November 2014 i



# Framework for Assessing Biogenic CO 2 

# Emissions

from Stationary Sources

United States Environmental Protection Agency, Offi ce of Air and Radiation

Office of Atmospheric Programs, Climate Change Divi sion

November 2014November 2014 ii 

## Executive Summary

FRAMEWORK FOR ASSESSING BIOGENIC CO 2 

### EMISSIONS FROM STATIONARY SOURCES

This report presents a methodological framework for assessing the extent to which the production, processing, and use of biogenic material at station ary sources results in a net atmospheric contribution of biogenic carbon dioxide (CO 2 ) emissions. Biogenic CO 2 emissions are defined as CO 2 emissions related to the natural carbon cycle, 1 as well as those resulting from the production, harvest, combustion, digestion, fermentation, decom position, and processing of biologically based materials. 2

The framework examines direct emissions of biogenic CO 2 from stationary sources that combust or process biogenic material, taking into account fact ors related to the biological carbon cycle. These factors include changes in biogenic carbon-based st ocks and emissions—known as “carbon fluxes” 3 —that occur (or are avoided) as a result of (1) fee dstock growth and harvest; (2) processing, transport, storage, and use of a bi ogenic feedstock at the stationary source; and/or (3) the possible alternative fate of biogenic feeds tock materials if not used for bioenergy. 4 The framework is a revision of the U.S. Environmental P rotection Agency’s (EPA’s) original September 2011 Draft Accounting Framework for Biogenic CO 2 Emissions from Stationary Sources and was developed in response to a peer review by the Bioge nic Carbon Emissions Panel of EPA’s Science Advisory Board (SAB Panel). In the review, 5 the SAB Panel stated that:
Carbon neutrality cannot be assumed for all biomass energy a priori. There are circumstances in which biomass is grown, harvested and combusted in a carbon neutral fashion but carbon neutrality is not an appropriate a priori assumption; it is a conclusion that should be reached only after consid ering a particular feedstock’s

1 The term “carbon cycle” is used to describe the fl ow of carbon (in various forms, for example, as CO 2 ) through the atmosphere, ocean, terrestrial biospher e, and lithosphere (IPCC, 2007a).

2 Biologically based feedstocks, also referred to in this report as “biogenic materials,” are non-fossi lized and biodegradable organic materials originating from mo dern or contemporarily grown plants, animals, or microorganisms (including products, by-products, re sidues, and wastes from agriculture, forestry, and related industries, as well as the non-fossilized and biode gradable organic fractions of industrial and munici pal wastes, including gases and liquids recovered from the decomposition of non-fossilized and biodegradab le organic material). These feedstocks do not include materials such as peat, coal, petroleum, natural ga s, and other products that are derived from biologic mater ials but are considered non-renewable during the ti me frame relevant to policymaking.

3 The IPCC defines “carbon flux” as the transfer of carbon from one carbon pool to another in units of measurement of mass per unit area and time (IPCC, 2 000). The term “flux” is used here to encompass bio genic CO 2 and methane emissions to the atmosphere, and remov al of carbon (C) from the atmosphere. Removal of C from the atmosphere is also referred to as “carbon sequestration” (EPA, 2014).

4 The framework includes consideration of carbon cyc le fluxes associated with or avoided by the convers ion of biogenic materials at stationary sources. For compl eteness, biogenic carbon and carbon-based gases—car bon dioxide and methane—are included within the framewo rk as applied to forest-, agriculture-, and waste sector-derived feedstocks. Throughout the report, t he term “biogenic CO 2 ” is often used to represent these carbon-based stocks and gases.

5 The final peer review report from the SAB Biogenic Carbon Emissions Panel on the draft framework was published on September 28, 2012 (Swackhamer and Kha nna, 2011).November 2014 iii

production and consumption cycle. There is consider able heterogeneity in feedstock types, sources and production methods and thus net biogenic carbon emissions will vary considerably.
Consistent with this statement, the revised framewo rk provides a description of the types of factors to consider when assessing biogenic CO 2 emissions and presents an equation that could be u sed to calculate the extent to which use of biogenic mater ials at a stationary source results in a net atmospheric contribution of biogenic CO 2 emissions. Although the framework does not reflect a full lifecycle assessment of all CO 2 emissions related to biogenic feedstock production and use, the framework provides a means to consider the role of the biological carbon cycle effects, including biogenic carbon and carbon-based fluxes, associated with biogenic feedstock growth, harvest, processing, and use. The methodology in this report includes technical elements that can be adapted to reflect a variety of policy scenarios ba sed on key decisions by the user, including type of baseline (e.g., reference point or anticipated), ti me frame of the assessment (e.g., future or historical, 20, 30, 50, 100 years), feedstock categ ories (i.e., waste-, agriculture-, and forest-deriv ed), and scale (e.g., state, regional, national). Many o f the decisions are dependent on the user’s goals and could be coupled with practical considerations, such as data availability and user type. As a result, the framework is designed to be flexible so that decisions on specific components can be made to accommodate different policy constructs. Th e types of decisions a user of the framework could encounter are identified throughout this repo rt, along with related considerations and implications of those decisions.

The body of this report describes the scientific ra tionale for the framework, presents the equation and terms on which the framework is based, and disc usses key issues that should be considered when applying the framework to a specific policy or program. It also discusses publicly available data sets that could be used in the framework, incl uding the EPA’s National Inventory of Greenhouse Gas Emissions and Sinks, the U.S. Depart ment of Agriculture (USDA) National Resources Conservation Service’s National Resources Inventory, the USDA Forest Service’s Forest Inventory Analysis, and the Energy Information Admi nistration’s Annual Energy Outlook. Technical documentation is included in a set of appendices th at present further detail on various elements of the framework and demonstrate how the framework cou ld be applied through illustrative examples.November 2014 iv



## Table of Contents

1. Introduction ......................................

................................................... ................................................... ......................... 1

1.1. Purpose of this Report ............................

................................................... ................................................... ..... 1

1.2. Background ........................................

................................................... ................................................... ............... 3

1.3. Scientific Basis for the Framework ................

................................................... ............................................ 5

1.4. Scope of the Framework.............................

................................................... ................................................... . 6

Biogenic Carbon and Carbon-Based Fluxes ...........

................................................... ....................... 6 Assessment Scope ..................................

................................................... ................................................. 7

Non-CO 2 Greenhouse Gases .................................

................................................... ................................ 9

1.5. Organization of the Report ........................

................................................... .................................................. 11

2. Biogenic Assessment Factor Equation ...............

................................................... .............................................. 11

2.1. Purpose of the Equation and Biogenic Attributes Con sidered ........................................... ............. 11

2.2. Overview of the Biogenic Assessment Factor Equation Terms ............................................ .......... 13

2.3. Primary Equation Terms ............................

................................................... .................................................. 14

Net Biogenic Emissions ( NBE ) .................................................

................................................... ........ 14 Potential Gross Emissions ( PGE ) .................................................

................................................... .... 14 Biogenic Assessment Factor ( BAF ) .................................................

.................................................. 15

2.4. Landscape Attribute Terms .........................

................................................... ............................................... 15

Net Growth on the Production Landscape ( GROW ) ................................................. .................. 16 Avoided Emissions (

AVOIDEMIT

) .................................................

................................................... .. 16 Total Net Change in Production Site Non-feedstock C arbon Pools ( SITETNC ) ............... 17 Leakage Associated with Feedstock Production ( LEAK ) ................................................. ......... 18

2.5. Process Attribute Terms ...........................

................................................... ................................................... 19

Feedstock Carbon Leaving the Supply Chain ( L ) ................................................. ........................ 20 Feedstock Carbon Embodied in Products ( P ) ................................................. .............................. 21

2.6. Interpreting the Biogenic Assessment Factor .......

................................................... .............................. 24

2.7. Possible Applications of the Biogenic Assessment Fa ctor .............................................. .................. 24

3. Representative and Customized Approaches to Landsca pe and Process Attributes ...................... 25

3.1. Representative Factor Approach ....................

................................................... .......................................... 25

3.2. Customized Factor Approach ........................

................................................... ............................................. 26

3.3. Hybrid Approach ...................................

................................................... ................................................... ....... 27November 2014 v

4. Technical Considerations ..........................

................................................... ................................................... ......... 27

4.1. Baselines .........................................

................................................... ................................................... ................. 27

Reference Point Baseline ..........................

................................................... .......................................... 28 Anticipated Baseline ..............................

................................................... ............................................... 30 Application of Different Baseline Approaches ......

................................................... ..................... 32

4.2. Temporal Scale ....................................

................................................... ................................................... .......... 33

Implications of Different Temporal Scales .........

................................................... ......................... 34 Potential Framework Applications of Time ..........

................................................... ...................... 35 Additional Technical Timing Issues ................

................................................... ............................... 37

4.3. Spatial Scale .....................................

................................................... ................................................... ............... 38

Smaller Spatial Scales ............................

................................................... .............................................. 39 Larger Spatial Scales..............................

................................................... ............................................... 41

International Considerations ......................

................................................... ...................................... 42 Spatial Scale Used in the Illustrative Applications in the Technical Appendices ........... 43

4.4. Relationship between Spatial and Temporal Scales ..

................................................... ....................... 43

4.5. Leakage ...........................................

................................................... ................................................... .................. 45

4.6. Feedstock Categorization ..........................

................................................... ................................................... 46

Temporal Scale and Feedstock Categorization .......

................................................... .................. 47 Spatial Scale and Feedstock Categorization ........

................................................... ....................... 47 Feedstock Categorization Used in the Framework ....

................................................... .............. 47

5. Discussion ........................................

................................................... ................................................... ......................... 48

6. Glossary of Terms .................................

................................................... ................................................... ................. 49

7. References ........................................

................................................... ................................................... ......................... 57

8. Technical Appendices to this Report ...............

................................................... ................................................. 6 3

• Appendix A: IPCC Inventory Approach to Accounting f or All Anthropogenic Greenhouse Gas
Emissions

• Appendix B: Temporal Scale

• Appendix C: Spatial Scale

• Appendix D: Feedstock Categorization and Definition s

• Appendix E: Discussion of Leakage Literature

• Appendix F: General Algebraic Representation of the Biogenic Assessment Factor Equations

• Appendix G: Illustrative Biogenic Process Attribute s

• Appendix H: Illustrative Biogenic Landscape Attribu tes Using a Retrospective Reference Point BaselineNovember 2014 vi

• Appendix I: Illustrative Forestry and Agriculture C ase Studies using a Retrospective Reference Point Baseline

• Appendix J: Anticipated Baselines: Background and K ey Modeling Considerations

• Appendix K: Future Anticipated Baseline Constructio n: Methodology and Results

• Appendix L: Illustrative Forestry and Agriculture C ase Studies using a Future Anticipated Baseline

• Appendix M: Summary of Illustrative Forestry and Ag riculture Results

• Appendix N: Assessing Biogenic CO 2 Emissions from Waste-Derived Feedstocks


## List of Figures
Figure 1: Diagram Illustrating the Basic Processes Addressed in the Framework. ....................... .............. 2 Figure 2: Various Carbon Flux Types and Timeframes.

................................................... ........................................ 8 Figure 3: Potential Range of Assessment Scope. ....

................................................... ................................................... 9 Figure 4: The Relative Size of Methane Emission Sou rces in the U.S. in 2012. ......................... ................... 10 Figure 5: Diagram with Equation Terms Included. ...

................................................... ........................................... 23November 2014 1



## 1. Introduction

1.1. Purpose of this Report

This report presents a methodological framework for assessing the extent to which the production, processing, and use of biogenic material at station ary sources results in a net atmospheric contribution of biogenic carbon dioxide (CO 2 ) emissions. Biogenic CO 2 emissions are defined as CO 2 emissions related to the natural carbon cycle, 6 as well as those resulting from the production, harvest, combustion, digestion, fermentation, decom position, and processing of biologically based 7 materials.

The intent of the framework is to evaluate biogenic CO 2 emissions from stationary sources that use biogenic feedstocks, given the unique ability of bi ogenic material to sequester CO 2 from the atmosphere over relatively short time frames throug h the process of photosynthesis. The framework focuses on changes in biogenic carbon-bas ed stocks and related emissions and sequestration—known as carbon fluxes (including CO 2 and methane fluxes)

— that occur (or are avoided) as a result of (1) feedstock growth and ha rvest; (2) processing, transport, and storage and use of a biogenic feedstock at the stationary sourc e; and/or (3) the possible alternative fate of the biogenic feedstock materials if not used for bioene rgy. 8 Although the framework does not reflect a full lifecycle assessment of all CO 2 emissions related to biogenic feedstock production and use, the framework provides a means to consider the role of the biological carbon cycle effects associated with biogenic feedstock growth, harvest, and proces sing. Figure 1 provides a simple graphic that shows the biogenic carbon pools and carbon-based fl ows that are represented within the framework.

6 The term “carbon cycle” is used to describe the fl ow of carbon (in various forms, for example, as CO 2 ) through the atmosphere, ocean, terrestrial biospher e, and lithosphere (IPCC, 2007a).

7 Biologically based feedstocks, also referred to in this report as “biogenic materials,” are non-fossi lized and biodegradable organic materials originating from mo dern or contemporarily grown plants, animals, or microorganisms (including products, by-products, re sidues, and wastes from agriculture, forestry, and related industries, as well as the non-fossilized and biode gradable organic fractions of industrial and munici pal wastes, including gases and liquids recovered from the decomposition of non-fossilized and biodegradab le organic material). These feedstocks do not include materials such as peat, coal, petroleum, natural ga s, and other products that are derived from biologic mater ials but are considered non-renewable during the ti me frame relevant to policymaking (years to decades).

8 The framework includes consideration of carbon cyc le fluxes associated with or avoided by the convers ion of biogenic materials at stationary sources. For compl eteness, biogenic carbon and carbon-based gases—car bon dioxide and methane—are included within the framewo rk as applied to forest, agriculture, and waste sec torderived feedstocks. Throughout the report, the term “biogenic CO 2 ” is used to represent these carbon-based stocks and gases.November 2014 2

Figure 1. Diagram Illustrating the Basic Processes Addressed in the Framework. The framework addresses carbon fluxes associated with production, processing, and use of biogenic feedstocks. The so lid, black arrows represent flows of carbon to and from the sy stem of feedstock growth, harvest, and use. The das hed, blue arrow represents physical transfers of the biogenic feedstock from the production landscape to the sta tionary source (transport-related emissions such as tractor or train fuel are not included in the framework).
The framework presents an equation and discusses th e terms of the equation, which are designed to account for the biogenic landscape and process a ttributes included in Figure 1 associated with feedstock production, processing, and use at statio nary sources. Additionally, the framework identifies and discusses the key decisions for a us er to make to inform the biogenic CO 2 flux assessment, including choice of baseline, temporal scale, feedstock categories, and spatial scale. A small number of illustrative examples based on hypo thetical situations or case studies are provided to help demonstrate the types of results and implic ations of these decisions. Beyond the context of evaluating biogenic carbon and carbon-based fluxes associated with biogenic feedstock production, processing, and use at a stationary source, the fra mework is intended to be a tool that is useful to a variety of stakeholders and flexible so it can be a dapted to fit the user’s programmatic needs.

The framework is an analytical methodology for asse ssing the extent to which there is a net atmospheric contribution of biogenic CO 2 emissions from the production, processing, and use of biogenic material at stationary sources. EPA has no t yet determined how the framework might be applied in any particular regulatory or policy cont exts or taken the steps needed for such implementation. In addition, the framework and this report may be refined further by EPA based on further scientific or public review. For these reas ons, the framework and this report are not a final agency action and do not establish or eliminate any regulatory requirements or create or remove rights or obligations enforceable by any party.November 2014 3



### 1.2. Background

On January 12, 2011, EPA announced a series of step s to address the treatment of biogenic CO 2 emissions from stationary sources, including a deta iled study of the scientific and technical issues associated with assessing these emissions. In Septe mber 2011, EPA released a Draft Accounting Framework for Biogenic CO 2 Emissions from Stationary Sources

. 9 The September 2011 report assessed various options for assessing biogenic CO 2 emissions from stationary sources and discussed related technical and scientific issues. This report was peer reviewed by members of the Biogenic Carbon Emissions Panel appointed by EPA’s Science Advisory Board (SAB Panel). 10 In addition to various recommendations and comments on the draft framework report, the SAB Panel stated in its review that:

Carbon neutrality cannot be assumed for all biomass energy a priori. There are circumstances in which biomass is grown, harvested and combusted in a carbon neutral fashion but carbon neutrality is not an appropriate a priori assumption; it is a conclusion that should be reached only after consid ering a particular feedstock’s production and consumption cycle. There is consider able heterogeneity in feedstock types, sources and production methods and thus net biogenic carbon emissions will vary considerably. (p. 17)

The majority of the SAB Panel also found that it is not appropriate to use the Intergovernmental Panel on Climate Change (IPCC) national accounting methodologies to evaluate biogenic CO 2 emissions at individual stationary sources:

The IPCC inventories, a static snapshot of emission s at any given point in time, are a reporting convention that has no associated connect ions to policies or implementation. These inventories do not explicitly link biogenic C O 2 emission sources and sinks to stationary sources, nor do they provide a mechanism for measuring changes in emissions as a result of changes in the building an d operation of stationary sources using biomass. (p. 17) 11

Though the SAB Panel did agree with some basic tene ts of the 2011 draft framework report, they also found some technical elements lacking:

9 The 2011 Draft Accounting Framework for Biogenic CO 2 Emissions from Stationary Sources is available at www.epa.gov/climatechange/ghgemissions/biogenic-emi ssions.html

. 10 The final peer review report from the SAB Biogenic Carbon Emissions Panel on the draft framework was published on September 28, 2012 (Swackhamer and Kha nna, 2011). See SAB Review of the Draft Accounting Framework for Biogenic CO 2 Emissions from Stationary Sources (September 2011). Information about the SAB peer review process for the September 2011 draft fr amework is available at http://yosemite.epa.gov/sab/sabproduct.nsf/0/2F9B57 2C712AC52E8525783100704886.

11 See Appendix A for further discussion of the IPCC guidelines for GHG emissions accounting from all se ctors at the national level.November 2014 4

The Framework

is a step forward in advancing our understanding o f how to account for biogenic emissions.... It covers many of the complica ted issues associated with the accounting of biogenic CO 2 emissions from stationary sources and acknowledges that its choices will have implications for the estimates of CO 2 emissions obtained. These [choices] include those raised by SAB and discussed above, related to the choice of baseline, region selection and the averaging of emi ssions/stocks over space and time. However, the solutions offered in many cases, parti cularly those related to the use of harvested wood for bioenergy, lack transparency or a scientific justification. (p. 40)

Specifically, the SAB Panel recommended more consid eration of different spatial and temporal scales, different baselines to better capture the a dditional effects of changes in biogenic feedstock use, broader discussions on leakage 12 and soil carbon implications, and the concept of r egional feedstock-specific calculations and default assessm ent factor values (rather than individual stationary source-specific assessment factor calcul ations).

Various stakeholders from the environmental communi ty, industry, academia, and government commented on the 2011 draft framework and SAB proce ss and shared relevant research and data to inform EPA’s evaluation of the scientific and te chnical issues associated with biogenic CO 2 emissions assessment. Many of these stakeholders fo cused on key issues similar to those included in the SAB Panel recommendations, although they did not always provide the same recommendations. For example, numerous stakeholders agreed with the SAB Panel’s assertion that the framework should consider alternative fate path ways and methane emissions (especially in the context of waste-derived feedstocks like municipal solid waste [MSW]). Overall, various stakeholders submitted different recommendations on baseline approaches, temporal and spatial scales, and consideration of leakage with regard to these feedstocks. 13

This report addresses central SAB Panel recommendat ions and stakeholder comments regarding the 2011 draft report. 14 Specifically, this report includes a more comprehe nsive discussion and analysis, including detailed technical appendices, on the following topics: (1) baseline approaches; (2) spatial and temporal scale decisions and implic ations; (3) inclusion of alternative fate analysis for certain feedstocks and methane; (4) leakage; an d (5) illustrative regional feedstock-specific

12 This report uses the IPCC definition of “leakage,” which is the indirect impact of a targeted activit y in a certain place at a certain time on carbon storage a t another place or time (IPCC, 2000).

13 Stakeholder comments on the draft 2011 framework a s well as the SAB Panel deliberations can be access ed at http://yosemite.epa.gov/sab/sabproduct.nsf/0/2F9 B572C712AC52E8525783100704886. In addition, stakeholders have provided studies and analysis to EPA directly since the SAB has released their peer review.

14 Some recommendations, such as “including a descrip tion of [the framework’s] regulatory context and specifying the boundaries for regulating upstream a nd downstream emissions while implementing the regulation” and including “how possible regulatory measures under the Clean Air Act may relate to othe r policies that affect land use changes or the combus tion/oxidation of products from the point sources t hat will release carbon or other greenhouse gases” (p. 42), are not reflected in this updated report because su ch parameters and boundaries may differ per applicatio n. Some program applications of the framework may include parameters and boundaries as appropriate to that program or policy.November 2014 5

calculations using existing data sources and models and resulting example regional biogenic assessment factor values.



### 1.3. Scientific Basis for the Framework

The framework is based on the fundamentals of the b iological carbon cycle. The biological carbon cycle is the flow of carbon in various forms throug h the atmosphere, ocean, terrestrial biosphere, and lithosphere (IPCC, 2007a), and this transfer of carbon is generally called a carbon flux. 15 Carbon resides in the atmosphere mostly as CO 2 , but also as methane (CH 4 ), carbon monoxide (CO), and a variety of minor compounds. Through photosynt hesis, plants sequester carbon from the atmosphere to produce wood, sugars, carbohydrates, and other plant products that are, in turn, consumed by animals for food, shelter, and energy ( IPCC, 2007b; King et al., 2007). Carbon is returned to the atmosphere via respiration by plant s and animals, industrial processes including combustion, wildfires, or decomposition. Carbon tha t is not returned to the atmosphere is stored on land in plants and vegetation, in soils, or in sedi ments and plants in fresh water or marine environments (IPCC, 2007b; King et al., 2007).

The biological cycling of carbon occurs on relative ly short timescales. When cycled on the surface of Earth, carbon typically moves between pools on the scale of less than a year, to several years, to decades. For example, over the course of a single y ear, carbon can be removed from the atmosphere by herbaceous plant growth and returned to the atmo sphere via decomposition. Over decades, carbon can be removed from the atmosphere by a grow ing tree and then released back to the atmosphere when the tree is burned or decomposes. C arbon may transit from living plants and trees to storage in litter, soil, and other pools p rior to returning to the atmosphere (except in certain pathways and pools where carbon is stored l onger term, for example, mineralized soil carbon).

In contrast to the relatively short timescale of th e biological carbon cycle, carbon in fossil fuel reservoirs, such as coal seams and oil and gas depo sits, was removed from the atmosphere by plants over millions of years but was not returned to the atmosphere through the natural processes described above. Instead, because of geologic proce sses, the carbon that accumulated in these deposits has been isolated from the active biologic al cycling of carbon to and from the atmosphere. 16 Without human intervention, carbon in fossil fuel reservoirs could remain isolated from the biogeochemical cycling of carbon long into the future.

Carbon is also stored in peat, which forms when pla nt material, usually in marshy areas, is inhibited from fully decaying by acidic and anaerobic conditi ons. Today’s peatlands have formed over

15 The IPCC defines “carbon flux” as the transfer of carbon from one carbon pool to another in units of measurement of mass per unit area and time (IPCC, 2 000). The term “flux” is used here to encompass bio genic carbon dioxide and methane emissions to the atmosph ere, and removal of carbon from the atmosphere. Removal of carbon from the atmosphere is also refer red to as carbon sequestration (EPA, 2014).

16 Other processes cycle carbon on geological timesca les, for example, carbon is vented by volcanoes, re leased from erosion of carbonate rocks (e.g., limestone), and deposited as sediments in marine and terrestria l basins (see IPCC, 2007b; King et al., 2007).November 2014 6

thousands of years, and the carbon in them has also remained largely isolated from the relatively short-term biological cycling of carbon. Peat is no t renewable on policy-relevant time scales. 17

Anthropogenic extraction and oxidation (e.g., combu stion) of the carbon in fossil fuel reservoirs and peatlands returns this isolated carbon to the atmos phere and into the biogeochemical carbon cycle. Collectively, the combustion of fossil fuels and pe at, along with the decrease in the amount of carbon stored in vegetation, have led to an increas e in CO 2 concentration in the atmosphere over the last 200 years (Le Quéré et al., 2009).

A particularly important foundation of the framewor k is the distinction between modern biological materials (e.g., non-fossil) that circulate carbon on policy-relevant time frames and materials such as fossil fuels or peat that circulate carbon on mu ch longer geologic timescales. A key implication of this distinction is that the production and use of biogenic feedstocks and subsequent biogenic CO 2 emissions from stationary sources will not inevitab ly result in an increased net flux of biogenic CO 2 to the atmosphere within a policy-relevant time sca le, unlike CO 2 emissions from combustion of fossil fuels. 18 Specifically, the net atmospheric contribution of CO 2 resulting from biogenic CO 2 emissions from stationary sources may differ from t he contribution resulting from the same amount of fossil fuel CO 2 emissions from stationary sources.



### 1.4. Scope of the Framework

Biogenic Carbon and Carbon-Based Fluxes

The carbon and carbon-based fluxes evaluated in thi s framework include biogenic carbon-based emissions and removals. The former describes the tr ansfer of carbon in the form of CO 2 and CH 4 from a reservoir 19 to the atmosphere, and the latter, known as carbon sequestration, describes the transfer or uptake of carbon in the form of CO 2 from the atmosphere to a reservoir. The framework examines shorter term fluxes of carbon that are ant hropogenically induced by the production and use of biomass (see Figure 2, upper left quadrant). Although the framework addresses net atmospheric contributions of biogenic CO 2 , it is critical to recognize that all CO 2 emissions contribute to the radiative balance of the atmosphe re regardless of their origin, whether biogenic

17 For the purpose of this study, references to a pol icy-relevant time scale is the time frame of concer n required for stabilization of atmospheric GHG conce ntrations to avoid “dangerous anthropogenic interfe rence with the climate system” (UNFCCC, 1994). See http://unfccc.int/essential_background/convention/b ackground/items/1353.php.

18 For example, it is possible to harvest and consume biomass such that, when averaged over an annual growing cycle and only looking at the feedstock car bon itself (i.e., not considering any other carbon pools or biogenic carbon losses), the amount harvested and b urned in a year is exactly balanced by the amount t hat grows during the year. In this theoretical case, th e mass of carbon in the biosphere (i.e., in the liv ing organisms on Earth) would be the same at the end of the year as it was at the beginning, and the net flux to (em issions) and from (sequestration) the atmosphere would be ze ro, averaged over a year. However, if the assessmen t starts with harvest and then the harvested feedstoc k is not replaced by growth (via replanting), then there will be a net increase in the amount of CO 2 in the atmosphere.

19 A carbon pool is a system with the capacity to acc umulate or release carbon, including the atmosphere
, forest biomass, wood products, and soils. The term “reservoir” describes carbon pools other than the atmosphere (IPCC, 2000).November 2014 7

or fossil. 20 Other than the differences in the isotope ratios o f 14 C to 12 C or 13 C to 12 C, the physical attributes of CO 2 released from processing, combustion, and decompos ition of biogenic material are the same as those of CO 2 released from any other process, including fossil fuel combustion. In other words, no matter what the original source of the CO 2 , the behavior of the molecules in the atmosphere in terms of radiative forcing, chemical reactivity, and residence time is effectively the

same.
Though “natural” and “biogenic” are sometimes used interchangeably in the context of fluxes, the IPCC distinguishes between them for greenhouse gas (GHG) inventory purposes (IPCC, 2006). Specifically, whether fluxes are “natural” or “anth ropogenic” depends on the cause or origin of the emissions, whereas the carbon cycle time frame, as discussed above, determines whether fluxes are “biogenic.” Biogenic emissions from the consumption of biogenic feedstocks at stationary sources would be classified as both anthropogenic and bioge nic. See Appendix A for further discussion of the IPCC’s approach to anthropogenic GHG emissions accounting.

Assessment Scope
The scope for assessing the net atmospheric contrib utions of biogenic CO 2 emissions can be narrow or quite broad, depending on the purposes and objec tives of assessment. 

### The framework is designed to assess the complex and shorter term bio logical carbon cycle fluxes associated with growth, harvest, transport, processing, and use of a biogenic feedstock at a stationary source. Thus, as presented in Figure 3, the assessment scope incl udes (1) the direct biogenic CO 2 emissions from a stationary source stack; (2) the carbon cycle eff ects of feedstock growth and harvest on the landscape where the feedstock is produced, as well as emissions associated with processing the feedstock for use at the stationary source

. Depending on the application of the framework, an assessment may also include (3) biological carbon c ycle effects related to leakage, 21 such as indirect land use change induced by displaced feedstock or f eedstock substitute production.

20 Radiative forcing is a function of the total conce ntration of CO 2 and other GHGs in the atmosphere and is not dependent on the origin of CO 2 (biogenic or fossil-based). Radiative forcing is t he change in the net (downward minus upward) irradiance (expressed in W/ m 2 ) at the tropopause due to a change in an external driver, such as, for example, a change in the conce ntration of CO 2

. 21 See Part 4 and Appendix E for a discussion of leak age.November 2014 8

Figure 2: Various Carbon Flux Types and Time Frames

. Carbon fluxes with the atmosphere can be defined in terms of (1) the long-term carbon cycle common t o fossil fuels versus the short-term carbon cycle c ommon to biogenic feedstocks and (2) natural versus anthropo genic origin of emissions.
Assessment scopes that extend beyond this framework include a full lifecycle analysis ([d] in Figure 3), which would take into account all upstream and downstream GHG emissions and sequestration related to feedstock production and use, including from all fossil fuel inputs used, for example, to power machinery used to harvest and transport bioge nic feedstocks. 22 The framework provides a way to address a different, narrower issue: how to assess the extent to which the production,

22 Lifecycle analysis, as defined in the RFS Regulatory Impact Analysis (E PA, 2010), is a compilation and evaluation of the inputs, outputs, and the potentia l environmental impacts of a product system through out its life cycle. In the context of GHG emissions assessm ents, lifecycle GHG emissions are the aggregate qua ntity of GHGs related to the full fuel life cycle (including direct emissions and significant indirect emission s such as emissions from land use changes), including all sta ges of fuel and feedstock production and distributi on, from feedstock generation and extraction through distrib ution, delivery, and use of the finished fuel by th e ultimate consumer, where the mass values for all GHGs are ad justed to account for their relative global warming potential (U.S Congress, 2007). See the following p ublications for the application of lifecycle analys is to biofuel-related GHG emissions: Berry et al., 1998; EPA, 2010; Heller et al., 2004; Heller et al., 2003 ; Keoleian and Volk, 2005; Mann and Spath, 1997; Spath and Man n, 2004; Spitzley and Keoleian, 2005. Other definitions and applications of lifecycle analysis may differ f rom those presented above.November 2014 9

processing, and use of biogenic material at station ary sources results in a net atmospheric contribution of biogenic CO 2 emissions. In this way, this framework aligns clos er with typical fossil fuel stationary source analyses that generally do n ot account for emissions related to fuel extraction and transport. This framework also does not include analysis of temperature change impacts, radiative forcing, and social costs and benefits re lated to biogenic feedstock use for energy ([e] in Figure 3). Different applications of the framework could require evaluation of these factors, and they could potentially be incorporated into an anal ysis using the framework. 23

Figure 3: Potential Range of Assessment Scope. The framework focuses on the first three boxes (a t hrough c). Though an equation term for leakage is included in the framework equation, a method for calculatin g leakage is not provided in this report. This framework does no t include a complete lifecycle GHG emissions analys is (d) or include analysis of temperature change impacts, rad iative forcing, and impacts on net social benefits (e).

Non-CO 2 Greenhouse Gases

The framework can take into consideration CH 4 emissions that are avoided when certain feedstocks, including waste-derived feedstocks, are used for energy. 24 CH 4 constitutes a significant

23 The scope of this framework does not assess or propose methods for assessing radiative forcing (IPCC, 2007b; Cherubini et al., 2011), changes in total at mospheric concentrations of CO 2 and other GHGs (IPCC, 2007b), potential impacts of increasing levels of a tmospheric CO 2 (changes in precipitation, temperature, etc.), or the societal costs and benefits of mitiga ting GHGs (Interagency Working Group, 2010). See IP CC (2007a) for a discussion of potential climate chang e impacts and vulnerabilities.

24 The amount of methane emitted from forest and agri culture-derived feedstocks combusted at a stationar y source is typically small and depends, in part, on the engineering conditions at the individual statio nary source (e.g., Lee et al., 2010). Broader effects such as radiative forcing, temperat ure change, and impacts on net social benefits (e) Lifecycle analysis of total GHG emissions (all upst ream and downstream GHG emissions and fluxes related to feedstock production and use, inc luding from all fossil fuel inputs) (d) Biological carbon cycle effects from leakage (inclu ding indirect land-use change related to displaced feedstock or feedstock substitute production (c) Biological carbon cycle effects of feedstock growth and/or harvest at feedstock production landscape; and feed stock processing and use (transport, storage) for use at stationary source (b) Direct biogenic CO 2 stack emissions at a stationary source (a)

Broader effects such as radiative forcing, temperat ure change, and impacts on net social benefits (e) Lifecycle analysis of total GHG emissions (all upst ream and downstream GHG emissions and fluxes related to feedstock production and use, inc luding from all fossil fuel inputs) (d) Biological carbon cycle effects from leakage (inclu ding indirect land-use change related to displaced feedstock or feedstock substitute production (c) Biological carbon cycle effects of feedstock growth and/or harvest at feedstock production landscape; and feed stock processing and use (transport, storage) for use at stationary source (b) Direct biogenic CO 2 stack emissions at a stationary source (a)November 2014 10

portion of biogenic carbon- based fluxes associated with waste -derived feedstocks, including those from landfills or MSW.

In the context of agriculture- and forest-derived f eedstocks, the framework can take into consideration landscape CH 4 emissions that are avoided when biogenic feedstock materials such as residues are collected and used for energy, instead of being open-burned or left to decay on the production landscape. However, in the United States , CH 4 is not a significant contributor to landscape carbon-based emissions related to the gro wth and harvest of biogenic feedstocks because most forest- and agriculture-derived feedst ocks are produced in upland areas rather than in areas with higher moisture content, such as rice paddies or wetlands (see Figure 4). 25 These areas do not typically generate CH 4 emissions (Anderson et al., 2010) or, in some case s, have a small negative net CH 4 fluxes (EPA, 2013b).

Figure 4: The Relative Size of Methane Emission Sour ces in the U.S. in 2012 (EPA, 2014).
Since the framework focuses specifically on biogeni c carbon-based fluxes associated with feedstock production and use at stationary sources, it does n ot include an analysis of nitrogen or nitrogen dioxide (N 2 O) emissions. 26 However, the framework equation is fle xible and thus could be used to

25 The largest sources of methane in the United States, other than waste management and rice production, are not typically associated with biogenic feedstock pr oduction and processing.

26 Nitrogen is naturally present in the atmosphere as part of the global nitrogen cycle and has a variet y of natural sources and forms. Nitrogen is essential fo r biomass growth and other important biological fun ctions, and limited usable nitrogen can limit biomass growt h. Anthropogenic nitrous oxide emissions result fro m agricultural fertilizer application, fossil fuel co mbustion, wastewater management, and industrial pro cesses and interact with the global nitrogen cycle. Howeve r, nitrogen is not carbon

- based and is not directly a part of the biological carbon cycle (though it is acknowled ged that it does indirectly as a factor of biomass growth).November 2014 11

consider non-carbon-based gases in a specific program or policy application where t he scope allows for and/or necessitates inclusion of non

- carbon

- based gases. 27



### 1.5. Organization of the Report

The next section of this report, Part 2, presents the biogenic assessment factor ( BAF ) equation and defines the terms it includes. Part 3 discusses a v ariety of ways the equation could be applied using representative and/or customized landscape and proc ess factors. Part 4 presents important technical aspects, such as baseline assessment meth od, temporal and spatial scales, leakage, and feedstock categorization, which require considerati on when applying the framework for use in a specific policy or program. Part 5 discusses key is sues that emerged in the course of designing the framework and highlights possible avenues for furth er exploration and improvement. Parts 6 and 7 include the glossary of terms used in the document and the list of references. Part 8 presents an outline of the technical appendices, which provide detailed background and illustrative case studies. Appendices A through G offer detailed disc ussion of the technical considerations used in the framework, including IPCC methods, temporal and spatial scale, feedstock categorization, process attributes, leakage, and an algebraic repre sentation of the framework. Appendices H through M provide example applications of the frame work, using publically available data applied in hypothetical scenarios, to generate illustrative values for the framework equation terms and assessment factors per different baselines, feedsto ck, and region combinations and other variations to the technical aspects of the equation. Appendix N provides an illustrative assessment of wastederived feedstocks.



## 2. Biogenic Assessment Factor Equation

2.1. Purpose of the Equation and Biogenic Attributes Con sidered

This framework presents an equation, called the bio genic assessment factor ( BAF ) equation, which includes terms representing different aspects of th e biological carbon cycle dynamics associated with biogenic feedstock growth, harvest, processing , and use at stationary sources. The equation is meant to serve as a tool that can be modified to me et the needs of policy or program applications that involve assessment of the net atmospheric cont ribution of biogenic CO 2 emissions associated with biogenic feedstock production, processing, and use at stationary sources.

The biogenic attributes related to feedstock produc tion and use fall into two categories:

(1) Biogenic landscape attributes: biogenic feedsto ck carbon and carbon-based fluxes that are associated with the landscape where a biogenic feed stock is grown and harvested/collected, and/or those that are associated with avoided emissions fr om feedstock use. The biogenic landscape attributes associated with feedstock production inc lude:

27 Appendix M includes a sensitivity run estimating t he potential impacts of including N 2 O for illustrative purposes.November 2014 12

• Net biogenic carbon sequestered or emitted through feedstock growth and removals on the feedstock production landscape;

• Avoided emissions associated with feedstocks that w ould have eventually decomposed or been burned on the production site if not removed o r some other alternative fate;

• Net biogenic carbon emissions or sequestration from non-feedstock biogenic carbon pools on the production landscape associated with land us e change (e.g., converting forests to agriculture) or land management or change in land m anagement (e.g., harvesting practices); and

• Emissions associated with leakage, such as indirect land use change from displaced feedstock or feedstock substitute production. 28

(2) Biogenic process attributes: biogenic feedstock carbon fluxes that occur during transit, processing, or use of the feedstock. The biogenic p rocess attributes associated with feedstock processing at the stationary source include:

• Biogenic feedstock carbon that leaves the stationar y source supply chain as losses during transportation, storage (decomposition), and proces sing; and

• Biogenic feedstock carbon embodied in products. Pro ducts could pass out of the supply chain and exit the stationary source either prior t o use (e.g., lumber) or after use (e.g., ash, biochar). 29

The inclusion of process attributes provides two be nefits. First, process attributes allow for a complete mass balance calculation to understand the fate of the feedstock carbon. A mass balance approach allows for an assessment of (1) the propor tion of biogenic feedstock carbon that enters the process; (2) the proportion of the feedstock ca rbon that passes through a stationary source as products; (3) the amount that is emitted to the atm osphere through losses during transport, storage, and processing; and (4) the amount that is emitted through the stack.

Second, inclusion of these process attributes allow s the framework to reflect differences in physical properties across feedstocks, such as different res ponses to the applied storage conditions (e.g., temperature and/or moisture) and conversion process es. The quantity of post-process products, such as ash, is largely determined by the feedstock used (Hiltunen et al., 2008), the combustion process, and feedstock moisture content. Generally, biogenic feedstocks produce more postcombustion materials than fossil fuels due to diffe rent characteristics such as lower heating values and more moisture content in biomass. 30

28 Indirect land use change is one form of leakage, w hich is the indirect impact that a targeted activit y in a certain place at a certain time has on carbon stora ge at another place or time (IPCC, 2000).

29 In specific applications of the framework, this pr ocess attribute category could be used to represent carbon capture and storage (CCS) if appropriate.

30 For example, see the Biomass Energy Center’s typica l heating values of different fuels (www.biomassenergycentre.org.uk).November 2014 13



### 2.2. Overview of the Biogenic Assessment Factor Equation Terms

This section first presents an overview of the BAF equation and terms, each of which are explored in depth in the following subsections–starting with the primary terms of the equation, followed by the biogenic landscape attribute terms and then the process attribute terms. Landscape attribute terms are determined by feedstock type and geograph ic feedstock production site. Process attribute terms are primarily influenced by and thu s may be evaluated by a combination of feedstock type, feedstock production region, and st ationary source process.

Net biogenic emissions ( NBE ) represents the net atmospheric biogenic CO 2 contributions associated with biogenic feedstock production, proc essing, and use at a stationary source. NBE is calculated by multiplying the potential gross emiss ions ( PGE ) of the biogenic feedstock input when used at a stationary source by the feedstock’s biog enic landscape attributes, and then by factors that adjust for biogenic feedstock carbon losses th at may occur during storage, transport, or processing, as well as biogenic feedstock carbon th at passes through the process as products instead of stack emissions.

NBE is calculated as:

 = ()(	
 + 

 +  + 
) (  ) () (EQ. 1)

Where:

• PGE is the carbon content of the biogenic feedstock us ed by a specific entity (or generally consumed). This is a quantity could be measured or estimated at different points of assessment (e.g., at the boiler mouth, stationary s ource gate, feedstock production site, or at the stack: wherever the point of assessment need s to be. Thus, this term can have different values indicated by subscripts, represent ing different points along the supply chain).

• (GROW + AVOIDEMIT + SITETNC + LEAK) represents the landscape emissions effect. The landscape emissions effect is the sum of four unitl ess factors that relate the total biogenic carbon content of the feedstock grown at the feedst ock production site, i.e., ( PGE * L ), to related landscape biogenic carbon pools.

o GROW

accounts for net feedstock growth on the biogenic feedstock production landscape (includes the specific feedstock carbon p ool only).

o AVOIDEMIT

accounts for the avoidance of estimated biogenic e missions that could have occurred on the feedstock landscape without bi ogenic feedstock removal (e.g., avoided decomposition or burning) or per an alterna tive management strategy.

o SITETNC

accounts for the estimated total net change in non

- feedstock carbon pools on the feedstock production site due to land use ma nagement or changes in land use and/or management associated with feedstock production.

o LEAK accounts for the potential leakage associated with biogenic feedstock production and use at stationary sources.

• L is a unitless adjustment factor greater than or equ al to one that represents biogenic feedstock carbon that leaves the supply chain (e.g. , via transit or decomposition, deviatedNovember 2014 14 for use as a product) between the feedstock production site and input int o the conversion process at a stationary source. L scales PGE , as it was measured at the point of assessment, up to account for any losses during transportation or storage between the feedstock production site and the point of assessment. PGE times L is thus the carbon content of the biomass that was grown at the feedstock production site in order to deliver the quantity of feedstock measured at the point of assessment.

• P is a unitless adjustment factor between zero and on e, equal to the share of the carbon content of the feedstock at the point of assessment that is emitted to the atmosphere by a stationary source (versus that which is embedded in products). In effect, this term also reflects the share of carbon that remains in produc ts, that is not emitted to the atmosphere or is sold and eventually emitted to the atmosphere by a downstream user.

The BAF equation makes the estimate of NBE relative to the PGE

that result from using a biogenic feedstock at stationary source. The BAF can be calculated by dividing NBE by PGE :


 = 

 = ()(	



) (  ) ()
 (EQ. 2)

Therefore:


 = (	
 + 

 +  + 
) (  ) () (EQ. 3)
The equations above are designed to transform a mea surable or estimated quantity—the carbon content of biogenic feedstock used at the point of assessment ( PGE )—into a quantity that cannot be directly measured—the net atmospheric biogenic CO 2 contributions of associated with the biogenic feedstock production, processing, and use, that the entity is responsible for or is generally consumed ( NBE

).



### 2.3. Primary Equation Terms

Net Biogenic Emissions ( NBE )

The NBE term represents the net atmospheric biogenic CO 2 contributions associated with different stages of biogenic feedstock production, processing , and use at a stationary source. The biogenic landscape attributes ( GROW, AVOIDEMIT, SITETNC, and LEAK , if included) and the biogenic process attributes ( L and P ) can be combined with PGE to estimate the NBE

associated with the specific biogenic feedstock and stationary source process. NBE is expressed a unit of mass.

Potential Gross Emissions ( PGE )

The PGE term represents the carbon content (expressed as u nit of mass) of biogenic feedstock and is a quantity that could be measured or estimated a t different points of assessment. The point of assessment may differ per policy or program applica tion and could include sites such as the boiler mouth, the stationary source gate, the forest or fa rm production site, or the stack. Thus, the framework, which is designed to be flexible enough such that it can be applied to a variety of programs with different requirements, allows for id entification and assessment of the biogenic feedstock carbon at different points along the supp ly chain, from the production site through the stationary source process.November 2014 15

Here is a simple example of how PGE could be evaluated at different points of assessme nt: the biogenic feedstock CO 2 potential harvested at a production site (point of assessment PGE 0 ) is 100 tons CO 2 . Assume that there are no feedstock carbon losses and no pre

- combustion products leaving the supply chain prior to entry into the co mbustion process (point of assessment PGE 1 ). Because there are no carbon losses between these two points of assessment, L = 1, and PGE 0 = PGE 1 = 100 tons CO 2 . However, if the point of assessment is after the combustion process, the biogenic CO 2 emitted from the stack would be represented by PGE S , which would be adjusted by P to reflect any post-combustion feedstock carbon in products (e .g., ash). Other examples of how L and P adjust PGE between the feedstock production landscape and lat er points of assessment are included in Appendix G.

Biogenic Assessment Factor ( BAF )

The BAF is a unitless factor that represents the net atmos pheric biogenic CO 2 contribution associated with using a biogenic feedstock at a sta tionary source, taking into consideration biogenic landscape and process attributes associated with fe edstock production, processing, and use at a stationary source, relative to the amount of biogen ic feedstock consumed. This term represents a ratio of the net biogenic carbon cycle effects from all stages of the growth, harvest/collection, processing, and use of a biogenic feedstock (i.e., the NBE ) relative to the PGE

of the biogenic feedstock consumed for energy (and resulting in sta ck emissions) at a stationary source. The purpose of deriving the unitless BAF value under this framework is to provide a univers al metric that could be applied to assess the net atmospheric biogenic CO 2 contribution of biogenic feedstock use by a particular type of source without regard t o the particular amount of feedstocks used . Once derived from assessing the carbon fluxes and mass balance associ ated with the production or use of a particular mass of feedstock in a specific type o f process, the BAF value may then be applied to examine the net atmospheric contribution of any amo unt of the relevant feedstock used by a particular stationary source or type of stationary source for which the BAF value is representative. A discussion on interpreting BAF estimates is included later in this section, after descriptions of the landscape and process attribute terms.



### 2.4. Landscape Attribute Terms

The biogenic landscape attributes described below c an be calculated in different ways, depending on the nature of the analysis being conducted and r elated parameters, such as the type of baseline being used or whether the analysis is retrospective (looking back in time) or prospective (looking forward in time). Thus, the calculation of each com ponent, units used for calculations, and their values 31 can differ according to how the framework parameters are applied (e.g., bas eline, spatial scale, temporal scale). The estimated biogenic land scape attribute terms are unitless values. 32 The text below provides some general examples of differ ent ways to calculate these terms using different baseline approaches, in an effort to help explain what the terms represent and what kind

31 For example, the use of relative values compared wi th the carbon contained in the quantity of feedstoc k being used in the calculation ( PGE )

. 32 However, the framework can be adapted to use units instead of unitless values as needed for a specifi c application.November 2014 16

of data they could include. Part 3 of this report d iscusses in detail the different types of baseline approaches, and the technical appendices include il lustrative calculations of these terms using different regions, feedstocks, and baselines.

Net Growth on the Production Landscape ( GROW )

The GROW

term represents net feedstock growth (or removals) on the production landscape. This term includes biogenic carbon directly associated w ith the feedstock carbon pool. For example, if the feedstock under consideration is roundwood, the n the GROW term would present the net change in biogenic carbon embodied in roundwood at the production landscape at the chosen scale, but it would not include changes in other carbon po ols on the production landscape, such as soil carbon or detrital carbon (which are captured in an other term, see SITETNC ). The GROW

term can be calculated in different ways, including with dif ferent baseline approaches.

For example, changes in the roundwood carbon stock (and related fluxes generated by feedstock growth and harvest) in a landscape could be evaluat ed between two points in time using historical data available for that landscape ; this method would reflect a retrospective reference point basel ine approach. Alternatively, a future anticipated basel ine approach could be used to evaluate the estimated, or relative, difference between two (or more) future potential scenarios. This approach could use, for example, recently measured roundwood data as a benchmark of biogenic feedstock carbon on the landscape in question and then create projections of roundwood management activities (and related biogenic fluxes) over time using biophysical and economic modeling to reflect potential environmental and market conditio ns in business-as-usual (BAU) and alternative scenarios. If the feedstock is or is related to an annual crop with a one-year growing cycle (e.g., co rn stover), the GROW term could equal 0, because during that one year th e carbon sequestered during growth is equal to the amount removed from the land scape.

Avoided Emissions (

AVOIDEMIT

)

The AVOIDEMIT

term accounts for the avoidance of estimated bioge nic emissions that could have occurred on the feedstock landscape without biogeni c feedstock removal (e.g., avoided decomposition or burning) or per an alternative man agement strategy (as in the case of, for example, waste -derived feedstock analysis ). Examples include forest and agricultural residues that—in the absence of feedstock removal—would natu rally decay or would be combusted onsite as part of the land management practice employed atthe production site. In the case of wastederived feedstocks like landfill gas and MSW, emiss ions could occur as part of the natural decay of the waste, with some of the carbon likely transform ed to CH 4 . This term focuses on the emissions fluxes related to the fate of the feedstock itself and does not include soil carbon implications of biogenic feedstock removal (this is captured within SITETNC , explained below).

The AVOIDEMIT

term can be calculated in different ways, as neces sitated per different baselines, feedstocks, and, in some cases, data availability. For example, with a strict interpretation of the retrospective reference point baseline, one cannot consider measured avoided emissions, because an assessment of avoided emissions requires a compa rison between two scenarios (with and without the feedstock consumption), not just lookin g at observed data between two points in time. However, outside of a strict application of the ret rospective reference point baseline approach, oneNovember 2014 17

can estimate avoided emissions by constructing a co unterfactual baseline (a type of anticipated baseline) analysis of what action and related emiss ions occurred historically versus an alternative action and estimated related emissions, or one may use estimates from the literature pertaining to possible avoided emissions from certain actions (se e Appendix H).

Using logging residues as an example, a future anti cipated baseline approach could estimate the future potential biogenic carbon-based emissions th at would have occurred if the logging residue was not used for energy and instead was open-burned or left to decompose. This approach derives an avoided emissions estimate by comparing a projec ted BAU future, in which the logging residues are open-burned or left on the forest floor at the harvest site, versus a projected alternative future
, in which all or some portion of the logging residue s are removed from the site due to increased demand for that feedstock.

As another example, MSW is a biogenic waste-derived feedstock composed of material that has been discarded, and the final disposition of this m aterial must be managed in some fashion. If the waste-derived feedstock were not processed or used by a stationary source, it would have been managed through an alternative strategy with an alt ernative emissions pathway. Therefore, for waste-derived feedstocks, AVOIDEMIT represents the avoided biogenic emissions that cou ld have occurred per an alternative management strategy ins tead of per the feedstock’s use in bioenergy production, relative to biogenic feedstock consumpt ion.

Total Net Change in Production Site Non-feedstock C arbon Pools ( SITETNC )

The SITETNC term accounts for the estimated total net change in non
- feedstock carbon pools on the feedstock production site due to land use managemen t, or changes in land use and/or management associated with feedstock production. SITETNC explicitly refers to those carbon pools on the feedstock production other than the carbon in the f eedstock, such as soil carbon, mineral carbon, below

- ground biomass, detrital pools, and others as they relate to the feedstock in question. Thus, the carbon pools included in SITETNC do not overlap with those measured for the other t erms in the framework equation (i.e., GROW and AVOIDEMIT

).

Different land uses or land management systems can engender different average or equilibrium carbon stock values for the landscape carbon pools. Land use activities, including changing from one land use or management system to another, may r esult in a net change in overall carbon stocks (including soil carbon and other carbon pools on th e landscape) over some period of time as the new use or system reaches equilibrium. If land use or management changes occur due to biog enic feedstock production, these changes can result in net emissions or net se questration, 33 depending on the feedstock type, the previous land use activi ty, and extent of the change. Changes in land use or management can lead to altered emissions profiles that can occur over a period of years.

33 For example, if land under conventional crop cultiv ation with conventional till practices is converted to produce dedicated energy crops such as switchgrass, the resulting net landscape emissions could be neg ative, indicating hi gher levels of sequestration with switchgrass produ ction (from increased carbon in the soil and root systems) than the previous land use practice.November 2014 18

Examples of land use or management changes that are captured in SITETNC might include planting a dedicated energy crop on land previously forested , switching from an annual to a perennial crop on existing agricultural land, or removing logging residues from forestland where such residues had previously been left on the forest floor and so me portion of that carbon entered into the soil carbon and possibly also the mineral carbon pools.

For example, changing land use to produce a dedicat ed energy crop such as switchgrass on farmland previously used to cultivate wheat could r esult in increased soil carbon levels, due to the extensive root systems of switchgrass and the elimi nation of tillage. In this instance, the value for SITETNC would be negative, indicating a net sequestration o f carbon resulting from the land use change. Conversely, if forest were cleared to plant switchgrass, this action would res ult in initial losses in soil carbon storage caused by disturbance during harvest, the lack of any further logging residues left to add to the soil carbon pool, losse s from the understory carbon pools associated with forested lands, and a loss in standing carbon that would have occurred if trees were replanted instead of switchgrass. In this instance, the value for SITETNC would likely be positive, indicating a net emission of biogenic CO 2 resulting from the land use change.

Deriving a value for SITETNC for a feedstock can also differ according to the b aseline approach used (as well as per the availability of data, spatial s cale, and other factors). Using a retrospective reference point baseline approach, one could assess changes in measured non-feedstock biogenic carbon pools between two points in time. For exampl e, if an increased amount of corn stover were removed from the production landscape between the r eference points, measurements may show a decline in soil carbon pool levels. This decrease m ay have occurred because of the increased stover removal or possibly that practice in addition to ot her influences (such as changes in tillage rates or fertilizer application, for example). Using a futur e anticipated baseline approach, one could simulate the anticipated BAU conditions (in this case, corn stover remains on the landscape) versus an alternative projected scenario in which the stover is removed. This baseline approach allows for evaluation of the relative estimated impact of remo ving the stover on the landscape soil carbon pool.

Leakage Associated with Feedstock Production ( LEAK )

The LEAK

term represents the potential leakage associated with changes in bi ogenic feedstock production and use at stationary sources. The IPCC defines leakage as the estimated indirect impacts of a targeted activity in a certain place a t a certain time on carbon storage at another place or time (IPCC, 2000). In the context of this report, leakage represents a ny biogenic carbon flux impacts outside of the biogenic feedstock productio n assessment boundary that can be attributed to the biogenic feedstock production activities (e. g., replacement of diverted crop, livestock, or forest products due to a change in land use from co nventional products to biogenic feedstocks). Leakage can be positive—indicating increased emissi ons outside the assessment boundary (e.g., if unmanaged forests become managed forests in order t o meet displaced market demand for traditional forest products)—or negative— indicating increased sequestration outside the assessment boundary (e.g., changes in biomass marke ts cause some landowners to convert idle cropland to forest, thereby increasing carbon stock s on the landscape).November 2014 19

One form of leakage—indirect land use change attrib utable to the production of a biogenic feedstock— can be challenging to quantify, because it often in volves a number of complex socioeconomic dynamics (e.g., trade, market interac tions) as well as biophysical impacts that occur outside of the biogenic feedstock production site a nd for which data may or may not be available. However, indirect land use change can result in sig nificant emissions if it occurs at a large scale an d involves conversion of land with relatively large p reexisting carbon stocks.

The framework includes an equation term for leakage; however, the illustrative calculations presented in the a ppendices do not explicitly quantify leakage or pro vide a method to do so. 34 With the equation as described above in this section, it would be straightforward to include the effects of leakage if they were estimated in the context of a specific policy or program.



### 2.5. Process Attribute Terms

Feedstock carbon can exit the supply chain through process
- specific factors other than as emissions from the stack. For example, losses during transpor tation of the feedstock, storage along the supply chain (e.g., due to decay), and handling can occur and can reduce the amount of biogenic feedstock available for processing or conversion. Representat ion of these supply chain losses allows for more accurate representation of the actual volumes of fe edstock harvested to meet feedstock demand and related landscape attributes.

Feedstocks may be harvested specifically for energy production or for the production of products (e.g., wood products like paper or lumber) that may consume some portion of the feedstock, with either sale of the remaining feedstock material or for onsite conversion processes (e.g., bioenergy generation). The NBE

equation allows for accounting of biogenic feedsto ck carbon in products that exit the supply chain before or after the conversio n process but are not emitted from the stack during the conversion process. This feedstock carbo n may pass through an entity to other end users (including secondary stationary sources) or possibl e longer -term carbon storage . Examples of products that may exit the supply chain prior to co nversion at the primary stationary source include:

• Wood material in commercial products (lumber, wood pulp, panel products);

• Wood residuals sold/transferred to a separate stati onary source for use as raw material or fuel;

• Bark sold to a separate (secondary) stationary sour ce for fuel or other end users for mulch;

• Agricultural by-products (e.g., stover, stalks, str aws, husks, hulls) sold/transferred to a separate stationary source for use as fuel; and

• Pulping by-products (tall oil, turpentine).

Examples of products that may exit the supply chain after conversion include:

• Distillers grains (from ethanol production);

• Ethanol; and

34 See Appendix E for a discussion of the literature o n leakage.November 2014 20

• Bottom ash, flyash, or biochar (e.g., materials con taining unburned carbon).

The biogenic process attributes in the NBE equation perform two separate functions. First, th ey relate the quantity of feedstock measured at the po int of assessment to the quantity that enters the supply chain at the feedstock production site (e.g. , farm or forest) to deliver the measured amount of feedstock to a particular (primary) stationary s ource. This adjustment is accomplished by the L term, which accounts for losses that occur and prod ucts that are produced between the farm or forest and the point of assessment. The second func tion of the process attributes is to determine the share of the total net biogenic landscape emissions associated with the amount of feedstock that was grown at the farm or forest that exit the stati onary source as stack emissions (and conversely, the remaining share that is associated with carbon that remains in products that could be passed to other uses or other entities). This adjustment is a ccomplished by the P term and accounts for products that are produced at any point in the supp ly chain and accounts for any losses that those products share responsibility for. The following se ctions describe the L and P terms in general, and there is detailed discussion of L and P in Appendix G and different illustrative equation pathways for these terms in Appendix F.

Feedstock Carbon Leaving the Supply Chain ( L )

The L term facilitates the link between the quantity of feedstock entering the supply chain at the feedstock production site (e.g., farm or forest) an d the quantity of feedstock measured at the point of assessment (e.g., the quantity received at the s tationary source or the quantity of feedstock that enters the stationary source process). Specifically, the term tracks any differences in th e mass of biogenic feedstock carbon between the feedstock pro duction site and the point of use for energy at a particular stationary source (i.e., the feedstock conversion process).

L is a unitless adjustment factor greater than or equ al to one that represents biogenic feedstock carbon that leaves the supply chain (e.g., lost via transit or decomposition, deviated for use as a product) between the feedstock production site and input into the conversion process at a stationary source. After harvest or collection at t he production site, feedstock carbon could leave the supply chain for a specific source as physical losses ( LOSS ) 35 that occur during transportation to and storage and processing at a stationary source ( e.g., decomposition during storage) or as products that exit the supply chain for a given sou rce prior to the conversion process at that source ( PROD , as further discussed in next section 36 ). If feedstock carbon exits the chain for a specif ic source, this means that more biogenic feedstock was harvested/collected than is actually used in the conversion process at that stationary source. 37 Therefore, to reflect the production landscape biogenic carbon fluxes associated with the feedstoc k produced, these deviations of feedstock

35 Different forms of physical losses ( LOSS ) could occur at different points along the supply chain. If multiple LOSS values are included, they would be reflected withi n the aggregated L term.

36 Different products could be produced at different points along the supply chain and PROD represents the carbon within a product. If multiple PROD values are included, they can be subscripted and u sed to calculate the P term (which represents the carbon not embodied in products).

37 As discussed later in this report, the collected c arbon content that leaves a primary stationary sour ce as product in the form of a fuel may be used by a seco ndary stationary source.November 2014 21

carbon need to be reflected in the biogenic carbon mass balance assessment. The L term differs from equation terms that address landscape biogenic attributes, which are based on feedstock type and the geographic location of the feedstock produc tion landscape (such as GROW
, AVOIDEMIT

, and SITETNC ). This term therefore is not dependent on landscap e attributes but rather feedstock and process type.

L is represented as a ratio between biogenic feedsto ck carbon that enters the supply chain at the farm or forest and biogenic feedstock carbon measur ed at the point of assessment. L can be calculated by using measurements of products and lo sses taken along the supply chain prior to entry into the conversion process to relate biogeni c carbon feedstock measured at the stationary source with the amount that originally entered the supply chain. Alternatively, L could be determined by dividing the harvested feedstock at t he feedstock production site by the quantity entering the conversion process. For example, assum ing no pre-conversion products, if the biogenic feedstock measures 100 tons CO 2 e at the harvest site and then measures 90 tons CO 2 e at the boiler mouth, L would be representing biogenic feedstock losses (v ia physical losses, decomposition, etc.) with a result of a positive 1.11. 38 For some feedstocks, it may be possible to generat e representative values for L based on standard patterns of products and losses in the supply chain and/or related literature estimates.

For the purposes of the L term, any biogenic carbon that leaves the supply c hain before being measured at the stationary source is assumed to be emitted to the atmosphere. (The P term will later account for any biogenic carbon that remains in products.) The biogenic feedstock physical losses are valued positively (using atmospheric acc ounting) because these losses represent an emission associated with procuring the biogenic fee dstock for energy use. This value allows for recognition of the additional feedstock that was ha rvested and related additional landscape effects that may have occurred but were not actually consum ed for energy by adjusting the landscape attribute equation terms upward.

Feedstock Carbon Embodied in Products ( P )

The P term uses the share of biogenic carbon feedstock t hat is emitted out of the stationary source stack and the share that is embedded in products (PROD) to link the stationary source to the total net landscape emissions associated with the full am ount of biogenic feedstock that entered the supply chain. The P

term, therefore, allows the proportion of biogenic feedstock carbon that is not emitted from the stationary source stack but instea d is embodied in products that exit the stationary source post

- conversion (e.g., ash, ethanol, distillers grains, biochar) or pre -conversion (biogenic feedstock material in products that leave the supply chain between the feedstock production site and conversion) to pass through the mass balance accounting as carbon not emitted by the stationary source. It also allocates any carbon losses between the sta tionary source and any products produced after the losses occurred.

38 1.11 = 100 tons harvested/90 tons input to bioenerg y conversion processNovember 2014 22

The P term differs from equation terms that address landscape biogenic attributes, which are based on feedstock type and the geographic location of th e feedstock production landscape (such as GROW
, AVOIDEMIT

, and SITETNC

). The P term apportions biogenic feedstock carbon between products exiting the supply chain (non

- emitted) and stack emissions and therefore is not dependent on landscape attributes but rather feedst ock and process type.

Values for the P

term could differ per feedstock type and stationary sour ce process . Some feedstocks have various alternative uses and produc t possibilities due to their biophysical properties. Stationary sources will have different purposes in terms of product manufacture and/or energy production as well as different technical pr ocesses and efficiencies within those processes that may yield varying levels of pre- and post-conv ersion product materials.

For example, an ethanol plant transforms biogenic f eedstock (e.g., corn) into ethanol fuel through a fermentation process. A portion of the carbon conta ined in the biogenic feedstock that is input to the conversion process forms the basis of the refin ed ethanol fuel. While fermentation results in process stack emissions of CO 2 from the stationary source, which may include some portion of carbon from the biogenic feedstock, the majority of carbon within the biogenic feedstock entering the stationary source fermentation process actually exits the stationary source not as stack emissions but as product (i.e., ethanol) or by-prod uct (e.g., distillers grains). 39 Although the vast majority of the biogenic carbon embodied in the eth anol will be subsequently emitted as CO 2 by mobile emissions sources that use the ethanol as fu el, these “downstream” CO 2 emissions are not emitted by the stationary source that produced the ethanol. As a result, this portion of the biogenic feedstock carbon is not included in the final resul t when calculating the assessment factor for the biogenic CO 2 emissions from the ethanol facility.

As illustrated by the example above, the P term carves out the carbon contained in biogenic feedstock carbon that is not emitted from the stati onary source stack but passes through the stationary source supply chain versus the biogenic carbon that it emitted from the stack; however, the carbon embodied in products leaving the station ary source may be subsequently used by another stationary source. Thus, the biogenic carbo n stored in products from the producing or primary stationary source could in turn be consider ed as a feedstock (secondary use feedstock) input to a different stationary source (secondary s tationary source) conversion process for energy.

Using sawmill mill residues that are sold to a sepa rate stationary source as an example, if a secondary stationary source uses the mill residues procured from the primary stationary source (the sawmill in this example) as biogenic input for energy, then the mill residues could be considered a biogenic feedstock and the framework c ould be applied as necessary. However, those emissions from the secondary stationary source that uses the mill residues for energy are not

39 Some secondary products generated during productio n of primary products, such as distillers grains created during ethanol production, currently have a lternative non-energy market uses, such as livestoc k feed. If, however, distillers’ grains were instead used f or energy at the stationary source, they would like ly be considered a biogenic feedstock input and thus may require assessment with the framework.November 2014 23

included among the biogenic CO 2 emissions from the primary stationary source that produced the biogenic feedstock (mill residues).

Figure 5 includes the landscape and process attribu tes as reflected by the specific equation terms within the simple flow diagram graphic presented in Figure 1 above. The landscape attributes are defined by the initial point of assessment or the f eedstock production site. Calculation of all remaining equation terms is predicated on emissions changes on the landscape. That is, each unit of biogenic CO 2 transferred from the landscape to the stationary s ource is adjusted to reflect net emissions on the landscape that occurred due to the growth and harvest of that feedstock. As Figure 5 shows, once the primary feedstock is harvested, b iogenic process attributes can be evaluated at various points of assessment (both outside of and w ithin the stationary source), and these terms are used to further adjust the net biogenic emissio ns that occur when the feedstock is converted to
energy.

Figure 5: Diagram with Equation Terms Included. This figure is the same as Figure 4 except that in addition to illustrating the basic processes (i.e., biogenic carbon and carbon fluxes) addressed in the framewo rk, this graphic also includes the equation terms that represent eac h of these basic process elements. The framework ad dresses biogenic carbon flows and fluxes associated with th e production, processing, and use of a biogenic fee dstock, including biogenic carbon impacts at the production landscape ( GROW, AVOIDEMIT, SITETNC ) as well as indirect impacts on landscapes other than the production lan dscape ( LEAK ); biogenic feedstock carbon losses during transport, storage, and processing of the feedstock ( L ); and biogenic feedstock carbon that is embodied i n products that exit the supply chain or stationary source in means other than the stack ( P ).November 2014 24



### 2.6. Interpreting the Biogenic Assessment Factor

A policy or program could define and interpret the BAF results differently. For the purposes of this report, the BAF

results can be interpreted as follows:

• A BAF of 0 means that 0% of the direct biogenic CO 2 emissions at a stationary source contribute additional biogenic CO 2 to the atmosphere—as compared with the baseline or reference condition—due to biological carbon cyclin g associated with feedstock production and use. In other words, changes in net biogenic CO 2 emissions and sequestration from biological carbon cycle processes and other offsite and process factors exactly offset the direct biogenic CO 2 emissions from the stationary source.

• A BAF of 1 means that 100% of the direct biogenic CO 2 emissions at a stationary source contribute additional biogenic CO 2 to the atmosphere—as compared with the baseline or reference condition—due to biological carbon cyclin g associated with feedstock production and use . In other words, biological carbon cycle processes and other offsite and process factors do not counterbalance any of the direct bio genic CO 2 emissions from the stationary

source.

• A BAF value between 0 and 1, such as 0.2 or 0.5, means th at biological carbon cycle effects related to biogenic feedstock growth, harvest, and use counterbalance a proportion of the direct biogenic CO 2 emissions from a stationary source (e.g., a 0.2 BAF means a net atmospheric contribution of 20% of the direct bioge nic CO 2 emissions, while the remaining 80% is counterbalanced by landscape and process att ributes).

• A negative BAF

value, such as −0.2, means that the biological car bon cycle effects of biogenic feedstock growth, harvest, and use more than fully counterbalance the direct biogenic CO 2 emissions at the stationary source. Negative values represent an emissions reduction that occurs as the result of a stationary source’s use o f a biogenic feedstock. In this case, 20% more CO 2 is sequestered (or 20% more CO 2 emissions are avoided) as a result of stationary source use of a biogenic feedstock than is directly emitted by a stationary source.

• A BAF value greater than 1 means that the biological carb on cycle effects related to biogenic feedstock growth, harvest, and use result in a net CO 2 emissions increase, over and above the CO 2 emissions coming directly from the facility. For example, where direct land use change causes a substantial decline in carbon stock s on the landscape, or where leakage effects are included and found to be substantial, a BAF value may be greater than 1.

When all the equation terms are included, the estimated value of the BAF depends on the relative magnitudes of NBE and PGE . Higher values of NBE that approximate or exceed PGE lead to larger BAF values. Smaller values of NBE will result in small or negligible BAF values.



### 2.7. Possible Applications of the Biogenic Assessment Fa ctor

The BAF , or elements of the BAF equation, could be applied in various policy scena rios. For example, a possible scenario may include calculatin g BAF values that may be used to determine whether to regulate direct biogenic CO 2 emissions from a stationary source. The framework could also be used to calculate an adjusted CO 2 emissions value at a stationary source by multiply ing a BAF value by the direct emissions of biogenic CO 2 from the individual stationary source. To conductNovember 2014 25

these calculations, the framework equation and its terms may need to be adjusted or modified to accommodate only the specific parameters required f or a particular application. F or example, if an application of the framework is limited to an asses sment of the environmental conditions of the landscape, the equation may only include use of the landscape attribute terms ( GROW
, AVOIDEMIT, SITETNC , and LEAK

).



## 3. Representative and Customized Approaches to Landsca pe and Process Attributes

This section describes three ways biogenic assessme nt factor equation terms and BAF s could be used: a “representative factor” approach, a “customized factor ” approach, and a hybrid approach. For each approach, this section discusses an exampl e of how a program or policy might apply the approach to calculate BAF equation teams and BAF values. These approaches are for illustrative purposes only and not meant to be prescriptive or s pecific to any particular policy or program.



### 3.1. Representative Factor Approach

A representative factor approach could include pre

- calculated, representative BAF values for specific feedstocks that take into account the land scape attributes and process attributes associated with that feedstock. Specifically , this approach could use an assessment of the biog enic landscape attributes (based on feedstock type and production site/ region where the feedstock is produced) as well as the process attributes (based on the type o f stationary source process and types of biomass handling such as transport and storage) to generate generally applicable values for those attributes. These representative factor values could be calcula ted using various spatial and temporal scales, baselines, and feedstock and process types as neede d, and could be presented in a simple format (e.g., look

- up tables). Using the representative factor approach, stationar y sources would not need to make case-specific measurements and calculations of carbon stocks and fluxes for each term in the BAF equation in order to calculate a BAF for each feedstock used.

The representative values generated through this ap proach could require updating every 5 to 10 years to ensure that the values reflect updated dat a , including measured changes to biogenic landscape attributes, changes in process technologi es, improvements in carbon measurement technologies, and other information necessary to up date tools and methods used to calculate the representative values.

Using th e representative factor approach, the following inf ormation would likely be needed by users to calculate a BAF

value:

• Type of biogenic feedstock ;

• Region where the feedstock was produced ;

• Type of stationary source process;

• Transportation and storage meth ods and duration of feedstock storage ; and

• Measured or estimated amount of feedstock used or stack emissions.November 2014 26



### 3.2. Customized Factor Approach

A customized factor approach would enable stationary sources to calculate their own values for each of the terms in the BAF equation, rather than using representative values as discussed above. Under this approach, the biogenic landscape and pro cess attributes would be calculated using parameters specific to the specific stationary sour ce using a specific biogenic feedstock.

For landscape attributes, a customized factor appro ach could require calculation of site-specific values for equation terms that characterize the dis tinct landscape where a feedstock is produced and harvested. For example, a stationary source wou ld likely need information from the forest landowner regarding forest growth and removals to c alculate a value representing net feedstock growth on the production landscape. The stationary source may also need data on delivered quantities of biogenic feedstock, as well as any bi ogenic feedstock losses that occur while the feedstock is being transported or stored prior to u se.
A number of technical and implementation issues wou ld need to be addressed to implement a customized factor approach, including:

• Identification of appropriate data collection appro aches for carbon stocks and carbon fluxes, by feedstock type and framework equation te rm;

• Identification of the appropriate level of data qua lity (possibly including statistical sampling protocols and acceptable standard error th resholds);

• Development of protocols for identifying the distin ct landscape where a biogenic feedstock is produced, including identification of landowners hip and management practices;

• Development of protocols to validate monitoring and reporting, if necessary 40 ; and

• Development of protocols , or modification of existing protocols, if needed to track a biogenic feedstock from the specific landscape wher e it was produced to the stationary source where it was used. 41

A customized factor approach may provide for greater flexibility, and p ossibly greater accuracy, in instances where a stationary source can identify th e specific land base where a biogenic feedstock was grown and harvested. Such an approach would als o allow a stationary source to provide greater specificity about the process attribute-rel ated biogenic CO 2 fluxes that occur onsite at a stationary source, as a result of biogenic feedstoc k storage and processing. This approach would allow a stationary source to apply detailed knowled ge about specific stationary source technology

40 For example, this might involve protocols for indep endent verification of measurement and reporting, including protocols for accreditation of verifiers.

41 It should be noted that a representative feedstock approach might also need to employ a limited chain- ofcustody tracking process in certain instances. For example, tracking might be needed to identify a bio genic feedstock’s source region, considering the potentia l for inter -regio nal transportation of feedstocks. Tracking might also be necessary where the feedstock type is not easily identifiable from visual inspection or chemical analysis alone. For example, the origin of wood chi ps may not be easily identifiable through visual in spection alone, because wood chips could be sourced from dif ferent woody biomass feedstocks, such as roundwood, forest residues, or mill residues, or might contain a mixture of woody biomass from multiple feedstock types or source regions.November 2014 27

and operations that may differ from assumptions app lied in developing representative factor values for process attributes.



### 3.3. Hybrid Approach

Depending on the availability o f information, a combination of both approaches des cribed above could be used. A hybrid approach could involve usin g a customized factor for some of the terms in the BAF equation and a representative factor for the other terms. For example, consider a stationary source that uses logging residues from the Northeas tern U. S. The stationary source could use the representative factor values provided for landscape attributes associated with logging residues from the region (equation terms GROW, SITETNC
, AVOIDEMIT

, and LEAK

) along with customized factors for process attributes that the entity calc ulates from information on its processes. Conversely, representative factors could be provide d for general stationary source processes, and the stationary source could calculate customized fa ctor values for landscape attributes associated with the landscape producing the logging residues i t uses.


## 4. Technical Considerations

The sections below describe key technical and metho dological components to consider when evaluating biogenic CO 2 fluxes related to a stationary source’s use of biogenic feedstocks:

• Baselines

• T emporal scale

• Spatial scale

• L eakage

• Feedstock cat egorization

The implications of making particular choices with respect to each of these key issues are discussed below, and the technical appendices provide illustr ative examples of how these considerations might be applied in practice.



### 4.1. Baselines

In basic terms, defining a baseline creates a found ation for comparing changes to a system over time or at different points in time. 42 A baseline can be constant over time or it can var y with time to represent some measured, anticipated, or desired ch ange. However, the choice of baseline largely depends on the analysis or application at hand. The choice of which baseline approach to use also

42 Definitions for baseline do vary but can be define d as “the reference for measurable quantities from which an alternative outcome can be measured” (IPCC, 2007 c) or “the baseline (or reference) is the state aga inst which change is measured. It might be a ‘current ba seline,’ in which case it represents observable, pr esent-day conditions. It might also be a ‘future baseline,’ w hich is a projected future set of conditions exclud ing the driving factor of interest. Alternative interpretat ions of the reference conditions can give rise to m ultiple baselines” (IPCC, 2007c).November 2014 28

depends in part on the time frame in which the anal ysis should take place, the context in which the approach is applied, and the available data.

The framework methodology involves a baseline again st which the emissions and sequestration associated with the production, processing, and use of biogenic feedstocks at stationary sources can be estimated and analyzed. Ultimately, the determin ation of which baseline to use can influence the outcome of the assessment and will likely depend on the specific context in which the framework is applied.

This framework examines two baseline approaches, al though other baselines could be used when applying the framework. The first baseline approach is the reference point baseline, which assesses the estimated net change in biogenic CO 2 fluxes and/or carbon stocks between two points in time (Fargione et al., 2008; UNFCCC, 2009). The second b aseline approach, the anticipated baseline, assesses the estimated net changes of biogenic CO 2 fluxes and/or carbon stocks between two scenarios at the same specified point in time. The anticipated baseline approach estimates the difference between a BAU scenario and an alternativ e counterfactual scenario that includes changes in environmental, economic, and/or policy condition s (Searchinger et al., 2009; Sohngen and Sedjo, 2000).

The reference point and anticipated baseline approa ches can both be used for prospective or retrospective analyses. A prospective analysis asse sses the potential impact of a particular policy or change in biomass use on future biogenic CO 2 fluxes and/or carbon stocks, 43 while a retrospective analysis can examine the impact that an existing po licy or biomass utilization practice has made on net biogenic CO 2 fluxes in the past. Prospective analyses commonly use the anticipated baseline approach to compare two future scenarios. A referen ce point baseline may also be used in a prospective analysis that compares modeled alternat ive future biogenic CO 2 fluxes to modeled or observed biogenic CO 2 fluxes at an earlier point in time. Retrospective analyses can use the reference point approach to compare current or rece nt biogenic CO 2 fluxes to historical biogenic CO 2 fluxes, because this approach allows for the possi bility of using only observed data. However, an anticipated baseline may also be used in a retro spective analysis comparing current/recent biogenic CO 2 fluxes with modeled counterfactual current/recent biogenic CO 2 fluxes.

The two baseline approaches discussed above can pro vide useful insights in the assessment of biogenic CO 2 fluxes. There is no single correct answer for whic h baseline to choose, because different baselines help answer different questions . The sections below describe how reference point baselines and anticipated baselines can be co nstructed and applied and discuss the implications of using either baseline approach in t he framework.

Reference Point Baseline
The reference point approach can be applied in situ ations that seek to measure what has or has not occurred on the landscape between two different poi nts in time. The reference point baseline

43 The rest of this section uses the term biogenic CO 2 fluxes as the unit of evaluation to represent chan ges in carbon stocks as well as methane fluxes.November 2014 29

approach seeks to answer the question, “ Is there more or less carbon stored in the system ( e.g., at the biogenic feedstock production site or region) at th e end of an assessment period than there was at the beginning?

” This approach allows for estimation of net carbon changes between different points in time. It establishes as the baseline the carbon sto ck (or carbon-based flux) on a given land base (i.e
., total stocks of carbon stored in vegetation and soi ls) at a given point in time (or over a given time interval). Land based carbon stocks or fluxes can t hen be evaluated against this reference point.

A reference point approach could be applied in a re trospective analysis using observed current (time t) carbon stocks and observed past (time t−1) carbon stocks, or in a prospective analysis, using future (time t+1) values instead of observed values. One potential benefit of the retrospective reference point baseline is that it enables the use r to rely on historic carbon stock data, which may be available for selected feedstocks (e.g., U.S. Fo rest Service Data for roundwood). Some of the illustrative BAF calculations provided in the technical appendices o f this report apply the reference point baseline retrospectively, relying primarily o n historical observed or modeled data or related estimates from the literature. To develop the basel ine under this retrospective reference point approach, it is appropriate to look at carbon stock s in the present and/or in the recent past to see how those stocks change between the two points of e valuation. 44

There are some limitations with using this approach . For example, land use, land use change, and forestry (LULUCF) data are often more uncertain tha n data from other sectors (e.g., energy) due to the complexity of the natural systems and the varie ty of anthropogenic uses and activities involved. Also, although tools and methods to monitor and mod el these systems exist, land-based ecosystems are ever changing and differ across different parts of the country, making it difficult to measure them regularly and robustly. Challenges can result from numerous factors, including insufficient data or information about a physical or economic pr ocess being evaluated, model/data specification, inherent statistical uncertainties, etc. Other limitations with the reference point baseline approach stem from gaps in data coverage ( e.g., some data sets are not national or do not exist for certain carbon pools or management practi ces), size of samples, and frequency of samples. Furthermore, when conducted at the regional level, the impacts of external drivers on the biogenic carbon fluxes on the production landscape may not b e identifiable.

In addition, other limitations may result from usin g the retrospective reference point baseline approach, including the inability to attribute land scape biogenic carbon fluxes directly to stationary source use, or to assess additionality. Additionali ty is a criterion for assessing whether an activity has resulted in biogenic carbon emission reductions or removals in addition to those that would have occurred in the absence of the activity. Addit ionality can be gauged as the difference in net atmospheric CO 2 emissions with and without changes in biogenic fee dstock use (Murray et al., 2007). If the goal of an analysis is to assess addi tionality (i.e., what would have happened in the absence of increased/decreased biomass use) or the potential impact of a marginal user of biogenic

44 This baseline could include a value from carbon st ocks in one year or an average over a range of year s.November 2014 30

feedstocks, the use of the retrospective reference point baseline (as defined here) would not be the most appropriate choice because it does not include comparison with a counterfactual scenario. 45

Unless conducted at a small scale, the retrospectiv e reference point approach does not show the extent to which the increased or decreased use of a biogenic feedstock at a specific stationary source is contributing to the net carbon stock chan ge.
At larger scales like, for example, the regional le vel, the retrospective reference point approach can indicate whether there has been a net change of the feedstock carbon stock within a given region. If historic data are readily available to assess the d ifference in regional feedstock carbon stocks between two points in time, then the reference poin t offers a practical method for determining if the area where the feedstock is harvested is a sour ce or a sink (as discussed in Appendices H and I). In certain contexts, this observed carbon stock cha nge can serve as a proxy for the net emissions contribution of biogenic feedstock production and h arvest. However, relevant historic data may not exist for retrospective assessment of particular fe edstocks (such as agricultural by-products or energy crops not currently grown at commercial scal e).

The retrospective reference point approach is prese nted in the appendices of this report as an example to show the functionality of the framework using historical data on regional land carbon stock changes to assess changes in net carbon stock s between two points in time retrospectively. These illustrative applications of the retrospectiv e reference point baseline use U.S. LULUCF and agriculture data to establish feedstock- and region -specific reference carbon stocks to estimate landscape changes in carbon stock pools associated with the production and use of a specific biogenic feedstock over a historic time frame. Thes e changes are reflected in the illustrative landscape attribute values presented in Appendix H and the case studies in Appendix I.

Anticipated Baseline
The anticipated baseline approach provides a means to gauge the incremental impact of biogenic feedstock production and use for energy, especially in the case of forest-derived feedstocks or feedstock production that causes land use and/or ma nagement changes (e.g., afforestation to provide wood for energy). The anticipated baseline approach seeks to answer the question, “Is more or less carbon stored in the system over time compa red to what could have been stored in the absence of changes in biogenic feedstock use?” This approach allows for estimation of the carbon stock levels (and associated carbon benefits or losses) that mig ht have existed over time in the absence of biogenic feedstock use, or what could have happened under an alternate fate (i.e., a counterfactual scenario to what actually took place, which is the basis of how avoided emissions can be estimated).

45 The SAB Panel found that the reference point appro ach was limited in that it could not identify and a ssess additionality: “[the reference point] approach is not adequate in cases where feedstocks accumulate over long time periods because it does not allow for the esti mation of the incremental effect on greenhouse gas emissions over time of feedstock use. To gauge the incremental effect on forest carbon stocks due to t he use of forest-derived woody biomass..., an anticipated basel ine approach is needed” (SAB Panel peer review, p. 33).November 2014 31

Typically, applications of an anticipated baseline first establish a BAU baseline scenario, 46 a reference level that establishes historic or simula tes future anticipated biogenic feedstock use and related environmental and socioeconomic conditions and impacts along a specified time scale. The BAU baseline estimates are then compared with an al ternative scenario of changed (e.g., increased or decreased) biogenic feedstock demand. The result ing difference between these scenarios can indicate the possible impacts of biogenic feedstock use.
The anticipated baseline approach differs from the reference point baseline approach in several key ways, including:

• The anticipated baseline is a comparison between tw o distinct scenarios, not two points in time. Although there is a time element to the antic ipated baseline approach, the basis of comparison for evaluating emissions changes is the difference between two modeled
scenarios.

• Because it is a comparison between scenarios, the a nticipated baseline approach also allows for the evaluation of additionality (landscape emis sions with and without additional feedstock harvest).

• The anticipated baseline approach relies on models that can project alternative futures, whereas a reference point approach can only require historic observed or modeled data.

A challenge with an anticipated baseline approach l ies in how to define what would have been expected to occur in the BAU or counterfactual scen arios. In a retrospective application, the challenge is to consider what the counterfactual “w ithout biomass” scenario would have been, after the period has ended (e.g., would more or less biom ass have been used than what actually was used). In a prospective application, one would need to simulate the future BAU biomass consumption and future socioeconomic and environmen tal conditions (including carbon stocks and fluxes related to the biomass consumption) as well as the counterfactual scenario. These baselines could be established through various means, such as dynamic modeling or extrapolation of historic trends, each with different related kinds of uncert ainty.

Uncertainties exist when applying future anticipate d baselines, including insufficient data or information about a physical or economic process be ing evaluated, model/data specification, inherent statistical uncertainties, etc. The refere nce point section discussed limitations related to LULUCF-related data, which are also relevant for fu ture anticipated baselines based on that data. Also, estimation of future BAU or counterfactual sc enarios for LULUCF is often more challenging than such efforts with other sectors (e.g., energy) because it involves generating future projections of the many complex natural systems, anthropogenic activities, and related carbon stocks and fluxes. The development of the future anticipated B AU baseline and counterfactual scenarios require simulations or projections of future biophy sical, policy, and economic conditions, which often rely on the best available information and ex pectations to inform model parameters and assumptions. Model parameters and assumptions about future or counterfactual economic, policy,

46 BAU baseline cases could assume that future trends follow those of the past and may or may not involv e anticipated policy and/or market changes in the fut ure.November 2014 32

and biophysical conditions play a significant role in resulting estimates of future or counterfactual potential carbon stocks and other impacts from chan ges in biogenic feedstock demand and use.

In the technical appendices, the framework uses the anticipated baseline in a prospective manner, referred to as the future anticipated baseline, and relies on historical biophysical and economic information as well as expectations of future envir onmental and biophysical conditions to develop illustrative future BAU baseline and alternative sc enarios.. Future anticipated baselines have been broadly applied to evaluate the potential future im pacts of specific policies or the marginal impacts of an action, holding everything else constant betw een future scenarios. 47 This baseline approach allows for evaluation of possible effects of statio nary source biogenic feedstock demand changes on the biogenic feedstock production landscape and rel ated net biogenic CO 2 fluxes over time by capturing the complex interactions between biogenic feedstock production and forest product markets, including: biogenic feedstock demand; mark et-driven changes in planting, management, and harvest regimes; direct land use management or land use change emissions; and possible leakage (market substitution or other indirect land use changes and related GHG implications). This approach also allows for consideration of alternate fates (e.g., what would happen to the feedstock if not combusted for energy) and regional differenc es as well as behavioral responses to market
incentives.

Application of Different Baseline Approaches
This report recognizes that the approach to develop ing baselines may differ according to the specific application of the framework; therefore, t he framework methodology provides flexibility to support both reference point and anticipated baseli nes and possibly others. Application of this framework in a specific program or policy may invol ve use of either baseline approach in a retrospective or prospective time frame, some combi nation of the two, or some other approach.

The choice of which baseline approach to apply nece ssarily involves tradeoffs, which must be considered by the user of the framework. Also, depe nding on the application of the framework, choices need to be made about other framework compo nents, such as spatial or temporal scale. Important considerations when determining an approp riate baseline approach include:

• Data availability: In the absence of adequate observational data that can be used to estimate carbon stock changes between two points in time, the anticipated baseline approach could be used to simulate the change in em issions from increased biomass use.

• Model Availability: Though both baselines can be limited by data avail ability, the anticipated baseline also depends on economic model s with certain components (discussed in Appendix J). Depending on the feedstock, or the choice of spatial or temporal scale,

47 In contrast, with a retrospective reference point approach there may have been other factors that cha nged in the historical period (e.g., economic growth, na tural disturbance, new government policy encouragin g tree planting) besides a change in biogenic feedstock pr oduction for energy use that affect biogenic feedst ock carbon levels on the assessment landscape, but the impact of those factors cannot be separated from th e impact of bioenergy. This is an important distincti on between the retrospective reference point and fu ture anticipated baseline approaches.November 2014 33

necessary models/model components may not exist or be accessible. For instance, using economic models to simulate changes in emissions at a spatially disaggregated level (state or sub-state) may not be possible at the time of as sessment and could require new model development specific to the assessment region.

• Specific policy or program goals: Choice of baseline approach should account for the specific goals of the policy or program. If a progr am requires an evaluation of the additional impact of a new source of feedstock consumption, th en the reference point baseline approach would not be appropriate. However, if prog rams require an assessment of historic emissions, a reference point approach could be nece ssary. 48

To highlight the various issues surrounding baselin es and facilitate consideration of each approach at the regional level, the two different baselines are applied to generate illustrative values in the appendices as follows: retrospective reference poin t analysis of forest- and agriculture-derived feedstocks in Appendices H and I; future anticipate d baseline of forest- and agriculture-derived feedstocks in Appendices K and L 49 ; Appendix M gives an overall comparison and discus sion of different examples presented in appendices from bot h baseline approaches. For the waste-derived feedstocks analysis in Appendix N, an anticipated b aseline (counterfactual) assessment between different alternative fate pathways and related avo ided emissions is presented. 50



### 4.2. Temporal Scale

Assessing biological phenomena inherently necessita tes considerations of time. Although different terrestrial and atmospheric processes unfold over t ime frames ranging from moments to millennia, policy is typically implemented over time frames no t as far ranging. There are different perspectives about how to assess future emissions t rajectories (e.g., Dornburg and Marland, 2008; Fargione, 2008; Kendall et al., 2009; Levasseur et al., 2010; Walker et al., 2010; Cherubini et al, 2011; Mitchell et al., 2012; Helin et al., 2013; Mi ner et al., 2014).

48 For example, with the reference point baseline app roach applied regionally, it is possible to estimat e the change in net carbon stocks between two points in t ime but not possible to estimate how many additiona l (or how many fewer) stationary sources are using the bi ogenic feedstock or the impact per each individual user on the regional landscape. With the future anticipa ted approach in a regional application, different l evels of potential future biogenic feedstock demand from and related landscape results (e.g., net forest carbon fluxes) within a region are estimated and compared against a regional BAU reference scenario. Though estimated future increased demand of biogenic feedstocks from a specific region implicitly reflects the impacts of additional users, this approach also cannot isolate the marginal impacts of each additional feedstock user, unless the model(s) used is detailed and disaggrega ted enough to allow for such detailed analysis. Disaggregation at the level of SubRegional Timber S upply (SRTS) model (Abt et al., 2009; Prestemon and Abt, 2002), for instance, is intended to capture impacts on individual wood basket regions.

49 Appendix H presents illustrative biogenic landscap e attributes using the retrospective reference poin t baseline, which are applied in example case studies in Appendix I. Appendix K discusses illustrative f uture anticipated baseline construction methods based on zero biomass use, constant 2009 levels of biogenic feedstock use, and various Annual Energy Outlook (A EO)-derived scenarios. A subset of these baselines is then compared with simulated alternative future sce narios with increased production and use of biogeni c feedstocks for energy and industrial processes at s tationary sources in Appendix L.

50 The analysis for waste-derived feedstocks consists of counterfactual exercise, comparing different pr actices and alternative fates.November 2014 34

GHG emissions assessment can be sensitive to accoun ting time frames, emissions flux trajectories, and reporting periods. In some situations, direct e missions of all types, including from biological phenomena, are accounted for over a single year, in terms of tons of emissions per year. For example, in national-scale GHG inventories submitte d by Parties to the United Nations Framework Convention on Climate Change (UNFCCC), emissions ar e estimated and reported for all sectors and Parties over a calendar year (UNFCCC, 2006). The U. S. GHG Reporting Program also has annual emissions reporting requirements (EPA, 2013a).

However, biogenic CO 2 fluxes related to the production and use of some b iogenic feedstocks can take place over periods of time longer than a year, or even decades, because of the time needed to grow to a harvestable size (in the case of trees) a nd the time needed for some landscape effects related to feedstock production to unfold (such as soil carbon pool effects and decay rates). The potential mismatch of temporal scales between direc t emissions coming out of the stack when a biogenic feedstock is used and longer term factors, including carbon sequestration via feedstock growth or carbon accumulation in soil, adds to the complexity of biogenic CO 2 assessments. Thus, it may be necessary to distinguish between the “emissi ons horizon” and the “assessment horizon.” The emissions horizon is the period of time during which the biogenic carbon-based fluxes resulting from actions taking place today actually occur, while the assessment horizon is a period of time selected for the analysis of the carbon fluxes . In effect, these time horizons can differ significantly.

When establishing a temporal scale for assessing th e biogenic landscape attributes and related biogenic CO 2 fluxes, determinations should include:

• Whether to include all past and/or future estimated biogenic carbon-based emissions related to biogenic feedstock production or the emi ssions horizon (e.g., from potential avoided decay from feedstock use or long-term impac ts on soil carbon stocks);

• Whether to establish a specified time frame over wh ich emissions fluxes are accounted for (which could be shorter or longer than the emission s horizon) or just emissions that occur in the year the biogenic feedstock is used or the a ccounting horizon; and

• Whether future emissions and/or sequestration shoul d be valued the same as emissions occurring today or whether future emissions should be discounted over time.

Implications of Different Temporal Scales
The selection of temporal scale could affect the re sults of any analysis evaluating biogenic CO 2 fluxes associated with biogenic feedstock productio n. Considerations related to the choice of temporal scale can differ with feedstock type and g eographic location (e.g., growth rates of different tree species in different locations). Also, determi ning the temporal scale for assessing biogenic CO 2 fluxes has implications and tradeoffs related to me asurement, reporting, and verification of estimates and the ability to reflect carbon dynamic s of feedstock production over time.

In terms of the science, there is no single correct answer for the choice of a timescale for assessment: different timescales allow for evaluati on of different questions and contexts. Therefore,November 2014 35

the choice of timescale is largely dependent on spe cific applications of the framework and related needs or preferences for accounting time such as us e of discount rates, inclusion of estimated future climate change impacts, radiative forcing, o r any other parameters for analysis.

There are important tradeoffs to consider when choo sing a temporal scale for assessment, especially in the context of a prospective analysis , when considering feedstocks with long rotations, and in applications where past biogenic CO 2 fluxes and activities may be included. The followi ng factors should be taken into account to help determ ine the most appropriate temporal scale:

• Feedstock choice: Different feedstocks have different growth and har vest intervals, so the choice of temporal scale could vary depending on wh at feedstock type is being evaluated. For example, if the feedstock is an annual crop wit h a one-year growing cycle (e.g., corn stover), a shorter time frame could be sufficient. However, for forest-derived biomass, a longer-term assessment would likely be necessary to capture emissions changes over full growth and harvest cycles (which can range from 20 to 80 years for managed forest systems in the United States).

• Region: Biophysical landscape attributes vary from region to region, so depending on where the assessment takes place, a longer temporal scale might be necessary. For example, setting an assessment time scale nationally for the United States for roundwood might not be appropriate, because the growth and harvest inte rvals for short-rotation tree species in the Southeastern U.S. are much different than manag ed forest systems elsewhere in the U.S. Setting a temporal scale that is too short could ca use an assessment to miss decades of tree growth and harvest cycles and would inaccurately re present investment decisions driving land use and management in the Pacific Northwest. C onversely, very long time frames (e.g., 100+ years) can obscure important shorter term land use activities and related biogenic CO 2 fluxes.

• Data Availability: Also important is the availability of data and/or models for conducting assessments. For instance, a one-year assessment sc ale using the retrospective reference point baseline would not be possible if assessment of the feedstock relies on observational data that are compiled only every 5+ years.

• Policy or program application: Finally, determination of temporal scale should ali gn with the specific goals of the policy or program that th e framework is applied to (e.g., baseline time frames for initial assessment or renewal of re quirements under existing programs).

Potential Framework Applications of Time

The framework can address the issue of time differe ntly depending on the policy or program application of the framework and the baseline used in that application. The discussion here focuses on addressing time within each of the two baseline approaches described above and used in the technical appendices: the retrospective reference p oint baseline and future anticipated baseline.

When using a retrospective reference point baseline (comparing two points in time historically), explicit choices need to be made about (1) the rang e of historical years included in the accounting period (e.g., will the time between reference point s be 5, 10, 20 years?) and (2) at what point in time to start the analysis. The answer to either or both of these questions could be based in part onNovember 2014 36

data availability. For example, because of differen t data collection methods, some parts of the country may have more historical and/or more recent forestry data than others. It may be useful to establish specific beginning and/or end points of t he analysis period to ensure the analysis uses a time frame where data for all parts of the country are available. In the illustrative applications in the technical appendices, the retrospective referen ce point baseline approach for calculating biogenic landscape attributes evaluates historical data from the last 5- or 10-year period of available data where possible (Appendix H).

When an assessment is conducted using a future anti cipated baseline, integrating time into the assessment of forward-looking phenomena is inherent in the approach, but decisions about how to do so could affect the outcomes (as discussed in th e previous subsection). The future anticipated baseline approach offers more flexibility in terms of what temporal scale could be used, but choices of temporal scale can in turn affect outcomes. Ther efore, determining the appropriate analysis temporal scale for the future anticipated baseline approach will largely depend on the needs and goals of the assessment. For example, an assessment that primarily focuses on feedstocks that have a one-year growing cycle may find that a shorter as sessment time frame would be more appropriate compared with an assessment that focuse s on forest-derived feedstocks, where the harvest cycles and landscape carbon effects occur o ver much longer time frames.

The technical appendix on temporal scale (Appendix B) discusses several approaches for assessing multiyear carbon-based fluxes into the future: (1) front-loading; 51 (2) year-to-year carryover; 52 and (3) annualized carryover. 53 For illustrative purposes in the technical appendi ces, the future anticipated baseline approach uses a 50-year simula tion horizon into the future using a year-toyear carryover construct (as reflected in Appendice s L and M). This temporal scale is long enough to capture carbon dynamics of longer rotation feeds tock species and short enough to detect significant biogenic CO 2 fluxes related to biogenic feedstock production. T he year-to-year carryover approach and time frames used can offer insights ab out the potential future impacts of biogenic feedstock production and use per the case study ass umptions; they were chosen for use in this report for illustrative purposes only and are not m eant to be prescriptive.

51 The front-loading approach accounts for the net bi ogenic CO 2 fluxes that will occur over the established assessment time frame associated with biogenic feed stock production and use, and then these fluxes are accounted for upfront in the year that the biogenic feedstock is consumed.

52 In year-to-year carryover accounting, the net biog enic CO 2 fluxes associated with feedstock production and use in the current year are counted as well as any net biogenic CO 2 fluxes that occur on the feedstock production landscape in the current year due to fee dstock use in previous years (e.g., decay, soil car bon pools changing to a new equilibrium state) and thus are “ carried over.”

53 Annualized carryover is a type of carryover accoun ting in which the net biogenic CO 2 fluxes associated with biogenic feedstock production and use are added tog ether (as in front- loading) but then divided by a certain number of years to generate an annualized biogenic CO 2 flux value per unit of feedstock. This approach accounts for cumulative emissions over the impact h orizon and then divides those emissions equally ove r the reporting horizon.November 2014 37 Additional Technical Timing Issues
Because of practical limitations related to data av ailability and to better represent general trends rather than episodic fluctuations, it may be necess ary to aggregate data from a multiyear period and to apply this multiyear average to annual fluxe s. The frequency of data collection for key inputs such as carbon sequestration occurring on the lands cape may not be annual, and/or annual data may not be publicly available. For example, the For est Inventory and Analysis (FIA) dataset collected by the USDA Forest Service is updated usi ng methods that reflect full U.S. coverage every 5 to 10 years. Depending on the type and location of the measurement, the carbon pool being measured, and the availability of annual data, it m ay be more practical to use an average value representing the mean over a period of years rather than an annual value.

Biogenic CO 2 emissions and carbon sequestration levels may also vary from year to year due to weather, other natural or anthropogenic disturbance s, or market conditions (e.g., demand) such that even if annual data were available, a single y ear’s value is not likely to represent the overall trends in biogenic carbon stocks and CO 2 fluxes. Therefore, even if data are available for finer temporal scales, it may still be appropriate to app ly a multiyear average. Even with multiyear averaging, there may be implications for overall re sults related to patterns and trends in the observed data. 54 Because different biomass feedstocks have differen t growth cycles and turnover times, it may also be appropriate to average over d ifferent time periods for different feedstocks, depending on temporal characteristics of the feedst ock.

If a multiyear integration is applied to biogenic C O 2 flux assessment, then it must be decided when in time the assessment period actually starts: befo re or after land is converted, at planting, at harvest, or some point in between. The importance o f this decision when the assessment period starts largely depends on the scale of the framewor k application. The choice of when to start an assessment period matters substantially at smaller scales, whereas assessments at larger scales are less sensitive to this aspect of timing because eff ects may be lost due to other activities affecting biogenic CO 2 fluxes over the larger landscape. In the case of f orest feedstocks at a small assessment scale, for example, assessment could start at any p oint in a standard harvest rotation.

No matter when the assessment begins, the total car bon accumulation over the lifetime of the forest might be the same, but if the time frame chosen doe s not correspond to exactly one full harvest rotation, then the starting point can impact result s. Selecting the point of harvest as the starting point for assessment, especially in conjunction wit h a short assessment time frame, might overestimate the emissions associated with the prod uction and use of the forest feedstock. If assessment starts at planting when rapid carbon seq uestration occurs, again in conjunction with a short time frame (less than one rotation), then an underestimate of the net biogenic CO 2 emissions associated with production and use of the forest fe edstock is more likely. Other timing

54 For example, a one-time land use conversion from o ne practice to another (e.g., when forested land is harvested and converted to agricultural crop produc tion instead of being reforested) results in a larg e emissions pulse in 1 year but is then followed by a few years of low sequestration. If an annual avera ge is calculated over this short time period (starting wi th the harvest), the result may show higher average emissions than if the average was taken over a long er period of time.November 2014 38

considerations include assumptions about length of feedstock use for bioenergy, ongoing use or one-time flux, and assumptions about other markets over time that may need to be considered in applying the future anticipated baseline approach.



### 4.3. Spatial Scale

Depending on the policy application, the selection of spatial scale could affect the results of any analysis evaluating GHG emissions and sequestration (Galik and Abt, 2012). The following factors should be taken into account when considering the c hoice of spatial scale:

• Feedstock Characteristics: Different feedstocks will have different availabil ity, yields, and production practices.

• Landscape Characteristics: Differences in physical attributes of the landscap e and carbon dynamics.

• Statistical Precision and Uncertainty: Statistical sampling and data accuracy vary across different spatial scales.

• Indirect Effects: The amount of leakage in an assessment varies acro ss different spatial scales.

The considerations related to spatial scale listed above can also differ with feedstock type and geographic location (e.g., variations in feedstock characteristics across different locations). Determining the spatial scale for an assessment fra mework has implications related to measurement, precision of estimates, data reporting and verification, the ability to reflect carbon dynamics in the feedstock production area, and cros s-boundary exchanges of biogenic feedstocks. The choice of spatial scale can be greatly influenc ed by the availability and accuracy of data and the precision with which one can measure and/or model f eedstock production as well as market dynamics. The characteristics of the land base—incl uding ownership type and resulting management regime, 55 as well as biophysical characteristics such as spe cies and soil types, climatic conditions, and water availability—are another impo rtant consideration when evaluating the choice of spatial scale.

The spatial scale for assessment could occur along a continuum from site specific to global, and there are tradeoffs related to accounting at differ ent points along this continuum. For example, the level of data accuracy varies with choice of spatia l scale. When carbon stocks are estimated at a larger spatial scale (e.g., global, national, regio nal) through statistical sampling, the increase in sample size provides more precision (i.e., smaller sampling errors), though resolution on incremental impacts of increased biomass demand can be lost. Global scale may be necessary in some cases to do a qualitative or quantitative asse ssment on potential leakage, such as indirect land-use effects in other countries. National scal e could be appropriate for feedstocks for which there is little or no variety in production and con sumption patterns (e.g., waste-derived, some

55 The ratio of forest growth to harvest for private forests in the conterminous United States is 1.3, w hile the same ratio on public lands is 5.3 (DOE, 2011). As t hese lands are currently managed, an area with a la rge proportion of publicly owned land would therefore b e more likely to have lower levels of harvest (and higher levels of growth) than a similar area with more pri vate land ownership (DOE, 2005, 2011).November 2014 39

industrial by-products). Moving from a larger scale to a smaller scale increases the ability to account for differences in feedstock species, produ ction site environmental conditions, and local market factors. However, moving to a smaller assess ment scale may ignore potential leakage effects. For smaller scales, the estimates tend to be less reliable due to a lack of statistical power associated with small sample size (Westfall et al., 2013). Estimates at smaller scales could be derived from other sources such as special inventor ies or surveys (i.e., thorough inventories conducted as part of a forest management plan). Ass essment at this scale does allow for evaluation of incremental impacts of increased biomass demand on specific sites.

In terms of the science, there is no single correct answer for the choice of a spatial scale for assessment: different spatial scales allow for eval uation of different questions and contexts. Therefore, the choice of spatial scale is largely d ependent on specific applications of the framework and related needs or preferences, considering the i mplications of feedstock and landscape characteristics, statistical precision and uncertai nty, indirect effects, or any other parameters. Thi s report evaluates the implications of different spat ial choices and for illustrative purposes uses a landscape (regional) scale to generate example biog enic assessment factor calculations. The technical issues one would need to consider to make an appropriate choice of spatial scale are discussed briefly below and are considered in more detail in Appendix C.

Smaller Spatial Scales
The finest level of resolution (aside from a single tree) is the stand or field level. Assessments at the stand or field level can allow for a direct linkage between the biogenic CO 2 fluxes occurring on the landscape and the stationary source. This small sca le of assessment could capture management practices as well as direct land use changes, inclu ding evaluation of what alternative or previous land uses might have been. For example, the cultiva tion of the same feedstock could have different landscape effects if grown on fallow land versus re placing existing crops. Choice of a small assessment scale might be appropriate for a policy or program application assessing biogenic CO 2 emissions from a relatively closed stationary sourc e production system; that is, a stationary source that relies on a single feedstock sourced from a si ngle production area and that has limited impact on aggregate markets.

However, such a small assessment scale cannot take into account potential broader market impacts captured with larger regions. For instance, biogeni c feedstock production and related carbon stocks within the evaluated area might be increasing, but this increase could have market ramifications that in turn cause shifts in production of the feed stock or other commodities outside the assessment area. Also, assessments at smaller scale s can be challenging because data would need to be collected and/or modeled for each feedstock prod uction site from which a stationary source procures feedstocks and sites might need to be moni tored for long periods of time (e.g., in cases of long rotation periods such as in Pacific Northwest forests). If the precise location of feedstock production is not known, one might still be able to generalize a fuelshed into a region encompassingNovember 2014 40

local and likely sources. 56 Fuelsheds can be shared by nearby facilities, and they may change over time as supply and market dynamics change (e.g., de mand, product substitution).

With the data collected when the feedstock is recei ved at a stationary source, it may be possible to determine the precise location of the feedstock pro duction site, or it may only be possible to know the broad geographic origin. For example, facilitie s operating primarily on long-term procurement contracts will likely use the same feedstock produc tion sites year after year, and the geographic location of those sites can be known. In such cases , measurement and analysis of productionrelated biogenic CO 2 fluxes at a localized scale is possible. On the oth er hand, for stationary sources operating using aggregated feedstocks (e.g., agricu ltural residues from multiple landowners aggregated at a centralized site) or for feedstocks that require storage and may become mixed (e.g., logging residues), it may be difficult to know prec isely the location of feedstock production. In thes e cases, only the broad geographic region might be id entified. Also, for some feedstocks, production sites may vary from year to year (e.g., forestry op erations that may not return to the same location for decades after harvest).

By the same token, analysis at a small scale (i.e., stand level) may obscure the impacts of a coherent management (e.g., silvicultural practices such as t hinnings 57 ) regime on a broader landscape. For example, consider an analysis focused on one stand of timber over a few decades. If the assessment starts at point of harvest, then over the course of the analyses the carbon stocks may return to their original levels but only if the stand regrows and s equesters an equivalent amount of carbon as when it was first harvested. Using a stand-level as sessment approach in this example, the production and use of the biogenic feedstock yields considerable biogenic CO 2 emissions in the short term (due to the harvest), with no net emissi ons in the long term. On the other hand, consider a timber fuelshed with multiple, multi-aged stands: at any given point in time, some stands are harvested while others are growing. At the fuelshed scale, a carbon balance over the full suite of stands may be achieved in the short term if harvest s account for less carbon than the increase in carbon in fuelshed feedstock biomass. Furthermore, if the sampling effort is insufficient for

56 For example, several analyses have used a circular fuelshed with a straight-line or road-distance rad ius as an approximation for forest-related feedstocks (50 miles straight line, Galik and Abt, 2012; 30 miles road distance, Brinkman and Munsell, 2012).

57 Thinnings as a silvicultural management strategy h as been practiced in the U.S. for some time (e.g., Smith et al., 1997), and is commonplace in even-aged managed forest stocks, par ticularly in regions like the Southeast U.S., where “pre-commercial” and “commercial” thinnings a re used as a stock improvement strategy and in some cases an income generation strategy (e.g., the sale of bioma ss or fuelwood may also help to offset cost of pre- commercial work). Some studies have evaluated the potential for and p ractices for thinning and thinned materials to supply bioenergy markets (e.g., Manley & Richardson , 1995; Egnell & Björheden, 2013) . Other works note that the “widespread” practice of thinning to reduce pot ential fuels for wildfire, particularly in the west ern U.S., and reduce the amount of carbon emissions potential ly released through wildfire (e.g., Evans & Finkral , 2009). Literature suggested that increased demand for bioe nergy could alter forest management, possibly resul ting increased harvest/use of low-quality wood material, such as materials from commercial or pre-commercia l thinnings or small roundwood. These potential shift s have in turn increased attention to the role of h arvest guidelines and best practice systems to guide bioen ergy-related forest management decisions (e.g., Kit tler, et al., 2012; Lattimore et al., 2009). For more discus sions and different perspectives on thinnings, also see Biomass Energy Resource Center, 2012; Kerr and Hauf e, 2011; Miner et al, 2014;November 2014 41

adequate coverage of small land areas, then the sam ple size for the area of interest will be small, resulting in low statistical power and an inconclus ive assessment of carbon stocks and fluxes.

Larger Spatial Scales
Existing data can be readily aggregated and present ed at larger scales such as states, regions, and nationally. For example, states and the federal gov ernment have existing data collection systems relevant to tracking landscape-scale biogenic carbo n cycling, such as forest inventories, land surveys, and tax records. However, certain small st ates (e.g., Rhode Island) may not be large enough (given the current sampling effort or plot density) to offer adequate or accurate data on forest growth and removals, because the associated samplin g errors may be too large. States may also implement policies and regulations focusing on fore sts and agricultural lands, such that all land in a given state may share some basic market and regulat ory influences. State-level assessments could become complicated, however, if feedstocks are boug ht and sold across state boundaries where laws differ from one another (in terms of actual me asurements and accounting). 58

The next spatial scale larger than a state level co uld be geographic regions, which may include multiple states. Regions may be large enough that d ata on carbon stocks and land use change are largely available and (assuming sampling is adequat e) less subject to error from small sample sizes. Depending on how they are defined, regions may be s mall enough to capture principal differences in biophysical characteristics, such as temperature and species types in different regions across the United States. For forest feedstocks, for example, regions could be defined on the basis of relative homogeneity of characteristics such as forest types , growth rates, and climate. Also, determining a regional scale might include considerations related to market characteristics, with multistate regions forming coherent markets for biogenic feeds tocks 59 and the ability of regions to capture market effects. Regional boundaries must be drawn c arefully to ensure the region is large enough to offer adequate data accuracy and availability, yet small enough to reflect carbon dynamics and meaningfully link to the “drivers of change,” (i.e. , the actions of feedstock users, to landscape carb on stock changes). One potential difficulty with asses sments at the regional spatial scale is that each region may encompass multiple states and political boundaries. Also at regional scales, there is less resolution on the incremental impact of increased b iogenic feedstock demand from specific entities and/or on specific sites than assessments on smalle r scales.

With larger spatial scales like regional or nationa l scales, it is possible that smaller-scale trends in carbon stocks could be masked, since carbon stocks could be declining in some areas and increasing

58 One difficulty with defining spatial scale with a political boundary is accounting for biogenic feeds tock transfers across boundaries. For example, wood-usin g mills in one state often purchase and transport w ood across state boundaries (Teeter et al., 2006). This adds accounting complexity because biogenic CO 2 fluxes from feedstock production may occur in a different region than biogenic CO 2 fluxes from feedstock use.

59 An example of a fixed regional framework is the EP A Emissions & Generation Resource Integrated Databa se (eGRID) region structure. eGRID is used for calcula ting GHG emissions related to electricity generatio n. Subregions nest within regions defined by the North American Electric Reliability Corporation (NERC). Regions vary widely in size, from small portions of an individual state to areas encompassing portions of seven large states. For more information on NERC an d eGRID regions, consult www.epa.gov/egrid.November 2014 42

in others. 60 For example, if one region has a declining carbon stock while another region has a slightly higher level of increasing stock, a nation al-scale analysis that averaged across all regions could indicate that feedstock carbon overall is inc reasing and could possibly encourage more feedstock removal including from the region with de clining stock, which could be problematic depending on the goals of the policy being implemen ted. Using the same scenario and applying a regional scale illustrates potential market effects of scale: if an assessment indicates that feedstoc ks from one region have increasing stock and thus coul d receive a more desirable assessment factor, this outcome could possibly shift demand to that re gion (if all else is equal, such as policies, tax structures, etc.).

Similarly, a national scale for agricultural biogen ic feedstock production might also mask regional and local differences in length of the growing seas on, yield per acre, above- and belowground net primary productivity, management practices (e.g., t illage or amendment application), previous land uses (in the case of land use conversion), and soil type and texture (Eagle et al., 2010). On the othe r hand, national-level assessments can give high-leve l insights concerning biogenic CO 2 fluxes from forests and agricultural landscapes and biogenic fe edstock market interactions. Lastly, if assessments take place at the national scale, then domestic indirect land use change and related emissions leakage between subnational regions or st ates would be implicitly captured (more discussion on leakage in Section 4.5 and in Appendi x E).

Some waste-derived feedstock materials, such as MSW , may have some regional variability, including the composition of waste (which can vary from community to community within a region) and regional climate factors that affect me thane (CH 4 ) oxidation via cover soils at managed landfills (Bogner et al., 2007; EPA, 2009; Spokas a nd Bogner, 2011). However, there is a lack of literature describing the degree to which compositi on of MSW can vary from region to region. Although composition of MSW may vary regionally, th is mainly contributes to potential generation amounts of CO 2 and CH 4 in a given landfill, whereas the framework methodo logy for waste-derived feedstocks focuses on how the CO 2 and CH 4 from MSW is treated when used in one activity vers us another. As a result, CO 2 and CH 4 from MSW could be treated similarly across the U.S ., i.e., differences would be based on the amount of waste g enerated, not on variation in the composition).

International Considerations
The pricing and flow of biogenic feedstocks and rel ated commodities globally have the potential to significantly affect domestic demand and supply dyn amics and could thus affect U.S. and international feedstock and related commodity produ ction sites and related biogenic CO 2 fluxes. For example, recently increasing pellet demand rela ted to renewable energy policies in the European Union is significantly influencing land us e and production activities in the Southeastern United States (Spelter, 2009; Cocchi, 2011; NREL, 2 013). Conversely, international feedstock

60 At larger scales, changes in carbon stocks could b e more heavily influenced by a variety of factors i n addition to biogenic feedstock use for energy, incl uding natural (e.g., hurricanes, pest, fire) and an thropogenic (e.g., development, park designations) drivers.November 2014 43

production and potential feedstock imports to the U nited States could affect the domestic biogenic feedstock market and related land use activities an d biogenic CO 2 fluxes (EIA, 2012).

Also, increases in U.S. biogenic feedstock producti on could affect traditional commodity markets and potentially trigger leakage effects internation ally. For example, if biogenic feedstock demand causes domestic land use to shift in biogenic feeds tock production, the demand for the displaced crops could be made up by converting lands abroad i nto cultivation to meet that demand. A global analysis of changes in feedstock demand and related commodity market and land use activities could estimate the potential directionality and mag nitude of these effects.

The framework is scalable and can be applied at var ious spatial scales, from a small scale (plot/entity-level) to global. For example, at the global level, it could be used to assess internationally produced feedstock trade flows, dep ending on policy requirements and/or international agreements related to GHG emissions a ssessments. However, for some U.S. domestic policy analyses (e.g., certain waste-derived feedst ocks), application of the framework on a global scale may not be required. The illustrative assessm ent factor calculations presented in the technical appendices of this report are conducted on a domest ic regional scale and do not evaluate treatments for exports and imports of international feedstocks or include a global scale assessment.

Spatial Scale Used in the Illustrative Applications in the Technical Appendices
The examples presented in the technical appendices use a regional spatial scale 61 (although, as discussed above, the methodology is scalable). At a regional scale, land areas are large enough that accurate data are available to represent trends in carbon stocks and land use change, but the regional scale is small enough that key differences in regional biophysical characteristics, such as growth and decomposition rates, as well as dynamics of regional feedstock markets, are captured (see Appendix C for additional technical details on spatial scale considerations and Appendices H through M for the illustrative case studies discuss ions).



### 4.4. Relationship between Spatial and Temporal Scales

Broadly speaking, the balance between carbon seques tration and carbon emitted on a given landscape can be estimated by measuring the change in the carbon stocks in biologic material over a given period of time (using a reference point bas eline approach) or by comparing relative estimated volumes along different projection scenar ios (using an anticipated baseline approach). For annual agricultural crops, for example, this ca lculation for the feedstock itself (aside from nonfeedstock biogenic CO 2 fluxes from, for example, soil carbon or land use change effects) can be fairly straightforward; typically at any scale, the amount of biogenic feedstock harvested and used during one year may be fully replaced by feedstock growth during the same year.

However, when considering biogenic feedstocks with rotation ages longer than one year (such as a forest or some dedicated bioenergy crops such as sw itchgrass) or in cases where biogenic feedstock

61 The SAB Biogenic Carbon Panel recommended that EPA consider regional-level assessment factor values (SAB, 2012) but did not suggest specific regions.November 2014 44

production has longer term landscape effects (such as changes in soil carbon pool equilibrium or in volumes of materials that decay), the scales of tim e and space can begin to interact, adding complexity to any assessment of biogenic feedstock production activities and related biogenic CO 2 fluxes. Hypothetically, if one hectare of forest is harvested and combusted, measurement of the tree biomass at the beginning and end of the year will s how a net release of biogenic CO 2 to the atmosphere, because combustion emissions exceeded c arbon uptake during growth on that hectare for that year. If, however, one measures the mass o f trees on that hectare at the beginning and end of a longer period, for example, a 60-year period ( assuming this is longer than the rotation age of the forest species and thus includes at least one h arvest), this may show that over time the mass of trees—and thus carbon stock—on that hectare is unch anged. In this example, using the longer time frame for integration shows that harvest and combus tion (emissions) are eventually equivalent to growth (sequestration).

The following hypothetical example illustrates that the same result can occur with a short time period and larger spatial scale. If 59 hectares of forest are growing uninterrupted alongside the 1 hectare that is harvested and reestablished during 1 year, the total mass of forest in the full 60 hectares is largely the same at the beginning and e nd of the year. This is because the carbon emitted from harvesting the 1 hectare has been counterbalan ced by growth in the remaining 59 hectares and new seedlings on the harvested area. In this ca se, then, overall equivalence is established for a single year in the 60-hectare parcel. 62

The simple examples above illustrate the complex in terplay between spatial and temporal scales when assessing biogenic CO 2 fluxes. Although integration over time and integra tion over space are very similar, they are not identical. Both cases ab ove demonstrate that returning to the preexisting carbon stock in the biosphere is not the same as re turning to the circumstance that would have prevailed had there been no harvest, because emissi ons and sequestration have both occurred in the meantime (not only in regard to the feedstock i tself, but to the landscape on which it is grown, for example, soil carbon). Integration, or lack of integration, over time confronts the additional issue of whether there is a time-dependent “value” of carbon: do current emissions have the same value as future emissions? In other words, does car bon sequestration occurring over the next 60 years compensate for carbon emitted to the atmosphe re today? Can carbon sequestration occurring over the prior 60 years be “banked” to counterbalan ce the carbon emitted today? This framework does not specifically propose or restrict the use o f time values for evaluation, because various time values may be desired in specific policy or program applications (see Appendix B for further detail on temporal scale issues, including a discussion on the valuation of time and discounting).

Ultimately, carbon stocks or flows that have high v ariability at fine spatial or temporal scales may have much less variability when averaged over large r areas or longer temporal scales. Integrating over long temporal scales may reduce the variabilit y observed at small spatial scales, and integrating over large areas may reduce the variabi lity observed over small temporal scales.

62 These are hypothetical examples that are not meant to be representative of typical conditions in the United States (i.e., annual growth on 59 ha equals standin g biomass harvested on 1 ha or a 60-year rotation p eriod is not typical for all U.S. regions).November 2014 45

However, it is not safe to assume that integrating over large areas and long time frames is always the best option. Large spatial scales and long temp oral scales are not necessarily the most accurate way to conduct specific policy/program assessments because the combination of the two may not offer the level of detail needed (e.g., biophysical differences in species/landscapes, shorter time frames and/or subregional analysis needed for polic y analysis) and may mask important smallerscale impacts. In essence, averaging over space may yield the same numeric result as averaging over time, but the implications can be quite differ ent, as illustrated above.



### 4.5. Leakage

This framework relies on the IPCC definition of “le akage,” which is defined as the indirect impacts of a targeted activity in a certain place at a cert ain time on carbon storage at another place or time (IPCC, 2000). In the context of biogenic CO 2 fluxes, leakage represents any biogenic CO 2 flux impacts outside of a biogenic feedstock production assessme nt scope that can be attributed to the production activities (e.g., replacement of diverte d crop, livestock, or forest products on other land s due to a change in land use from conventional produ cts to biogenic feedstocks). Leakage is an externality common to many policies and occurs in m any different contexts (e.g., benefits of local tourism extending beyond the region or technologica l innovation spreading from one firm to several others). Leakage, both positive and negativ e 63 and depending on the nature and magnitude of the market shifts, could have biogenic CO 2 flux effects that range from fairly minimal to qui te large.

One of the primary drivers of leakage is economics, including market interactions that can result in changes in production as well as land use activitie s. Increased demand for biogenic feedstocks might cause higher prices for those feedstocks, whi ch in turn might trigger more production of biomass feedstocks. These increases in production c an lead to a succession of land use changes, including the possible conversion of previously for ested land or other high-carbon ecosystems to lower carbon systems and the release of carbon stor ed in soils and vegetation. In globally integrated markets, increased demand for biogenic f eedstocks within the assessment area may lead to increased feedstock (or feedstock substitute) pr oduction outside the assessment boundaries. Thus, associated emissions may shift from assessed regions to unassessed regions. If leakage leads to biogenic CO 2 fluxes outside the assessment area, then not accou nting for it could bias the results toward lower or higher overall emission impacts (lo wer if leakage is negative, higher if leakage is positive).

One form of leakage, indirect land use change, may occur in an unplanned and unanticipated manner. Detecting leakage resulting from land use c hange outside an assessment boundary is complex and involves considering multiple variables , including connectedness of output and land markets, mobility of labor and capital, consumer fl exibility, producer flexibility, availability of

63 Negative leakage indicates net emissions generated outside the assessment scope due to changed feedst ock production within the assessment scope. Positive le akage indicates net sequestration generated outside the assessment scope due to changed feedstock productio n within the assessment scope.November 2014 46

alternative lands for production, and the ability o f producers to change their emissions profile without modifying production (Henders and Ostwald, 2012; Wunder, 2008).

Determining the value of these numerous variables i s difficult, and the literature offers a disparate picture of leakage magnitude (Kim and Dale, 2011; M urray, 2008). Several different leakage measurement strategies have been used in the litera ture, but these strategies show large variations in the type and quality of data required and the es timates they provide.

Although leakage is difficult to determine, in some circumstances it may be a significant factor in determining net biogenic CO 2 fluxes. The framework, therefore, can accommodate calculations of leakage effects; however, the framework does not ch oose or develop a specific methodology for identifying and evaluating these effects. If an app lication of this framework requires leakage analysis, the method chosen for such an analysis sh ould reflect the needs/parameters of the specific policy or program context. Within the context of the Clean Air Act, not all re gulatory actions explicitly require a leakage assessment. However, o ne example of a specific policy application that includes leakage analysis is the Renewable Fuel Sta ndard (RFS). The RFS took into account indirect effects of biofuel production and use, including in ternational leakage effects such as indirect land use change and related GHG emissions. 64 Appendix E provides more information on a variety of leakage estimates in the literature.



### 4.6. Feedstock Categorization

The delineation of different feedstock categories a llows for groupings of feedstocks that share similar attributes for the purpose of assessment wi thin this framework. These attributes include:

• Feedstocks with similar biogenic landscape attribut es (e.g., feedstock turnover time);

• Feedstocks with similar temporal characteristics;

• Feedstocks with similar management techniques;

• Feedstocks with similar process attributes (e.g., c ombustion process types); and

• Feedstocks with similar market or alternative uses.

The scientific literature on biogenic feedstock cla ssification systems is limited and varies, with categorizations designed to be most useful within a particular study or presentation. As a result, a new categorization system is developed and used in this framework. When approaching feedstock categorization, a few key considerations should be taken into account: these considerations are outlined below, followed by presentation of the fee dstock categorization used in the framework. Details on various feedstock classification systems found in the literature for other purposes can be found in Appendix D.

64 For more information on the Renewable Fuel Standar d, please see the RFS Regulatory Impact Analysis (E PA, 2010).November 2014 47 Temporal Scale and Feedstock Categorization
One consideration when classifying biogenic feedsto cks is the inherently different turnover times (i.e., growth cycles and management regimes) of dif ferent biogenic feedstocks. In most cases, feedstocks with similar growth and harvest characte ristics can be given the same treatment or considerations in the framework. For example, for m aterials typically harvested and replenished within one year, such as many conventional agricult ural crops or residues, there may be no change in the net biogenic CO 2 contribution to the atmosphere from the growth and harvest of the feedstock itself. However, there may be changes in the overall production site non-feedstock carbon pools (i.e., contained in agricultural soils, detri tal pools, or elsewhere in the landscape as a resul t of the feedstock management and/or harvest) that shoul d be considered. For feedstocks with longer turnover times, such as those derived from forests, the relative rates of feedstock harvest and replenishment are related to the spatial and tempor al scales considered, as discussed in previous
sections.

For feedstocks that, if not used as a biogenic feed stock and left on the landscape, would decompose over long periods of time, such as logging residues , it is important to acknowledge the pattern of decomposition over time if possible. If left onsite , logging residues can decay and release CO 2 to the atmosphere and soils naturally over periods ranging from years to decades, depending on biophysical characteristics and forest management p ractices. Harvest and combustion of these residues, on the other hand, converts the residue m aterial to CO 2 in a very short time.

Spatial Scale and Feedstock Categorization
For forest-derived and agriculture-derived feedstoc ks, there also are distinct differences in species type, feedstock growth, decomposition, and climate and ecosystem characteristics in different parts of the United States. Disaggregating feedstock cate gories into subcategories could allow for analysis of feedstock differences per different geographic p roduction locations. Assessing feedstocks by landscape or regional characteristics can allow for evaluation of biophysical and economic patterns in growth and decay by region, as well as for regio nally specific calculation of the emissions and sequestration associated with feedstock production.

Feedstock Categorization Used in the Framework
The feedstock classification system used here appli es three broad feedstock categories: forestderived, agriculture-derived, and waste-derived fee dstocks. These feedstocks are further subdivided into subcategories, as follows (see Appe ndix D for details):

Forest-derived feedstocks:

• Roundwood

• Logging residues

• Industrial processing by-products, which includes t wo subcategories:

o Those with no current alternative market uses such as pulping liquor

o Those with current alternative market uses, such as wood mill residues (e.g., chips, bark, and sawdust)November 2014 48

Agriculture-derived feedstocks:

• Conventional agricultural crops

• Crop residues

• Dedicated energy crops, such as:

o Short-rotation woody crops

o Switchgrass

• Industrial processing by-products, which includes t wo subcategories:

o Those with no current alternative market uses such as shells, husks, and cobs

o Those with current alternative market uses, such as animal fats, oils, and greases, distillers grains

Waste-derived feedstocks:

• Municipal solid waste, including food waste

• Animal waste/litter

• Wastewater

The framework could also be used to consider second ary use feedstocks. Secondary use feedstocks are biogenic materials that exit a particular stati onary source (i.e., primary stationary source) or t he stationary source supply chain where the original b iogenic feedstock material is transformed into a product or by-product and are then used for energy production at a different stationary source (i.e., secondary stationary source). For example, suppose a secondary stationary source uses for energy an agriculture- or forest-derived industrial proces sing by-product such as distillers’ grains or woody slabs or residuals from a primary stationary source. If necessary, the landscape attributes from the original biogenic feedstock at the primary source could be used at the secondary entity. Hypothetically, the same treatment could be used in the case of energy products such as pellets or ethanol used for energy at a secondary stationary s ource.
For purposes of this report, biogenic feedstocks ha ve been classified broadly into the feedstock categories identified above based on the physical a nd other attributes those feedstocks possess. This categorization does not represent a formal or legal definition, nor does it intend to replace any existing legal definitions. The framework is design ed to be flexible so it can be modified as needed to be applicable to nearly all domestic biogenic fe edstocks currently in use or under consideration for bioenergy production. However, new and unconven tional, or otherwise unanticipated, feedstocks may emerge over time and may fit under t he categories above or may necessitate new categories.


## 5. Discussion

This report presents a methodological framework for assessing the extent to which the production, processing, and use of biogenic material at station ary sources results in a net atmospheric contribution of biogenic CO 2 emissions. It is a revision of EPA’s original Sept ember 2011 Draft Accounting Framework for Biogenic CO 2 Emissions from Stationary Sources and is responsive to aNovember 2014 49

peer review of that draft report conducted by the Biogenic Carbon Emissions Panel of EPA’s Science Advisory Board (SAB Panel).

The revised framework is an analytical tool designe d for many users, including facilities and companies that produce biogenic feedstocks and/or u se biogenic feedstocks in energy production at stationary sources, and policymakers considering different GHG-related policies and programs. Users of this framework can review the biogenic ass essment factor ( BAF ) equation presented in Part 2; consider the different possible approaches— representative, customized, or hybrid— presented in Part 3; and examine in Part 4 the key decisions for a user to make to inform the biogenic emissions assessment, including choice of baseline, temporal scale, feedstock categories, and spatial scale. Illustrative applications of the framework are included in in the technical appendices.

The discussion of the scientific elements associate d with biogenic CO 2 fluxes and the presentation of the assessment method and illustrative calculations aim to provide a technical foundation for assessing the extent to which the production, proce ssing, and use of biogenic material at stationary sources results in a net atmospheric contribution o f biogenic CO 2 emissions. In addition to the key decisions discussed in the report, stakeholders may wish to consider additional factors when making this type of assessment, including:

• Key policy considerations and associated statutory and regulatory structure of different programs.

• Other complementary policies related to biogenic CO 2 emissions. For example, policies affecting land owners might influence the way land and feedstocks are managed, such as forest conservation, zoning, and biomass certificat ion programs (e.g., owners of stationary sources may also be landowners and may b e able to demonstrate that their feedstocks all come from lands that are managed in ways that maintain or increase carbon stocks).

• Energy efficiency: Different types of stationary so urces (e.g., combined heat and power) use biomass with varying degrees of efficiency (e.g ., tons of feedstock required per megawatt or British thermal unit), and efficiency m ay be an important consideration under certain policies and programs.

• Scale/scope of program: Large-scale programs affect ing entire industries on an annual basis may require different technical approaches th an programs on a smaller scale.

• Timing: Different program applications may require biogenic CO 2 emissions from a stationary source to be evaluated over specific tim e frames and/or at specific points in time (e.g., on a prospective basis that would apply over the life of the stationary source, annually).


## 6. Glossary of Terms

Additionality: A criterion for assessing whether an activity has r esulted in GHG emission reductions or removals in addition to what would ha ve occurred in its absence.November 2014 50

Anthropogenic: Resulting from or produced by human beings.

Anticipated Baseline: A baseline that assesses the estimated net changes of biogenic CO 2 fluxes and/or carbon stocks between two scenarios at the s ame specified point in time (e.g., the estimated difference between a business-as-usual [ BAU ] scenario and an alternative counterfactual scenar io that includes changes in environmental, economic, and/or policy conditions.

Assessment Framework: A methodology for assessing the net atmospheric contribution of biogenic CO 2 emissions from a stationary source that combusts or processes biogenic feedstocks taking into account factors related to the biological carbon cycle . These factors include biogenic landscape attributes, including carbon-based stock changes and fluxes (including carbon dioxide and methane fluxes) that occur (or are avoided) on the landscape associated with or affected by feedstock production and biogenic process attributes related to processing (transporting, storing, processing) and use of a biogenic feedstock at the stationary source, and/or the alternative fate of biogenic materials if not used for bioenergy.

AVOIDEMIT: Landscape attribute term AVOIDEMIT accounts for the avoidance of estimated biogenic emissions that could have occurred on the feedstock landscape without biogenic feedstock removal (e.g., avoided decomposition) or per an alt ernative management strategy (e.g., wastederived feedstocks).

Baseline: The baseline (or reference) is any datum against which change i s measured. Such a datum serves as the “reference” against which other condi tions or changes can be compared. Examples of baselines include an anticipated baseline or reference point baseline

.
Biochar: Charcoal created by pyrolysis of biogenic feedstock

. Bioenergy: Energy derived from biomass

. Biogenic Assessment Factor ( BAF ): The BAF

is a unitless factor that represents the net atmospheric biogenic CO 2 contribution associated with using a biogenic feedstock at a stationary

source , taking into consideration biogenic landscape and process attributes associated with feedstock production, processing, and use at a stationary source , relative to the amount of biogenic feedstock consumed. This term represents a ratio of the net b iogenic carbon cycle effects from all stages of growth, harvest/collection, processing, a nd use of a biogenic feedstock (i.e., the NBE ) relative to the PGE

of the biogenic feedstock consumed for energy (and resulting in stack emissio ns)

at a stationary source

. Biogenic Attributes: Attributes associated with biogenic feedstock production, such as sequestration in the biogenic feedstock , avoided emissions

from residues that would have eventually decomposed onsite if they were not used in the stationary source
, emissions or sequestration associated with direct land management or direct land use change , and leakage ( such as indirect land use change) associated with biogenic feedstock production.November 2014 51

Biogenic CO 2 Emissions: Carbon dioxide emissions related to the natural carbon cycle as well as those resulting from the production, harvest, combu stion, digestion, fermentation, decomposition, or processing of biologically based materials.

Biogenic Feedstock: Biologically based materials that are used for combustion, conversion
, product processes, or otherwise decompose at or pas s through a stationary source

Biologically based: Non-fossilized and biodegradable organic material o riginating from modern or contemporarily grown plants, animals, or microorgan isms (including products, by-products
, residues and wastes from agriculture, forestry, and related industries, as well as the non-fossilized and biodegradable organic fractions of industrial a nd municipal wastes, including gases and liquids recovered from the decomposition of non-fossilized and biodegradable organic material).

Biomass: Organic material that is living or dead (e.g., tre es, crops, grasses, tree litter, roots), above and belowground and both). Biomass literally means living matter, but the term is also used for any organic material derived from plant and animal tiss ue. In the context of bioenergy
, biomass is any material of biological origin, excluding material e mbedded in geological formations and transformed to fossil.

Biosphere : The part of the Earth system comprising all ecosy stems and living organisms, in the atmosphere, on land (terrestrial biosphere), or in the oceans (marine biosphere), including derived dead organic matter, such as litter, soil organic m atter, and oceanic detritus.

Black Liquor: Industrial paper manufacture involves a procedure known as the Kraft process, where wood is converted into wood pulp and then int o paper. The process produces a by-product referred to as black liquor (i.e., a liquid mixture of pulping residues [such as lignin and hemicellulose] and inorganic chemicals from the Kraft process [such as sodium hydroxide and sodium sulfide]).

By-product: A material of value produced as a residual of, or incidental to, the conversion process.

Carbon: Chemical element with symbol C and a common atomic weight of 12. The abundance and unique diversity of organic compounds that it forms make this element the chemical basis for all known life.

Carbon Capture and Storage (CCS): CCS refers to a set of technologies that can reduce carbon dioxide (CO 2 ) emissions

from stationary sources of CO 2 through a three-step process that includes capture and compression of CO 2 from stationary sources ; transport of the captured CO 2 (usually in pipelines); and storage of that CO 2 in geologic formations, such as deep saline format ions, oil and gas reservoirs, and unmineable coal seams.

Carbon Cycle: The term used to describe the flow of carbon (in various forms, e.g., as carbon dioxide ) through the atmosphere, ocean, terrestrial biosph ere, and lithosphere.

Carbon Dioxide (CO 2 ): A naturally occurring gas, which is also produced t hrough anthropogenic activities such as burning fossil fuels (such as oil, gas, and coal) from fossil carbon deposits, burning biomass
, land use changes, and other industrial processes. It is the principa l anthropogenicNovember 2014 52

greenhouse gas that affects the Earth’s radiative balance. It is t he reference gas against which other greenhouse gases are measured and therefore has a global warming potential of 1.

Carbon Dioxide Equivalent (CO 2 e): The atmospheric impact ( global warming potential [GWP] ) of 1 metric ton of a primary greenhouse gas in terms of the equivalent number of metric tons o f CO 2 emissions

. For example, 1 metric ton of methane (CH 4 ) has the same GWP as 25 metric tons of CO 2 . Therefore, 1 metric ton CH 4 = 25 metric tons CO 2 e.

Carbon Flux: Transfer of carbon from one carbon pool to another in units of measurement of mass per unit of area and time. The term is used here to encompass both biogenic carbon dioxide and methane

.
Carbon Pool: A system with the capacity to accumulate or release carbon . Examples of carbon pools are forest biomass, wood products, soils, and the a tmosphere.

Carbon Stocks: See reservoir

.

Conversion: Technologies or processes that convert biomass into energy directly, in the form of heat or electricity, or may convert it to another f orm, such as liquid biofuel or combustible biogas. Examples of biomass conversion processes include bi omass-fired or co-fired boilers, biomass gasification or pyrolysis systems, ethanol fermenta tion processes, and anaerobic digesters.

Direct Land-Use Change: Land use change that occurs when land within the assessment system boundaries of a framework that was previously in another land use is brought into production for a biogenic feedstock.

Discounting: A treatment of time approach, in which all of the future net carbon flux impacts associated with using the biogenic feedstock are given a net present value using a discount rat e.

Distiller Grains: Materials remaining after the starch fraction of co rn is fermented with selected yeasts and enzymes to produce ethanol and carbon dioxide . After complete fermentation, the alcohol is removed by distillation.

Emissions: Release of greenhouse gases and/or their precursors into the atmosphere over a specified area and period of time. A transfer of carbon ( flux ) from a reservoir

to the atmosphere carbon pool. Direct emissions are defined at the point in the production process where they are released or transferred and are attributed to that point in the production process (the “point of emission”), whether it is a sector, a technology, o r an activity. In the context of an activity, emissions may refer to net emissions from the entire production process or direct emissions from particular points in the production process such as the stack or flue at a stationary source or the growth or decomposition of feedstock at the point of harvest or in transit to a stationary source. When applied in the context of a sector, emissions from coal-fired power plants are considered direct emissions from the energy supply sector. Indirect emissions , or emissions “allocated to the end-use sector,” refer to the energy use in end-use sectors and acco unt for the emissions associated with the upstream production of the end-use energy. For exam ple, some emissions associated with electricity generation can be attributed to the buildings secto r, corresponding to the building sector’s use of electricity.November 2014 53

Feedstock: See biogenic feedstock

.
Fossil Fuel: Natural gas, petroleum, coal, or any form of carbon -based solid, liquid, or gaseous fuel derived from fossil hydrocarbon deposits.

Fossil Fuel Emissions: Emissions of greenhouse gases

(in particular CO 2 ) resulting from the combustion of fossil fuels

. Front-loading: A treatment of time approach, which accounts for a ll of the future net biogenic CO 2 flux es associated with the removal of the biogenic feedstock are added together and accounted for in the year in which the removed biogenic feedstock is consumed at the stationary source

.
Fuelshed: An aggregate of areas from which feedstock may be d rawn for a specific facility.

Global Warming Potential (GWP): An index, based on radiative properties of well-mi xed greenhouse gases , measuring the radiative forcing of a unit mass of a given well-mixed greenhouse gas in the present-day atmosphere integrated over a chosen time horizon, relative to that of carbon dioxide . The GWP represents the combined effect of the dif fering times these gases remain in the atmosphere and their relative effectiveness in abso rbing outgoing thermal infrared radiation.

Greenhouse Gases (GHGs): Gaseous constituents of the atmosphere, both natural and anthropogenic , that absorb and emit radiation at specific wavele ngths within the spectrum of thermal infrared radiation emitted by the Earth’s s urface, the atmosphere itself, and by clouds. This property causes the greenhouse effect. Water vapor (H 2 O), carbon dioxide (CO 2 ), nitrous oxide (N 2 O), methane (CH 4 ), and ozone (O 3 ) are the primary greenhouse gases in the Earth’s a tmosphere. Moreover, there are a number of entirely human-made greenhouse gases in the atmosphere, such as the halocarbons and other chlorine- and bromine- containing substances, addressed under the Montreal Protocol. Beside CO 2 , N 2 O, and CH 4 , the Kyoto Protocol deals with the greenhouse gase s sulfur hexafluoride (SF 6 ), hydrofluorocarbons (HFCs), and perfluorocarbons (PFCs).

GROW: The GROW

term in the BAF

equation represents net feedstock growth (or remov als) on the feedstock production landscape.

Indirect Land-Use Change: Land use change that occurs outside the assessment scope due to changes in biogenic feedstock production activities and land use within the assessment scope.

L: The process attribute term L is a unitless adjustment factor greater than or eq ual to one that represents biogenic feedstock carbon that leaves the supply chain (e.g., via tran sit or decomposition, deviated for use as a product) between the feedstoc k production site and input into the conversion process at a stationary source

. L scales PGE , as it was measured at the point of assessment, up to account for any losses during transportation or sto rage between the feedstock production site and the point of assessment. PGE times L is thus the carbon content of the biomass that was grown at the feedstock production site in order to deliver t he quantity of feedstock measured at the point of

assessment.November 2014 54

Landscape Attributes: biogenic feedstock carbon and carbon -based fluxes that are associated with the landscape where a biogenic feedstock is grown and harvested/collected or avoided emissio ns from feedstock use.

Land Use: Land use refers to the total of arrangements, acti vities, and inputs undertaken in a certain land cover type (a set of human actions). T he term land use is also used in the sense of the social and economic purposes for which land is mana ged or used (e.g., grazing, timber extraction, residential, commercial, and conservation).

Land Use Change: A change in the use or management of land by humans , which may lead to a change in land cover.

LEAK: The landscape attribute term LEAK accounts for the potential leakage associated with biogenic feedstock production and use at stationary sources

. Leakage: Leakage refers to the indirect impact that a target ed activity in a certain place at a certain time has on carbon storage at another place or time.

Lifecycle Analysis: Compilation and evaluation of the inputs, outputs, and the potential environmental impacts of a product system throughou t its life cycle. In the context of greenhouse
gas assessments, lifecycle greenhouse gas emissions are the aggregate quantity of greenhouse gases resulting from the full fuel cycle, including all s tages of fuel and feedstock production and distribution, from feedstock generation and extract ion through distribution and delivery and use of the finished fuel.

LOSS: Represents the actual tons of carbon lost in transp ortation or storage along the supply chain rather than being emitted during conversion to ener gy at the stationary source

. Managed Forest: All forests subject to some kind of human interacti ons (notably commercial management, harvest of industrial roundwood [logs] and fuelwood, production and use of wood commodities, and forest managed for amenity value o r environmental protection if specified by the country), with defined geographical boundaries.

Methane (CH 4 ): A hydrocarbon that is a greenhouse gas produced through anaerobic (without oxygen) decomposition of waste in landfills, animal digestion, decomposition of animal wastes, production and distribution of natural gas and oil, coal production, and incomplete fossil fuel combustion.

Natural: Having or constituting a classification based on fe atures existing in nature. Carbon fluxes are categorized as natural if the flux is caused by something beyond human control.

Net Biogenic Emissions ( NBE ): The NBE

term represents the net atmospheric biogenic CO 2 contributions associated with different stages of biogenic feedstock production, processing, and use

at a stationary source . The biogenic landscape attributes ( GROW, AVOIDEMIT, SITETNC, and LEAK , if included) and the biogenic process attributes ( L and P ) can be combined with PGE to estimate the
NBE associated with the specific biogenic feedstock and stationary source process.November 2014 55

Nitrous Oxide (N 2 O): A greenhouse gas emitted through soil cultivation practices, especi ally the use of commercial and organic fertilizers, fossil fuel combustion, nitric acid production, and biomass burning.

P: The process attribute term P is a unitless adjustment factor between zero and on e, equal to the share of the carbon content of the feedstock at the point of assessment that is emitted to the atmosphere by a stationary source (versus that which is embedded in products). In eff ect, this term also reflects the share of carbon that remains in products, that is either not emitt ed to the atmosphere or is sold and eventually emitted to the atmosphere by a downstream user.

Photosynthesis: The process by which plants take carbon dioxide from the air (or bicarbonate in water) to build carbohydrates, releasing oxygen in the process. There are several pathways of photosynthesis

with different responses to atmospheric carbon dioxide concentrations.

Policy-Relevant Timescale: The time frame of concern required for stabilizing atmospheric greenhouse gas concentrations to avoid dangerous anthropogenic interference with the climate system.
Potential Gross Emissions ( PGE ): Potential gross emissions, represented by PGE , is the carbon content of the biogenic feedstock used by a specific entity (or generally consumed). This is a quantity that could be measured or estimated at dif ferent points of assessment (e.g., at the boiler mouth, stationary source gate, feedstock production site, or at the stack: w herever the point of assessment needs to be. Thus, this term can have di fferent values indicated by subscripts, representing different points along the supply chai n).

Primary Biogenic Feedstock: Biogenic feedstock derived directly from harvested /collected biomass , such as acquired directly from the land on which they grew or from a third party that harvested, collected, or aggregated the biomass

.
Process Attributes: Attributes associated with processing or using biogenic feedstock , such as biogenic carbon that is embodied in products such as ethanol or bi ogenic CO 2 fluxes caused by losses that occur during biogenic feedstock storage or processing.

PROD: Represents the actual tons of carbon stored in fina l products or by-products along the supply chain rather than being emitted during conve rsion to energy at the stationary source

. Pyrolysis: Pyrolysis

is the heating of an organic material, such as biomass , in the absence of oxygen. The material is decomposed into combustible gases a nd biochar . Most of these combustible gases can be condensed into a combustible liquid called p yrolysis oil (bio-oil), though there are some permanent gases (CO 2 , CO, H 2 , light hydrocarbons). Pyrolysis of biomass

thus produces three products: bio-oil, biochar , and syngas. The proportion of these products depe nds on several factors including the composition of the biogenic feedstock and process parameters. All things being equal, the yield of bio-oil is optimized under fast pyroly sis conditions (i.e., when the pyrolysis temperatur e is around 500 °C and the heating rate is high [ i.e., 1,000 °C/s ] ). Processes that use slower heating rates are called slow pyrolysis, and biochar is usually the major product of such processes.November 2014 56

Radiative Forcing: Radiative forcing is the change in the net, downward minus upward, i rradiance (expressed in W/m 2 ) at the tropopause due to a change in an external driver of climate change, such as, for example, a change in the concentration of carbon dioxide or the output of the Sun. Radiative forcing is computed with all tropospheric properties held fixed at their unperturbed values and after allowing for stratospheric tempera tures, if perturbed, to readjust to radiativedynamical equilibrium. Radiative forcing is called instantaneous if no change in stratosphe ric temperature is accounted for. For the purposes of t his report, radiative forcing is further defined as the change relative to the year 1750 and, unless ot herwise noted, refers to a global and annual average value. Radiative forcing is not to be confused with cloud radiative forcing , a similar terminology for describing an unrelated measure of the impact of clouds on the irradiance at the top of the atmosphere.

Reference Point Baseline: A baseline that represents the estimated net change of biogen ic CO 2 fluxes and/or carbon stocks between two points in time.

Reservoirs: A component of the climate system, other than the a tmosphere, that has the capacity to store, accumulate, or release a substance of concer n, including carbon , a greenhouse gas
, or a greenhouse gas precursor. Oceans, soils, and forests are examples of reservoirs of carbon . The absolute quantity of the substance of concern held within a reservoir at a specified time is called the carbon stock

.
Roundwood: The portion of tree biomass that would be defined as merchantable (is of sufficient quality for market sale)
, according to existing forest inventory definitions. This includes trees of commercial species that have good form (e.g., not h ollow or “cull”) and that are large enough to be harvested and includes the main bole or stem, but n ot branches or tops.

Scope: Components that could be included as part of the sy stem boundary for an assessment framework

. Sequestration: The addition of a substance of concern to a reservoir . The uptake of carbon

containing substances, in particular carbon dioxide , is often called ( carbon ) sequestration

.
Sink: Any process, activity, or mechanism that removes a greenhouse gas , an aerosol, or a precursor of a greenhouse gas

from the atmosphere.

SITETNC: The landscape attribute term SITETNC accounts for the estimated total net change in non-feedstock carbon pools on the feedstock product ion site due to land use management or land

use or management changes associated with feedstock pr oduction.

Source: Any process or activity that releases a greenhouse gas , an aerosol, or a precursor of a greenhouse gas into the atmosphere.

Stationary Source: For the purpose of this report, a stationary source is any building, facility, structure, or installation that emits or may emit biogenic CO 2 gases

.November 2014 57

Year-to-year Carryover: A treatment of time approach, which accounts for t he emissions and/or sequestration associated with a unit of biogenic feedstock production, harvest, and use in the year in which the emissions

actually occur.

Working Forest: Those portions of the forest resource within a reg ion that are likely to be used for biogenic feedstock

production. Excluded from working forests are, for example, protected forest areas, areas not conducive to harvest due to physic al conditions (e.g., inoperable soils or steep slopes), regulatory restrictions (e.g., elevation l imits in the Northeast), or economic feasibility (e .g., large distance to transportation networks).



## 7. References

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## 8. Technical Appendices to this Report

The technical appendices to this report contain the detailed background and computations necessary to explain how the methodologies for calc ulating the various terms in the BAF equation were developed to generate the illustrative BAF values for sample feedstocks.

Appendix A details the relation of the framework to the IPCC greenhouse gas inventory methods. Appendices B through D describe the preliminary technical decisions requi red to implement the framework. Specifically, Appendix B provides a theoretical discussion of the key consi derations related to the treatment of time in an assessment f ramework. Appendix C discusses spatial scale considerations and explains the implications of cho osing different scales, including the regional scale. Appendix D describes the categorization of biogenic feedstock s used in this framework.

Appendix E presents a discussion of leakage literature. Appendix F provides a general algebraic representation of the biogenic assessment factor eq uations, which includes detailed examples of how the equation could be adjusted to accommodate d ifferent supply chain permutations. Appendix G discusses the technical aspects of the biogenic pr ocess attributes, equation terms related to use of biogenic feedstock in different s tationary source types ( P and L ) and provides a discussion of the literature as well as illustrativ e values for different feedstocks and for sample stationary source applications. Appendix H presents a discussion of the illustrative equation terms related to the biogenic landscape attributes of fee dstock production and harvest ( GROW, AVOIDEMIT, and SITETNC ) for forest- and agriculture-derived feedstocks in the context of the retrospective reference point baseline and provides illustrative calculations of these terms for example feedstock/region combinations. Appendix I presents the illustrative BAF calculations for the example feedstock/region combinations and examp le process attribute values for the retrospective reference point baseline.

Appendix J provides a discussion defining what an anticipated baseline is and how it can be applied in different ways, as well as important con siderations to apply when choosing a modeling method for applying a future anticipated baseline i n the context of assessing biogenic emissions from stationary sources. Appendix K describes the methodology used to develop various illustrative future anticipated baselines for use i n the following appendix. Appendix L presents illustrative applications of the future anticipated baseline approach to specific feedstock/region combinations for forest- and agriculture-derived fe edstocks to generate example equation terms and BAF calculations. Appendix M provides a synopsis of the illustrative BAF calculations from the retrospective reference point and future anticipate d baseline approaches and sensitivities using both approaches. Appendix N describes an evaluation of waste-derived feedstock s, such as municipal solid waste and wastewater treatment.