogenic CO 2 only (though a sensitivity evaluating the impact of incl uding N 2 O is included in this analysis). This approach includes carbon in agricultural and forest ry soils, and carbon stored in forest and agricultural biomass (additional details provided b elow).

2 Discussed here as a possible method, but this meth od was not employed to avoid further complexity, as many different methods could be discussed and employed u sing a future anticipated baseline approach. Theref ore, the illustrative results tables do not include this cat egory.November 2014 L-6



## 3. Mapping and Interpreting Future Anticipated Baselin e Data and Illustrative Results
This section presents which FASOM-GHG data componen ts are mapped to BAF equation terms as well as how BAF

equation terms are calculated using these data ove r a specified simulation time horizon. Specifically, FASOM-GHG projections are us ed to derive representative values for regional net growth ( GROW

), total net carbon change on the feedstock product ion region ( SITETNC ), and avoided emissions from feedstock harvest or collect ion ( AVOIDEMIT

) in each simulation period. These terms are aggregated into a net biogenic emis sions ( NBE ) term, which is used along with the total additional biogenic CO 2 (calculated directly from the feedstock-specific b iomass 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 po int and the future anticipated baseline approach is tha t the equation terms ( PGE, GROW, SITETNC, AVOIDEMIT

, and NBE

) as defined and applied within the future anticipa ted baseline approach do not represent the absolute emissions associated wit h the terms but rather the additional, or relative, emissions compared with an alternate pote ntial future.


### 3.1. FASOM-GHG Data Component Mapping to BAF Terms Deriving BAF

equation term values from FASOM-GHG output data co mponents involves aggregating the various emissions components into a single valu e. Table L-1 lists specific carbon-based GHG flux categories from FASOM-GHG simulations and the BAFequation term associated with each carbonbased GHG flux account. Note that non-CO 2 emissions from crop and livestock management, carb on 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 w ith 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 SITETNCNovember 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 abo ve FASOM-GHG elements and how they are calculated in the model are included in the Supplem ental Information section in this appendix.


### 3.2. Potential Gross Emissions ( PGE )

Because the BAF

as calculated using the future anticipated baselin e approach is a measure of emissions intensity, PGE , which also varies by time ( t ), is the estimated biogenic feedstock consumed (in terms of CO 2 ) in the case study (“ CS ”) that is additional to the estimated biogenic feedstock consumed (in terms of CO 2 ) in the alternate baseline (“ AB ”):

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


### 3.3. Net Growth ( GROW ) Similar to the retrospective reference point baseli ne approach, the future anticipated baseline treats GROW

as the net landscape biogenic CO 2 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 GROW t, where the i

index specifically represents the individual biogenic CO 2 fluxes from the SE roundwood column of Table L-1 l abeled “ GROW ” (afforestation harvest flux, afforestation tree car bon flux, etc.) It should be noted that the fluxes considered in set i

include both forest growth and removals, thus yiel ding a net growth value for each time period, t . Under this approach, each flux account is the dif ference 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 bioge nic CO 2 emissions fluxes not directly related to the actual biogenic feedstock growth (fo r each time period in the simulation). This factor includes changes in carbon stored in soils, non-har vested biomass, and potentially other pools. The change in site carbon, SITE k,”CS”,t, is the sum of a set of k biogenic CO 2 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 diff erence 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 a pproach, AVOIDEMIT represents the relative difference in avoided biogenic emissions between sc enarios. Each “ AVOID” term in equation 4 below represents the avoided biogenic emissions wit hin a particular scenario. Letting AVOID h,”CS”,t represent the sum of the set of h biogenic CO 2 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 em issions 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-le vel CO 2 emissions (emissions from harvesting and using the biogenic feedstock) betwee n scenarios (calculated as the sum of all landscape-level CO 2 emissions). This is represented as:

  = 	 
  + 	  + 
		  (EQ. L.5)


### 3.7. Biogenic Assessment Factor (BAF) Thus, the biogenic assessment factor is ratio of th e net biogenic emissions ( NBE t ) to the potential growth emissions ( PGE t ), 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 thr ee case studies include positive and negative values. Positive values indicate a net flux of emis sions (harvest or land use change emissions outweigh biogenic CO 2 sequestration on the landscape), whereas negative v alues indicate net sequestration (biogenic CO 2 sequestration on the landscape outweighs harvest or land use change emissions). However, determination of how and wheth er negative values would be applied in practice would depend on the policy or program bein g analyzed.

BAF results can be illustrated in a variety of context s, 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 comparisonNovember 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 BAF s can vary widely from period to period as land management and forest harvest interv als adjust to the new biomass demand shock. This explains the variable nature of the per iodic calculations. A 2015–2060 average is calculated to represent the average periodic BAF over the entire time frame of the analysis. Cumulative BAF s are calculated by taking the cumulative value of each term in the BAF equation over time. The cumulative value offers in sight into potential anthropogenic biogenic carbon-based emissions effects over a spec ified 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 b aseline as the basis of analysis led to different BAF values. The Zero Biomass baseline comparison to th e case study projection captures all anticipated biomass users, whereas the Constant Bio mass comparison focuses on new users. Depending on the policy application of the framewor k, either of these approaches may be more appropriate. Also, the means for considering the re sults over time (averaged per-period BAF s versus cumulative) led to different BAF values. Per-period values, calculated using the for mulas from the section above, illustrate BAF values specific to an individual point in time, whi ch might be useful in some policy applications but not relevant for others. Given the nature of modeling methods employed, periodic BAF s can vary widely from period to period as land man agement and forest harvest intervals adjust to the new biomass demand shock. The supplemental data and information section provi des the illustrative results and discussion for the various feedstock- and region-specific case stu dies. Data presented in this supplemental section include projected equation term values for each sim ulation period for emissions fluxes and cumulative emissions, using the average and margina l counterfactual approaches.


### 4.2. Overview of the Illustrative Results Table L-2 provides illustrative values for NBE and the BAF

s 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 bioge nic feedstock consumption from the additional 1 million dry ton feedstock demand shock. All BAF equation terms presented in Table L-2 can be replic ated based on the cumulative “average” value tables provided in the Supplemental Information sec tion of this appendix, referencing the 2055– 2060 simulation period. Table L-2 presents estimated landscape attributes f or each of the three case studies and concludes with two illustrative BAF values (with and without default process attribute s P and L , which areNovember 2014 L-10

assumed to be 1 and 1.1, respectively, for consiste ncy 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 colum ns could yield a positive or negative value, depending on the relative difference in these cumul ative 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 addition al biogenic feedstock demand in the case study. Relative net growth (in the fifth column) is the su m of relative growth and relative removals. Dividing this absolute emissions change by the regi onal 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 ca rbon-based fluxes included in AVOIDEMIT and SITETNC

for this application).

NBE (eighth column) is the sum of all relative landsca pe 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 bio genic feedstocks for energy generation (in million tCO 2 e) 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 reabsorb ed by the landscape.November 2014 L-11

Table L-2. Illustrative BAF Values for the Future Anticipated Baseline Case St udies: 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 tCO 2 e) Relative

Removals Emissions (million tCO 2 e) Relative

Net Growth (GROW = Relative growth – relative

r emovals) (million tCO 2 e) Relative

Avoided Emissions (AVOIDEMIT) (million tCO 2 e) Relative

Net L andscape Emissions (SITE_TNC ) (million tCO 2 e) Net

Biogenic Emissions (NBE): Sum of all relative carbon fluxes (million tCO 2 e) Potential

Gross Emissions (PGE): All Additional Biogenic FeedstockConsumpt ion (million tCO 2 e) Assessment

Factor (BAF) (Ratio of relative total carbon flux to relative feedstock flux) Adjustment

Factor (BAF ) withProcessBased Equation Terms P and L

SE Roundwood 201 5

–

2060 − 37 17

− 20

− 0.6

− 3

− 24 672

− 0.0 3

− 0.0 3

PNW Logging Residues 201 5

–

2060 − 14 16 2 0 4 7 155 0.0 4 0.0 4

CB Corn Stover 201 5

–

2060 NA NA 0 0 16 16 108 0.1 5 0. 1 6



## 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, manageme nt techniques, hardwood/softwood mixes, and forest products industry. For example, softwood pla ntation pine systems are common in the Southeast, and such plantations involve more intens ive management but shorter rotations than typical hardwood stands in other regions such as th e 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 S tudy Table L-3 displays average periodic and cumulative biogenic CO 2 emissions results for the marginal estimated landscape factor calculations for three s eparate 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 bi ogenic CO 2 consumption for the feedstock case study relative to the AEO Reference baseline. Estim ated per-period landscape factors vary over time for the marginal case, reflecting the cyclical nature of terrestrial CO 2 fluxes from forest management, though this variation is smoothed by av eraging over time. Initially, emissions intensity is negative and relatively large in magni tude, reflecting a net increase in carbon

3 Note that an estimated landscape factor has the sa me 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 ant icipated long-term demand shift for roundwoodderived biomass. That is, landowners plant new tree s and delay harvests in an effort to meet this long-term increase in demand. Furthermore, the Southeast region is a unique regio n with historically high levels of observed land use exchanges between agriculture and forestry (Wea r and Gries, 2002; Milesi et al., 2003). This phenomenon is evident in the Southeast case study r esults, 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 i tself. These land use changes can cause large periodic flu ctuations in SITETNC emissions as new sources of carbon sequestration from afforested stands affe ct the projected terrestrial carbon balance (as seen in 2035 and 2040). In addition to land use cha nge, differences in forest management techniques and shorter rotations in the Southeast r elative to other regions lead to more variability in the GROW

term as high levels of harvest emissions occur mor e frequently and forest carbon stocks recover more rapidly. Table L-3. Southeast Roundwood Landscape Factor Res ults (Marginal User).

Case Study Term Emissions Projection Method Time Period

2015–2030 2015–2045 2015–2060SE RoundwoodMarginal User ------ additional emissions (t CO 2 ) from AEO Reference case baseline level ------ GROWPer 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 CO 2 ) from AEO Reference baseline level GROWCumulative -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 BAF

s are smoother and less variable overall when compa red with the periodic BAF s. 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 CO 2 emissions relative to the AEO Reference baseline.November 2014 L-13

5.1.2. Average Effects for the Southeast Roundwood Case St udy Average effects are displayed in Table L-4. These r esults include the net change in biomass consumption and emissions for existing levels of co nsumption, planned expansion, and the additional case study feedstock requirement. Period ic landscape factors are more stable (less variable) under this approach than the marginal eff ects above, in part because the additional biogenic CO 2 in the denominator includes the biomass consumptio n already projected to take place. Changes in the denominator are not overwhelmed by t he large landscape-level emissions changes present in the numerator. Estimated landscape facto rs for the “average user” are positive at the beginning of the simulation horizon when the increa se 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 landsc ape factors fluctuate over time and the average by 2060 is less than 0 at −0.07. Table L-4. Southeast Roundwood Landscape Factor Res ults (Average User).

Case Study Term Emissions Projection Method Time Period

2015–2029 2015–2044 2015–2060SE RoundwoodAverage User ------ additional emissions (t CO 2 ) from Zero Biomass baseline level ------ GROWPer 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 CO 2 ) from Zero Biomass baseline level GROWCumulative 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 tot al in 2060 to the periodic average (−0.03). Note that this result differs from the previous “margina l” user landscape factor. An “average” value includes the landscape-level emissions effect of al l biomass users (current, planned, and the additional roundwood consumption source), whereas t he 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 manageme nt changes early in the simulation horizon (afforestation, longer forest rotations) in anticip ation 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 baselin e, so the resulting change from AEO Reference to the roundwood feedstock case is only capturing t he additional emissions and biomass consumption attributable to the additional roundwoo d demand source. Figure L-1 compares thisNovember 2014 L-14

case study’s cumulative average user trend BAFs with the average regional BAF s for the AEO Reference case baseline scenario presented in Appen dix K (when comparing the AEO Reference case baseline to the Zero Biomass Baseline scenario ).

Figure L-1. Comparison of Average Cumulative Landsc ape Factors in the Southeast Region for the AEO Reference Case Baseline and Roundwood Case Stud y Relative to the Zero Biomass Scenario. 

### 5.2. Pacific Northwest (PNW) Logging Residues The PNW logging residues case study simulates a dem and for additional feedstock that is met entirely from soft and hardwood logging residues. T his case study helps illustrate the potential biogenic CO 2 effects of increased demand for logging residues a s 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 analys is only includes the western portion of the PNW where cool, relatively dry summers and mild, we t winters yield highly productive Douglas-fir, hemlock, and spruce forests. A full evaluation of t he PNW region would require including the eastern portions of Oregon and Washington, which ar e primarily agricultural regions with limited market interaction with the area included in this a ssessment.
5.2.1. Marginal Effects for the PNW Logging Residue Case S tudy Table L-5 displays landscape factors for the PNW lo gging residue case study (marginal user case). Unlike the roundwood case studies previously examin ed, projected marginal BAFs are positive at the beginning of the analysis, quickly become negat ive through the near to medium term, and then become positive toward the end of the simulation ho rizon. 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 2055Cumulative BAF

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

and cumulative marginal user calculations. Early in the simulation horizon, the model projects that the additional biomass requirement leads to increas ed forest harvest emissions. An increase in logging residue demand leads to a net increase in r oundwood harvests for other products in order to meet the additional residue demand. Then, affore station and forest management responses to the feedstock requirement lead to an increase in bi ogenic carbon sequestration (hence, large negative values for GROW ), resulting in negative landscape factors from 202 0–2040. Over the long term, however, this effect flips as harvest emissio ns outweigh growth in landscape-level biogenic carbon sequestration. The large emissions increase in GROW leads to high positive values for the periodic lan dscape 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, a nd the cumulative landscape factor in 2055 is slightly higher at 0.3. Thus, relative to the AEO R eference baseline, isolated expansion in logging residue consumption in the PNW would lead to a slig ht increase in biogenic CO 2 emissions. Table L-5. PNW Logging Residue Landscape Factor Res ults (Marginal User).

Case Study Term Emissions Projection Method Time Period

2015–2030 2015–2045 2015–2060PNW Logging ResidueMarginal User ------ additional emissions (t CO 2 ) from AEO Reference case baseline level ------ GROWPer 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 CO 2 ) from AEO Reference case baseline level GROWCumulative -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 St udy For the PNW Logging Residue case, average user land scape factors, or the combined effects of current consumption, planned expansion, and the add itional feedstock consumption source, trend toward 0 over time (Table L-6). Net emissions decre ase rapidly initially due to additional tree planting and changes in forest management in respon se to the anticipated feedstock demand. Figure L-2 provides a comparison of cumulative land scape factor values for the AEO Reference and PNW logging residue scenarios, respectively, relati ve 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, bu t a slight increase in emissions over the longNovember 2014 L-16

term due to sustained harvest emissions that increa se with the demand for logging residues. The cumulative landscape factor is close to 0 (0.04) bu t positive in the 2050–2060 assessment period. Table L-6. PNW Logging Residue Landscape Factor Res ults (Average User).

Case Study Term Emissions Projection Method Time Period

2015–2030 2015–2045 2015–2060PNW Logging ResAverage User ------ additional emissions (t CO 2 ) from Zero Biomass baseline level ------ GROWPer 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 CO 2 ) from Zero Biomass baseline level GROWCumulative 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 Landsc ape Factors in the PNW Region for the AEO Reference Case and PNW Logging Residue Case Study Re lative to the Zero Biomass Scenario.November 2014 L-17



### 5.3. Corn Belt Corn Stover The Corn Belt corn stover case study applies the sa me additional 1 million ton biomass shock over time to the Corn Belt region and requires this addi tional 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 round wood scenarios. The GROW term defaults to 0 for agricultural biomass sources in this methodolog y. The assumption is that, with annual crops, biogenic CO 2 “growth” in this context equals what is harvested (removed) from the system for energy generation. However, because this effort see ks 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 st udy is that for each alternative future baseline, a significant amount of corn residue is projected t o be harvested in the Corn Belt for producing cellulosic ethanol to meet the RFS2 advanced biofue l 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 relativel y 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 prop ortion of conventional tillage to no-till and conservation ti ll stays relatively constant (thus, there are only minor biogenic soil carbon effects from increased r esidue harvesting). The majority of SITETNC emissions are due to forest harvest fluxes and smal l levels of deforestation for crop production in response to the additional feedstock demand. Table L-7. Corn Belt Corn Stover Landscape Factor R esults (Marginal User).

Case Study Term Emissions Projection Method Time Period

2015–2030 2015–2045 2015–2060CB Corn StoverMarginal User ------ additional emissions (t CO 2 ) from AEO Reference case baseline level ------ GROWPer 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 addition al feedstock usage (t CO 2 ) from AEO Reference case baseline level GROWCumulative 0 0 0 SITETNC 2,645 7,838 5,435 AVOIDEMIT 0 1.7 2.0November 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, endin g up at 0.08. Thus, 40 years after the initial corn stover demand shock, only a small portion of biogenic CO 2 emissions from additional corn stover removals are not balanced by landscape biogenic CO 2 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 ar e relatively stable, average effects fluctuate considerably over time in the Corn Belt region (Tab le L-8). The overall trend is similar to the alternative baseline Corn Belt regional results pre sented 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). Howev er, the additional corn stover requirement increases net biogenic CO 2 emissions (hence the positive periodic flux values in the marginal case), which essentially shifts the BAF trajectory up for the majority of the simulation h orizon. Note that the two BAF

trajectories below converge over the long term, in dicating a rise in land use change emissions in the AEO Reference baseline in the long term. In general, these results show that although biogen ic CO 2 emissions from corn stover biomass removals in the Corn Belt might be predominately of fset by landscape-level CO 2 accumulation, additional expansion of corn stover demand could in crease the value of agricultural land relative to other uses, which could drive land use change and i ncrease net emissions (especially if the land is converted to agricultural use from forestry). Howev er, even with the resulting emissions effects, biogenic CO 2 emissions from corn stover consumption in this sce nario are almost fully offset by landscape-level CO 2 changes in this case study scenario.

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

Case Study Term Emissions Projection Method Time Period

2015–2030 2015–2045 2015–2060CB Corn StoverAverage User ------ additional emissions (t CO 2 ) from Zero Biomass baseline levels ------ GROWPer 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 0.81November 2014 L-19

Case Study Term Emissions Projection Method Time Period

2015–2030 2015–2045 2015–2060

Cumulative additional emissions (t CO 2 ) from Zero Biomass baseline level
GROWCumulative 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 Landsc ape Factors in the Corn Belt Region for the AEO Reference Case Baseline and Corn Stover Case Stu dy Relative to the Zero Biomass Scenario. 

## 6. Summary

The illustrative BAF

values presented in this appendix do not reflect a ny 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 model ing system. The goal of this exercise is to illustrate the functionality of the future anticipa ted 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 differe nt temporal and spatial scales that could be used, and choices pertaining to these factors can i mpact 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 2055Cumulative BAF

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

Ultimately, the illustrative case studies and estim ated values in this appendix are meant to demonstrate the flexibility of the framework as wel l as the importance of decisions made in terms of how results are to be calculated. Therefore, dec isions about time, space, data aggregation, etc., a ll should be specific to the policy or program to whic h the framework is applied. Appendix M provides an overview and discussion of i llustrative case study results as well as sensitivities derived from both the retrospective r eference point and future anticipated baseline applications. In that appendix, the future anticipa ted results reflect the comparison of the 1 million ton increased feedstock scenario to the Zero Biomas s scenario to provide an estimate of the average biogenic CO 2 emissions effect for all existing and planned biog enic feedstock consumption at national and regional scales and also applies the c umulative calculation method. 

## 7. References
Milesi, C., Elvidge, C. D., Nemani, R. R., & Runnin g, S. W. (2003). Assessing the impact of urban land development on net primary productivity in the sout heastern United States. Remote Sensing of Environment 86 (3):401-410. Wear, D. N. and Greis, J.G. (2002). The Southern Fo rest 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 pr oducts. In addition, the model tracks GHG emission reductions in other sectors caused by miti gation actions in the forest and agricultural sectors. In addition to CO 2 , FASOM-GHG’s accounting also includes CH 4 , and N 2 O. In this section, we provide additional information on the CO 2 accounting functions and parameters used within the model. To compare landscape-level emissions across baselin es, the following CO 2 flux categories are aggregated to yield a total net emissions flux. Thi s aggregation is calculated for every time step in the simulation horizon (5-year time steps). Then, a nnual 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 con text of longer rotation woody biomass (i.e., roundwood). Short rot ation 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 CO 2 flux; thus, growth would be in balance with remova ls). The agricultural feedstocks complete an entire grow th/harvest/combustion cycle entirely within aNovember 2014 L-21

year (and thus any CO 2 sequestration in the feedstock would also be balan ced by its removal and use). In FASOM-GHG the net forest carbon growth fro m 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 reg ionally and nationally. Note that this net change in tree carbon would include both growth of trees t hat did not get harvested as well as a loss associated with the trees that did get harvested (r emovals). FASOM-GHG tree carbon calculation is based on two p rimary sources: timber yields and a set of factors that convert those yields to carbon. With t he exception of the Pacific Northwest-West (PNWW) region, the timber yields come from the ATLA S 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 Si mulator (FVS) and then averaged over strata. The yields include options for partial harvesting r egimes of one or more thinning entries only in the PNWW, Southeast (SE), and South central (SC) region s. The conversion of yields for all management regimes including those that involve partial harves ts to carbon are based on Smith et al. (2006). The growing stock volumes ( V A ) from the FASOM-GHG yield tables are in thousands of cubic feet per acre and therefore must be converted to cubic m eters per hectare ( V H ) 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 w eighted 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 tre e carbon and young stand adjustment from an update by Jim Smith in 2007. The L1 , L2 , and L3 parameters 5 displayed in Table L-9 are for the live tree mass equation, the D2 and D3 parameters are for the dead tree mass equation, an d the C1 parameter is used to “ramp up” the mass in young st ands (because they may have no growing stock volume). Table L-9. FASOM-GHG Live and Dead Tree Biomass Equ ation 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 u p to total volumes as the model has been updated. 5 Note that these are the values we use after the we ighted 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 9.889November 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 ( C live ):

, -./0 = 1% − 0 2 3450 ,% 6 7  8% + 8 ∙ # $ 89  (EQ. L.8)
And dead mass ( C dead ):

, :04: = 1% − 0 2 3450 ,% 6 7  ; ∙ #

$ ;9  (EQ. L.9)
And converted to tree carbon per acre ( C tree ) based on half of the mass being the carbon conten t:

, <=00 = > , -./0 ?, :04: @

&.A∙.)B% (EQ. L.10)

8.1.2. FASOM-GHG Functions Relating to Changes in Site Emi ssions
The SITETNC

term represents the feedstock production site-leve l difference in the net CO 2 flux to the atmosphere when biogenic feedstocks are used fo r bioenergy compared with a previous use/activity considering both emissions and sequest ration 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 understo ry 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). Upo n 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 utilizat ion can be evaluated by simply taking the difference between scenarios. The litter and unders tory carbon is based on a forest floor equation along with estimates of understory and coarse woody debris. Unlike tree carbon, these valuesNovember 2014 L-24

adapted from Smith et al. (2007) are based solely o n 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 24.3November 2014 L-25

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

C = 2 (∙450 DE450 6?,∙0 32 450

; 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 DE450 6

.)B% + > CF:?,G: @ ∙8∙&.A

%&&∙.)B% (EQ. L.12)
In addition to the litter, understory, and coarse w oody debris carbon pools, FASOM-GHG tracks soil carbon. The approach used is adapted from earlier w ork by Birdsey (1996a), which had fixed forestland carbon values in all regions except the South 6 that varied by region, while Smith et al. (2006) have all carbon in forest soils assumed to b e constant over time but varied by region and forest type. Birdsey (1996a) also has soil carbon e stimates 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 forest land steady-state soil carbon values. To keep soil carbon values consistent across land use types (cro p, pasture, and forest), FASOM-GHG does not use any of the Birdsey (1996a) or Smith et al. (2006) v alues 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 Cons tants 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 stan d.November 2014 L-26

A regression analysis of the Appendix 3 afforestati on soil carbon values (Birdsey, 1996b) as a quadratic function of forest stand age after pastur e 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 Paramete rs.

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 max imum soil carbon value achieved when the FASOM-GHG minimum harvest ages for each region, own er, forest type, site class, and management intensity are used in the C soil equation. The values obtained and used for the regi onal forest soil constants are provided in Table L-13.

Table L-13. FASOM-GHG Forest Soil Carbon Constant b y 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 14.617November 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 amoun t 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 ( ∆

C soil ) associated with a land movement from pasture (C past soil ) to forest (C for soil ) using the soil carbon conversion rate, S t , in year t would be calculated using the following equation:

∆ KLM = >  N% −   @ 2 KLM OLP − KLM

QRK 6 (EQ. L.14)

8.1.3. FASOM-GHG Functions Relating to Changes in Avoided Emissions In addition to the litter, understory, and coarse w oody 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 logg ing residues can be either used or left to decompose onsite based on region and forest type. S pecific 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 De composition 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 0.05 7 Hardwood 0.084 0.084 0.069 0.082 0.082 0.082 0.082 0.082 0.0 82



## 9. References
Birdsey, R.A. 1996a. “Carbon Storage for Major Fore st Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest manage ment 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 pa sture reversion to forest.” In Forests and Global Change. Volume II: Forest management opportu nities 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 Mana gement—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, 2 007. 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 car bon 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 Agri culture, 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 20 40. 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 alternat ive policy options. EPA/600/3-93/093. Corvallis, OR: U.S. Environmental Protection Agency , Environmental Research Laboratory.