November 2014 i 

 

Framework for Assessing Biogenic CO2 Emissions 

from Stationary Sources 

United States Environmental Protection Agency, Office of Air and Radiation 

Office of Atmospheric Programs, Climate Change Division 

November 2014 
November 2014 ii 

Executive Summary 

FRAMEWORK FOR ASSESSING BIOGENIC CO2 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 stationary sources results in a net atmospheric 

contribution of biogenic carbon dioxide (CO2) emissions. Biogenic CO2 emissions are defined as CO2

emissions related to the natural carbon cycle,1 as well as those resulting from the production, 

harvest, combustion, digestion, fermentation, decomposition, and processing of biologically based 

materials.2 

The framework examines direct emissions of biogenic CO2 from stationary sources that combust or 

process biogenic material, taking into account factors related to the biological carbon cycle. These 

factors include changes in biogenic carbon-based stocks and emissions—known as “carbon 

fluxes”3—that occur (or are avoided) as a result of (1) feedstock growth and harvest; 

(2) processing, transport, storage, and use of a biogenic feedstock at the stationary source; and/or 

(3) the possible alternative fate of biogenic feedstock materials if not used for bioenergy.4 The 

framework is a revision of the U.S. Environmental Protection Agency’s (EPA’s) original September 

2011 Draft Accounting Framework for Biogenic CO2 Emissions from Stationary Sources and was 

developed in response to a peer review by the Biogenic 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 considering a particular feedstock’s 

 

1 The term “carbon cycle” is used to describe the flow of carbon (in various forms, for example, as CO2) 

through the atmosphere, ocean, terrestrial biosphere, and lithosphere (IPCC, 2007a). 

2 Biologically based feedstocks, also referred to in this report as “biogenic materials,” are non-fossilized and 

biodegradable organic materials originating from modern or contemporarily grown plants, animals, or 

microorganisms (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 and municipal 

wastes, including gases and liquids recovered from the decomposition of non-fossilized and biodegradable 

organic material). These feedstocks do not include materials such as peat, coal, petroleum, natural gas, and 

other products that are derived from biologic materials but are considered non-renewable during the time 

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, 2000). The term “flux” is used here to encompass biogenic 

CO2 and methane emissions to the atmosphere, and removal 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 cycle fluxes associated with or avoided by the conversion of 

biogenic materials at stationary sources. For completeness, biogenic carbon and carbon-based gases—carbon 

dioxide and methane—are included within the framework as applied to forest-, agriculture-, and waste 

sector-derived feedstocks. Throughout the report, the term “biogenic CO2” 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 Khanna, 2011). 
November 2014 iii 

production and consumption cycle. There is considerable heterogeneity in feedstock 

types, sources and production methods and thus net biogenic carbon emissions will 

vary considerably. 

Consistent with this statement, the revised framework provides a description of the types of factors 

to consider when assessing biogenic CO2 emissions and presents an equation that could be used to 

calculate the extent to which use of biogenic materials at a stationary source results in a net 

atmospheric contribution of biogenic CO2 emissions. Although the framework does not reflect a full 

lifecycle assessment of all CO2 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 based on key decisions by the user, including type of

baseline (e.g., reference point or anticipated), time frame of the assessment (e.g., future or 

historical, 20, 30, 50, 100 years), feedstock categories (i.e., waste-, agriculture-, and forest-derived), 

and scale (e.g., state, regional, national). Many of 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. The types of decisions a user of the framework 

could encounter are identified throughout this report, along with related considerations and 

implications of those decisions. 

The body of this report describes the scientific rationale for the framework, presents the equation 

and terms on which the framework is based, and discusses 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, including the EPA’s National Inventory of 

Greenhouse Gas Emissions and Sinks, the U.S. Department of Agriculture (USDA) National 

Resources Conservation Service’s National Resources Inventory, the USDA Forest Service’s Forest 

Inventory Analysis, and the Energy Information Administration’s Annual Energy Outlook. Technical 

documentation is included in a set of appendices that present further detail on various elements of 

the framework and demonstrate how the framework could 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-CO2 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 Considered ........................................................ 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 Carbon 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 Factor ................................................................ 24

3. Representative and Customized Approaches to Landscape and Process Attributes ...................... 25

3.1. Representative Factor Approach ................................................................................................................. 25

3.2. Customized Factor Approach ........................................................................................................................ 26

3.3. Hybrid Approach ................................................................................................................................................ 27
November 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 ................................................................................................................... 63

• Appendix A: IPCC Inventory Approach to Accounting for All Anthropogenic Greenhouse Gas 

Emissions 

• Appendix B: Temporal Scale 

• Appendix C: Spatial Scale 

• Appendix D: Feedstock Categorization and Definitions 

• Appendix E: Discussion of Leakage Literature 

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

• Appendix G: Illustrative Biogenic Process Attributes 

• Appendix H: Illustrative Biogenic Landscape Attributes Using a Retrospective Reference 

Point Baseline 
November 2014 vi 

• Appendix I: Illustrative Forestry and Agriculture Case Studies using a Retrospective 

Reference Point Baseline 

• Appendix J: Anticipated Baselines: Background and Key Modeling Considerations 

• Appendix K: Future Anticipated Baseline Construction: Methodology and Results 

• Appendix L: Illustrative Forestry and Agriculture Case Studies using a Future Anticipated 

Baseline 

• Appendix M: Summary of Illustrative Forestry and Agriculture Results 

• Appendix N: Assessing Biogenic CO2 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 Sources in the U.S. in 2012. ............................................ 10 

Figure 5: Diagram with Equation Terms Included. ................................................................................................. 23
November 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 stationary sources results in a net atmospheric 

contribution of biogenic carbon dioxide (CO2) emissions. Biogenic CO2 emissions are defined as CO2

emissions related to the natural carbon cycle,6 as well as those resulting from the production, 

harvest, combustion, digestion, fermentation, decomposition, and processing of biologically based7

materials. 

The intent of the framework is to evaluate biogenic CO2 emissions from stationary sources that use 

biogenic feedstocks, given the unique ability of biogenic material to sequester CO2 from the 

atmosphere over relatively short time frames through the process of photosynthesis. The 

framework focuses on changes in biogenic carbon-based stocks and related emissions and 

sequestration—known as carbon fluxes (including CO2 and methane fluxes) — that occur (or are 

avoided) as a result of (1) feedstock growth and harvest; (2) processing, transport, and storage and 

use of a biogenic feedstock at the stationary source; and/or (3) the possible alternative fate of the 

biogenic feedstock materials if not used for bioenergy.8 Although the framework does not reflect a 

full lifecycle assessment of all CO2 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 processing. Figure 1 provides a simple graphic that 

shows the biogenic carbon pools and carbon-based flows that are represented within the 

framework. 

 

6 The term “carbon cycle” is used to describe the flow of carbon (in various forms, for example, as CO2) 

through the atmosphere, ocean, terrestrial biosphere, and lithosphere (IPCC, 2007a). 

7 Biologically based feedstocks, also referred to in this report as “biogenic materials,” are non-fossilized and 

biodegradable organic materials originating from modern or contemporarily grown plants, animals, or 

microorganisms (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 and municipal 

wastes, including gases and liquids recovered from the decomposition of non-fossilized and biodegradable 

organic material). These feedstocks do not include materials such as peat, coal, petroleum, natural gas, and 

other products that are derived from biologic materials but are considered non-renewable during the time 

frame relevant to policymaking (years to decades). 

8 The framework includes consideration of carbon cycle fluxes associated with or avoided by the conversion of 

biogenic materials at stationary sources. For completeness, biogenic carbon and carbon-based gases—carbon 

dioxide and methane—are included within the framework as applied to forest, agriculture, and waste sectorderived feedstocks. Throughout the report, the term “biogenic CO2” 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 solid, black 

arrows represent flows of carbon to and from the system of feedstock growth, harvest, and use. The dashed, blue 

arrow represents physical transfers of the biogenic feedstock from the production landscape to the stationary 

source (transport-related emissions such as tractor or train fuel are not included in the framework). 

The framework presents an equation and discusses the terms of the equation, which are designed 

to account for the biogenic landscape and process attributes included in Figure 1 associated with 

feedstock production, processing, and use at stationary sources. Additionally, the framework 

identifies and discusses the key decisions for a user to make to inform the biogenic CO2 flux 

assessment, including choice of baseline, temporal scale, feedstock categories, and spatial scale. A 

small number of illustrative examples based on hypothetical situations or case studies are provided 

to help demonstrate the types of results and implications 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 framework is intended to be a tool that is useful to a

variety of stakeholders and flexible so it can be adapted to fit the user’s programmatic needs. 

The framework is an analytical methodology for assessing the extent to which there is a net 

atmospheric contribution of biogenic CO2 emissions from the production, processing, and use of 

biogenic material at stationary sources. EPA has not yet determined how the framework might be 

applied in any particular regulatory or policy contexts 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 reasons, 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 steps to address the treatment of biogenic CO2

emissions from stationary sources, including a detailed study of the scientific and technical issues 

associated with assessing these emissions. In September 2011, EPA released a Draft Accounting 

Framework for Biogenic CO2 Emissions from Stationary Sources.

9 The September 2011 report 

assessed various options for assessing biogenic CO2 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 considering a particular feedstock’s 

production and consumption cycle. There is considerable 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 CO2

emissions at individual stationary sources: 

The IPCC inventories, a static snapshot of emissions at any given point in time, are a 

reporting convention that has no associated connections to policies or implementation. 

These inventories do not explicitly link biogenic CO2 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 and operation of stationary sources 

using biomass. (p. 17)11

Though the SAB Panel did agree with some basic tenets of the 2011 draft framework report, they 

also found some technical elements lacking: 

 

9 The 2011 Draft Accounting Framework for Biogenic CO2 Emissions from Stationary Sources is available at 

www.epa.gov/climatechange/ghgemissions/biogenic-emissions.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 Khanna, 2011). See SAB Review of the Draft Accounting 

Framework for Biogenic CO2 Emissions from Stationary Sources (September 2011). Information about the SAB 

peer review process for the September 2011 draft framework is available at 

http://yosemite.epa.gov/sab/sabproduct.nsf/0/2F9B572C712AC52E8525783100704886. 

11 See Appendix A for further discussion of the IPCC guidelines for GHG emissions accounting from all sectors 

at the national level. 
November 2014 4 

The Framework is a step forward in advancing our understanding of how to account for 

biogenic emissions.... It covers many of the complicated issues associated with the 

accounting of biogenic CO2 emissions from stationary sources and acknowledges that its 

choices will have implications for the estimates of CO2 emissions obtained. These 

[choices] include those raised by SAB and discussed above, related to the choice of 

baseline, region selection and the averaging of emissions/stocks over space and time. 

However, the solutions offered in many cases, particularly those related to the use of 

harvested wood for bioenergy, lack transparency or a scientific justification. (p. 40) 

Specifically, the SAB Panel recommended more consideration of different spatial and temporal 

scales, different baselines to better capture the additional effects of changes in biogenic feedstock 

use, broader discussions on leakage12 and soil carbon implications, and the concept of regional 

feedstock-specific calculations and default assessment factor values (rather than individual 

stationary source-specific assessment factor calculations). 

Various stakeholders from the environmental community, industry, academia, and government 

commented on the 2011 draft framework and SAB process and shared relevant research and data 

to inform EPA’s evaluation of the scientific and technical issues associated with biogenic CO2

emissions assessment. Many of these stakeholders focused 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 pathways 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 recommendations and stakeholder comments regarding 

the 2011 draft report.14 Specifically, this report includes a more comprehensive discussion and 

analysis, including detailed technical appendices, on the following topics: (1) baseline approaches; 

(2) spatial and temporal scale decisions and implications; (3) inclusion of alternative fate analysis 

for certain feedstocks and methane; (4) leakage; and (5) illustrative regional feedstock-specific 

 

12 This report uses the IPCC definition of “leakage,” which is the indirect impact of a targeted activity in a 

certain place at a certain time on carbon storage at another place or time (IPCC, 2000). 

13 Stakeholder comments on the draft 2011 framework as well as the SAB Panel deliberations can be accessed 

at http://yosemite.epa.gov/sab/sabproduct.nsf/0/2F9B572C712AC52E8525783100704886. 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 description of [the framework’s] regulatory context and 

specifying the boundaries for regulating upstream and downstream emissions while implementing the 

regulation” and including “how possible regulatory measures under the Clean Air Act may relate to other 

policies that affect land use changes or the combustion/oxidation of products from the point sources that will 

release carbon or other greenhouse gases” (p. 42), are not reflected in this updated report because such 

parameters and boundaries may differ per application. 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 biological carbon cycle. The biological carbon 

cycle is the flow of carbon in various forms through 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 CO2, but also as methane (CH4), carbon monoxide (CO), 

and a variety of minor compounds. Through photosynthesis, 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 plants and animals, industrial processes including 

combustion, wildfires, or decomposition. Carbon that is not returned to the atmosphere is stored on 

land in plants and vegetation, in soils, or in sediments and plants in fresh water or marine 

environments (IPCC, 2007b; King et al., 2007). 

The biological cycling of carbon occurs on relatively 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 year, carbon can be removed from the atmosphere 

by herbaceous plant growth and returned to the atmosphere via decomposition. Over decades, 

carbon can be removed from the atmosphere by a growing tree and then released back to the 

atmosphere when the tree is burned or decomposes. Carbon may transit from living plants and 

trees to storage in litter, soil, and other pools prior to returning to the atmosphere (except in 

certain pathways and pools where carbon is stored longer term, for example, mineralized soil 

carbon). 

In contrast to the relatively short timescale of the biological carbon cycle, carbon in fossil fuel 

reservoirs, such as coal seams and oil and gas deposits, 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 processes, the carbon that accumulated in these 

deposits has been isolated from the active biological 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 plant material, usually in marshy areas, is inhibited 

from fully decaying by acidic and anaerobic conditions. 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, 2000). The term “flux” is used here to encompass biogenic 

carbon dioxide and methane emissions to the atmosphere, and removal of carbon from the atmosphere. 

Removal of carbon from the atmosphere is also referred to as carbon sequestration (EPA, 2014). 

16 Other processes cycle carbon on geological timescales, for example, carbon is vented by volcanoes, released 

from erosion of carbonate rocks (e.g., limestone), and deposited as sediments in marine and terrestrial 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 not renewable on policy-relevant time scales. 17 

Anthropogenic extraction and oxidation (e.g., combustion) of the carbon in fossil fuel reservoirs and 

peatlands returns this isolated carbon to the atmosphere and into the biogeochemical carbon cycle. 

Collectively, the combustion of fossil fuels and peat, along with the decrease in the amount of 

carbon stored in vegetation, have led to an increase in CO2 concentration in the atmosphere over 

the last 200 years (Le Quéré et al., 2009). 

A particularly important foundation of the framework 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 much longer geologic timescales. A key implication of 

this distinction is that the production and use of biogenic feedstocks and subsequent biogenic CO2

emissions from stationary sources will not inevitably result in an increased net flux of biogenic CO2

to the atmosphere within a policy-relevant time scale, unlike CO2 emissions from combustion of 

fossil fuels.18 Specifically, the net atmospheric contribution of CO2 resulting from biogenic CO2

emissions from stationary sources may differ from the contribution resulting from the same 

amount of fossil fuel CO2 emissions from stationary sources. 

1.4. Scope of the Framework 

Biogenic Carbon and Carbon-Based Fluxes 

The carbon and carbon-based fluxes evaluated in this framework include biogenic carbon-based 

emissions and removals. The former describes the transfer of carbon in the form of CO2 and CH4

from a reservoir19 to the atmosphere, and the latter, known as carbon sequestration, describes the 

transfer or uptake of carbon in the form of CO2 from the atmosphere to a reservoir. The framework 

examines shorter term fluxes of carbon that are anthropogenically induced by the production and 

use of biomass (see Figure 2, upper left quadrant). Although the framework addresses net 

atmospheric contributions of biogenic CO2, it is critical to recognize that all CO2 emissions 

contribute to the radiative balance of the atmosphere regardless of their origin, whether biogenic 

 

17 For the purpose of this study, references to a policy-relevant time scale is the time frame of concern 

required for stabilization of atmospheric GHG concentrations to avoid “dangerous anthropogenic interference 

with the climate system” (UNFCCC, 1994). See

http://unfccc.int/essential_background/convention/background/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 carbon itself (i.e., not considering any other carbon pools or 

biogenic carbon losses), the amount harvested and burned in a year is exactly balanced by the amount that 

grows during the year. In this theoretical case, the mass of carbon in the biosphere (i.e., in the living organisms 

on Earth) would be the same at the end of the year as it was at the beginning, and the net flux to (emissions) 

and from (sequestration) the atmosphere would be zero, averaged over a year. However, if the assessment 

starts with harvest and then the harvested feedstock is not replaced by growth (via replanting), then there 

will be a net increase in the amount of CO2 in the atmosphere. 

19 A carbon pool is a system with the capacity to accumulate 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 of 14C to 12C or 13C to 12C, the physical 

attributes of CO2 released from processing, combustion, and decomposition of biogenic material are 

the same as those of CO2 released from any other process, including fossil fuel combustion. In other 

words, no matter what the original source of the CO2, 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 “anthropogenic” 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 biogenic. 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 contributions of biogenic CO2 emissions can be narrow 

or quite broad, depending on the purposes and objectives of assessment. The framework is 

designed to assess the complex and shorter term biological 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 includes (1) the direct biogenic CO2 emissions from 

a stationary source stack; (2) the carbon cycle effects 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 cycle effects related to leakage,21 such as indirect 

land use change induced by displaced feedstock or feedstock substitute production. 

 

20 Radiative forcing is a function of the total concentration of CO2 and other GHGs in the atmosphere and is not 

dependent on the origin of CO2 (biogenic or fossil-based). Radiative forcing is the change in the net 

(downward minus upward) irradiance (expressed in W/m2) at the tropopause due to a change in an external 

driver, such as, for example, a change in the concentration of CO2. 

21 See Part 4 and Appendix E for a discussion of leakage. 
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 to fossil fuels versus the short-term carbon cycle common to 

biogenic feedstocks and (2) natural versus anthropogenic 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 biogenic 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 (EPA, 2010), is a compilation and 

evaluation of the inputs, outputs, and the potential environmental impacts of a product system throughout its 

life cycle. In the context of GHG emissions assessments, lifecycle GHG emissions are the aggregate quantity of 

GHGs related to the full fuel life cycle (including direct emissions and significant indirect emissions such as 

emissions from land use changes), including all stages of fuel and feedstock production and distribution, from 

feedstock generation and extraction through distribution, delivery, and use of the finished fuel by the ultimate 

consumer, where the mass values for all GHGs are adjusted to account for their relative global warming

potential (U.S Congress, 2007). See the following publications for the application of lifecycle analysis 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 Mann, 2004; Spitzley and Keoleian, 2005. Other definitions 

and applications of lifecycle analysis may differ from those presented above. 
November 2014 9 

processing, and use of biogenic material at stationary sources results in a net atmospheric 

contribution of biogenic CO2 emissions. In this way, this framework aligns closer with typical fossil 

fuel stationary source analyses that generally do not 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 related 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 analysis using the framework.23 

Figure 3: Potential Range of Assessment Scope. The framework focuses on the first three boxes (a through 

c). Though an equation term for leakage is included in the framework equation, a method for calculating leakage is 

not provided in this report. This framework does not include a complete lifecycle GHG emissions analysis (d) or 

include analysis of temperature change impacts, radiative forcing, and impacts on net social benefits (e). 

Non-CO2 Greenhouse Gases 

The framework can take into consideration CH4 emissions that are avoided when certain 

feedstocks, including waste-derived feedstocks, are used for energy.24 CH4 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 atmospheric concentrations of CO2 and other GHGs (IPCC, 

2007b), potential impacts of increasing levels of atmospheric CO2 (changes in precipitation, temperature, 

etc.), or the societal costs and benefits of mitigating GHGs (Interagency Working Group, 2010). See IPCC 

(2007a) for a discussion of potential climate change impacts and vulnerabilities. 

24 The amount of methane emitted from forest and agriculture-derived feedstocks combusted at a stationary 

source is typically small and depends, in part, on the engineering conditions at the individual stationary 

source (e.g., Lee et al., 2010). 

Broader effects such as radiative forcing, temperature change, and impacts on net social benefits (e)

Lifecycle analysis of total GHG emissions (all upstream and downstream GHG emissions and 

fluxes related to feedstock production and use, including from all fossil fuel inputs) (d)

Biological carbon cycle effects from leakage (including 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 feedstock 

processing and use (transport, storage) for use at stationary 

source (b)

Direct biogenic CO2 stack emissions at 

a stationary source (a) 

Broader effects such as radiative forcing, temperature change, and impacts on net social benefits (e)

Lifecycle analysis of total GHG emissions (all upstream and downstream GHG emissions and 

fluxes related to feedstock production and use, including from all fossil fuel inputs) (d)

Biological carbon cycle effects from leakage (including 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 feedstock 

processing and use (transport, storage) for use at stationary 

source (b)

Direct biogenic CO2 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 feedstocks, the framework can take into 

consideration landscape CH4 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, CH4 is not a significant contributor to 

landscape carbon-based emissions related to the growth and harvest of biogenic feedstocks 

because most forest- and agriculture-derived feedstocks 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 CH4 emissions (Anderson et al., 2010) or, in some cases, have a small 

negative net CH4 fluxes (EPA, 2013b). 

Figure 4: The Relative Size of Methane Emission Sources in the U.S. in 2012 (EPA, 2014). 

Since the framework focuses specifically on biogenic carbon-based fluxes associated with feedstock 

production and use at stationary sources, it does not include an analysis of nitrogen or nitrogen 

dioxide (N2O) emissions.26 However, the framework equation is flexible 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 production and processing. 

26 Nitrogen is naturally present in the atmosphere as part of the global nitrogen cycle and has a variety of 

natural sources and forms. Nitrogen is essential for biomass growth and other important biological functions, 

and limited usable nitrogen can limit biomass growth. Anthropogenic nitrous oxide emissions result from 

agricultural fertilizer application, fossil fuel combustion, wastewater management, and industrial processes 

and interact with the global nitrogen cycle. However, nitrogen is not carbon-based and is not directly a part of 

the biological carbon cycle (though it is acknowledged 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 the 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 variety of ways the equation could be applied using 

representative and/or customized landscape and process factors. Part 4 presents important 

technical aspects, such as baseline assessment method, temporal and spatial scales, leakage, and 

feedstock categorization, which require consideration when applying the framework for use in a 

specific policy or program. Part 5 discusses key issues that emerged in the course of designing the 

framework and highlights possible avenues for further 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 discussion of the technical considerations used in 

the framework, including IPCC methods, temporal and spatial scale, feedstock categorization, 

process attributes, leakage, and an algebraic representation of the framework. Appendices H 

through M provide example applications of the framework, using publically available data applied 

in hypothetical scenarios, to generate illustrative values for the framework equation terms and 

assessment factors per different baselines, feedstock, 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 Considered 

This framework presents an equation, called the biogenic assessment factor (BAF) equation, which 

includes terms representing different aspects of the 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 meet the needs of policy or program applications 

that involve assessment of the net atmospheric contribution of biogenic CO2 emissions associated 

with biogenic feedstock production, processing, and use at stationary sources. 

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

(1) Biogenic landscape attributes: biogenic feedstock carbon and carbon-based fluxes that are 

associated with the landscape where a biogenic feedstock is grown and harvested/collected, and/or 

those that are associated with avoided emissions from feedstock use. The biogenic landscape 

attributes associated with feedstock production include: 

 

27 Appendix M includes a sensitivity run estimating the potential impacts of including N2O 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 would have eventually decomposed or 

been burned on the production site if not removed or some other alternative fate; 

• Net biogenic carbon emissions or sequestration from non-feedstock biogenic carbon pools 

on the production landscape associated with land use change (e.g., converting forests to 

agriculture) or land management or change in land management (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 process attributes associated with feedstock 

processing at the stationary source include: 

• Biogenic feedstock carbon that leaves the stationary source supply chain as losses during 

transportation, storage (decomposition), and processing; and

• Biogenic feedstock carbon embodied in products. Products could pass out of the supply 

chain and exit the stationary source either prior to use (e.g., lumber) or after use (e.g., ash, 

biochar).29 

The inclusion of process attributes provides two benefits. 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 proportion of biogenic feedstock carbon that enters 

the process; (2) the proportion of the feedstock carbon that passes through a stationary source as 

products; (3) the amount that is emitted to the atmosphere through losses during transport, 

storage, and processing; and (4) the amount that is emitted through the stack. 

Second, inclusion of these process attributes allows the framework to reflect differences in physical 

properties across feedstocks, such as different responses to the applied storage conditions (e.g., 

temperature and/or moisture) and conversion processes. 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 different characteristics such as lower heating values 

and more moisture content in biomass.30 

 

28 Indirect land use change is one form of leakage, which is the indirect impact that a targeted activity in a 

certain place at a certain time has on carbon storage at another place or time (IPCC, 2000). 

29 In specific applications of the framework, this process attribute category could be used to represent carbon 

capture and storage (CCS) if appropriate. 

30 For example, see the Biomass Energy Center’s typical 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 geographic feedstock production site. Process 

attribute terms are primarily influenced by and thus may be evaluated by a combination of 

feedstock type, feedstock production region, and stationary source process. 

Net biogenic emissions (NBE) represents the net atmospheric biogenic CO2 contributions 

associated with biogenic feedstock production, processing, and use at a stationary source. NBE is 

calculated by multiplying the potential gross emissions (PGE) of the biogenic feedstock input when 

used at a stationary source by the feedstock’s biogenic landscape attributes, and then by factors 

that adjust for biogenic feedstock carbon losses that may occur during storage, transport, or 

processing, as well as biogenic feedstock carbon that 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 used 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 source gate, feedstock production site, or 

at the stack: wherever the point of assessment needs to be. Thus, this term can have 

different values indicated by subscripts, representing different points along the supply 

chain). 

• (GROW + AVOIDEMIT + SITETNC + LEAK) represents the landscape emissions effect. The 

landscape emissions effect is the sum of four unitless factors that relate the total biogenic 

carbon content of the feedstock grown at the feedstock 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 pool only). 

o 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 burning) or per an alternative 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 management 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 equal to one that represents biogenic 

feedstock carbon that leaves the supply chain (e.g., via transit or decomposition, deviated 
November 2014 14 

for use as a product) between the feedstock 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 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 one, 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 products, 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 measurable 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 CO2 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 CO2 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 unit of mass) of biogenic feedstock and 

is a quantity that could be measured or estimated at different points of assessment. The point of 

assessment may differ per policy or program application and could include sites such as the boiler 

mouth, the stationary source gate, the forest or farm 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 identification and assessment of the biogenic 

feedstock carbon at different points along the supply 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 assessment: the 

biogenic feedstock CO2 potential harvested at a production site (point of assessment PGE0) is 100 

tons CO2. Assume that there are no feedstock carbon losses and no pre-combustion products 

leaving the supply chain prior to entry into the combustion process (point of assessment PGE1). 

Because there are no carbon losses between these two points of assessment, L = 1, and PGE0 = PGE1

= 100 tons CO2. However, if the point of assessment is after the combustion process, the biogenic 

CO2 emitted from the stack would be represented by PGES, 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 later points of assessment are included in 

Appendix G. 

Biogenic Assessment Factor (BAF) 

The BAF is a unitless factor that represents the net atmospheric biogenic CO2 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 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 stack emissions) at a stationary source. The 

purpose of deriving the unitless BAF value under this framework is to provide a universal metric 

that could be applied to assess the net atmospheric biogenic CO2 contribution of biogenic feedstock 

use by a particular type of source without regard to the particular amount of feedstocks used. Once 

derived from assessing the carbon fluxes and mass balance associated with the production or use of 

a particular mass of feedstock in a specific type of process, the BAF value may then be applied to 

examine the net atmospheric contribution of any amount 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 can be calculated in different ways, depending 

on the nature of the analysis being conducted and related 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 component, units used for calculations, and their 

values31 can differ according to how the framework parameters are applied (e.g., baseline, spatial 

scale, temporal scale). The estimated biogenic landscape attribute terms are unitless values.32 The 

text below provides some general examples of different 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 with the carbon contained in the quantity of feedstock 

being used in the calculation (PGE). 

32 However, the framework can be adapted to use units instead of unitless values as needed for a specific 

application. 
November 2014 16 

of data they could include. Part 3 of this report discusses in detail the different types of baseline 

approaches, and the technical appendices include illustrative 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 with the feedstock carbon pool. For example, if 

the feedstock under consideration is roundwood, then 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 pools on the production landscape, such as soil 

carbon or detrital carbon (which are captured in another term, see SITETNC). The GROW term can 

be calculated in different ways, including with different baseline approaches. 

For example, changes in the roundwood carbon stock (and related fluxes generated by feedstock 

growth and harvest) in a landscape could be evaluated between two points in time using historical 

data available for that landscape; this method would reflect a retrospective reference point baseline 

approach. Alternatively, a future anticipated baseline 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 conditions 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., corn 

stover), the GROW term could equal 0, because during that one year the carbon sequestered during 

growth is equal to the amount removed from the landscape. 

Avoided Emissions (AVOIDEMIT) 

The AVOIDEMIT term 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 burning) or per an alternative management 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 naturally decay or would be combusted onsite 

as part of the land management practice employed at the production site. In the case of wastederived feedstocks like landfill gas and MSW, emissions could occur as part of the natural decay of 

the waste, with some of the carbon likely transformed to CH4. 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 necessitated 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 comparison between two scenarios (with and 

without the feedstock consumption), not just looking at observed data between two points in time. 

However, outside of a strict application of the retrospective reference point baseline approach, one 
November 2014 17 

can estimate avoided emissions by constructing a counterfactual baseline (a type of anticipated 

baseline) analysis of what action and related emissions 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 (see Appendix H). 

Using logging residues as an example, a future anticipated baseline approach could estimate the 

future potential biogenic carbon-based emissions that 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 projected 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 residues 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 material 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 alternative emissions pathway. Therefore, for 

waste-derived feedstocks, AVOIDEMIT represents the avoided biogenic emissions that could have 

occurred per an alternative management strategy instead of per the feedstock’s use in bioenergy 

production, relative to biogenic feedstock consumption. 

Total Net Change in Production Site Non-feedstock Carbon 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 management, 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 feedstock, 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 terms 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 result in a net change in overall carbon stocks 

(including soil carbon and other carbon pools on the landscape) over some period of time as the 

new use or system reaches equilibrium. If land use or management changes occur due to biogenic 

feedstock production, these changes can result in net emissions or net sequestration,33 depending 

on the feedstock type, the previous land use activity, 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 cultivation with conventional till practices is converted to 

produce dedicated energy crops such as switchgrass, the resulting net landscape emissions could be negative, 

indicating higher levels of sequestration with switchgrass production (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 some portion of that carbon entered into the soil 

carbon and possibly also the mineral carbon pools. 

For example, changing land use to produce a dedicated energy crop such as switchgrass on 

farmland previously used to cultivate wheat could result in increased soil carbon levels, due to the 

extensive root systems of switchgrass and the elimination of tillage. In this instance, the value for 

SITETNC would be negative, indicating a net sequestration of carbon resulting from the land use 

change. Conversely, if forest were cleared to plant switchgrass, this action would result 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, losses 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 CO2 resulting from the land use change. 

Deriving a value for SITETNC for a feedstock can also differ according to the baseline approach used 

(as well as per the availability of data, spatial scale, 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 example, if an increased amount of corn stover were 

removed from the production landscape between the reference points, measurements may show a 

decline in soil carbon pool levels. This decrease may have occurred because of the increased stover 

removal or possibly that practice in addition to other influences (such as changes in tillage rates or

fertilizer application, for example). Using a future 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 removing 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 biogenic feedstock 

production and use at stationary sources. The IPCC defines leakage as the estimated indirect 

impacts of a targeted activity in a certain place at a certain time on carbon storage at another place

or time (IPCC, 2000). In the context of this report, leakage represents any biogenic carbon flux 

impacts outside of the biogenic feedstock production 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 conventional products to biogenic feedstocks). 

Leakage can be positive—indicating increased emissions outside the assessment boundary (e.g., if 

unmanaged forests become managed forests in order to meet displaced market demand for 

traditional forest products)—or negative—indicating increased sequestration outside the 

assessment boundary (e.g., changes in biomass markets cause some landowners to convert idle 

cropland to forest, thereby increasing carbon stocks on the landscape). 
November 2014 19 

One form of leakage—indirect land use change attributable to the production of a biogenic 

feedstock—can be challenging to quantify, because it often involves a number of complex 

socioeconomic dynamics (e.g., trade, market interactions) as well as biophysical impacts that occur 

outside of the biogenic feedstock production site and for which data may or may not be available. 

However, indirect land use change can result in significant emissions if it occurs at a large scale and 

involves conversion of land with relatively large preexisting carbon stocks. 

The framework includes an equation term for leakage; however, the illustrative calculations

presented in the appendices do not explicitly quantify leakage or provide 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 transportation 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. Representation of these supply chain losses allows for more 

accurate representation of the actual volumes of feedstock 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 feedstock carbon in products that 

exit the supply chain before or after the conversion process but are not emitted from the stack 

during the conversion process. This feedstock carbon may pass through an entity to other end users 

(including secondary stationary sources) or possible longer-term carbon storage. Examples of 

products that may exit the supply chain prior to conversion at the primary stationary source 

include: 

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

• Wood residuals sold/transferred to a separate stationary source for use as raw material or 

fuel; 

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

• Agricultural by-products (e.g., stover, stalks, straws, 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 on leakage. 
November 2014 20 

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

The biogenic process attributes in the NBE equation perform two separate functions. First, they 

relate the quantity of feedstock measured at the point 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 source. This adjustment is accomplished by the L

term, which accounts for losses that occur and products that are produced between the farm or 

forest and the point of assessment. The second function 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 stationary 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 accomplished by the P term and accounts for 

products that are produced at any point in the supply chain and accounts for any losses that those 

products share responsibility for. The following sections 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) and the quantity of feedstock measured at the point 

of assessment (e.g., the quantity received at the stationary source or the quantity of feedstock that 

enters the stationary source process). Specifically, the term tracks any differences in the mass of 

biogenic feedstock carbon between the feedstock production 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 equal 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 the 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 source prior to the conversion process at that source 

(PROD, as further discussed in next section36). If feedstock carbon exits the chain for a specific 

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 feedstock 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 within 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 used to calculate 

the P term (which represents the carbon not embodied in products). 

37 As discussed later in this report, the collected carbon content that leaves a primary stationary source as 

product in the form of a fuel may be used by a secondary 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 production landscape (such as GROW, AVOIDEMIT, and 

SITETNC). This term therefore is not dependent on landscape attributes but rather feedstock and 

process type. 

L is represented as a ratio between biogenic feedstock carbon that enters the supply chain at the 

farm or forest and biogenic feedstock carbon measured at the point of assessment. L can be 

calculated by using measurements of products and losses taken along the supply chain prior to 

entry into the conversion process to relate biogenic 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 the feedstock production site by the quantity 

entering the conversion process. For example, assuming no pre-conversion products, if the biogenic 

feedstock measures 100 tons CO2e at the harvest site and then measures 90 tons CO2e at the boiler 

mouth, L would be representing biogenic feedstock losses (via physical losses, decomposition, etc.) 

with a result of a positive 1.11.38 For some feedstocks, it may be possible to generate 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 chain 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 accounting) because these losses represent an 

emission associated with procuring the biogenic feedstock for energy use. This value allows for 

recognition of the additional feedstock that was harvested and related additional landscape effects 

that may have occurred but were not actually consumed 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 that 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 amount 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 instead 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 stationary source and any 

products produced after the losses occurred. 

 

381.11 = 100 tons harvested/90 tons input to bioenergy conversion process 
November 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 the 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 feedstock and process type. 

Values for the P term could differ per feedstock type and stationary source process. Some 

feedstocks have various alternative uses and product 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 processes and efficiencies within those processes 

that may yield varying levels of pre- and post-conversion product materials. 

For example, an ethanol plant transforms biogenic feedstock (e.g., corn) into ethanol fuel through a 

fermentation process. A portion of the carbon contained in the biogenic feedstock that is input to 

the conversion process forms the basis of the refined ethanol fuel. While fermentation results in 

process stack emissions of CO2 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-product (e.g., distillers grains).39 Although the vast 

majority of the biogenic carbon embodied in the ethanol will be subsequently emitted as CO2 by 

mobile emissions sources that use the ethanol as fuel, these “downstream” CO2 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 result when calculating the assessment factor for the 

biogenic CO2 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 stationary 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 stationary source may be subsequently used by 

another stationary source. Thus, the biogenic carbon stored in products from the producing or 

primary stationary source could in turn be considered as a feedstock (secondary use feedstock) 

input to a different stationary source (secondary stationary source) conversion process for energy. 

Using sawmill mill residues that are sold to a separate 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 could 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 production of primary products, such as distillers grains 

created during ethanol production, currently have alternative non-energy market uses, such as livestock feed. 

If, however, distillers’ grains were instead used for energy at the stationary source, they would likely be 

considered a biogenic feedstock input and thus may require assessment with the framework. 
November 2014 23 

included among the biogenic CO2 emissions from the primary stationary source that produced the 

biogenic feedstock (mill residues). 

Figure 5 includes the landscape and process attributes 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 feedstock production site. Calculation of all 

remaining equation terms is predicated on emissions changes on the landscape. That is, each unit of 

biogenic CO2 transferred from the landscape to the stationary source 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, biogenic process attributes can be evaluated at 

various points of assessment (both outside of and within the stationary source), and these terms 

are used to further adjust the net biogenic emissions 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 framework, this graphic 

also includes the equation terms that represent each of these basic process elements. The framework addresses 

biogenic carbon flows and fluxes associated with the production, processing, and use of a biogenic feedstock, 

including biogenic carbon impacts at the production landscape (GROW, AVOIDEMIT, SITETNC) as well as indirect 

impacts on landscapes other than the production landscape (LEAK); biogenic feedstock carbon losses during 

transport, storage, and processing of the feedstock (L); and biogenic feedstock carbon that is embodied in 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 CO2 emissions at a stationary source 

contribute additional biogenic CO2 to the atmosphere—as compared with the baseline or 

reference condition—due to biological carbon cycling associated with feedstock production 

and use. In other words, changes in net biogenic CO2 emissions and sequestration from 

biological carbon cycle processes and other offsite and process factors exactly offset the 

direct biogenic CO2 emissions from the stationary source. 

• A BAF of 1 means that 100% of the direct biogenic CO2 emissions at a stationary source 

contribute additional biogenic CO2 to the atmosphere—as compared with the baseline or 

reference condition—due to biological carbon cycling 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 biogenic CO2 emissions from the stationary 

source. 

• A BAF value between 0 and 1, such as 0.2 or 0.5, means that biological carbon cycle effects 

related to biogenic feedstock growth, harvest, and use counterbalance a proportion of the 

direct biogenic CO2 emissions from a stationary source (e.g., a 0.2 BAF means a net 

atmospheric contribution of 20% of the direct biogenic CO2 emissions, while the remaining 

80% is counterbalanced by landscape and process attributes). 

• A negative BAF value, such as −0.2, means that the biological carbon cycle effects of biogenic 

feedstock growth, harvest, and use more than fully counterbalance the direct biogenic CO2

emissions at the stationary source. Negative values represent an emissions reduction that 

occurs as the result of a stationary source’s use of a biogenic feedstock. In this case, 20% 

more CO2 is sequestered (or 20% more CO2 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 carbon cycle effects related to biogenic 

feedstock growth, harvest, and use result in a net CO2 emissions increase, over and above 

the CO2 emissions coming directly from the facility. For example, where direct land use 

change causes a substantial decline in carbon stocks 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 Factor 

The BAF, or elements of the BAF equation, could be applied in various policy scenarios. For 

example, a possible scenario may include calculating BAF values that may be used to determine 

whether to regulate direct biogenic CO2 emissions from a stationary source. The framework could 

also be used to calculate an adjusted CO2 emissions value at a stationary source by multiplying a 

BAF value by the direct emissions of biogenic CO2 from the individual stationary source. To conduct 
November 2014 25 

these calculations, the framework equation and its terms may need to be adjusted or modified to 

accommodate only the specific parameters required for a particular application. For example, if an 

application of the framework is limited to an assessment 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 Landscape 

and Process Attributes 

This section describes three ways biogenic assessment factor equation terms and BAFs could be 

used: a “representative factor” approach, a “customized factor” approach, and a hybrid approach. 

For each approach, this section discusses an example 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 specific 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 landscape attributes and process attributes associated 

with that feedstock. Specifically, this approach could use an assessment of the biogenic 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 of 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 calculated using various spatial and temporal scales, 

baselines, and feedstock and process types as needed, and could be presented in a simple format 

(e.g., look-up tables). Using the representative factor approach, stationary 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 approach could require updating every 5 to 10 

years to ensure that the values reflect updated data, including measured changes to biogenic 

landscape attributes, changes in process technologies, improvements in carbon measurement 

technologies, and other information necessary to update tools and methods used to calculate the 

representative values. 

Using the representative factor approach, the following information 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 methods 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 process attributes would be calculated using 

parameters specific to the specific stationary source using a specific biogenic feedstock. 

For landscape attributes, a customized factor approach could require calculation of site-specific 

values for equation terms that characterize the distinct landscape where a feedstock is produced 

and harvested. For example, a stationary source would likely need information from the forest 

landowner regarding forest growth and removals to calculate 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 biogenic feedstock losses that occur while the 

feedstock is being transported or stored prior to use. 

A number of technical and implementation issues would need to be addressed to implement a 

customized factor approach, including: 

• Identification of appropriate data collection approaches for carbon stocks and carbon 

fluxes, by feedstock type and framework equation term; 

• Identification of the appropriate level of data quality (possibly including statistical 

sampling protocols and acceptable standard error thresholds); 

• Development of protocols for identifying the distinct landscape where a biogenic feedstock 

is produced, including identification of landownership and management practices; 

• Development of protocols to validate monitoring and reporting, if necessary40; and 

• Development of protocols, or modification of existing protocols, if needed to track a 

biogenic feedstock from the specific landscape where it was produced to the stationary 

source where it was used.41

A customized factor approach may provide for greater flexibility, and possibly greater accuracy, in 

instances where a stationary source can identify the specific land base where a biogenic feedstock 

was grown and harvested. Such an approach would also allow a stationary source to provide 

greater specificity about the process attribute-related biogenic CO2 fluxes that occur onsite at a 

stationary source, as a result of biogenic feedstock storage and processing. This approach would 

allow a stationary source to apply detailed knowledge about specific stationary source technology 

 

40 For example, this might involve protocols for independent 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 biogenic 

feedstock’s source region, considering the potential for inter-regional 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 chips may not be easily identifiable through visual inspection 

alone, because wood chips could be sourced from different 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 applied in developing representative factor values 

for process attributes. 

3.3. Hybrid Approach 

Depending on the availability of information, a combination of both approaches described above 

could be used. A hybrid approach could involve using 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 Northeastern 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 calculates from information on its processes. 

Conversely, representative factors could be provided for general stationary source processes, and 

the stationary source could calculate customized factor values for landscape attributes associated 

with the landscape producing the logging residues it uses. 

4. Technical Considerations 

The sections below describe key technical and methodological components to consider when 

evaluating biogenic CO2 fluxes related to a stationary source’s use of biogenic feedstocks: 

• Baselines 

• Temporal scale 

• Spatial scale 

• Leakage 

• Feedstock categorization 

The implications of making particular choices with respect to each of these key issues are discussed 

below, and the technical appendices provide illustrative examples of how these considerations 

might be applied in practice. 

4.1. Baselines 

In basic terms, defining a baseline creates a foundation 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 vary with time to 

represent some measured, anticipated, or desired change. 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 defined as “the reference for measurable quantities from which 

an alternative outcome can be measured” (IPCC, 2007c) or “the baseline (or reference) is the state against 

which change is measured. It might be a ‘current baseline,’ in which case it represents observable, present-day 

conditions. It might also be a ‘future baseline,’ which is a projected future set of conditions excluding the 

driving factor of interest. Alternative interpretations of the reference conditions can give rise to multiple 

baselines” (IPCC, 2007c). 
November 2014 28 

depends in part on the time frame in which the analysis should take place, the context in which the 

approach is applied, and the available data. 

The framework methodology involves a baseline against 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 determination 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, although 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 CO2 fluxes and/or carbon stocks between two points in time 

(Fargione et al., 2008; UNFCCC, 2009). The second baseline approach, the anticipated baseline, 

assesses the estimated net changes of biogenic CO2 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 alternative counterfactual scenario that includes changes 

in environmental, economic, and/or policy conditions (Searchinger et al., 2009; Sohngen and Sedjo, 

2000). 

The reference point and anticipated baseline approaches can both be used for prospective or 

retrospective analyses. A prospective analysis assesses the potential impact of a particular policy or

change in biomass use on future biogenic CO2 fluxes and/or carbon stocks,43 while a retrospective 

analysis can examine the impact that an existing policy or biomass utilization practice has made on 

net biogenic CO2 fluxes in the past. Prospective analyses commonly use the anticipated baseline 

approach to compare two future scenarios. A reference point baseline may also be used in a 

prospective analysis that compares modeled alternative future biogenic CO2 fluxes to modeled or 

observed biogenic CO2 fluxes at an earlier point in time. Retrospective analyses can use the 

reference point approach to compare current or recent biogenic CO2 fluxes to historical biogenic 

CO2 fluxes, because this approach allows for the possibility of using only observed data. However, 

an anticipated baseline may also be used in a retrospective analysis comparing current/recent 

biogenic CO2 fluxes with modeled counterfactual current/recent biogenic CO2 fluxes. 

The two baseline approaches discussed above can provide useful insights in the assessment of 

biogenic CO2 fluxes. There is no single correct answer for which baseline to choose, because 

different baselines help answer different questions. The sections below describe how reference 

point baselines and anticipated baselines can be constructed and applied and discuss the 

implications of using either baseline approach in the framework. 

Reference Point Baseline 

The reference point approach can be applied in situations that seek to measure what has or has not 

occurred on the landscape between two different points in time. The reference point baseline 

 

43 The rest of this section uses the term biogenic CO2 fluxes as the unit of evaluation to represent changes 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 the 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 stock (or carbon-based flux) on a given land base (i.e., 

total stocks of carbon stored in vegetation and soils) at a given point in time (or over a given time 

interval). Land based carbon stocks or fluxes can then be evaluated against this reference point. 

A reference point approach could be applied in a retrospective 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 user to rely on historic carbon stock data, which may 

be available for selected feedstocks (e.g., U.S. Forest Service Data for roundwood). Some of the 

illustrative BAF calculations provided in the technical appendices of this report apply the reference 

point baseline retrospectively, relying primarily on historical observed or modeled data or related 

estimates from the literature. To develop the baseline under this retrospective reference point 

approach, it is appropriate to look at carbon stocks in the present and/or in the recent past to see 

how those stocks change between the two points of evaluation.44 

There are some limitations with using this approach. For example, land use, land use change, and 

forestry (LULUCF) data are often more uncertain than data from other sectors (e.g., energy) due to 

the complexity of the natural systems and the variety of anthropogenic uses and activities involved. 

Also, although tools and methods to monitor and model 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 process 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 practices), 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 be identifiable. 

In addition, other limitations may result from using the retrospective reference point baseline 

approach, including the inability to attribute landscape biogenic carbon fluxes directly to stationary

source use, or to assess additionality. Additionality 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. Additionality can be gauged as the difference in net 

atmospheric CO2 emissions with and without changes in biogenic feedstock use (Murray et al., 

2007). If the goal of an analysis is to assess additionality (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 stocks in one year or an average over a range of years. 
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 retrospective 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 change. 

At larger scales like, for example, the regional level, 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 difference in regional feedstock carbon stocks 

between two points in time, then the reference point offers a practical method for determining if 

the area where the feedstock is harvested is a source or a sink (as discussed in Appendices H and I). 

In certain contexts, this observed carbon stock change can serve as a proxy for the net emissions 

contribution of biogenic feedstock production and harvest. However, relevant historic data may not 

exist for retrospective assessment of particular feedstocks (such as agricultural by-products or 

energy crops not currently grown at commercial scale). 

The retrospective reference point approach is presented 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 stocks between two points in time retrospectively. 

These illustrative applications of the retrospective 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. These 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 management 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 compared 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 might 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 approach was limited in that it could not identify and assess 

additionality: “[the reference point] approach is not adequate in cases where feedstocks accumulate over long 

time periods because it does not allow for the estimation of the incremental effect on greenhouse gas 

emissions over time of feedstock use. To gauge the incremental effect on forest carbon stocks due to the use of 

forest-derived woody biomass..., an anticipated baseline 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 simulates 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 alternative scenario of changed (e.g., increased 

or decreased) biogenic feedstock demand. The resulting 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 two distinct scenarios, not two points in 

time. Although there is a time element to the anticipated 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 anticipated baseline approach also allows 

for the evaluation of additionality (landscape emissions 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 lies in how to define what would have been 

expected to occur in the BAU or counterfactual scenarios. In a retrospective application, the 

challenge is to consider what the counterfactual “without biomass” scenario would have been, after 

the period has ended (e.g., would more or less biomass 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 environmental 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 uncertainty. 

Uncertainties exist when applying future anticipated baselines, including insufficient data or 

information about a physical or economic process being evaluated, model/data specification, 

inherent statistical uncertainties, etc. The reference point section discussed limitations related to 

LULUCF-related data, which are also relevant for future anticipated baselines based on that data. 

Also, estimation of future BAU or counterfactual scenarios 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 BAU baseline and counterfactual scenarios 

require simulations or projections of future biophysical, policy, and economic conditions, which 

often rely on the best available information and expectations 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 involve 

anticipated policy and/or market changes in the future. 
November 2014 32 

and biophysical conditions play a significant role in resulting estimates of future or counterfactual 

potential carbon stocks and other impacts from changes 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 environmental and biophysical conditions to develop 

illustrative future BAU baseline and alternative scenarios.. Future anticipated baselines have been 

broadly applied to evaluate the potential future impacts of specific policies or the marginal impacts 

of an action, holding everything else constant between future scenarios.47 This baseline approach 

allows for evaluation of possible effects of stationary source biogenic feedstock demand changes on 

the biogenic feedstock production landscape and related net biogenic CO2 fluxes over time by 

capturing the complex interactions between biogenic feedstock production and forest product 

markets, including: biogenic feedstock demand; market-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 differences as well as behavioral responses to market 

incentives. 

Application of Different Baseline Approaches 

This report recognizes that the approach to developing baselines may differ according to the 

specific application of the framework; therefore, the framework methodology provides flexibility to 

support both reference point and anticipated baselines and possibly others. Application of this 

framework in a specific program or policy may involve use of either baseline approach in a 

retrospective or prospective time frame, some combination of the two, or some other approach. 

The choice of which baseline approach to apply necessarily involves tradeoffs, which must be 

considered by the user of the framework. Also, depending on the application of the framework, 

choices need to be made about other framework components, such as spatial or temporal scale. 

Important considerations when determining an appropriate 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 emissions from increased biomass use. 

• Model Availability: Though both baselines can be limited by data availability, the 

anticipated baseline also depends on economic models 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 changed 

in the historical period (e.g., economic growth, natural disturbance, new government policy encouraging tree 

planting) besides a change in biogenic feedstock production for energy use that affect biogenic feedstock 

carbon levels on the assessment landscape, but the impact of those factors cannot be separated from the 

impact of bioenergy. This is an important distinction between the retrospective reference point and future 

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 assessment 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 program requires an evaluation of the additional 

impact of a new source of feedstock consumption, then the reference point baseline 

approach would not be appropriate. However, if programs require an assessment of historic 

emissions, a reference point approach could be necessary. 48 

To highlight the various issues surrounding baselines 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 point analysis of forest- and agriculture-derived 

feedstocks in Appendices H and I; future anticipated baseline of forest- and agriculture-derived 

feedstocks in Appendices Kand L49; Appendix M gives an overall comparison and discussion of 

different examples presented in appendices from both baseline approaches. For the waste-derived 

feedstocks analysis in Appendix N, an anticipated baseline (counterfactual) assessment between 

different alternative fate pathways and related avoided emissions is presented.50 

4.2. Temporal Scale 

Assessing biological phenomena inherently necessitates considerations of time. Although different 

terrestrial and atmospheric processes unfold over time frames ranging from moments to millennia, 

policy is typically implemented over time frames not as far ranging. There are different 

perspectives about how to assess future emissions trajectories (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; Miner et al., 2014). 

 

48 For example, with the reference point baseline approach applied regionally, it is possible to estimate the 

change in net carbon stocks between two points in time but not possible to estimate how many additional (or 

how many fewer) stationary sources are using the biogenic feedstock or the impact per each individual user 

on the regional landscape. With the future anticipated approach in a regional application, different levels 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 disaggregated enough to allow for such detailed analysis. 

Disaggregation at the level of SubRegional Timber Supply (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 landscape attributes using the retrospective reference point 

baseline, which are applied in example case studies in Appendix I. Appendix K discusses illustrative future 

anticipated baseline construction methods based on zero biomass use, constant 2009 levels of biogenic 

feedstock use, and various Annual Energy Outlook (AEO)-derived scenarios. A subset of these baselines is 

then compared with simulated alternative future scenarios with increased production and use of biogenic 

feedstocks for energy and industrial processes at stationary sources in Appendix L. 

50 The analysis for waste-derived feedstocks consists of counterfactual exercise, comparing different practices 

and alternative fates. 
November 2014 34 

GHG emissions assessment can be sensitive to accounting time frames, emissions flux trajectories, 

and reporting periods. In some situations, direct emissions 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 submitted by Parties to the United Nations Framework 

Convention on Climate Change (UNFCCC), emissions are 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 CO2 fluxes related to the production and use of some biogenic 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) and 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 direct 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 CO2 assessments. Thus, it 

may be necessary to distinguish between the “emissions 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 the biogenic landscape attributes and related 

biogenic CO2 fluxes, determinations should include: 

• Whether to include all past and/or future estimated biogenic carbon-based emissions 

related to biogenic feedstock production or the emissions horizon (e.g., from potential 

avoided decay from feedstock use or long-term impacts on soil carbon stocks); 

• Whether to establish a specified time frame over which emissions fluxes are accounted for 

(which could be shorter or longer than the emissions horizon) or just emissions that occur 

in the year the biogenic feedstock is used or the accounting horizon; and 

• Whether future emissions and/or sequestration should 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 results of any analysis evaluating biogenic CO2

fluxes associated with biogenic feedstock production. Considerations related to the choice of 

temporal scale can differ with feedstock type and geographic location (e.g., growth rates of different

tree species in different locations). Also, determining the temporal scale for assessing biogenic CO2 

fluxes has implications and tradeoffs related to measurement, reporting, and verification of 

estimates and the ability to reflect carbon dynamics 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 evaluation of different questions and contexts. Therefore, 
November 2014 35 

the choice of timescale is largely dependent on specific applications of the framework and related 

needs or preferences for accounting time such as use of discount rates, inclusion of estimated 

future climate change impacts, radiative forcing, or any other parameters for analysis. 

There are important tradeoffs to consider when choosing 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 CO2 fluxes and activities may be included. The following 

factors should be taken into account to help determine the most appropriate temporal scale: 

• Feedstock choice: Different feedstocks have different growth and harvest intervals, so the 

choice of temporal scale could vary depending on what feedstock type is being evaluated. 

For example, if the feedstock is an annual crop with 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 intervals for short-rotation tree species in 

the Southeastern U.S. are much different than managed forest systems elsewhere in the U.S. 

Setting a temporal scale that is too short could cause an assessment to miss decades of tree 

growth and harvest cycles and would inaccurately represent investment decisions driving 

land use and management in the Pacific Northwest. Conversely, very long time frames (e.g., 

100+ years) can obscure important shorter term land use activities and related biogenic CO2

fluxes. 

• Data Availability: Also important is the availability of data and/or models for conducting 

assessments. For instance, a one-year assessment scale 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 align with 

the specific goals of the policy or program that the framework is applied to (e.g., baseline 

time frames for initial assessment or renewal of requirements under existing programs). 

Potential Framework Applications of Time 

The framework can address the issue of time differently 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 point 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 range of historical years included in the accounting 

period (e.g., will the time between reference points 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 on 
November 2014 36 

data availability. For example, because of different 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 the 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 reference 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 anticipated 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 the 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. Therefore, 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 assessment time frame would be more 

appropriate compared with an assessment that focuses on forest-derived feedstocks, where the 

harvest cycles and landscape carbon effects occur over 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 appendices, the future 

anticipated baseline approach uses a 50-year simulation horizon into the future using a year-toyear carryover construct (as reflected in Appendices L and M). This temporal scale is long enough 

to capture carbon dynamics of longer rotation feedstock species and short enough to detect 

significant biogenic CO2 fluxes related to biogenic feedstock production. The year-to-year carryover 

approach and time frames used can offer insights about the potential future impacts of biogenic 

feedstock production and use per the case study assumptions; they were chosen for use in this 

report for illustrative purposes only and are not meant to be prescriptive. 

 

51 The front-loading approach accounts for the net biogenic CO2 fluxes that will occur over the established 

assessment time frame associated with biogenic feedstock 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 biogenic CO2 fluxes associated with feedstock production and 

use in the current year are counted as well as any net biogenic CO2 fluxes that occur on the feedstock 

production landscape in the current year due to feedstock use in previous years (e.g., decay, soil carbon pools 

changing to a new equilibrium state) and thus are “carried over.” 

53 Annualized carryover is a type of carryover accounting in which the net biogenic CO2 fluxes associated with 

biogenic feedstock production and use are added together (as in front- loading) but then divided by a certain 

number of years to generate an annualized biogenic CO2 flux value per unit of feedstock. This approach 

accounts for cumulative emissions over the impact horizon and then divides those emissions equally over the 

reporting horizon. 
November 2014 37 

Additional Technical Timing Issues 

Because of practical limitations related to data availability and to better represent general trends 

rather than episodic fluctuations, it may be necessary to aggregate data from a multiyear period 

and to apply this multiyear average to annual fluxes. The frequency of data collection for key inputs 

such as carbon sequestration occurring on the landscape may not be annual, and/or annual data 

may not be publicly available. For example, the Forest Inventory and Analysis (FIA) dataset 

collected by the USDA Forest Service is updated using 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 may be more practical to use an average value 

representing the mean over a period of years rather than an annual value. 

Biogenic CO2 emissions and carbon sequestration levels may also vary from year to year due to 

weather, other natural or anthropogenic disturbances, or market conditions (e.g., demand) such 

that even if annual data were available, a single year’s value is not likely to represent the overall 

trends in biogenic carbon stocks and CO2 fluxes. Therefore, even if data are available for finer 

temporal scales, it may still be appropriate to apply a multiyear average. Even with multiyear 

averaging, there may be implications for overall results related to patterns and trends in the 

observed data.54 Because different biomass feedstocks have different growth cycles and turnover 

times, it may also be appropriate to average over different time periods for different feedstocks, 

depending on temporal characteristics of the feedstock. 

If a multiyear integration is applied to biogenic CO2 flux assessment, then it must be decided when 

in time the assessment period actually starts: before or after land is converted, at planting, at 

harvest, or some point in between. The importance of this decision when the assessment period 

starts largely depends on the scale of the framework 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 effects may be lost due to other activities affecting 

biogenic CO2 fluxes over the larger landscape. In the case of forest feedstocks at a small assessment 

scale, for example, assessment could start at any point in a standard harvest rotation. 

No matter when the assessment begins, the total carbon accumulation over the lifetime of the forest 

might be the same, but if the time frame chosen does not correspond to exactly one full harvest 

rotation, then the starting point can impact results. Selecting the point of harvest as the starting 

point for assessment, especially in conjunction with a short assessment time frame, might 

overestimate the emissions associated with the production and use of the forest feedstock. If 

assessment starts at planting when rapid carbon sequestration occurs, again in conjunction with a 

short time frame (less than one rotation), then an underestimate of the net biogenic CO2 emissions 

associated with production and use of the forest feedstock is more likely. Other timing 

 

54 For example, a one-time land use conversion from one practice to another (e.g., when forested land is

harvested and converted to agricultural crop production instead of being reforested) results in a large 

emissions pulse in 1 year but is then followed by a few years of low sequestration. If an annual average is 

calculated over this short time period (starting with the harvest), the result may show higher average

emissions than if the average was taken over a longer 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 choice of spatial scale: 

• Feedstock Characteristics: Different feedstocks will have different availability, yields, and 

production practices. 

• Landscape Characteristics: Differences in physical attributes of the landscape 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 across 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 framework has implications related to 

measurement, precision of estimates, data reporting and verification, the ability to reflect carbon 

dynamics in the feedstock production area, and cross-boundary exchanges of biogenic feedstocks. 

The choice of spatial scale can be greatly influenced by the availability and accuracy of data and the

precision with which one can measure and/or model feedstock production as well as market 

dynamics. The characteristics of the land base—including ownership type and resulting 

management regime,55 as well as biophysical characteristics such as species and soil types, climatic 

conditions, and water availability—are another important 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 different points along this continuum. For example, the 

level of data accuracy varies with choice of spatial scale. When carbon stocks are estimated at a 

larger spatial scale (e.g., global, national, regional) 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 assessment on potential leakage, such as indirect 

land-use effects in other countries. National scale could be appropriate for feedstocks for which 

there is little or no variety in production and consumption 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, while the 

same ratio on public lands is 5.3 (DOE, 2011). As these lands are currently managed, an area with a large 

proportion of publicly owned land would therefore be more likely to have lower levels of harvest (and higher 

levels of growth) than a similar area with more private 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, production site environmental conditions, and local 

market factors. However, moving to a smaller assessment 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 inventories or surveys (i.e., thorough inventories 

conducted as part of a forest management plan). Assessment 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 evaluation of different questions and contexts. 

Therefore, the choice of spatial scale is largely dependent on specific applications of the framework 

and related needs or preferences, considering the implications of feedstock and landscape 

characteristics, statistical precision and uncertainty, indirect effects, or any other parameters. This 

report evaluates the implications of different spatial choices and for illustrative purposes uses a 

landscape (regional) scale to generate example biogenic 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 CO2 fluxes occurring on the 

landscape and the stationary source. This small scale of assessment could capture management 

practices as well as direct land use changes, including evaluation of what alternative or previous 

land uses might have been. For example, the cultivation of the same feedstock could have different 

landscape effects if grown on fallow land versus replacing existing crops. Choice of a small 

assessment scale might be appropriate for a policy or program application assessing biogenic CO2

emissions from a relatively closed stationary source production system; that is, a stationary source 

that relies on a single feedstock sourced from a single 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, biogenic 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 feedstock or other commodities outside the 

assessment area. Also, assessments at smaller scales can be challenging because data would need to 

be collected and/or modeled for each feedstock production site from which a stationary source 

procures feedstocks and sites might need to be monitored 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 encompassing 
November 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., demand, product substitution). 

With the data collected when the feedstock is received at a stationary source, it may be possible to 

determine the precise location of the feedstock production site, or it may only be possible to know 

the broad geographic origin. For example, facilities operating primarily on long-term procurement 

contracts will likely use the same feedstock production sites year after year, and the geographic 

location of those sites can be known. In such cases, measurement and analysis of productionrelated biogenic CO2 fluxes at a localized scale is possible. On the other hand, for stationary sources 

operating using aggregated feedstocks (e.g., agricultural 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 precisely the location of feedstock production. In these 

cases, only the broad geographic region might be identified. Also, for some feedstocks, production 

sites may vary from year to year (e.g., forestry operations 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 thinnings57) 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 sequesters an equivalent amount of carbon as 

when it was first harvested. Using a stand-level assessment approach in this example, the 

production and use of the biogenic feedstock yields considerable biogenic CO2 emissions in the 

short term (due to the harvest), with no net emissions 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 harvests 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 radius 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 has been practiced in the U.S. for some time (e.g., Smith et 

al., 1997), and is commonplace in even-aged managed forest stocks, particularly in regions like the Southeast U.S., 

where “pre-commercial” and “commercial” thinnings are used as a stock improvement strategy and in some cases an 

income generation strategy (e.g., the sale of biomass or fuelwood may also help to offset cost of pre-commercial 

work). Some studies have evaluated the potential for and practices 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 potential fuels for wildfire, particularly in the western U.S., 

and reduce the amount of carbon emissions potentially released through wildfire (e.g., Evans & Finkral, 2009). 

Literature suggested that increased demand for bioenergy could alter forest management, possibly resulting 

increased harvest/use of low-quality wood material, such as materials from commercial or pre-commercial 

thinnings or small roundwood. These potential shifts have in turn increased attention to the role of harvest 

guidelines and best practice systems to guide bioenergy-related forest management decisions (e.g., Kittler, et 

al., 2012; Lattimore et al., 2009). For more discussions and different perspectives on thinnings, also see 

Biomass Energy Resource Center, 2012; Kerr and Haufe, 2011; Miner et al, 2014; 
November 2014 41 

adequate coverage of small land areas, then the sample size for the area of interest will be small, 

resulting in low statistical power and an inconclusive assessment of carbon stocks and fluxes. 

Larger Spatial Scales 

Existing data can be readily aggregated and presented at larger scales such as states, regions, and 

nationally. For example, states and the federal government have existing data collection systems 

relevant to tracking landscape-scale biogenic carbon cycling, such as forest inventories, land 

surveys, and tax records. However, certain small states (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 sampling errors may be too large. States may also 

implement policies and regulations focusing on forests and agricultural lands, such that all land in a

given state may share some basic market and regulatory influences. State-level assessments could 

become complicated, however, if feedstocks are bought and sold across state boundaries where 

laws differ from one another (in terms of actual measurements and accounting).58 

The next spatial scale larger than a state level could be geographic regions, which may include 

multiple states. Regions may be large enough that data on carbon stocks and land use change are 

largely available and (assuming sampling is adequate) less subject to error from small sample sizes. 

Depending on how they are defined, regions may be small 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 feedstocks59 and the ability of regions to capture 

market effects. Regional boundaries must be drawn carefully 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 carbon 

stock changes). One potential difficulty with assessments 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 biogenic feedstock demand from specific entities 

and/or on specific sites than assessments on smaller scales. 

With larger spatial scales like regional or national 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 feedstock 

transfers across boundaries. For example, wood-using mills in one state often purchase and transport wood 

across state boundaries (Teeter et al., 2006). This adds accounting complexity because biogenic CO2 fluxes 

from feedstock production may occur in a different region than biogenic CO2 fluxes from feedstock use. 

59 An example of a fixed regional framework is the EPA Emissions & Generation Resource Integrated Database 

(eGRID) region structure. eGRID is used for calculating GHG emissions related to electricity generation. 

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 and 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 national-scale analysis that averaged across all regions 

could indicate that feedstock carbon overall is increasing and could possibly encourage more 

feedstock removal including from the region with declining stock, which could be problematic 

depending on the goals of the policy being implemented. Using the same scenario and applying a 

regional scale illustrates potential market effects of scale: if an assessment indicates that feedstocks 

from one region have increasing stock and thus could receive a more desirable assessment factor, 

this outcome could possibly shift demand to that region (if all else is equal, such as policies, tax 

structures, etc.). 

Similarly, a national scale for agricultural biogenic feedstock production might also mask regional 

and local differences in length of the growing season, yield per acre, above- and belowground net 

primary productivity, management practices (e.g., tillage or amendment application), previous land 

uses (in the case of land use conversion), and soil type and texture (Eagle et al., 2010). On the other 

hand, national-level assessments can give high-level insights concerning biogenic CO2 fluxes from 

forests and agricultural landscapes and biogenic feedstock 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 states would be implicitly captured (more 

discussion on leakage in Section 4.5 and in Appendix 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 methane (CH4) oxidation via cover soils at managed 

landfills (Bogner et al., 2007; EPA, 2009; Spokas and Bogner, 2011). However, there is a lack of 

literature describing the degree to which composition of MSW can vary from region to region. 

Although composition of MSW may vary regionally, this mainly contributes to potential generation 

amounts of CO2 and CH4 in a given landfill, whereas the framework methodology for waste-derived 

feedstocks focuses on how the CO2 and CH4 from MSW is treated when used in one activity versus 

another. As a result, CO2 and CH4 from MSW could be treated similarly across the U.S., i.e., 

differences would be based on the amount of waste generated, not on variation in the composition). 

International Considerations 

The pricing and flow of biogenic feedstocks and related commodities globally have the potential to 

significantly affect domestic demand and supply dynamics and could thus affect U.S. and 

international feedstock and related commodity production sites and related biogenic CO2 fluxes. 

For example, recently increasing pellet demand related to renewable energy policies in the 

European Union is significantly influencing land use and production activities in the Southeastern 

United States (Spelter, 2009; Cocchi, 2011; NREL, 2013). Conversely, international feedstock 

 

60 At larger scales, changes in carbon stocks could be more heavily influenced by a variety of factors in 

addition to biogenic feedstock use for energy, including natural (e.g., hurricanes, pest, fire) and anthropogenic 

(e.g., development, park designations) drivers. 
November 2014 43 

production and potential feedstock imports to the United States could affect the domestic biogenic 

feedstock market and related land use activities and biogenic CO2 fluxes (EIA, 2012). 

Also, increases in U.S. biogenic feedstock production could affect traditional commodity markets 

and potentially trigger leakage effects internationally. For example, if biogenic feedstock demand 

causes domestic land use to shift in biogenic feedstock production, the demand for the displaced 

crops could be made up by converting lands abroad into 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 magnitude of these effects. 

The framework is scalable and can be applied at various 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, depending on policy requirements and/or 

international agreements related to GHG emissions assessments. However, for some U.S. domestic 

policy analyses (e.g., certain waste-derived feedstocks), application of the framework on a global 

scale may not be required. The illustrative assessment factor calculations presented in the technical 

appendices of this report are conducted on a domestic 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 scale61 (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 discussions). 

4.4. Relationship between Spatial and Temporal Scales 

Broadly speaking, the balance between carbon sequestration 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 baseline approach) or by comparing relative 

estimated volumes along different projection scenarios (using an anticipated baseline approach). 

For annual agricultural crops, for example, this calculation for the feedstock itself (aside from nonfeedstock biogenic CO2 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 switchgrass) 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 time and space can begin to interact, adding 

complexity to any assessment of biogenic feedstock production activities and related biogenic CO2

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 show a net release of biogenic CO2 to the 

atmosphere, because combustion emissions exceeded carbon uptake during growth on that hectare 

for that year. If, however, one measures the mass of 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 harvest), this may show that over time the mass of 

trees—and thus carbon stock—on that hectare is unchanged. In this example, using the longer time 

frame for integration shows that harvest and combustion (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 end of the year. This is because the carbon emitted 

from harvesting the 1 hectare has been counterbalanced by growth in the remaining 59 hectares 

and new seedlings on the harvested area. In this case, then, overall equivalence is established for a 

single year in the 60-hectare parcel.62 

The simple examples above illustrate the complex interplay between spatial and temporal scales 

when assessing biogenic CO2 fluxes. Although integration over time and integration over space are 

very similar, they are not identical. Both cases above demonstrate that returning to the preexisting 

carbon stock in the biosphere is not the same as returning to the circumstance that would have 

prevailed had there been no harvest, because emissions and sequestration have both occurred in 

the meantime (not only in regard to the feedstock itself, 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 carbon sequestration occurring over the next 60 

years compensate for carbon emitted to the atmosphere today? Can carbon sequestration occurring 

over the prior 60 years be “banked” to counterbalance the carbon emitted today? This framework 

does not specifically propose or restrict the use of 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 variability at fine spatial or temporal scales may 

have much less variability when averaged over larger areas or longer temporal scales. Integrating 

over long temporal scales may reduce the variability observed at small spatial scales, and 

integrating over large areas may reduce the variability 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 standing biomass harvested on 1 ha or a 60-year rotation period 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 temporal 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 policy 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 different, as illustrated above. 

4.5. Leakage 

This framework relies on the IPCC definition of “leakage,” which is defined as the indirect impacts 

of a targeted activity in a certain place at a certain time on carbon storage at another place or time

(IPCC, 2000). In the context of biogenic CO2 fluxes, leakage represents any biogenic CO2 flux impacts 

outside of a biogenic feedstock production assessment scope that can be attributed to the 

production activities (e.g., replacement of diverted crop, livestock, or forest products on other lands 

due to a change in land use from conventional products to biogenic feedstocks). Leakage is an 

externality common to many policies and occurs in many different contexts (e.g., benefits of local 

tourism extending beyond the region or technological innovation spreading from one firm to 

several others). Leakage, both positive and negative63 and depending on the nature and magnitude 

of the market shifts, could have biogenic CO2 flux effects that range from fairly minimal to quite 

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 activities. Increased demand for biogenic feedstocks 

might cause higher prices for those feedstocks, which in turn might trigger more production of 

biomass feedstocks. These increases in production can lead to a succession of land use changes, 

including the possible conversion of previously forested land or other high-carbon ecosystems to 

lower carbon systems and the release of carbon stored in soils and vegetation. In globally 

integrated markets, increased demand for biogenic feedstocks within the assessment area may lead 

to increased feedstock (or feedstock substitute) production outside the assessment boundaries. 

Thus, associated emissions may shift from assessed regions to unassessed regions. If leakage leads 

to biogenic CO2 fluxes outside the assessment area, then not accounting for it could bias the results 

toward lower or higher overall emission impacts (lower 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 change outside an assessment boundary is 

complex and involves considering multiple variables, including connectedness of output and land 

markets, mobility of labor and capital, consumer flexibility, producer flexibility, availability of 

 

63 Negative leakage indicates net emissions generated outside the assessment scope due to changed feedstock 

production within the assessment scope. Positive leakage indicates net sequestration generated outside the 

assessment scope due to changed feedstock production within the assessment scope. 
November 2014 46 

alternative lands for production, and the ability of producers to change their emissions profile 

without modifying production (Henders and Ostwald, 2012; Wunder, 2008). 

Determining the value of these numerous variables is difficult, and the literature offers a disparate 

picture of leakage magnitude (Kim and Dale, 2011; Murray, 2008). Several different leakage 

measurement strategies have been used in the literature, but these strategies show large variations 

in the type and quality of data required and the estimates they provide. 

Although leakage is difficult to determine, in some circumstances it may be a significant factor in 

determining net biogenic CO2 fluxes. The framework, therefore, can accommodate calculations of 

leakage effects; however, the framework does not choose or develop a specific methodology for 

identifying and evaluating these effects. If an application of this framework requires leakage 

analysis, the method chosen for such an analysis should reflect the needs/parameters of the specific 

policy or program context.Within the context of the Clean Air Act, not all regulatory actions 

explicitly require a leakage assessment. However, one example of a specific policy application that 

includes leakage analysis is the Renewable Fuel Standard (RFS). The RFS took into account indirect 

effects of biofuel production and use, including international 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 allows for groupings of feedstocks that share 

similar attributes for the purpose of assessment within this framework. These attributes include: 

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

• Feedstocks with similar temporal characteristics; 

• Feedstocks with similar management techniques; 

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

• Feedstocks with similar market or alternative uses. 

The scientific literature on biogenic feedstock classification 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 feedstock 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 Standard, please see the RFS Regulatory Impact Analysis (EPA, 

2010). 
November 2014 47 

Temporal Scale and Feedstock Categorization 

One consideration when classifying biogenic feedstocks is the inherently different turnover times 

(i.e., growth cycles and management regimes) of different biogenic feedstocks. In most cases, 

feedstocks with similar growth and harvest characteristics can be given the same treatment or 

considerations in the framework. For example, for materials typically harvested and replenished 

within one year, such as many conventional agricultural crops or residues, there may be no change 

in the net biogenic CO2 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, detrital pools, or elsewhere in the landscape as a result of 

the feedstock management and/or harvest) that should 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 temporal scales considered, as discussed in previous 

sections. 

For feedstocks that, if not used as a biogenic feedstock 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 CO2 to the 

atmosphere and soils naturally over periods ranging from years to decades, depending on 

biophysical characteristics and forest management practices. Harvest and combustion of these 

residues, on the other hand, converts the residue material to CO2 in a very short time. 

Spatial Scale and Feedstock Categorization 

For forest-derived and agriculture-derived feedstocks, 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 categories into subcategories could allow for analysis 

of feedstock differences per different geographic production 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 regionally specific calculation of the emissions and 

sequestration associated with feedstock production. 

Feedstock Categorization Used in the Framework 

The feedstock classification system used here applies three broad feedstock categories: forestderived, agriculture-derived, and waste-derived feedstocks. These feedstocks are further 

subdivided into subcategories, as follows (see Appendix D for details): 

Forest-derived feedstocks: 

• Roundwood 

• Logging residues 

• Industrial processing by-products, which includes two 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 two 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 secondary use feedstocks. Secondary use feedstocks 

are biogenic materials that exit a particular stationary source (i.e., primary stationary source) or the 

stationary source supply chain where the original biogenic 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 processing 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 source. 

For purposes of this report, biogenic feedstocks have been classified broadly into the feedstock 

categories identified above based on the physical and 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 designed to be flexible so it can be modified as needed 

to be applicable to nearly all domestic biogenic feedstocks currently in use or under consideration 

for bioenergy production. However, new and unconventional, or otherwise unanticipated, 

feedstocks may emerge over time and may fit under the 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 stationary sources results in a net atmospheric 

contribution of biogenic CO2 emissions. It is a revision of EPA’s original September 2011 Draft 

Accounting Framework for Biogenic CO2 Emissions from Stationary Sources and is responsive to a 
November 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 designed for many users, including facilities and 

companies that produce biogenic feedstocks and/or use 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 assessment 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 associated with biogenic CO2 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, processing, and use of biogenic material at stationary 

sources results in a net atmospheric contribution of biogenic CO2 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 CO2 emissions. For example, policies 

affecting land owners might influence the way land and feedstocks are managed, such as 

forest conservation, zoning, and biomass certification programs (e.g., owners of 

stationary sources may also be landowners and may be 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 sources (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 may be an important consideration 

under certain policies and programs. 

• Scale/scope of program: Large-scale programs affecting entire industries on an annual 

basis may require different technical approaches than programs on a smaller scale. 

• Timing: Different program applications may require biogenic CO2 emissions from a 

stationary source to be evaluated over specific time 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 resulted in GHG emission 

reductions or removals in addition to what would have 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 CO2 fluxes 

and/or carbon stocks between two scenarios at the same specified point in time (e.g., the estimated 

difference between a business-as-usual [BAU] scenario and an alternative counterfactual scenario 

that includes changes in environmental, economic, and/or policy conditions. 

Assessment Framework: A methodology for assessing the net atmospheric contribution of 

biogenic CO2 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 alternative management strategy (e.g., wastederived feedstocks).

Baseline: The baseline (or reference) is any datum against which change is measured. Such a datum 

serves as the “reference” against which other conditions 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 CO2 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 biogenic carbon cycle effects from all 

stages of 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 stack emissions) 

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 CO2 Emissions: Carbon dioxide emissions related to the natural carbon cycle as well as 

those resulting from the production, harvest, combustion, 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 pass through a stationary source

Biologically based: Non-fossilized and biodegradable organic material originating from modern or 

contemporarily grown plants, animals, or microorganisms (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 and 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., trees, 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 tissue. In the context of bioenergy, biomass is any 

material of biological origin, excluding material embedded in geological formations and 

transformed to fossil. 

Biosphere: The part of the Earth system comprising all ecosystems 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 matter, 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 into 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 (CO2) emissions from stationary sources of CO2 through a three-step process that includes 

capture and compression of CO2 from stationary sources; transport of the captured CO2 (usually in 

pipelines); and storage of that CO2 in geologic formations, such as deep saline formations, 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 biosphere, and lithosphere. 

Carbon Dioxide (CO2): A naturally occurring gas, which is also produced through 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 principal anthropogenic 
November 2014 52 

greenhouse gas that affects the Earth’s radiative balance. It is the reference gas against which other 

greenhouse gases are measured and therefore has a global warming potential of 1. 

Carbon Dioxide Equivalent (CO2e): 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 of CO2

emissions. For example, 1 metric ton of methane (CH4) has the same GWP as 25 metric tons of CO2. 

Therefore, 1 metric ton CH4 = 25 metric tons CO2e. 

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 atmosphere.

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 form, such as liquid biofuel or combustible biogas. 

Examples of biomass conversion processes include biomass-fired or co-fired boilers, biomass 

gasification or pyrolysis systems, ethanol fermentation 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 rate. 

Distiller Grains: Materials remaining after the starch fraction of corn 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, or 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 account for the emissions associated with the 

upstream production of the end-use energy. For example, some emissions associated with electricity 

generation can be attributed to the buildings sector, 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 CO2) resulting from the 

combustion of fossil fuels. 

Front-loading: A treatment of time approach, which accounts for all of the future net biogenic CO2

fluxes 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 drawn for a specific facility. 

Global Warming Potential (GWP): An index, based on radiative properties of well-mixed 

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 differing times these gases remain in the 

atmosphere and their relative effectiveness in absorbing outgoing thermal infrared radiation. 

Greenhouse Gases (GHGs): Gaseous constituents of the atmosphere, both natural and 

anthropogenic, that absorb and emit radiation at specific wavelengths within the spectrum of 

thermal infrared radiation emitted by the Earth’s surface, the atmosphere itself, and by clouds. This 

property causes the greenhouse effect. Water vapor (H2O), carbon dioxide (CO2), nitrous oxide 

(N2O), methane (CH4), and ozone (O3) are the primary greenhouse gases in the Earth’s atmosphere. 

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 CO2, N2O, and CH4, the Kyoto Protocol deals with the greenhouse gases 

sulfur hexafluoride (SF6), hydrofluorocarbons (HFCs), and perfluorocarbons (PFCs). 

GROW: The GROW term in the BAF equation represents net feedstock growth (or removals) 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 equal to one that 

represents biogenic feedstock carbon that leaves the supply chain (e.g., 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. 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. 
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 emissions 

from feedstock use. 

Land Use: Land use refers to the total of arrangements, activities, and inputs undertaken in a 

certain land cover type (a set of human actions). The term land use is also used in the sense of the 

social and economic purposes for which land is managed 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 targeted 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 throughout 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 stages of fuel and feedstock production and 

distribution, from feedstock generation and extraction through distribution and delivery and use of 

the finished fuel. 

LOSS: Represents the actual tons of carbon lost in transportation or storage along the supply chain 

rather than being emitted during conversion to energy at the stationary source. 

Managed Forest: All forests subject to some kind of human interactions (notably commercial 

management, harvest of industrial roundwood [logs] and fuelwood, production and use of wood 

commodities, and forest managed for amenity value or environmental protection if specified by the 

country), with defined geographical boundaries. 

Methane (CH4): 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 features 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 CO2

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 (N2O): A greenhouse gas emitted through soil cultivation practices, especially 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 one, 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 products, that is either not emitted 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 different points of assessment (e.g., at the boiler 

mouth, stationary source gate, feedstock production site, or at the stack: wherever the point of 

assessment needs to be. Thus, this term can have different values indicated by subscripts, 

representing different points along the supply chain). 

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 biogenic CO2 fluxes caused by 

losses that occur during biogenic feedstock storage or processing. 

PROD: Represents the actual tons of carbon stored in final products or by-products along the 

supply chain rather than being emitted during conversion 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 and biochar. Most of these combustible gases 

can be condensed into a combustible liquid called pyrolysis oil (bio-oil), though there are some 

permanent gases (CO2, CO, H2, light hydrocarbons). Pyrolysis of biomass thus produces three 

products: bio-oil, biochar, and syngas. The proportion of these products depends 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 pyrolysis conditions (i.e., when the pyrolysis temperature 

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, irradiance 

(expressed in W/m2) 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 temperatures, if perturbed, to readjust to radiativedynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric 

temperature is accounted for. For the purposes of this report, radiative forcing is further defined as 

the change relative to the year 1750 and, unless otherwise 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 biogenic CO2

fluxes and/or carbon stocks between two points in time. 

Reservoirs: A component of the climate system, other than the atmosphere, that has the capacity to 

store, accumulate, or release a substance of concern, 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 hollow or “cull”) and that are large enough to be 

harvested and includes the main bole or stem, but not branches or tops. 

Scope: Components that could be included as part of the system boundary for an assessment 

framework. 

Sequestration: The addition of a substance of concern to a reservoir. The uptake of carboncontaining 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 production site due to land use management or land 

use or management changes associated with feedstock production.

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 CO2 gases. 
November 2014 57 

Year-to-year Carryover: A treatment of time approach, which accounts for the 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 region 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 physical conditions (e.g., inoperable soils or steep 

slopes), regulatory restrictions (e.g., elevation limits in the Northeast), or economic feasibility (e.g., 

large distance to transportation networks). 

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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 calculating 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 required to implement the 

framework. Specifically, Appendix B provides a theoretical discussion of the key considerations 

related to the treatment of time in an assessment framework. Appendix C discusses spatial scale 

considerations and explains the implications of choosing different scales, including the regional 

scale. Appendix D describes the categorization of biogenic feedstocks used in this framework. 

Appendix E presents a discussion of leakage literature. Appendix F provides a general algebraic 

representation of the biogenic assessment factor equations, which includes detailed examples of 

how the equation could be adjusted to accommodate different supply chain permutations. 

Appendix G discusses the technical aspects of the biogenic process attributes, equation terms 

related to use of biogenic feedstock in different stationary source types (P and L) and provides a 

discussion of the literature as well as illustrative 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 feedstock 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 example 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 considerations to apply when choosing a modeling 

method for applying a future anticipated baseline in the context of assessing biogenic emissions 

from stationary sources. Appendix K describes the methodology used to develop various 

illustrative future anticipated baselines for use in the following appendix. Appendix L presents 

illustrative applications of the future anticipated baseline approach to specific feedstock/region 

combinations for forest- and agriculture-derived feedstocks 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 anticipated baseline approaches and sensitivities using 

both approaches. Appendix N describes an evaluation of waste-derived feedstocks, such as 

municipal solid waste and wastewater treatment.