U.S. Environmental Protection Agency
Office of Resource Conservation and Recovery
Documentation for Greenhouse Gas Emission and
Energy Factors Used in the Waste Reduction Model
(WARM)
Management Practices Chapters
December 2023
EPA-530-R-23-018
vvEPA
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WARM Version 16
Table of Contents
December 2023
Table of Contents
1 Source Reduction 1-1
2 Recycling 2-1
3 Anaerobic Digestion 3-1
4 Composting 4-1
5 Combustion 5-1
6 Landfilling 6-1
7 Energy Impacts 7-1
8 Economic Impacts 8-1
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WARM Version 16
Source Reduction
December 2023
1 SOURCE REDUCTION
This chapter describes the development of material-specific emission factors for source
reduction in EPA's Waste Reduction Model (WARM). Source reduction, or waste prevention, refers to
practices that reduce the amount of materials entering the waste stream, including changes in the
design, manufacture, purchase or use of materials. This document provides examples of source
reduction and a summary of how EPA estimated the GHG benefits from source reduction of materials.
1.1 TYPES OF SOURCE REDUCTION
Source reduction can result from any activity that reduces the amount of a material or
agricultural input needed and therefore used to make products or food.1 Some specific examples of
source reduction practices are:
• Redesigning products to use fewer materials (e.g., lightweighting, material substitution).
• Reusing products and materials (e.g., a refillable water bottle).
• Extending the useful lifespan of products.
• Avoiding using materials in the first place (e.g., reducing junk mail, reducing demand for
uneaten food).
In addition to the activities above, there are limited circumstances where the emission factors
can be used to estimate GHG benefits of substituting one material or product for another material or
product. Section 1.3.2 presents considerations for estimating the GHG effects of material substitution.
1.2 A SUMMARY OF THE GHG IMPLICATIONS OF SOURCE REDUCTION
When a material is source reduced, GHG emissions associated with producing the material
and/or manufacturing the product and managing the post-consumer waste are avoided. Consequently,
source reduction provides GHG emission benefits by: (1) avoiding the "upstream" GHGs emitted in the
raw material acquisition, manufacture or production and transport of the source-reduced material; (2)
increasing the amount of carbon stored in forests (when wood and paper products are source reduced);
and (3) avoiding the downstream GHG emissions from waste management.
Because many materials are manufactured from a mix of virgin and recycled inputs, the quantity
of virgin material production that is avoided is not always equal to the quantity of material source
reduced. Therefore, to estimate GHG emissions associated with source reduction, WARM uses a mix of
virgin and recycled inputs, based on the national average for each material. However, WARM also allows
users to evaluate the benefits of source reducing materials manufactured from 100 percent virgin
inputs, instead of a mix of virgin and recycled inputs. For some materials, such as food waste and some
wood products, it is either not possible or very uncommon to use recycled inputs during material
production, so WARM always assumes material production using 100 percent virgin inputs.
WARM assumes that source reduction of paper and wood products increases the amount of
carbon stored in forests by reducing the amount of wood harvested. For more information on the
calculations that went into creating the forest carbon storage offset, see the Forest Carbon Storage
chapter.
In order to measure the full GHG impact of source reduction, the user must compare the GHG
emissions from source reduction to the GHG emissions of another materials management option. For
example, a user could compare the benefits from source reducing one short ton of office paper instead
1 The source reduction pathway was added for food waste in June 2014 into WARM Version 13.
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of sending the paper to the landfill. This approach enables policy-makers to evaluate, on a per-ton basis,
the overall difference in GHG emissions between (1) source reducing one short ton of material, and (2)
manufacturing and then managing (post-consumer) one short ton of the same material. For most
materials, source reduction has lower GHG emissions than the other materials management options.2
Exhibit 1-1 presents the net emissions factors of all management options in order to provide
context for the emissions associated with upstream product and material manufacturing characterized
by the source reduction emission factors in WARM as compared to other management practices.
2 The most notable exception is for aluminum cans, where recycling benefits are higher. For aluminum cans, the
net source reduction emissions for the current mix of inputs are smaller than the net recycling emissions. This is
because of two factors: (1) the large difference in GHG emissions between virgin and recycled manufacture of
aluminum cans and (2) the relatively high recycled content (68 percent) in aluminum cans. In this instance, source
reduction is relatively less beneficial because of the high recycled content of a "virgin" can.
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WARM Version 16 Source Reduction December 2023
Exhibit 1-1: Greenhouse Gas Emissions from Management Options Modeled in WARM (MTC02E/Short Ton of Material)
Material
Net Source
Reduction
Emissions for
Current Mix of
Inputs
Net Source
Reduction
Emissions
for 100%
Virgin Inputs
Net
Recycling
Emissions
Net
Composting
Emissions
Net
Combustion
Emissions
Net
Landfilling
Emissions
Net Wet
Anaerobic
Digestion
with curing
Net Wet
Anaerobic
Digestion
with Direct
Application
Net Dry
Anaerobic
Digestion
with curing
Net Dry
Anaerobic
Digestion
with Direct
Application
Corrugated Containers
(5.58)
(8.09)
(3.14)
NA
(0.49)
0.18
NA
NA
NA
NA
Magazines/Third-class Mail
(8.57)
(8.86)
(3.07)
NA
(0.35)
(0.43)
NA
NA
NA
NA
Newspaper
(4.68)
(5.74)
(2.71)
NA
(0.56)
(0.85)
NA
NA
NA
NA
Office Paper
(7.95)
(8.23)
(2.86)
NA
(0.47)
1.13
NA
NA
NA
NA
Phonebooks
(6.17)
(6.17)
(2.62)
NA
(0.56)
(0.85)
NA
NA
NA
NA
Textbooks
(9.02)
(9.32)
(3.10)
NA
(0.47)
1.13
NA
NA
NA
NA
Mixed Paper (general)
(6.07)
(7.61)
(3.55)
NA
(0.49)
0.07
NA
NA
NA
NA
Mixed Paper (primarily residential)
(6.00)
(7.64)
(3.55)
NA
(0.49)
0.02
NA
NA
NA
NA
Mixed Paper (primarily from offices)
(7.37)
(7.93)
(3.58)
NA
(0.45)
0.11
NA
NA
NA
NA
Food Waste
(3.66)
(3.66)
NA
(0.15)
(0.13)
0.50
(0.06)
(0.14)
(0.04)
(0.10)
Food Waste (non-meat)
(0.76)
(0.76)
NA
(0.15)
(0.13)
0.50
(0.06)
(0.14)
(0.04)
(0.10)
Food Waste (meat only)
(15.10)
(15.10)
NA
(0.15)
(0.13)
0.46
(0.06)
(0.14)
(0.04)
(0.10)
Beef
(30.09)
(30.09)
NA
(0.15)
(0.13)
0.43
(0.06)
(0.14)
(0.04)
(0.10)
Poultry
(2.45)
(2.45)
NA
(0.15)
(0.13)
0.49
(0.06)
(0.14)
(0.04)
(0.10)
Grains
(0.62)
(0.62)
NA
(0.15)
(0.13)
1.37
(0.06)
(0.14)
(0.04)
(0.10)
Bread
(0.66)
(0.66)
NA
(0.15)
(0.13)
0.99
(0.06)
(0.14)
(0.04)
(0.10)
Fruits and Vegetables
(0.44)
(0.44)
NA
(0.15)
(0.13)
0.23
(0.06)
(0.14)
(0.04)
(0.10)
Dairy Products
(1.75)
(1.75)
NA
(0.15)
(0.13)
0.48
(0.06)
(0.14)
(0.04)
(0.10)
Yard Trimmings
NA
NA
NA
(0.11)
(0.17)
(0.20)
NA
NA
(0.09)
(0.35)
Grass
NA
NA
NA
(0.11)
(0.17)
0.12
NA
NA
0.00
(0.06)
Leaves
NA
NA
NA
(0.11)
(0.17)
(0.53)
NA
NA
(0.14)
(0.53)
Branches
NA
NA
NA
(0.11)
(0.17)
(0.54)
NA
NA
(0.22)
(0.73)
HDPE
(1.42)
(1.52)
(0.76)
NA
1.29
0.02
NA
NA
NA
NA
LDPE
(1.80)
(1.80)
NA
NA
1.29
0.02
NA
NA
NA
NA
PET
(2.17)
(2.21)
(1.04)
NA
1.24
0.02
NA
NA
NA
NA
LLDPE
(1.58)
(1.58)
NA
NA
1.29
0.02
NA
NA
NA
NA
PP
(1.52)
(1.54)
(0.79)
NA
1.29
0.02
NA
NA
NA
NA
PS
(2.50)
(2.50)
NA
NA
1.65
0.02
NA
NA
NA
NA
PVC
(1.93)
(1.93)
NA
NA
0.66
0.02
NA
NA
NA
NA
Mixed Plastics
(1.87)
(1.94)
(0.93)
NA
1.26
0.02
NA
NA
NA
NA
PLA
(2.45)
(2.45)
NA
(0.13)
(0.63)
(1.64)
NA
NA
NA
NA
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Net Source
Net Source
Net Wet
Anaerobic
Digestion
with curing
Net Wet
Net Dry
Anaerobic
Digestion
with curing
Net Dry
Reduction
Reduction
Net
Net
Net
Net
Anaerobic
Anaerobic
Material
Emissions for
Emissions
Recycling
Composting
Combustion
Landfilling
Digestion
Digestion
Current Mix of
for 100%
Emissions
Emissions
Emissions
Emissions
with Direct
with Direct
Inputs
Virgin Inputs
Application
Application
Desktop CPUs
(20.86)
(20.86)
(1.49)
NA
(0.66)
0.02
NA
NA
NA
NA
Portable Electronic Devices
(29.83)
(29.83)
(1.06)
NA
0.65
0.02
NA
NA
NA
NA
Flat-Panel Displays
(24.19)
(24.19)
(0.99)
NA
0.03
0.02
NA
NA
NA
NA
CRT Displays
NA
NA
(0.57)
NA
0.45
0.02
NA
NA
NA
NA
Electronic Peripherals
(10.32)
(10.32)
(0.36)
NA
2.08
0.02
NA
NA
NA
NA
Hard-Copy Devices
(7.65)
(7.65)
(0.56)
NA
1.20
0.02
NA
NA
NA
NA
Mixed Electronics
(20.79)
(20.79)
(0.90)
NA
0.34
0.02
NA
NA
NA
NA
Aluminum Cans
(4.80)
(10.99)
(9.13)
NA
0.03
0.02
NA
NA
NA
NA
Aluminum Ingot
(7.48)
(7.48)
(7.20)
NA
0.03
0.02
NA
NA
NA
NA
Steel Cans
(3.03)
(3.64)
(1.83)
NA
(1.59)
0.02
NA
NA
NA
NA
Copper Wire
(6.72)
(6.78)
(4.49)
NA
0.03
0.02
NA
NA
NA
NA
Mixed Metals
(3.65)
(6.22)
(4.39)
NA
(1.02)
0.02
NA
NA
NA
NA
Glass
(0.53)
(0.60)
(0.28)
NA
0.03
0.02
NA
NA
NA
NA
Asphalt Concrete
(0.11)
(0.11)
(0.08)
NA
NA
0.02
NA
NA
NA
NA
Asphalt Shingles
(0.19)
(0.19)
(0.09)
NA
(0.35)
0.02
NA
NA
NA
NA
Carpet
(3.68)
(3.68)
(2.38)
NA
1.10
0.02
NA
NA
NA
NA
Clay Bricks
(0.27)
(0.27)
NA
NA
NA
0.02
NA
NA
NA
NA
Concrete
NA
NA
(0.01)
NA
NA
0.02
NA
NA
NA
NA
Dimensional Lumber
(2.11)
(2.11)
(l-66)a
NA
(0.58)
(0.92)
NA
NA
NA
NA
Drywall
(0.22)
(0.22)
0.03
NA
NA
(0.06)
NA
NA
NA
NA
Fiberglass Insulation
(0.38)
(0.48)
NA
NA
NA
0.02
NA
NA
NA
NA
Fly Ash
NA
NA
(0.87)
NA
NA
0.02
NA
NA
NA
NA
Medium-density Fiberboard
(2.41)
(2.41)
NA
NA
(0.58)
(0.85)
NA
NA
NA
NA
Structural Steel
(1.67)
(3.42)
(1.93)
NA
NA
0.02
NA
NA
NA
NA
Vinyl Flooring
(0.58)
(0.58)
NA
NA
(0.31)
0.02
NA
NA
NA
NA
Wood Flooring
(4.03)
(4.03)
(3.68)
NA
(0.74)
(0.86)
NA
NA
NA
NA
Tires
(4.30)
(4.46)
(0.38)
NA
0.50
0.02
NA
NA
NA
NA
Mixed Recyclables
NA
NA
(2.80)
NA
(0.42)
0.03
NA
NA
NA
NA
Mixed Organics
NA
NA
NA
(0.13)
(0.15)
0.18
NA
NA
(0.06)
(0.21)
Mixed MSW
NA
NA
NA
NA
0.01
0.31
NA
NA
NA
NA
a Modeled as Reuse in WARM.
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1.3 APPLYING EMISSION FACTORS TO SPECIFIC SOURCE REDUCTION STRATEGIES
1.3.1 Calculating the Energy and GHG Emissions Benefits of Reuse
The GHG and energy benefits of reusing non-food materials or products multiple times before
they are sent for end-of-life management can be modeled using the source reduction pathway in
WARM. The process for calculating the GHG and energy benefits of reuse is as follows:
1. Using the downloadable (i.e., Excel-based) version of WARM, run the model using a baseline
scenario of landfilling, recycling, combustion or composting (depending on the likely fate of the
material or product if it is not reused), and an alternate scenario of source reduction. For
example, if the item was originally destined for a landfill and now will be reused, the baseline
scenario is landfilling.
2. Select whether the reused material is manufactured from 100 percent virgin inputs or the
current mix of virgin and recycled inputs.3 (The assumption that the material is manufactured
from 100 percent virgin inputs indicates an upper bound estimate of the benefits from reuse.)
3. Multiply the GHG emissions reduction result (i.e., "total change in GHG emissions" from WARM)
by the number of times the material is reused. The reuse number should equal one less than the
number of total uses to account for the production of the initial material.
This methodology for calculating the GHG benefits from reuse is summarized in the following
formula. Energy use can be similarly calculated by replacing the GHG emission factors with energy use
factors.
GHG Benefits of Reuse = (N-l) x (A)
Where,
N = Number of total uses
A = GHG benefits of the source reduction (alternate) pathway minus the baseline
pathway (i.e., "total change in GHG emissions" from WARM)
For example, consider reusable HDPE plastic crates, weighing 1,000 short tons total, used for
transporting bread to a grocery store. Assume that the crates are typically recycled after each use, but
could be reused up to 20 times before they are recycled. To calculate the GHG benefits of reusing the
crates, the user can run WARM using a baseline of recycling 1,000 short tons HDPE and an alternate
scenario of source reducing 1,000 short tons HDPE. Assuming that reusing the crates offsets the
production of HDPE crates that would otherwise have been manufactured from 100 percent virgin
inputs, WARM's results indicate that source reduction of 1,000 short tons of HDPE crates results in a net
emissions reduction of 760 MTC02E relative to the baseline recycling scenario.4
The GHG benefits should then be multiplied by 19 reuses (i.e., 20 total uses - 1 original use).
Energy use can be similarly calculated by replacing the GHG emission factors with energy use factors. In
equation form:
3 Some materials modeled in WARM utilize 100 percent virgin materials in the "current mix" of inputs. This is in
cases where information on the share of recycled inputs used in production is unavailable or is not a common
practice.
4 If reusing the crates offsets crates that would otherwise have been manufactured from the current mix of virgin
and recycled inputs, source reduction of 1,000 short tons HDPE would result in a net emissions reduction of 661
MTC02E relative to the baseline recycling scenario.
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GHG Benefits of Reuse = 19 x (source reduction of 1,000 short tons HDPE - recycling of 1,000 short tons
HDPE)
100% virgin inputs (upper bound for reductions):
GHG Benefits of Reuse = 19 x (760 MTC02E) = 14,440 MTC02E
1.3.2 Calculating the Energy and GHG Emissions Benefits of Material Substitution
The analysis of source reduction is based on an assumption that source reduction is achieved by
practices such as lightweighting, double-sided copying and material reuse. However, it is also possible to
source reduce one type of material by substituting another material. The GHG impact of this type of
source reduction is the net GHG benefits from source reduction of the original material and
manufacturing and disposing of the substitute material.
Where both the original material and the substitute material are available in WARM, the GHG
impacts of source reduction with material substitution may be estimated as long as users verify that the
material production and end-of-life pathways in WARM are representative of the materials involved in
the substitution. However, for cases where one of the materials in the substitution pair is not in WARM,
the user will only be able to calculate the GHG impact of the material used (without a comparison with
the potential substitute). The large number of materials that could be substituted for the materials
available in WARM, and the need for specific information on application of material substitution, make
an analysis of all potential substitutions prohibitive and highly uncertain.
In the case where both the material being replaced and its substitute are in WARM, the GHG
benefits can be estimated as described below. Note that this calculation cannot be run in WARM
because WARM requires the user to have the same material in the baseline and alternate scenarios:
1. Calculate the GHG emissions from manufacturing and end-of-life management of the original
material that will be replaced by the substitute material (i.e., the baseline scenario; see
equations below for an explanation of this calculation).
2. Calculate the GHG emissions from manufacturing and end-of-life management of the substitute
material (i.e., the alternate scenario; see equations below for an explanation of this calculation).
3. Calculate the mass substitution rate. The mass substitution rate is the number of tons of
substitute material used per ton of original material. For example, one ton of plastic containers
may serve the same function as two tons of glass containers. In this case, the mass substitution
rate would be 50%. In calculating the mass substitution rate, users should also account for any
difference in the number of times that a product made from the original material is used prior to
waste management, compared to the number of times a product made from the substitute
material will be used prior to waste management.
4. Calculate the net GHG benefits by subtracting the GHG emissions that would have been
generated to produce the baseline material from the GHG emissions generated by producing an
equivalent amount of the substitute materials.
This basic methodology for calculating the GHG benefits of material substitution is summarized
in the following formula. Energy use can be similarly calculated by replacing the GHG emission factors
with energy use factors.
GHG Benefits of Material Substitution = (EFalternate material * MS - EFbaseline material)
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Where,
EFalternate material = GHG emissions from production and end-of-life management of the
substitute material per unit of substitute material
EFbaseiine material = GHG emissions from production and end-of-life management of the
original material per unit of original material
MS = Material substitution rate i.e., amount of substitute material required to replace a
unit of the original material
Because source reduction GHG emission factors represent the benefits of avoided production of
materials, the GHG emissions generated by the production of materials can be calculated by taking the
absolute value of WARM'S source reduction factors. The energy or GHG emissions from end-of-life
management can be calculated using the various end-of-life materials management factors in WARM
(e.g., recycling, composting, combustion or landfilling). Consequently, the EFaiternative material and EFbaseiine
material terms are equal to:
EFalternate material ~ ~EFsource reduction, alternate material EFend-of-life management, alternate material
EFbaseiine material ~ ~EFsource reduction, baseline material EFend-of-life management, baseline material
Where,
EF source reduction = WARM emission factor for source reduction of the baseline and
alternative materials
EFend-of-iife management = WARM emission factor for the end-of-life management practice
(recycling, composting, combustion or landfilling) used to manage the baseline and
alternative materials
1.3.3 Calculating the Energy and GHG Emissions Benefits of Material Choice Across Material Life
In the case where only one material is available in WARM and not the substitute, a WARM user
can still calculate the total GHG impact across material life, including both upstream manufacturing and
downstream materials management. Source reduction GHG emission factors represent the benefits of
avoided production of materials. Therefore, the GHG emissions generated by the production of
materials can be calculated by taking the absolute value of WARM'S source reduction factors. The
energy or GHG emissions from end-of-life management can be calculated using the various end-of-life
materials management factors in WARM (i.e., recycling, composting, anaerobic digestion, combustion,
or landfilling). Using this approach, the energy and GHG emission impacts across the life of a material
can be calculated for baseline and alternative options using the following equations:
EFbaseiine material ~ ~EFsource reduction, baseline material EFend-of-life management, baseline material
EFalternate material ~ ~EFsource reduction, alternate material EFend-of-life management, alternate material
Where,
EF source reduction = WARM emission factor for source reduction of the baseline and
alternative materials
EF end-of-iife management = WARM emission factor for the end-of-life management practice
(recycling, composting, anaerobic digestion, combustion or landfilling) used to manage
the baseline and alternative materials
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Production and end-of-life impacts are automatically calculated and visually displayed in the
"Production + EOL" tabs within the WARM tool. See the Production and End-of-Life Impacts chapter for
additional information on the calculations and the visualizations.
1.4 LIMITATIONS
Because the data presented in this chapter were developed using data presented in the raw
materials and acquisition section of the Overview chapter (and the Forest Carbon Storage chapter), the
limitations discussed there also apply to the values presented here. Other limitations include:
• The source reduction factors for food waste materials are meant to capture the emissions
avoided through waste reduction. They are the closest pathway available in WARM to
approximate the benefits from food reuse and donation, but they likely overstate the benefits.
Applying source reduction factors to donated materials assumes that the donation completely
offsets the use of new materials, but this may not be the case. For example, edible food can be
donated to feed hungry people, and while this may offset the demand for other food, it is
unlikely that the donation will entirely offset the production of an equivalent amount of food.
Also, food donations could be reused for other purposes such as feed for livestock, which would
instead offset the production of traditional livestock feed. EPA is conducting research into how
to address food donation and food waste reuse in WARM.
• WARM allows users to model source reduction for several mixed material types: mixed paper
(all types), mixed metals, mixed plastics, food waste, food waste (meat only), and food waste
(non-meat). For these mixed material categories, all components can be individually source
reduced in WARM and users could reasonably implement activities or purchasing practices that
would reduce a representative mix of these materials. The other mixed materials in WARM—
mixed recyclables, mixed organics, and mixed MSW—cannot be source reduced because they
contain a broader mixture of materials at end-of-life where users could not reasonably
implement activities or purchasing practices that reduce demand for all components.
Additionally, mixed MSW and mixed organics include waste materials for which there is no
source reduction pathway in WARM.
• There may be additional GHG impacts from disposal of industrial wastes, particularly paper
sludge at paper mills. Because of the complexity of analyzing these second-order effects and the
lack of data, EPA did not include them.
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2 RECYCLING
This chapter describes the development of material-specific emission factors for recycling in
EPA's Waste Reduction Model (WARM). A discussion of forest carbon storage, an important input in
calculating the emission benefits of paper product recycling, is also included in this chapter.
2.1 A SUMMARY OF THE GHG IMPLICATIONS OF RECYCLING
EPA defines recycling as "the separation and collection of wastes, their subsequent transformation
or remanufacture into usable or marketable products or materials, and the purchase of products made
from recyclable materials" (EPA, 2012). WARM considers the recycling of post-consumer materials,
which are defined as a "material or finished product that has served its intended use and has been
diverted or recovered from waste destined for disposal, having completed its life as a consumer item"
(EPA, 2014).
Recycling is a process that takes materials or products that are at end of life and transforms
them into either (1) the same product or (2) a secondary product (see discussion of open- and closed-
loop recycling). When a material is recycled, it is used in place of virgin inputs in the manufacturing
process, rather than being disposed of and managed as waste. Consequently, recycling provides GHG
reduction benefits in two ways, depending upon the material recycled: (1) it offsets a portion of
"upstream" GHGs emitted in raw material acquisition, manufacture and transport of virgin inputs and
materials, and (2) it increases the amount of carbon stored in forests (when wood and paper products
are recycled).
In calculating the first source of GHG reduction benefits, WARM assumes that recycling
materials does not cause a change in the amount of materials that would otherwise have been
manufactured. Because the amount of products manufactured stays the same, and the existing demand
for recycled content is the same, an increase in recycling leads to a displacement of virgin-sourced
materials.
For more information on the second source of GHG reduction benefits that are provided by
forest carbon storage, see the Forest Carbon Storage chapter.
2.1.1 Open- and Closed-Loop Recycling
Recycling processes can be broadly classified into two different categories: open-loop and
closed-loop recycling. Most of the materials in WARM are modeled in a closed-loop recycling process,
where end-of-life products are recycled into the same product. An example of a closed-loop recycling
process is recycling an aluminum can back into another aluminum can. Decisions about whether to
model materials in an open-loop or closed-loop process are based on how the material is most often
recycled and the availability of data.
For materials recycled in an open loop, the products of the recycling process (secondary
product) are not the same as the inputs (primary product). In open-loop emission factors, the GHG
benefits of material recycling result from the avoided emissions associated with the virgin manufacture
of the secondary products that the material is recycled into. Open-loop recycling does not account for
avoided emissions from manufacturing the primary material, since recycling the recycled material does
not displace manufacturing of the primary material. It only displaces manufacturing of the secondary
product. For example, electronics are recycled by dismantling the products and recovering and
processing the raw materials it contains for use in secondary products. Consequently, WARM calculates
the GHG benefit from recycling electronics based on the emissions displaced from extracting and
producing these secondary products from virgin inputs, rather than on the emissions displaced from
manufacturing an entire new electronic product. In applying this method, EPA considered only the GHG
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benefit for one generation of recycling (i.e., future benefits from recycling the secondary products into
additional products were not included).
The materials modeled as open-loop recycling processes in WARM are: mixed paper, corrugated
containers (partial open-loop),5 copper wire, carpet, electronics, concrete, tires, fly ash, asphalt shingles
and drvwall (partial open-loop).6 Corrugated containers and drywall are modeled as partial open-loop
because the recycling emission factors for these materials are a weighted average of a closed-loop
recycling pathway and an open-loop recycling pathway (e.g., 70 percent of recycled corrugated
containers are used in production of more corrugated containers, and 30 percent of corrugated
containers are recycled into boxboard). Fly ash is a special case: because it is a byproduct rather than a
primary product, it would be impossible to recycle into additional primary product. For more detail on
any of the materials mentioned, please refer to the material-specific chapter.
2.1.2 Material Losses
When any material is recovered for recycling, some portion of the recovered material is
unsuitable for use as a recycled input. This portion is discarded either in the recovery stage (i.e., at
collection and at the materials recovery facility) or in the manufacturing stage. Consequently, more than
one short ton of material must be recovered and processed to produce one short ton of new material
from the recycling process. Material losses are quantified and translated into loss rates. In this analysis,
EPA used estimates of loss rates provided by Franklin Associates, Limited (FAL, 2003), for steel,
dimensional lumber and medium-density fiberboard (the same materials for which FAL's energy data
were used, as described in the Source Reduction chapter). Loss rates for a number of other materials
were based on data compiled by EPA's Office of Research and Development (ORD) and the Research
Triangle Institute (RTI, 2004). Material-specific sources were consulted for the remaining materials.
These values are shown in Exhibit 2-1.
Exhibit 2-1: Loss Rates for Recovered Materials
(a)
(b)
(c)
(d)
(e)
Short Tons of
Short Tons of
Product Made per
Product Made
% of Recovered
Short Ton of
per Short Ton
Materials
Recycled Inputs in
Recovered
Retained in the
the Manufacturing
Materials
Material
Recovery Stage
Stage
(d = b x c)
Data Source3
Aluminum Cans
100
0.93
0.93
RTI, 2004
Aluminum cans used as
Aluminum Ingot
100
0.93
0.93
proxy
Steel Cans
100
0.98
0.98
FAL, 2003
Copper Wire
82
0.99
0.81
FAL, 2003
Glass
90
0.98
0.88
FAL, 2003; RTI, 2004
HDPE
91
0.84
0.76
FAL, 2018
PET
91
0.85
0.77
FAL, 2018
PP
91
0.85
0.78
FAL, 2018
Corrugated Containers
100
0.93
0.93
FAL, 2003; RTI, 2004
5 Note that corrugated containers are modeled using a partial open-loop recycling process. Roughly 70 percent of
the recycled corrugated containers are closed-loop (i.e., replaces virgin corrugated) and 30 percent is open-loop
(i.e., replaces boxboard).
6 Most recycled drywall is used for a variety of agricultural purposes, but can also be recycled back into new
drywall. Approximately 20 percent of recycled drywall is closed-loop (i.e., replaces virgin drywall) and 80 percent is
open-loop (i.e., used for agricultural purposes).
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(a)
(b)
(c)
(d)
(e)
Short Tons of
Short Tons of
Product Made per
Product Made
% of Recovered
Short Ton of
per Short Ton
Materials
Recycled Inputs in
Recovered
Retained in the
the Manufacturing
Materials
Material
Recovery Stage
Stage
(d = b x c)
Data Source3
Magazines/Third-Class Mail
95
0.71
0.67
FAL, 2003; RTI, 2004
Newspaper
95
0.94
0.90
FAL, 2003; RTI, 2004
Office Paper
91
0.66
0.60
FAL, 2003; RTI, 2004
Phone Books
95
0.71
0.68
FAL, 2003; RTI, 2004
Textbooks
95
0.69
0.66
FAL, 2003; RTI, 2004
Dimensional Lumber
80
1.00
0.80
Bergman et al., 2013
Desktop CPUs
100
See note b
See note b
See note b
Portable Electronic Devices
100
See note b
See note b
See note b
Flat-panel Displays
100
See note b
See note b
See note b
CRT Displays
100
See note b
See note b
See note b
Electronic Peripherals
100
See note b
See note b
See note b
Hard-copy Devices
100
See note b
See note b
See note b
Mixed Electronics
100
See note b
See note b
See note b
Carpet
100
1.00
1.00
FAL 2002a; see note c
Concrete
100
1.00
1.00
See note d
Fly Ash
100
1.00
1.00
See note d
Tires
90
0.86
0.80
Corti & Lombardi, 2004
Asphalt Concrete
100
1.00
1.00
Levis 2008e
Asphalt Shingles
100
0.07
0.93
Berenyi, 2007
Drywall
100
1.00
1.00
WRAP, 2008
World Steel Association,
Structural Steel
98
0.87
0.85
2015; AISC, 2015
a Franklin Associates, Ltd. (FAL) provided data for column (b), while the Research Triangle Institute (RTI) provided data for
column (c).
b The rate at which recycled inputs are recovered for new products in the manufacturing stage varies by electronic component.
See the Electronics chapter for more detail.
c A 0.5% loss rate was assumed for molded products from carpet recycling, based on data provided by FAL (2002a). No loss was
assumed for the carpet pad/cushion and carpet backing. Since molded products make up 25% of the materials recovered from
recycling carpet, the loss rate was weighted by this percentage to calculate the overall amount of material retained: (100% -
0.05% x 25%)/100 = 1.00.
d Due to the nature of the recycling process for fly ash and concrete, these materials are collected and recycled on a ton-per-ton
basis, offsetting the production of portland cement and virgin aggregates, respectively.
0 Loss rates for recycling asphalt concrete are less than 1% by mass. Because the recovered asphalt concrete is extremely
valuable and typically recovered on-site, the retention rate for recovered asphalt concrete is quite high.
Explanatory notes: The value in column (b) accounts for losses such as recovered newspapers that were unsuitable for
recycling because they were too wet. Column (c) reflects process waste losses at the manufacturing plant or mill. Column (d) is
the product of the values in columns (b) and (c).
2.1.3 Calculating the GHG Impacts of Recycling
WARM assesses the GHG emission implications of recycling from the point of waste generation
(i.e., starting at the point when the material is collected for recycling) through the point where the
recycled material or product has been manufactured into a new product for use. This includes all of the
GHG emissions associated with collecting, transporting, processing and recycling or manufacturing the
recycled material into a new product for use. To account for the emissions associated with virgin
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manufacture, WARM calculates a "recycled input credit" by assuming that the recycled material
avoids—or offsets—the upstream GHG emissions associated with producing the same amount of
material from virgin inputs.
The approach for calculating the recycled input credit depends upon whether the material is
recycled in a closed- or open-loop process. GHG emission reductions associated with closed-loop
manufacture using recycled inputs are calculated by taking the difference between (1) the GHG
emissions from manufacturing a material (accounting for loss rates) from 100 percent recycled inputs,
and (2) the GHG emissions from manufacturing an equivalent amount of the material from 100 percent
virgin inputs.
For open-loop recycling processes, the emission reductions are calculated by taking the
difference between (1) the GHG emissions from manufacturing a secondary product from 100 percent
recycled inputs, and (2) the GHG emissions from manufacturing an equivalent amount of the secondary
product (accounting for loss rates) from 100 percent virgin inputs.
The methodology for estimating resource acquisition and manufacturing emissions is described
in the WARM Background and Overview chapter. There are separate estimates for manufacturing
process emissions for virgin inputs and recycled inputs, and transportation for virgin inputs and recycled
inputs. For details on the components of the manufacturing process and transportation inputs, see the
WARM Background and Overview chapter.
The recycling GHG emission factors are provided in the chapters corresponding to each
individual material modeled in WARM. These GHG emission factors represent the GHG emissions
associated with recycling each material into a new product for use, minus a GHG emission offset for
avoiding the manufacture of an equivalent amount of the product from virgin inputs.
In evaluating the relative GHG reduction benefits of recycling compared to an existing materials
management practice (i.e., evaluating the benefits of recycling relative to source reduction, composting,
combustion or landfilling), the recycling GHG emission factors developed in WARM must be compared
against the corresponding emission factors for the existing management practice. For example, to
evaluate the GHG emission reductions from recycling one short ton of aluminum cans instead of sending
the same quantity to the landfill, the GHG emission factor for landfilling one short ton of aluminum cans
must be subtracted from the recycling emission factor for aluminum cans. Please see the WARM
Background and Overview chapter for additional explanation of the comparative aspect of WARM
emission factors.
2.2 RESULTS
The national average results of this analysis are shown in Exhibit 2-2. The net GHG emission
reductions from recycling of each material are shown in column (f). As stated earlier, these estimates of
net GHG emissions are expressed for recycling in absolute terms, and are not values relative to another
waste management option, although they must be used comparatively, as all WARM emission factors
must be. They are expressed in terms of short tons of waste input (i.e., tons of waste prior to
processing).
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Exhibit 2-2: Emission Factor for Recycling (MTC02E/Short Ton of Material Recovered)
(a)
(b)
(c)
(d)
(e)
(f)
Recycled
GHG Reductions
Recycled
Recycled Input
Input
from Using Recycled
Input Credit:3
Credit:3
Credit:3
Forest
Inputs Instead of
Process
Transportation
Process Non-
Carbon
Virgin Inputs
Material
Energy
Energy
Energy
Storage
(f = b + c + d+e)
Aluminum Cans
(5.37)
(0.04)
(3.72)
-
(9.13)
Aluminum Ingot
(4.00)
(0.03)
(3.18)
-
(7.20)
Steel Cans
(1.79)
(0.04)
-
-
(1.83)
Copper Wire
(4.44)
(0.05)
-
-
(4.49)
Glass
(0.12)
(0.02)
(0.14)
-
(0.28)
HDPE
(0.59)
(0.01)
(0.15)
-
(0.76)
LDPE
NA
NA
NA
NA
NA
PET
(0.77)
0.03
(0.30)
-
(1.04)
LLDPE
NA
NA
NA
NA
NA
PP
(0.65)
0.02
(0.16)
-
(0.79)
PS
NA
NA
NA
NA
NA
PVC
NA
NA
NA
NA
NA
PLA
NA
NA
NA
NA
NA
Corrugated Containers
(0.02)
(0.05)
(0.01)
(3.06)
(3.14)
Magazines/Third-Class Mail
(0.01)
-
-
(3.06)
(3.07)
Newspaper
(0.66)
(0.03)
-
(2.02)
(2.71)
Office Paper
0.21
-
(0.02)
(3.06)
(2.86)
Phone Books
(0.61)
-
-
(2.02)
(2.62)
Textbooks
(0.05)
-
-
(3.06)
(3.10)
(0.10)
-
-
(1.56)
(1.66)
Dimensional Lumberb
Medium-Density Fiberboard
NA
NA
NA
NA
NA
Food Waste
NA
NA
NA
NA
NA
Food Waste (meat only)
NA
NA
NA
NA
NA
Food Waste (non-meat)
NA
NA
NA
NA
NA
Beef
NA
NA
NA
NA
NA
Poultry
NA
NA
NA
NA
NA
Grains
NA
NA
NA
NA
NA
Bread
NA
NA
NA
NA
NA
Fruits and Vegetables
NA
NA
NA
NA
NA
Dairy Products
NA
NA
NA
NA
NA
Yard Trimmings
NA
NA
NA
NA
NA
Grass
NA
NA
NA
NA
NA
Leaves
NA
NA
NA
NA
NA
Branches
NA
NA
NA
NA
NA
Mixed Paper (general)
(0.38)
(0.11)
(0.01)
(3.06)
(3.55)
Mixed Paper (primarily residential)
(0.38)
(0.11)
(0.01)
(3.06)
(3.55)
Mixed Paper (primarily from offices)
(0.41)
(0.11)
(0.00)
(3.06)
(3.58)
Mixed Metals
(3.05)
(0.04)
(1.31)
-
(4.39)
Mixed Plastics
(0.70)
0.01
(0.24)
-
(0.93)
(0.21)
(0.04)
(0.07)
(2.49)
(2.80)
Mixed Recyclables
Mixed Organics
NA
NA
NA
NA
NA
Mixed MSW
NA
NA
NA
NA
NA
Carpet
(1.43)
(0.01)
(0.94)
-
(2.38)
Desktop CPUs
(1.47)
0.00
(0.04)
-
(1.49)
Portable Electronic Devices
(1.14)
0.01
0.04
-
(1.06)
Flat-panel Displays
(1.00)
0.01
(0.02)
-
(0.99)
CRT Displays
(0.55)
0.00
(0.04)
-
(0.57)
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(a)
(b)
(c)
(d)
(e)
(f)
Recycled
GHG Reductions
Recycled
Recycled Input
Input
from Using Recycled
Input Credit:3
Credit:3
Credit:3
Forest
Inputs Instead of
Process
Transportation
Process Non-
Carbon
Virgin Inputs
Material
Energy
Energy
Energy
Storage
(f = b + c + d+e)
Electronic Peripherals
(0.38)
0.02
(0.03)
-
(0.36)
Hard-Copy Devices
(0.56)
0.00
(0.02)
-
(0.56)
-
(0.90)
Mixed Electronics
(0.91)
0.01
(0.02)
Clay Bricks
NA
NA
NA
NA
NA
Concrete
(0.00)
(0.01)
-
-
(0.01)
Fly Ash
(0.42)
-
(0.45)
-
(0.87)
Tires
(0.46)
0.08
-
-
(0.38)
Asphalt Concrete
(0.03)
(0.05)
-
NA
(0.08)
Asphalt Shingles
(0.11)
0.02
-
NA
(0.09)
Drywall
-
0.02
-
-
0.03
Fiberglass Insulation
NA
NA
NA
NA
NA
Structural Steel
(1.06)
(0.10)
(0.78)
NA
(1.93)
Vinyl Flooring
NA
NA
NA
NA
NA
Wood Flooring
(0.16)
(0.17)
0.00
(3.26)
(3.68)
NA = Not applicable. For the plastic resin material types, only HDPE, PET, and PP recycling are modeled in WARM due to LCI
data availability.
- = Zero emissions.
Note that totals may not add due to rounding, and more digits may be displayed than are significant. Negative values denote
GHG emission reductions or carbon storage.
a Material that is recycled after use is then substituted for virgin inputs in the production of new products. This credit represents
the difference in emissions that results from using recycled inputs rather than virgin inputs. The credit accounts for loss rates in
collection, processing and remanufacturing. Recycling credit is based on closed- and open-loop recycling, depending on
material.
b Modeled as Reuse in WARM.
2.3 LIMITATIONS
The data presented in this document involve GHG emissions associated with the raw materials
and acquisition of materials; therefore, the limitations related to raw materials and acquisition for
specific material types are provided in respective material type chapters. Other limitations are as
follows:
• The recycling results are reported in terms of GHG emissions per short ton of material collected
for recycling. Thus, the emission factors incorporate assumptions on loss of material through
collection, sorting and remanufacturing. There is uncertainty in the loss rates: some materials
recovery facilities and manufacturing processes may recover or use recycled materials more or
less efficiently than as estimated here.
• Because the modeling approach assumes closed-loop recycling for most materials, it does not
fully reflect the prevalence and diversity of open-loop recycling. Most of the materials in this
analysis are recycled into a variety of manufactured products, not just into the original material.
Resource limitations prevent an exhaustive analysis of all of the recycling possibilities for each of
the materials analyzed.
• For the purpose of simplicity, EPA assumed that increased recycling does not change overall
demand for products. In other words, it was assumed that each incremental short ton of
recycled inputs would displace virgin inputs in the manufacturing sector. In reality, there may be
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a relationship between recycling and demand for products with recycled content since these
products may become cheaper as the supply of recycled materials increases.
2.4 REFERENCES
American Institute of Steel Construction. (2015). Hot-Rolled Structural Steel Sections - Life cycle
inventory methodology report.
Berenyi, E. B. (2007). Materials Recycling and Processing in the United States. Westport, CT:
Governmental Advisory Associates, Inc.
Bergman, Richard D.; Falk, Robert H.; Gu, Hongmei; Napier, Thomas R.; Meil, Jamie. (2013). Life-cycle
energy and GHG emissions for new and recovered softwood framing lumber and hardwood
flooring considering end-of-life scenarios. Res. Pap. FPL-RP-672. Madison, Wl: U.S. Department
of Agriculture, Forest Service, Forest Products Laboratory. 35 p.
Corti, A., & Lombardi, L. (2004). End life tyres: Alternative final disposal processes compared by LCA.
Energy. 29 (12-15), 2089-2108. doi:10.1016/j.energy.2004.03.014.
EPA (2014). EPA's Comprehensive Procurement Guideline Glossary.
http://www.epa.gOv/wastes/conserve/tools/cpg/resources.htm#glossary.
EPA (2012). Appendix C: Glossary, RCRA Orientation Manual 2011: Resource Conservation and Recovery
Act.
FAL (2003). Loss rates provided by in-house data from Franklin Associates, Ltd., Prairie Village, KS.
FAL (2002a). Energy and Greenhouse Gas Factors for Nylon Broadloom Residential Carpet. Prairie Village,
KS: Franklin Associates Ltd., July 3, 2002.
FAL (2018). Life Cycle Impacts for Postconsumer Recycled Resins: PET, HDPE, and PP. December 2018.
Conducted by Franklin Associates for APR. Available at
https://plasticsrecvcling.org/images/apr/2018-APR-Recycled-Resin-Report.pdf.
ICF Consulting. (1996). Memorandum to EPA Office of Solid Waste: "Methane Generation from Paper
Sludge," December.
Levis, J. W. (2008). A Life-Cycle Analysis of Alternatives for the Management of Waste Hot-Mix Asphalt,
Commercial Food Waste, and Construction and Demolition Waste. Raleigh: North Carolina State
University.
RTI. (2004). Unpublished database developed jointly by the Research Triangle Institute and the U.S.
Environmental Protection Agency Office of Research and Development.
World Steel Association. (2015). Steel in The Circular Economy-A life cycle perspective.
WRAP. (2008). Life Cycle Assessment of Plasterboard. Waste & Resources Action Programme. United
Kingdom. April 2008.
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3 ANAEROBIC DIGESTION
This chapter describes the development of anaerobic digestion emission factors for EPA's Waste
Reduction Model (WARM). Included are estimates of the net greenhouse gas (GHG) emissions from
anaerobic digestion of yard trimmings, food waste, and mixed organics waste.
3.1 A SUMMARY OF THE GHG IMPLICATIONS OF ANAEROBIC DIGESTION
During anaerobic digestion, degradable materials, such as yard trimmings and food waste, are
digested in a reactor in the absence of oxygen to produce biogas that is between 50-70 percent
methane (CH4). This biogas is then typically burned on-site for electricity generation.7 WARM includes
anaerobic digestion as a materials management option for yard trimmings, food waste, and mixed
organics (i.e., yard trimmings plus food waste). Although there are many different categories of food
waste, including food waste from residential sources, commercial sources, waste from specific types of
commercial entities, vegetables, and meat, EPA has not located satisfactory data on how the
characteristics of these different types of waste vary when managed at end of life. As a result, all food
waste is treated as one material in the anaerobic digestion management practice in WARM. The same
assumption was made for the landfilling, composting, and combustion pathways in WARM.
Anaerobic digestion is a biological process in which microorganisms break down organic
material in the absence of oxygen. While breaking down this matter, the microorganisms release biogas
and leave behind digested solids referred to as digestate. WARM'S approach to anaerobic digestion
assumes that the biogas is used for electricity generation and to heat the digester while the digestate is
ultimately applied to agricultural lands.
There are different types of digesters including wet and dry digesters. Wet digesters involve the
addition of water during the digestion process; the liquid resulting from digestion is recovered and
returned to the reactor once the process is complete. Dry digesters do not require the addition of water.
EPA developed separate estimates of emissions for wet anaerobic digesters and dry anaerobic digesters.
Due to the high amount of preprocessing that would be required, EPA assumed that wet digester
operators do not use yard trimmings as a feedstock. Therefore, dry digestion is the only digestion option
for yard trimmings and mixed organics. EPA also modeled two digestate management scenarios: the
direct application of digestate to land and the curing of digestate before land application. As modeled in
WARM, anaerobic digestion results in carbon dioxide (C02) emissions from transportation,
preprocessing and digester operations, carbon storage (associated with application of digestate to
agricultural soils), nitrogen and phosphorous fertilizer offsets, net electricity offsets, and where
applicable, digestate curing. Emissions estimates also include fugitive emissions of CH4 and nitrous oxide
(N20) produced during digestate decomposition.
3.2 CALCULATING THE GHG IMPACTS OF ANAEROBIC DIGESTION
The stages of an anaerobic digestion operation that contributed to the WARM anaerobic
digestion energy and emission factors include the following processes:
• Transport of materials
• Preprocessing and digester operations
• Biogas collection and utilization
• Curing and land application
• Fugitive CH4 and N20 emissions
7 The generated biogas can be used for other applications such as vehicle fuel or upgrading to pipeline-quality
natural gas. These biogas applications are not modeled in WARM.
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• Carbon storage
• Avoided fertilizer offsets
• Net electricity offsets
There are numerous configurations of anaerobic digestion facilities. WARM includes the
emissions associated with both a continuous single-stage, wet, mesophilic digester and a single-stage,
dry, mesophilic digester. Wet digestion is the most widely-used technology in practice (when including
the co-digestion of food waste with wastewater sludge or manure). The modeled wet digester is
assumed to process only food waste whereas the dry digester may accept food waste, yard trimmings
and mixed organics. Dry digestion systems are projected to represent the majority of anaerobic
digestion growth in the United States (The Environmental Research & Education Foundation, 2015).
Both the wet and dry digesters modeled in WARM may utilize the biogas produced to heat the reactor
and to generate electricity on-site. A majority of currently operational facilities beneficially use biogas
(The Environmental Research & Education Foundation, 2015). EPA assumed that the generated
electricity is used to power the anaerobic digestion facility and excess electricity is sold to the regional
electrical grid. Depending on the system type, the digestate removed from the reactor is dewatered and
can be aerobically cured. The resulting compost is land applied and assumed to store carbon and offset
nitrogen and phosphorus fertilizer use.
Exhibit 3-1 below shows a flow diagram of the different processes within anaerobic digestion.
Feedstock materials, such as food waste, are pre-processed. Pre-processing includes grinding, screening
and mixing the feedstock before it is fed into the digester. The digester releases biogas which is
combusted in an internal combustion engine to generate electricity and heat. The heat is captured and
used to heat the reactor while the net electricity generated is exported to the electrical grid, offsetting
grid electricity generation. The digestate is removed from the digester and, in the case of a wet digester,
dewatered. The digestate is either aerobically cured before land application or directly applied to
agricultural lands.
Exhibit 3-1: Flow Diagram of Anaerobic Digestion as Modeled in WARM
/
IK
Feedstocks
Feedstocks \
=w*
Digestate \
Compost j>
The process modeled within WARM results in biogenic CO2 emissions associated with
decomposition after the resulting compost is added to the soil. Because this C02 is biogenic in origin,
however, it is not counted as a GHG in the Inventory of U.S. Greenhouse Gas Emissions and Sinks and is
not included in this accounting of emissions and sinks.8
8 For more information on biogenic carbon emissions, see the text box, "CO2 Emissions from Biogenic Sources" in
the WARM Background and Overview chapter.
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The following sections provide additional detail on the data sources and methods used to
develop emissions factors. Section 3.2.1 describes the material properties required to model anaerobic
digestion. Section 3.2.2 describes the transport of materials in terms of the fossil fuels (diesel) used in
vehicles collecting and transporting waste to the anaerobic digestion facility and the post-consumer
transportation. Section 3.2.3 discusses the inputs required for the preprocessing and operation of the
anaerobic digester including fuel and electricity use, water requirements, and losses. Section 3.2.4
outlines the biogas collection process and the avoided emissions from the combustion of methane.
Section 3.2.5 describes the curing and land application process. Section 3.2.6 describes the fugitive CH4
and N20 emissions that occur during digestate curing and after land application. Section 3.2.7 describes
the components of carbon storage. Section 3.2.8 discusses the avoided nitrogen and phosphorous
fertilizer amounts and emissions from land application of digestate.
3.2.1 Calculating Material Properties
In modeling anaerobic digestion, EPA first determined the amount of carbon contained in
degradable materials that will be anaerobically digested. Although a large body of research exists on CH4
generation from mixed solid wastes, only a few investigators—most notably Dr. Morton Barlaz and
colleagues at North Carolina State University—have measured the behavior of specific waste wood,
paper, food waste and yard trimming components. The results of their experiments yield data on the
inputs—specifically the initial carbon contents, CH4 generation and carbon stored—that are required for
calculating material-specific emission factors for WARM.
The anaerobic digestion process requires eight material properties for each organic feedstock.
Net emission values are calculated for mixed yard trimmings and mixed organics based on the weighted
average emission factors for the constituent materials (i.e., food waste, branches, grass, and leaves).
Exhibit 3-2 shows the material properties based on the work of Dr. Barlaz and are consistent with the
methodology used for landfilling in WARM, as described in the Landfilling Chapter.
Exhibit 3-2: Material Properties by Material Type
Material
Moisture
Content
(%)
Initial
Carbon
Contentb
(%)
Initial
Nitrogen
Content3
(%)
Initial
Phosphorus
Content3 (%)
Volatile
Solids
Content3
(%)
Methane
Potential
(m3/dry
metric ton)
Percent of
Final Methane
Yield Reached8
(%)
Volatile
Solids
Destruction'
(%)
Food Waste
72.2%
49.5%
3.8%
0.51%
95.6%
369.0C
90.0%
75.0%
Branches
15.9%
49.4%
0.8%
0.20%
90.6%
106.0d
50.0%
47.5%
Grass
82.0%
44.9%
3.4%
0.20%
86.4%
194.8
90.0%
75.0%
Leaves
32.2%
45.5%
0.9%
0.20%
90.2%
65.3d
50.0%
47.5%
a Developed from Riber et al. (2009).
b Initial carbon content from Barlaz (1998).
c Mean of literature values reviewed; Hodge, K. L., Levis, J. W., DeCarolis, J. F., & Barlaz, M. A. (2016). Systematic evaluation of
industrial, commercial, and institutional food waste management strategies in the United States. Environmental Science &
Technology, 50(16), 8444-8452.
d Methane yield calculated from C-loss reported by Barlaz (1998).
0 Varies by process, retention time, and material decay rate. M0ller et al. (2009) used 70% for mixed organics, which was
increased to 90% for food waste and grass and reduced to 50% for branches and leaves.
f Used average for mesophilic reactors reported by EBMUD (2008) for food waste and grass and used average for municipal
wastewater solids for branches and leaves due to their higher lignin content.
The methane yield of food waste is the most critical input value, and a review of recent
literature shows that it can range from approximately 181 to 544 m3 CH4/dry ton. The mean of the
previous studies is 334 m3 CH4/ ton. The current version of WARM uses a factor of 369 m3 CH4/dry ton,
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Anaerobic Digestion
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which is within one standard deviation of the mean. This higher value was selected for consistency with
the current WARM landfill model.
3.2.2 Transport of Materials
WARM accounts for the GHG emissions resulting from fossil fuels used in vehicles collecting and
transporting waste to the anaerobic digestion facility. Exhibit 3-3 shows the diesel used for transporting
the feedstock and solids to the anaerobic digester and the post-consumer transportation. To calculate
the emissions, WARM relies on assumptions NREL's US Life Cycle Inventory Database (USLCI) (NREL,
2015). The NREL emission factor assumes a diesel, short-haul truck.
Exhibit 3-3: Diesel Use by Process and by Material Type for Dry Digestion
Material
Transportation
and Spreading
(Million Btu)
Post-Consumer
Transportation
(Million Btu)
Total Energy Required for
Dry Anaerobic Digestion
(Million Btu)
Total C02 Emissions from
Dry Anaerobic Digestion
(MTCOzE)
Food Waste
0.26
0.04
0.30
0.02
Yard Trimmings
0.29
0.04
0.33
0.02
Grass
0.26
0.04
0.30
0.02
Leaves
0.31
0.04
0.35
0.02
Branches
0.32
0.04
0.36
0.02
Mixed Organics
0.27
0.04
0.31
0.02
3.2.3 Preprocessing and Digester Operations
WARM models the electricity and diesel consumed during preprocessing and digester operation
for both wet and dry digestion based on literature values. Preprocessing includes grinding, screening
and mixing the feedstock before they are fed into the reactor. For the electricity used in operations, EPA
assumed the upper literature limit for the wet digestion system, as additional electricity is required for
pumping and mixing within the system (Moller et al., 2009). The lower literature limit was chosen for
the dry digestion system (Moller et al., 2009). Dry digestion requires more diesel for its operations as it
involves the additional use of front-end loaders to move materials. The reactor moisture content of wet
digestion systems is assumed to be higher than dry digestion systems. In the wet digestion system, the
digestate is dewatered and some liquids are recovered and returned to the reactor, with the remainder
being treated in a wastewater treatment plant (WWTP). For dry systems, the digestate is simply
removed without dewatering. Electricity is consumed during the dewatering process. Additional
operation assumptions are shown below in Exhibit 3-4.
Exhibit 3-4: Preprocessing and Reactor Operations Inputs and Assumptions for Wet and Dry Anaerobic Digestion
Facility Operation Inputs
Units
Wet Digestion
Assumptions
Dry Digestion
Assumptions
Source
Percent methane loss to leaks
%
2
2
WERF, 2012
Sanscartier et al. (2012) reports (2-5%)
House electricity demand
kWh/ton
45.4
18.1
Boldrin et al. (2011) (48.9 kWh/Mg)
M0ller et al. (2009) (20-50 kWh/Mg)
Sanscartier et al. (2012) (47-67 kWh/Mg for
Dufferin facility)
Dewatering electricity use
kWh/ton
68
0
Niuetal. (2013)
House diesel fuel use
L/ton
0.91
5.89
Boldrin et al. (2011) (0.9 L/Mg)
M0ller et al. (2009) (1.6 L/Mg)
Sanscartier et al. (2012) (0.3 L/Mg for
Dufferin facility)
WERF (2012) (6.5 L/Mg)
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Anaerobic Digestion
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Reactor moisture content
% wet
weight
95
70
Hansen et al. (2006)
Moisture content after
dewatering
% wet
weight
76
NA
Metcalf and Eddy (2003)
Percent dry mass Nitrogen
loss during AD
%
8
8
Developed from Hansen et al. (2006) based
on initial nitrogen content
Percent dry mass Phosphorus
loss during AD
%
0
0
Assumed
3.2.4 Biogas Collection and Avoided Emissions
The methane biogas produced during anaerobic digestion is collected and can be combusted to
produce heat and electricity. The recovery of heat and electricity from the combusted biogas offsets the
combustion of other fossil fuel inputs. WARM models the recovery of biogas for electricity generation
and assumes that this electricity offsets non-baseload electricity generation in the power sector.
Electricity generation from combustion of biogas is assumed to be unavailable for 15 percent of
operation time and the process is assumed to be 29 percent efficient (EPA, 2013) These values are
consistent with those used for landfill gas combustion in WARM, as described in the Landfilling Chapter.
WARM estimates the amount of methane that is collected by gas collection equipment. Exhibit
3-5 and Exhibit 3-6 show the mass of methane generated, leaked, flared, and combusted for energy by
material type for wet and dry digestion. The anaerobic digestion of food waste results in almost twice as
much electricity generation compared to yard trimmings and mixed organics due to its higher methane
yield. For all feedstocks, the excess heat captured from the engine is more than four times what is
needed to heat the digester.
Exhibit 3-5: Methane Generation, Treatment, and Use by Material Type for Dry Digestion
Mass of
Mass of
Mass of
Mass of
Methane
Energy from
Methane
Methane
Methane
Combusted
Combusted
Electricity
Net Electricity
Generated
Leaked
Flared
for Energy
Methane
Generation
to the Grid
Material
(kg/ton)
(kg/ton)
(kg/ton)
(kg/ton)
(MMBtu/ton)
(kWh/ton)
(kWh/ton)
Food Waste
60.0
1.20
8.81
49.9
2.37
201.2
182.8
Yard Trimmings
21.1
0.42
3.10
17.6
0.83
70.7
51.9
Grass
20.5
0.41
3.01
17.1
0.81
68.8
50.4
Leaves
14.4
0.29
2.11
12.0
0.57
48.2
29.1
Branches
28.9
0.58
4.26
24.1
1.14
97.1
77.7
Mixed Organics
41.8
0.84
6.15
34.8
1.65
140.3
121.8
Exhibit 3-6: Methane Generation, Treatment, and Use by Material Type for Wet Digestion
Material
Mass of
Methane
Generated
(kg/ton)
Mass of
Methane
Leaked
(kg/ton)
Mass of
Methane
Flared
(kg/ton)
Mass of
Methane
Combusted
for Energy
(kg/ton)
Energy
from
Combusted
Methane
(MMBtu/
ton)
Electricity
Generation
(kWh/ton)
Energy
Available
to Heat
Digester
(MMBtu/
ton)
Net
Electricity
to the Grid
(kWh/
ton)
Energy
Required
to Heat
Reactor
(MMBtu/
ton)
Food Waste
59.96
1.20
8.81
49.9
2.37
201.2
1.26
154.54
0.14
WARM applies non-baseload electricity emission rates to calculate the emissions offset from gas
energy recovery because the model assumes that incremental increases in energy recovery will affect
non-baseload power plants (i.e., power plants that are "demand-following" and adjust to marginal
changes in the supply and demand of electricity). EPA calculated non-baseload emission rates as the
average emissions rate from power plants that combust fuel and have capacity factors less than 0.8
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WARM Version 16
Anaerobic Digestion
December 2023
(EPA, 2015a). The methodology used for anaerobic digestion is consistent with landfilling in WARM, as
described in the Landfilling chapter.
The net electricity exported to the grid is the difference between the electricity generated from
biogas combustion and the electricity used in the anaerobic digestion process and, if modeled by the
user, digestate curing. The majority of the electricity use is due to material pre-processing and mixing.
Food waste uses less electricity for dewatering and screening because its higher moisture content
results in less solid digestate produced. Exhibit 3-7 illustrates the net electricity exported to the grid by
material type.
Exhibit 3-7: Electricity Exported by Material Type for Dry Digestion and Digestate Curing
Material
Net Electricity to Grid
(kWh/ton)
Net Greenhouse Gas Offset3
(MTC02e/ton)
Food Waste
182.80
0.14
Yard Trimmings
51.91
0.04
Grass
50.45
0.04
Leaves
29.05
0.02
Branches
77.70
0.06
Mixed Organics
121.76
0.09
a Based on national average grid mix.
3.2.5 Curing and Land Application
For both wet and dry anaerobic digestion systems, WARM estimates the emissions associated
with two scenarios for digestate beneficial use: curing the digestate and applying the resulting compost
to agricultural lands, or directly applying digestate to agricultural lands without curing.
In the case in which the digestate is cured, the solids are aerobically cured in turned windrows.
The resulting compost is then screened, transported to agriculture lands, and used in place of a portion
of the conventional nitrogen and phosphorus fertilizer that would be needed for the same agricultural
lands. EPA assumed that there are CH4 and N20 emissions released during the curing process. Less N20 is
emitted from the cured compost during land application than from compost that was directly applied
due in part to the N20 released during the curing process. Cured digestate also has a lower mass of
carbon stored after 100 years compared to digestate directly applied to agricultural lands. Exhibit 3-8
outlines the digestate curing input values and assumptions used to develop curing GHG emissions within
WARM. These inputs are used to calculate the diesel used during curing for mixing and windrow turning
and electricity use for screening. Section 3.2.7 and Section 3.2.8 further elaborate on the impact of
curing on fugitive emissions and carbon storage calculations.
Exhibit 3-8: Digestate Curing Inputs and Assumptions for Wet and Dry Digestion
Digestate Curing Parameters
Units
Value
Source
Curing fuel use (mixing windrow turning)
L/ton
1
Boldrin et al. (2009) Assumed 1/3 of 3L used for curing.
Nitrogen loss during curing
%
38.5
Average from Beck-Friis et al. (2000)
Carbon loss during curing
%
58
Boldrin et al. (2009) Average for open biowaste systems
Percent N loss as N20
%
1
Boldrin et al. (2009) Average for open biowaste systems
Percent C loss as CH4
%
1.3
Boldrin et al. (2009) Average for open biowaste systems
Screen electricity use
kWh/ton
0.882
Komilis and Ham (2004)
Mass volatile solids loss per mol C loss
g/mol C loss
12
Haug (1993)
Finished compost moisture content
%
40
Haug (1993)
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Anaerobic Digestion
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3.2.6 Fugitive Emissions of CH4 and N20 During Curing and After Land Application
In addition to the emissions from curing processes, WARM accounts for the fugitive CH4 and N20
emissions that occur during the curing process and after land application. These emissions are
dependent on whether the digestate is cured before land application. Exhibit 3-9 summarizes the CH4
and N20 emissions by material type. Food waste has greater N20 emissions and nitrogen fertilizer
offsets because it contains more initial nitrogen.
Exhibit 3-9: Methane and Nitrous Oxide Emissions During Curing and After Land Application for Wet and Dry
Digestion
Material
Methane Emitted
During Curing
(kg CH4/ton)
N20 Emitted
During Curing
(kg N20/ton)
N20 Emitted After
Land Application
when Cured
(kg N20/ton)
N20 Emitted After
Land Application
when not Cured
(kg N20/ton)
Food Waste
0.26
0.11
0.26
0.42
Yard Trimmings
1.28
0.06
0.15
0.25
Grass
0.32
0.06
0.15
0.24
Leaves
1.95
0.06
0.15
0.24
Branches
2.51
0.07
0.16
0.26
Mixed Organics
0.74
0.09
0.21
0.34
3.2.7 Carbon Storage
Similar to carbon from compost applied to agricultural lands, EPA assumed that carbon from
digestate applied to agricultural lands remains stored in the soil through two main mechanisms: direct
storage of carbon in depleted soils and carbon stored in non-reactive humus compounds. WARM
calculates the carbon storage impact of each carbon storage path separately and then sums them to
estimate the carbon storage factor associated with each short ton of organics composted. For more
information on carbon storage calculations, see section 2.4 in the Composting chapter, which includes
information on the Century model framework and simulations. EPA used the Century model to calculate
soil carbon storage by simulating soil organic matter pools. Exhibit 3-10 presents the soil carbon storage
by material type. The increased solids content of mixed organics causes increased carbon in the compost
when compared to compost from just food waste, and thus increased soil carbon storage credits.
Exhibit 3-10: Soil Carbon Storage by Material Type
Material
Soil Carbon Storage (kg C/ton)
Food Waste
8.96
Yard Trimmings
43.37
Grass
10.92
Leaves
66.18
Branches
85.46
Mixed Organics
25.01
3.2.8 Avoided Fertilizer Offsets
EPA assumed that digestate applied to agricultural land allows for some synthetic fertilizer use
to be avoided. Food waste is the primary feedstock for anaerobic digestion, and contains significant
amounts of nitrogen and phosphorus. Based on a review of literature, EPA calculated a nitrogen and
phosphorus fertilizer offset for anaerobically digested materials (Beck-Friis, Pell, Sonesson, Jonsson, &
Kirchmann, 2000). The literature values used for mineral nutrient equivalence and the emissions
intensity of nitrogen and phosphorous fertilizer use and application are shown in Exhibit 3-11.
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WARM Version 16 Anaerobic Digestion December 2023
Exhibit 3-11: Literature Values for Calculating Avoided Fertilizer Offsets
Use on Land Parameters
Units
Value
Source
Mineral Nutrient Equivalent for Nitrogen
kg N offset/kg N applied
0.4
M0lleretal. (2009)
Mineral Nutrient Equivalent for Phosphorus
kg N offset/kg P applied
1.0
M0lleretal. (2009)
GHG intensity of N fertilizer use and application
kg C02e/kg N
8.9
Boldrin etal. (2009)
GHG intensity of P fertilizer use and application
kg C02e/kg N
1.8
Boldrin etal. (2009)
Exhibit 3-12 presents the nitrogen and phosphorous fertilizer offset by material type. Food
waste has greater nitrogen fertilizer offsets than yard trimmings and mixed organics as it initially
contains more nitrogen.
Exhibit 3-12: Nitrogen and Phosphorous Fertilizer Offset by Material Type
Material
Nitrogen Fertilizer
Offset
(kg N/ton)
Phosphorous
Fertilizer Offset
(kg P/ton)
Nitrogen
Fertilizer Offset
(MTC02e/ton)
Phosphorous
Fertilizer Offset
(MTC02e/ton)
Food Waste
1.084
1.286
0.01
0.00
Yard Trimmings
0.643
0.844
0.01
0.00
Grass
0.628
0.323
0.01
0.00
Leaves
0.626
1.218
0.01
0.00
Branches
0.691
1.511
0.01
0.00
Mixed Organics
0.873
1.074
0.01
0.00
3.2.9 WARM Anaerobic Digestion Results
The net greenhouse gas emissions resulting from anaerobic digestion are calculated by summing
the emissions from the diesel for transportation and land application, fuel and electricity required for
operation, biogas collection and combustion of methane, curing and land application, fugitive emissions,
carbon storage, avoided fertilizer offsets and avoided electricity offsets. In WARM, the emissions from
anaerobic digestion are dependent on the user selection of one of two digestion scenarios (i.e., "Wet
Anaerobic Digestion," and "Dry Anaerobic Digestion") and one of two curing scenarios (i.e., "Cured
Digestate," and "Direct Application"). Exhibit 3-13 shows the GHG emissions from each sub-process for
the dry digestion of food waste and mixed organics with digestate curing. Exhibit 3-14 shows the GHG
emissions from dry digestion with direct land application.
Exhibit 3-15 shows the GHG emissions from wet digestion with digestate curing. Exhibit 3-16
shows the GHG emissions from wet digestion with direct land application.
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WARM Version 16
Anaerobic Digestion
December 2023
Exhibit 3-13: Components of the Dry Anaerobic Digestion Net Emission Factor by Material Type with Digestate
Curing (MTC02E/Short Ton)
Avoided
Avoided
Soil
Process
Net Emissions
Process
Utility
Fertilizer
Carbon
Non-
Transportation
(Post-
Material
Energy
Emissions
Application
Storage
Energy
Energy
Consumer)
Food Waste3 b
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Food Waste (meat only)
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Food Waste (non-meat)
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Beef
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Poultry
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Grains
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Bread
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Fruits and Vegetables
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Dairy Products
0.02
(0.14)
(0.01)
(0.03)
0.12
0.00
(0.04)
Yard Trimmings
0.02
(0.04)
(0.01)
(0.16)
0.09
0.00
(0.09)
Grass
0.02
(0.04)
(0.01)
(0.04)
0.07
0.00
0.00
Leaves
0.02
(0.02)
(0.01)
(0.24)
0.10
0.00
(0.14)
Branches
0.02
(0.06)
(0.01)
(0.31)
0.13
0.00
(0.22)
Mixed Organicsc
0.02
(0.09)
(0.01)
(0.09)
0.11
0.00
(0.06)
a Food waste material properties represent a weighted average of vegetable food waste and non-vegetable food waste.
b Although there are many different categories of food waste, including food waste from residential sources, commercial
sources, waste from specific types of commercial entities, vegetables, and meat, EPA has not located satisfactory data on how
the characteristics of these different types of waste vary when managed at end of life. As a result, all food waste is treated as
one material in the anaerobic digestion management practice in WARM.
c Mixed organics material properties represent a weighted average of branches, grass, leaves, vegetable food waste, and non-
vegetable food waste.
Exhibit 3-14: Components of the Dry Anaerobic Digestion Net Emission Factor by Material Type with Direct Land
Application (MTC02E/Short Ton)
Avoided
Avoided
Soil
Process
Net Emissions
Process
Utility
Fertilizer
Carbon
Non-
Transportation
(Post-
Material
Energy
Emissions
Application
Storage
Energy
Energy
Consumer)
Food Waste3 b
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Food Waste (meat only)
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Food Waste (non-meat)
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Beef
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Poultry
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Grains
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Bread
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Fruits and Vegetables
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Dairy Products
0.02
(0.14)
(0.02)
(0.08)
0.12
0.00
(0.10)
Yard Trimmings
0.02
(0.04)
(0.01)
(0.38)
0.06
0.00
(0.35)
Grass
0.02
(0.04)
(0.01)
(0.10)
0.06
0.00
(0.06)
Leaves
0.02
(0.02)
(0.01)
(0.58)
0.06
0.00
(0.53)
Branches
0.02
(0.06)
(0.01)
(0.75)
0.07
0.00
(0.73)
Mixed Organicsc
0.02
(0.09)
(0.01)
(0.22)
0.09
0.00
(0.21)
a Food waste material properties represent a weighted average of vegetable food waste and non-vegetable food waste.
b Although there are many different categories of food waste, including food waste from residential sources, commercial
sources, waste from specific types of commercial entities, vegetables, and meat, EPA has not located satisfactory data on how
the characteristics of these different types of waste vary when managed at end of life. As a result, all food waste is treated as
one material in the anaerobic digestion management practice in WARM.
c Mixed organics material properties represent a weighted average of branches, grass, leaves, vegetable food waste, and non-
vegetable food waste.
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WARM Version 16 Anaerobic Digestion December 2023
Exhibit 3-15: Components of the Wet Anaerobic Digestion Net Emission Factor by Material Type with Digestate
Curing (MTC02E/Short Ton)
Avoided
Avoided
Soil
Process
Net Emissions
Process
Utility
Fertilizer
Carbon
Non-
Transportation
(Post-
Material
Energy
Emissions
Application
Storage
Energy
Energy
Consumer)
Food Waste3 b
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Food Waste (meat only)
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Food Waste (non-meat)
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Beef
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Poultry
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Grains
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Bread
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Fruits and Vegetables
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Dairy Products
0.01
(0.12)
(0.02)
(0.03)
0.10
0.00
(0.06)
Yard Trimmings
NA
NA
NA
NA
NA
NA
NA
Grass
NA
NA
NA
NA
NA
NA
NA
Leaves
NA
NA
NA
NA
NA
NA
NA
Branches
NA
NA
NA
NA
NA
NA
NA
Mixed Organicsc
NA
NA
NA
NA
NA
NA
NA
a Food waste material properties represent a weighted average of vegetable food waste and non-vegetable food waste.
b Although there are many different categories of food waste, including food waste from residential sources, commercial
sources, waste from specific types of commercial entities, vegetables, and meat, EPA has not located satisfactory data on how
the characteristics of these different types of waste vary when managed at end of life. As a result, all food waste is treated as
one material in the anaerobic digestion management practice in WARM.
c Mixed organics material properties represent a weighted average of branches, grass, leaves, vegetable food waste, and non-
vegetable food waste.
NA = Not applicable
Exhibit 3-16: Components of the Wet Anaerobic Digestion Net Emission Factor by Material Type with Direct Land
Application (MTC02E/Short Ton)
Avoided
Avoided
Soil
Process
Net Emissions
Process
Utility
Fertilizer
Carbon
Non-
Transportation
(Post-
Material
Energy
Emissions
Application
Storage
Energy
Energy
Consumer)
Food Waste3 b
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Food Waste (meat only)
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Food Waste (non-meat)
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Beef
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Poultry
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Grains
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Bread
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Fruits and Vegetables
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Dairy Products
0.01
(0.12)
(0.03)
(0.08)
0.08
0.00
(0.14)
Yard Trimmings
NA
NA
NA
NA
NA
NA
NA
Grass
NA
NA
NA
NA
NA
NA
NA
Leaves
NA
NA
NA
NA
NA
NA
NA
Branches
NA
NA
NA
NA
NA
NA
NA
Mixed Organicsc
NA
NA
NA
NA
NA
NA
NA
a Food waste material properties represent a weighted average of vegetable food waste and non-vegetable food waste.
b Although there are many different categories of food waste, including food waste from residential sources, commercial
sources, waste from specific types of commercial entities, vegetables, and meat, EPA has not located satisfactory data on how
the characteristics of these different types of waste vary when managed at end of life. As a result, all food waste is treated as
one material in the anaerobic digestion management practice in WARM.
c Mixed organics material properties represent a weighted average of branches, grass, leaves, vegetable food waste, and non-
vegetable food waste.
NA = Not applicable
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3.3 LIMITATIONS
Because of data and resource constraints, this chapter does not explore the full range of
conditions, technologies, and practices for anaerobic digestion and how this range could affect the
results of this analysis. Instead, EPA has attempted to provide an analysis of GHG emissions and sinks
associated with anaerobic digestion of organics under a limited set of scenarios. In addition, the analysis
was limited by the scope of WARM, which is intended to present life-cycle GHG emissions of waste
management practices for selected material types, including food waste and yard trimmings.
This section compiles the limitations of the anaerobic digestion analysis described in this
chapter.
• This analysis did not consider the differences in anaerobic digestion emissions resulting from
digesting different food waste types. EPA may consider the need for additional research into
developing food type-specific anaerobic digestion factors for WARM.
• WARM assumes that the biogas generated during anaerobic digestion is used in an internal
combustion engine to generate electricity. This electricity then offsets grid electricity.
Throughout EPA's review of literature and stakeholder engagement, multiple other uses have
been identified for the biogas that have not been addressed here. These uses include upgrading
the gas to pipeline quality and converting it to either compressed natural gas or liquid natural
gas.
• WARM assumes that the digestate generated during anaerobically digesting organic waste is
applied to agricultural land, either after curing or without further processing. EPA's review of
literature and stakeholder engagement identified other uses for digestate that have not been
addressed within WARM. These uses include incinerating it for energy recovery and pelletizing it
for sale as a fertilizer substitute.
• The net GHG emissions from anaerobically digesting food waste are quite sensitive to food
waste methane yield assumptions. In discussions with stakeholders and in EPA's review of
literature, it was indicated that there was little evidence that different anaerobic digestion
reactor configurations have significantly different methane yields. Therefore, EPA believes that
the model presented in this chapter should provide reasonable estimates of the GHG emissions
from a wide range of anaerobic digestion configurations.
• This analysis calculates the GHG impacts of the anaerobic digestion of individual substrates as if
they were digested by themselves. In practice, food waste may be co-digested with manure of
wastewater treatment biosolids. It is assumed that the food waste behaves the same in
dedicated and in co-digestion facilities such that the analysis presented here is applicable across
many anaerobic digestion scenarios.
• As identified in the Composting Chapter, this analysis does not consider all soil conversation and
management pathways and the impact of those practices on carbon storage. Data and resource
restraints prevented EPA from using Century to evaluate the variation in carbon storage impacts
for a wide range of compost feedstocks (e.g., yard trimmings mixed with food waste, food waste
alone). EPA acknowledges that the modeling performed to determine the humus formation for
yard trimmings and food discards attempts to provide an analysis of GHG emissions and sinks
that reflect the set of scenarios available. This methodology and its limitations are further
explained in the Composting Chapter.
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3.4 REFERENCES
Arsova, L. (2010). Anaerobic digestion of food waste: Current status, problems and an alternative
product. Master's Thesis, New York, NY: Department of Earth and Environmental Engineering,
Columbia University, http://www.seas.columbia.edu/earth/wtert/sofos/arsova thesis.pdf.
Beck-Friis, B., Pell, M., Sonesson, U., Jonsson, H., and Kirchmann, H. (2000). Formation and Emission of
N20 and CH4 from Compost Heaps of Organic Household Waste. Environmental Monitoring and
Assessment. 62: 317-331.
Berglund, M. & Borjesson, P. (2006). Assessment of energy performance in the life-cycle of biogas
production. Biomass and Bioenergy, 30,254-266.
Boldrin, A., Neidel, T. L., Damgaard, A., Bhander, G. S., M0ller, J., & Christensen, T. H. (2011). Modelling
of environmental impacts from biological treatment of organic municipal waste in EASEWASTE.
Waste Management (New York, N.Y.), 31(4), 619-30. doi:10.1016/j.wasman.2010.10.025.
Bruun, S., Hansen, T.L., Christensen, T.H., Magid, J. & Jensen, L.S. (2006). Application of processed
organic municipal solid waste on agricultural land: a scenario analysis. Environmental Modeling
and Assessment, 11, 251-265.
The Environmental Research & Education Foundation (2015). Anaerobic Digestion of Municipal Solid
Waste: Report on the State of Practice. Retrieved from www.erefdn.org.
EPA. (2018a). Advancing Sustainable Materials Management: 2015 Fact Sheet. (EPA530-F-18-004).
Washington, DC: U.S. Government Printing Office. Retrieved from
https://www.epa.gov/sites/production/files/2018-
07/documents/2015 smm msw factsheet 07242018 fnl 508 002.pdf.
EPA. (2018b). Emissions & Generation Resource Integrated Database (eGRID). Available from EPA at
http://www.epa.gov/energy/egrid.
EPA. (2013). The Landfill Methane Outreach Program (LMOP) LFGE Benefits Calculator. Available online
at: http://www.epa.gov/lmop/proiects-candidates/lfge-calculator.html.
EPA (2008). Anaerobic Digestion of Food Waste, Prepared by East Bay Municipal Utility District, Oakland,
CA.
EPA (2000). Biosolids Technology Fact Sheet: Centrifuge Thickening and Dewatering. Office of Water,
Washington D.C., EPA 832-F-00-053, September 2000.
Fruergaard, T., Ekvall, T., Astrup, T. (2009). Energy use and recovery in waste management and
implications for accounting of greenhouse gases and global warming contributions. Waste
Management & Research, 27(8), 724-737.
IPCC (2011). Moomaw, W., P. Burgherr, G. Heath, M. Lenzen, J. Nyboer, A. Verbruggen, 2011: Annex II:
Methodology. In IPCC Special Report on Renewable Energy Sources and Climate Change
Mitigation, O. Edenhofer, R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S.
Hansen, T.L., Bhander, G.S., Christensen, T.H., Bruun, S. & Jensen, L.S. (2006). Life cycle modelling of
environmental impacts of application of processed organic municipal solid waste on agricultural
land (EASEWASTE). Waste Management & Research, 24, 153-166.
Haug, R.T. (1993). The Practical Handbook of Compost Engineering. CRC Press, Boca Raton, FL, USA.
Long, J. H. (2012). Environmental, Economic, and Process Evaluation of Anaerobic Co-digestion of Grease
Trap Waste with Municipal Wastewater Sludge. North Carolina State University.
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Lopez, V. M. (2015). Commercial Food Waste Feedstock Characterization for Anearobic Digestion. (MS
Thesis), North Carolina State University, Raleigh, NC.
M0ller, J., Boldrin, A., & Christensen, T. H. (2009). Anaerobic digestion and digestate use: accounting of
greenhouse gases and global warming contribution. Waste Management & Research: The
Journal of the International Solid Wastes and Public Cleansing Association, ISWA, 27(8), 813-24.
doi: 10.1177/0734242X09344876
National Renewable Energy Laboratory (2015). "U.S. Life Cycle Inventory Database." Retrieved from
https://www.lcacommons.gov.
Niu, D. et al. (2013). Greenhouse gases emissions accounting for typical sewage sludge digestion with
energy utilization and residue land application in China. Waste Management, 33(1), pp.123-8.
Available at: http://www.ncbi.nlm.nih.gov/pubmed/22884580.
NRAES (1998). Composting for municipalities: Planning and Design Considerations; Natural Resource,
Agriculture, and Engineering Service: Ithaca, New York, 1998.
Oshins, C., Block, D. (2000). Feedstock composition at composting sites. Biocycle, 41(9), 31-34.
Riber, C.; Petersen, C.; Christensen, T.H., (2009). Chemical composition of material fractions in Danish
household waste. Waste Manage., 29(4), 1251-1257.
Sanscartier, D., MacLean, H., Saville, B. (2011). Electricity Production from Anaerobic Digestion of
Household Organic Waste in Ontario: Techno-Economic and GHG Emission Analyses. Environ.
Sci. Technol., 2012, 46 (2), pp 1233-1242 Publication Date (Web): December 14, 2011 (Article)
DOI: 10.1021/es2016268.
WERF (2012). Sustainable Food Waste Evaluation, Alexandria, VA. OWSO5R07e,
http://www.werf.Org/a/ka/Search/Research Profile.aspx?Reportld=OWSQ5R07e.
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4 COMPOSTING
This guidance document describes the development of composting emission factors for EPA's
Waste Reduction Model (WARM). Included are estimates of the net greenhouse gas (GHG) emissions
from composting of yard trimmings and food waste, as well as mixed organics and polylactide (PLA)
biopolymer resin.9
4.1 A SUMMARY OF THE GHG IMPLICATIONS OF COMPOSTING
During composting, microbial decomposition aerobically transforms organic substrates into a
stable, humus-like material (Brown and Subler, 2007). Although small-scale composting, such as
backyard composting, occurs across the United States, WARM models composting only in central
composting facilities with windrow piles because data for small-scale composting or other large-scale
operations are insufficient.10 WARM includes composting as a materials management option for yard
trimmings, food waste, PLA, and mixed organics.
As modeled in WARM, composting results in some carbon storage (associated with application
of compost to agricultural soils), carbon dioxide (C02) emissions from transportation and mechanical
turning of the compost piles, in addition to fugitive emissions of methane (CH4) and nitrous oxide (N20)
produced during decomposition. To estimate the carbon storage from compost application, EPA
selected point estimates from the range of emission factors covering various compost application rates
and time periods. EPA chose the point estimates based on a typical compost application rate of 20 short
tons of compost per acre, averaged over four soil-crop scenarios.11 EPA selected the carbon storage
values for the year 2010 to maintain consistency with the forest carbon storage estimates discussed in
the Forest Carbon Storage chapter.12
4.2 CALCULATING THE GHG IMPACTS OF COMPOSTING
The stages of a composting operation with the potential to affect GHG flux include the following
processes:
• Collecting and transporting the organic materials to the central composting site.
• Mechanical turning of the compost pile.
• Non-C02 GHG emissions during composting (primarily CH4 and N20).
• Avoided fertilizer offset from direct application of compost.
• Storage of carbon after compost application to soils.
Composting also results in biogenic C02 emissions associated with decomposition, both during
the composting process and after the compost is added to the soil. Because this C02 is biogenic in origin,
9 Composting is not included as a material management pathway for paper because of insufficient information on
the GHG implications of composting paper products.
10 Windrows are a widely used method for composting yard trimmings and municipal solid waste, and they are
considered to be the most cost-effective composting technology (EPA, 1994; Coker, 2006).
11 EPA ran the composting simulation on two sites included in CENTURY: an eastern Colorado site with clay loam
soil and a southwestern Iowa site with silty clay loam soil. EPA simulated two harvest regimes on each site, one
where corn is harvested for silage and 95 percent of the above-ground biomass is removed and the other one
where corn is harvested for grain and the stover is left behind to decompose on the field.
12 For consistency with the paper recycling/source reduction analysis of forest carbon storage, EPA analyzed the
GHG implications of composting at the year 2010. EPA chose 2010 in the paper recycling/source reduction and
forest carbon analyses because it represented a delay of 5 to 15 years from the onset of the simulated period of
incremental recycling.
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however, it is not counted as a GHG in the Inventory of U.S. Greenhouse Gas Emissions and Sinks and is
not included in this accounting of emissions and sinks.13
Exhibit 4-1: Components of the Composting Net Emission Factor for Food Waste, Yard Trimmings, and Mixed
Organics
Composting of Post-Consumer Material
Transportation
and Turning of
Fugitive
Fertilizer
Soil Carbon
Net Emissions
Material Type
Compost
Emissions
Offset
Storage
(Post-Consumer)
PLA
0.03
0.10
(0.02)
(0.24)
(0.13)
Food Waste
0.03
0.08
(0.03)
(0.24)
(0.15)
Dairy Products
0.03
0.08
(0.03)
(0.24)
(0.15)
Grains
0.03
0.08
(0.03)
(0.24)
(0.15)
Bread
0.03
0.08
(0.03)
(0.24)
(0.15)
Fruits and Vegetables
0.03
0.08
(0.03)
(0.24)
(0.15)
Beef
0.03
0.08
(0.03)
(0.24)
(0.15)
Poultry
0.03
0.08
(0.03)
(0.24)
(0.15)
Food Waste (meat only)
0.03
0.08
(0.03)
(0.24)
(0.15)
Food Waste (non-meat)
0.03
0.08
(0.03)
(0.24)
(0.15)
Yard Trimmings
0.02
0.12
-
(0.24)
(0.11)
Grass
0.02
0.12
-
(0.24)
(0.11)
Leaves
0.02
0.12
-
(0.24)
(0.11)
Branches
0.02
0.12
-
(0.24)
(0.11)
Mixed Organics
0.03
0.10
(0.02)
(0.24)
(0.13)
- = Zero emissions.
Note that totals may not add due to rounding. Negative values denote GHG emission reductions or carbon storage.
Exhibit 4-1 shows the four components of the net emission factor for food waste, yard waste,
PLA, and mixed organics. Because of resource and model resolution constraints, the two approaches
EPA used in WARM to calculate carbon storage from compost application model only finished compost
and do not distinguish between compost feedstocks; therefore, the emission factors for each organic's
input are the same. The following sections provide further detail on the sources and methods used to
develop these emission factors. Section 4.2.1 describes how WARM accounts for GHG emissions during
transportation of composting materials and the physical turning of the compost. Section 4.2.2 describes
the estimates of fugitive emissions of CH4 and N20 for composting within WARM. Section 4.2.3 describes
the avoided fertilizer offset from direct application of compost to soil. Section 4.2.4 details the
methodology for calculating the carbon storage resulting from compost application in soils, and Sections
4.2.5 and 4.2.6 describe in greater detail the components of carbon storage.
4.2.1 C02 from Transportation of Materials and Turning of Compost
WARM includes emissions associated with transporting and processing of the compost in
aerated windrow piles. Transportation energy emissions occur when fossil fuels are combusted to
collect and transport yard waste and food waste to the composting facility and then to operate
composting equipment that turns the compost.14 To calculate the emissions for yard waste materials,
WARM relies on assumptions from FAL (1994) for the equipment emissions and NREL's US Life Cycle
Inventory Database (USLCI) (NREL, 2015). The NREL emission factor assumes a diesel, short-haul truck.
To calculate emissions for food waste material categories, WARM relies on assumptions from Oregon
13 For more information on biogenic carbon emissions, see the text box, "C02 Emissions from Biogenic Sources" in
the WARM Background and Overview chapter.
14 EPA did not count transportation emissions from delivery of finished compost from the composting facility to its
final destination.
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DEQ (2014) for the equipment emissions and NREL's USLCI (NREL, 2015). Mixed organics and PLA are
considered a representative blend of compostable waste, and use a weighted average of the yard and
food waste transportation and turning emission factors based on the assumed composition of mixed
organic waste.15 Exhibit 4-2 provides the transportation emission factor calculation.
Exhibit 4-2: Emissions Associated with Transporting and Turning Compost
Material Type
Diesel Fuel Required
to Collect and
Transport One Short
Ton (Million Btu)a
Electricity Required
to Turn the Compost
Piles (Million Btu)
Total Energy Required
for Transporting and
Turning Compost
(Million Btu)
Total C02 Emissions
from Transporting
and Turning Compost
(MTCOzE)
Yard Waste
0.04
0.22b
0.26
0.02
Food Waste
0.04
0.69 c
0.73
0.03
PLA
0.04
0.47
0.51
0.03
Mixed Organics
0.04
0.47
0.51
0.03
a Based on estimates from NREL's USLCI Database.
b Based on estimates in Table 1-17 in FAL, 1994, p.132.
c Based on estimates in Table 10 in Oregon DEQ, 2014, p.42.
4.2.2 Fugitive Emissions of CH4 and N20 During Composting
4.2.2.1 Background on Fugitive Emissions from Composting
During the composting process, microbial activity decomposes waste into a variety of
compounds, some of which are emitted from the compost pile as gases. The amount and type of end
products formed during these reactions depends on many factors, including the original nutrient
balance and composition of the waste, the temperature and moisture conditions of the compost, and
the amount of oxygen present in the pile. These processes result in the generation of small amounts of
CH4 and N20 gases, which contribute to the net GHG emissions associated with the composting
pathway.
The scientific literature suggests that there is a wide range of emissions for fugitive gases
generated during composting. Local factors can strongly influence the existence and extent of CH4 and
N20 emissions from composting piles. These local factors include:
• Aeration
• Density of compost
• Frequency of turning
• Feedstock composition
• Climate (temperature and precipitation)
• Size of compost piles
After reviewing a large number of studies, EPA found that Williams et al. (2019) provided the
most applicable results for WARM and forms the basis of EPA's estimates of fugitive emissions for
composted waste in WARM. The study characterizes CH4 and N20 emissions for both biowaste and
green waste in well-managed compost windrows across several weeks. Biowaste is composed of
separated organic household waste, including food waste. Green waste, or garden waste, is composed
primarily of plant waste such as grass and yard trimmings. In WARM, food waste is classified as a bio-
waste for the purposes of estimating fugitive emissions, whereas yard trimmings are classified as a
green waste.
15 WARM assumes that mixed organics is comprised of 53% food waste and 47% yard waste.
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4.2.2.2 Methane Generated from Composting
There is a consensus within the scientific literature that CH4 is emitted in measurable quantities
even in well-managed compost piles. Williams et. al (2019) measured the average emissions from
windrow composting on green waste and a mixture of food and green waste in California for one ton of
dry mass of waste. Green waste is defined as landscaping materials including grass, leaves and woody
materials. Mixed waste includes both food (8.6%) and green (91.4%) waste where food waste includes
pre-consumer food scraps from a university coffee house. Exhibit 4-3 provides a summary of these
emissions.
Exhibit 4-3: Fugitive CH4 Emissions from Composting Biowaste and Green Waste
Compost Feedstock
CH4 Emissions (MTC02E/ton)
Food Waste
0.07
Green waste
0.10
4.2.2.3 Nitrous Oxide Generated from Composting
Knowledge of the mechanism of N20 emissions from composting is significantly less developed
than that of either C02 or CH4 emissions. N20 is formed during both incomplete ammonium oxidation
and incomplete denitrification processes, but there is debate over which process is most important in
composting (Lou and Nair, 2009). While CH4 is usually detected near the bottom of piles where oxygen is
absent, N20 often forms closer to the surface. Exhibit 4-4 provides a summary of these emissions.
Exhibit 4-4: Fugitive N20 Emissions from Composting Biowaste and Green Waste
Compost Feedstock
N20 Emissions (MTC02E/ton)
Food Waste
0.01
Green waste
0.01
4.2.2.4 Summary of Fugitive Emissions Generated from Composting
Combining CH4 and N20 emissions, the net fugitive emissions from composting comprise 0.12
and 0.17 MTC02E/ton for food waste and green waste, respectively. For mixed organics and PLA, WARM
uses a weighted emission factor that considers the relative amounts of biowaste and green waste
composted in the United States.16 For an overview of fugitive emissions by material type, see Exhibit 4-5.
Exhibit 4-5: Total Fugitive Emissions from Composting, by Material Type
Fugitive Emissions
Material Type
(MTC02E/ton)
PLA
0.10
Food Waste
0.08
Yard Trimmings
0.12
Grass
0.12
Leaves
0.12.17
Branches
0.12
Mixed Organics
0.10
Note that totals from Exhibit 4-3 and Exhibit 4-4 may not add due to rounding.
4.2.3 Avoided Fertilizer Offset
EPA assumes that compost applied directly to soil allows for some synthetic fertilizer use to be
avoided. A life-cycle assessment harmonization study conducted by the Oregon DEQ (2014) reviewed
impacts from composting of food waste found that compost contains significantly smaller amounts of
16 WARM assumes that mixed organics is comprised of 53% food waste and 47% yard waste.
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nutrients compared to fertilizers. The Oregon DEQ (2014) study, which is based on end uses of home
gardens, landscapers, nurseries, and bulk agriculture, assumes that approximately 25% of synthetic
fertilizer use is displaced by end users applying food waste compost. Based on this information, EPA
assumes an offset of 0.03 MTC02e/ton associated with the application of composted food waste. Due to
a lack of available information, EPA also assumes that the compost generated during composting of yard
waste does not offset any synthetic fertilizer use when applied to agricultural land.
4.2.4 Carbon Storage Resulting from Compost Application to Soils
4.2.4.1 Background on Carbon Storage in Soils
The stock of carbon in soils is the result of a balance between inputs (usually plant matter) and
outputs (primarily C02 flux during decomposition of organic matter). The entire portion of carbon held in
the soil and undergoing decomposition is collectively referred to as "soil organic matter" (SOM) or "soil
organic carbon" (SOC). SOC is a mixture of different organic compounds that decompose at vastly
differing rates. Soils contain thousands of different SOC compounds that microbial degradation or
abiotic condensation reactions transform into new structures. The more complex of these molecular soil
structures tend to have a low decomposition rate and often are identified as humus (Davidson and
Janssens, 2006). Strong evidence exists that SOC decomposition decreases with increasing depth
(Meersmans et al., 2009). The top layers of soil generally contain organic matter (such as plant residues)
that decomposes quickly, meaning that carbon in this portion of the soil is likely to be relatively young.
The carbon dynamics in deeper soil layers and the driving factors behind vertical distribution of SOC are
poorly understood.
During composting, microbes degrade the original waste materials into organic compounds
through a variety of pathways. During this decomposition, approximately 80 percent of the initial
organic matter is emitted as C02 (Beck-Friis et al., 2000). The remainder of the organic compounds
eventually stabilize and become resistant to further rapid microbial decomposition (i.e., recalcitrant)
(Francou et al., 2008). Mature compost is characterized as containing a high percentage of these stable,
humic substances. When the compost is mature, nearly all of the water-soluble compounds (such as
dissolved organic carbon) will have leached out (Bernal et al., 1998).
While EPA is currently researching the mechanisms and magnitude of carbon storage, WARM
assumes that carbon from compost remains stored in the soil through two main mechanisms: direct
storage of carbon in depleted soils and carbon stored in non-reactive humus compounds. WARM
calculates the carbon storage impact of each carbon storage path separately and then adds them
together to estimate the carbon storage factor associated with each short ton of organics composted.
4.2.4.2 Soil Carbon Storage Calculation
To calculate soil carbon storage, EPA simulated soil organic matter pools using the Century
model, which is described in Section 4.2.5. EPA ran more than 30 scenarios with varied compost
application rates and frequency, site characteristics, fertilization rates, and crop residue management.
Based on this analysis, EPA concluded that while a single compost application does initially increase soil
carbon, the carbon storage rate declines with time after the application. Using a timeframe of 10 years
to calculate carbon storage, only a fraction of the initial carbon added remained in the soil at the end of
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that time period. EPA included this fraction of added carbon per short ton of compost that remained
present in the soil after 10 years in the WARM composting emission factor, as shown in Exhibit 4-1.17
4.2.4.3 Alternative Carbon Storage Hypotheses
When EPA first incorporated into WARM composting as a materials management option, the
agency conducted research but could not identify sufficient primary data that could be used to develop
quantitative estimates of the soil carbon storage benefits of compost. EPA developed modeling
approaches to investigate the possible effects of compost application on soil carbon storage. In addition
to the humus formation and depleted soils mechanisms mentioned earlier, EPA considered the following
two possible mechanisms for the effect of compost on soil carbon:
• Nitrogen in compost may stimulate higher productivity, thus generating more crop residues.
This fertilization effect would increase soil carbon because of the larger volume of crop residues,
which serves as organic matter input.
• The application of compost produces a multiplier effect by qualitatively changing the dynamics
of the carbon cycling system and increasing the retention of carbon from non-compost sources.
Some studies of other compost feedstocks (e.g., farmyard manure, legumes) have indicated that
the addition of organic matter to soil plots can increase the potential for storage of soil organic
carbon. The carbon increase apparently comes not only from the organic matter directly, but
also from retention of a higher proportion of carbon from residues of crops grown on the soil.
This multiplier effect could enable compost to increase carbon storage by more than its own
direct contribution to carbon mass accumulation.
EPA concluded from the Century simulations that a shortage of nitrogen can modestly increase
crop productivity with compost application, which results in higher inputs of crop residues into the soil
and an increased carbon storage rate. As noted above, our analysis assumed that farmers will supply
sufficient synthetic fertilizer to crops to maintain commercial yields, in addition to any compost added,
so that the soil carbon effect of nitrogen fertilization resulting from compost is relatively small. Although
several of the experts contacted cited persuasive qualitative evidence of the existence of a multiplier
effect, EPA was unable to develop an approach to quantify this process. More information on these two
hypotheses and why they were not included in the final carbon storage emission factor appears in
Section 4.3.
4.2.5 Century Model Framework and Simulations
4.2.5.1 Evaluating Possible Soil Carbon Models
As mentioned earlier, EPA's composting analysis included an extensive literature review and
interviews with experts to consider whether the application of compost leads to long-term storage of
carbon in soils. After determining that neither the literature review nor discussions with experts would
yield a basis for a quantitative estimate of soil carbon storage, EPA evaluated the feasibility of a
simulation modeling approach. EPA initially identified two simulation models with the potential to be
applied to the issue of soil carbon storage from compost application: (1) Century and (2) the Rothamsted
17 Note that if the time frame is extended to longer periods (and many of the recent discussions of agricultural and
forestry offsets in the context of carbon credits would indicate that 10 years is well below the consensus time
horizon), the fraction of added carbon per ton of compost that remains present in the soil would be smaller.
Although the selection of an appropriate time frame is not the subject of this documentation, EPA may later revisit
the choice of time frame.
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C (ROTHC-26.3) model.18 Both are peer-reviewed models that have structure and application that have
been described in scores of publications. The models share several features:
• Ability to run multiyear simulations.
• Capability to construct multiple scenarios covering various climate and soil conditions and
loading rates.
• Ability to handle interaction of several soil processes, environmental factors, and
management scenarios such as carbon: nitrogen (C:N) ratios, aggregate formation, soil
texture (e.g., clay content), and cropping regime.
Given the extensive application of Century in the U.S., its availability on the Internet, and its
ability to address many of the processes important to compost application, EPA decided to use Century
rather than ROTHC-26.3.
Description of the Century Soil Model
Century is a FORTRAN model of plant-soil ecosystems that simulates long-term dynamics of carbon,
nitrogen, phosphorus, and sulfur. It tracks the movement of carbon through soil pools—active, slow,
and passive—and can show changes in carbon levels as a result of the addition of compost.
In addition to soil organic matter pools, carbon can be found in surface (microbial) pools and in above-
and below-ground litter pools. The above-ground and below-ground litter pools are divided into
metabolic and structural pools based on the ratio of lignin to nitrogen in the litter. The structural
pools contain all of the lignin and have much slower decay rates than the metabolic pools. Carbon
additions to the system flow through the various pools and can exit the system (e.g., as C02, dissolved
carbon, or through crop removals).
The above-ground and below-ground litter pools are split into metabolic and structural pools based
on the ratio of lignin to nitrogen in the litter. The structural pools contain all of the lignin and have
much slower decay rates than the metabolic pools. The active pool of soil organic matter includes
living biomass, some of the fine particulate detritus, most of the non-humic material, and some of the
more easily decomposed fulvic acids. The active pool is estimated to have a mean residence time
(MRT) of a few months to 10 years (Metherell et al., 1993; Brady and Weil, 1999). The slow pool
includes resistant plant material (i.e., high lignin content) derived from the structural pool and other
slowly decomposable and chemically resistant components. It has an MRT of 15-100 years. The
passive pool of soil organic matter includes very stable materials remaining in the soil for hundreds to
thousands of years.
Century does not simulate increased formation of humic substances associated with organic matter
additions, nor does it allow for organic matter additions with high humus content to increase the
magnitude of the passive pool directly. (Because Century does not account for these processes, EPA
developed a separate analysis, described in this section.)
Century contains a submodel to simulate soil organic matter pools. Additional submodels address
nitrogen, phosphorus, sulfur, the water budget, leaching, soil temperature, and plant production, as
well as individual submodels for various ecosystems (e.g., grassland, cropland). The nitrogen
submodel addresses inputs of fertilizer and other sources of nitrogen, mineralization of organic
nitrogen, and uptake of nitrogen by plants.
18 This model was developed based on long-term observations of soil carbon at Rothamsted, an estate in the
United Kingdom where organic amendments have been added to soils since the 19th century.
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4.2.5.2 Century Simulations
For this analysis, EPA developed a basic agricultural scenario in Century where land was
converted from prairie to farmland (growing corn) in 1921 and remained growing corn through 2030.19
Several sets of detailed site characteristics from past modeling applications are available to
users in Century. EPA chose two settings: an eastern Colorado site with clay loam soil and a
southwestern Iowa site with silty clay loam soil. Both settings represent fairly typical Midwestern corn
belt situations where agricultural activities have depleted soil organic carbon levels. EPA then ran more
than 30 scenarios to examine the effect of the following variables on soil carbon storage:
• Compost application rate and frequency.
• Site characteristics (rainfall, soil type, irrigation regime)
• Fertilization rate
• Crop residue management
EPA adjusted compost application rates using the organic matter (compost) files for each
compost application rate included in the analysis. EPA then compared the effect of applying compost
annually for 10 years (1996-2005) at seven different application rates: 1.3, 3.2, 6.5, 10, 15, 20, and 40
wet short tons compost per acre (corresponding to 60-1,850 grams of carbon per square meter).20 EPA
also investigated the effect of compost application frequency on the soil carbon storage rate and total
carbon levels. EPA ran the model to simulate compost applications of 1.3 wet short tons compost/acre
and 3.2 wet short tons compost/acre every year for 10 years (1996-2005) and applications of 1.3 wet
short tons compost/acre and 3.2 wet short tons compost/acre applied every five years (in 1996, 2001,
and 2006). The simulated compost was specified as having 33 percent lignin,2117:1 C:N ratio,22 60:1
carbon-to-phosphorus ratio, and 75:1 carbon-to-sulfur ratio.23 EPA also ran a scenario with no compost
application for each combination of site-fertilization-crop residue management. This scenario allowed
19 EPA is conducting research into compost markets, and initial findings indicate that compost is not often used in
large-scale agricultural applications, but it is often applied in high-end markets, such as landscaping. Century and
other widely vetted soil carbon models, however, do not readily model the effects of composting on soil carbon for
non-agricultural scenarios. Because of this lack of data, EPA chose to simulate composting using the large-scale
agricultural scenarios available in Century. EPA is researching methods to improve these assumptions.
20 The model requires inputs in terms of the carbon application rate in grams per square meter. The relationship
between the carbon application rate and compost application rate depends on three factors: the moisture content
of compost, the organic matter content (as a fraction of dry weight), and the carbon content (as a fraction of
organic matter). Inputs are based on values provided by Dr. Harold Keener of Ohio State University, who estimates
that compost has a moisture content of 50 percent, an organic matter fraction (as dry weight) of 88 percent, and a
carbon content of 48 percent (as a fraction of organic matter). Thus, on a wet weight basis, 21 percent of compost
is carbon.
21 EPA estimated the percentage of lignin based on the lignin fractions for grass, leaves, and branches specified by
compost experts (particularly Dr. Gregory Evanylo at Virginia Polytechnic Institute and State University, and lignin
fractions reported in M.A. Barlaz [1997]). FAL provided an estimate of the fraction of grass, leaves, and branches in
yard trimmings in a personal communication with ICF Consulting, November 14,1995. Subsequently, FAL obtained
and provided data showing that the composition of yard trimmings varies widely in different states. The
percentage composition used here (50 percent grass, 25 percent leaves, and 25 percent branches on a wet weight
basis) is within the reported range.
22 The C:N ratio was taken from Brady and Weil (1999).
23 C:P and C:S ratios were based on the literature and conversations with composting experts, including Dr.
Gregory Evanylo at Virginia Polytechnic Institute and State University.
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EPA to control for compost application that is, to calculate the change in carbon storage attributable
only to the addition of compost.
Finally, EPA simulated two harvest regimes, one where the corn is harvested for silage (where
95 percent of the above-ground biomass is removed) and the other where corn is harvested for grain
(where the stover is left behind to decompose on the field). These simulations enabled EPA to isolate
the effect of the carbon added directly to the system in the form of compost, as opposed to total carbon
inputs, which include crop residues.
4.2.5.3 Analysis of Compost Application Impacts on Depleted Soils
The output data cover the period from 1900 through 2030. In general, EPA focused on the
difference in carbon storage between a baseline scenario where no compost was applied and a with-
compost scenario. EPA calculated the difference between the two scenarios to isolate the effect of
compost application. EPA converted output data in grams of carbon per square meter to MTC02E by
multiplying by area in square meters and multiplying by the molecular weight ratio of C02 to carbon.
To express results in units comparable to those for other sources and sinks, EPA divided the
increase in carbon storage by the short tons of organics required to produce the compost.24 That is, the
factors are expressed as a carbon storage rate in units of MTC02E per wet short ton of organic inputs
(not MTC02E per short ton of compost).
As Exhibit 4-6 illustrates, EPA's Century analysis found that the carbon storage rate declines with
time after initial application. The rate is similar across application rates and frequencies, and across the
site conditions that were simulated. Exhibit 4-6 shows results for the Colorado and Iowa sites, for the
10-, 20-, and 40-ton per acre application rates. As indicated on the graph, the soil carbon storage rate
varies from about 0.08 MTCE (0.30 MTC02E) per wet ton yard trimmings immediately after compost
application in 1997 to about 0.02 MTCE (0.07 MTC02E) per ton in 2030, 24 years after the last
application in 2006.
24 EPA assumed 2.1 tons of yard trimmings are required to generate 1 ton of composted yard trimmings; thus, to
convert the results in WARM (in MTC02E per wet ton yard trimmings) to MTC02E per wet ton of compost, multiply
by 2.1. To convert to MTC02E per dry ton compost, multiply values in WARM by 4.2 (assuming 50 percent moisture
content).
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The similarity across the various site conditions and application rates reflects the fact that the
dominant process controlling carbon retention is the decomposition of organic materials in the various
pools. As simulated by Century, this process is governed by first-order kinetics, i.e., the rate is
independent of organic matter concentration or the rate of organic matter additions.
When viewed from the perspective of total carbon, rather than as a storage rate per ton of
inputs to the composting process, both soil organic carbon concentrations and total carbon stored per
acre increase with increasing application rates (see Exhibit 4-7). Soil organic carbon concentrations
increase throughout the period of compost application, peak in 2006 (the last year of application), and
decline thereafter as a result of decomposition of the imported carbon. Exhibit 4-7 shows total carbon
storage (including baseline carbon) in soils on the order of 40 to 65 metric tons per acre. (The range
would be higher with higher compost application rates or longer term applications.)
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Exhibit 4-7: Total Soil C; Iowa Site, Corn Harvested for Grain
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4.2.5.4 Century Simulation of Nitrogen Fertilization Effect
While the decomposition of organic materials is the primary process driving soil carbon
retention, EPA's Century analysis also revealed several secondary effects of compost application,
including the effects of compost application on nitrogen availability and moisture retention. EPA
performed additional Century simulations to quantify the nitrogen fertilization effect, or the hypothesis
that mineralization of nitrogen in compost could stimulate crop growth, leading to production of more
organic residues and increased soil organic carbon levels. The strength of this effect varies, depending
on the availability of other sources of nitrogen (N). To investigate this hypothesis, EPA analyzed different
rates of synthetic fertilizer addition ranging from zero up to a typical rate to attain average crop yield
(Colorado site: 90 lbs N/acre; Iowa site: 124 lbs N/per acre). EPA also evaluated fertilizer application at
half of these typical rates.
Exhibit 4-8 shows the carbon storage rate for the Iowa site and the effect of nitrogen
fertilization. The two curves in the exhibit represent the difference in carbon storage between a with-
compost scenario (20 tons per acre) and a baseline, where compost is not applied. The nitrogen
application rates differ in the following ways:
• The curve labeled "Typical N application" represents application of 124 lbs per acre for both the
compost and baseline scenarios. Because the nitrogen added through the compost has little
effect when nitrogen is already in abundant supply, this curve portrays a situation where the
carbon storage is attributable solely to the organic matter additions in the compost.
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• The curve labeled "Half N application" represents application of 62 lbs per acre. In this scenario,
mineralization of nitrogen added by the compost has an incremental effect on crop productivity
compared to the baseline. The difference between the baseline and compost application runs
reflects both organic matter added by the compost and additional biomass produced in
response to the nitrogen contributed by the compost.
The difference in incremental carbon storage rates between the two fertilization scenarios is
less than 0.01 MTCE (0.03 MTC02E) per ton, indicating that the nitrogen fertilization effect is relatively
small. Note that this finding is based on the assumption that farmers applying compost also will apply
sufficient synthetic fertilizer to maintain economic crop yields. The effect would be larger if this
assumption is not well-founded or in situations where compost is applied as a soil amendment for road
construction, landfill cover, or similar situations.
4.2.6 Humus Formation Carbon Storage
Significant evidence exists that compost contains stable compounds, such as humus, and that
the carbon stored in that humus should be considered passive when added to the soil because it breaks
down much more slowly than crop residues. As mentioned earlier, the Century model does not allow
carbon inputs to flow directly into the passive pools; therefore, EPA used a bounding analysis to
estimate the upper and lower limits of this humus formation mechanism of carbon storage. This
bounding analysis rested on two primary variables: (1) the fraction of carbon in compost that is
considered very stable and (2) the rate at which passive carbon is degraded to C02. Based on the expert
judgment of Dr. Michael Cole from the University of Illinois, EPA found that between four to 20 percent
of the carbon in compost degrades very quickly, and the remainder can be considered either slow or
passive. Dr. Cole found 400 years to be the average of the reported sequestration times of carbon in the
soil. The upper and lower bounds of the rate of carbon storage in soils resulting from the humus effect
are shown in Exhibit 4-9. EPA took an average value of the upper and lower bounds after 10 years to
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estimate the carbon storage per short ton of compost that was stored in the passive carbon pool after
year 10.
In WARM's final calculation, EPA weighed the carbon values from the two carbon storage
mechanisms according to the estimated percentage of compost that is passive (assumed to be 52
percent), and then used the total to estimate the sequestration value associated with composting, as
shown in Exhibit 4-9.
Exhibit 4-9: Carbon Storage Resulting from Humus Effect, Bounding Estimate
Time since compost application (years)
4.2.6.1 Eliminating the Possibility of Double-Counting
EPA adopted the approach of adding the humus formation effect to the direct carbon storage
effect to capture the range of carbon storage benefits associated with compost application; however,
this dual approach creates the possibility of double counting because the Century simulation may
include both the direct carbon storage and humus formation effects. In an effort to eliminate double
counting, EPA evaluated the way that Century partitions compost carbon after it is applied to the soil.
To do so, EPA ran a Century model simulation of compost addition during a single year and
compared the results to a corresponding reference case without compost. EPA calculated the difference
in carbon in each of the Century pools for the two simulations and found that the change in the passive
pool represented less than 0.01 percent of the change in total carbon; therefore, Century is not adding
recalcitrant carbon directly to the passive pool. Next, EPA graphed the change in the passive pool over
time to ensure that the recalcitrant compost carbon was not being cycled from the faster pools into the
passive pool several years after the compost is applied. As Exhibit 4-10 shows, Century does not
introduce significant increments over the base case of recalcitrant carbon into the passive pool at any
time.
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Exhibit 4-10: Difference in Carbon Storage Between Compost Addition and Base Case Yearly Application with 20
Tons Compost
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Year
Based on the analysis, it appears that Century is appropriately simulating carbon cycling and
storage for all but the passive carbon introduced by compost application. Because passive carbon
represents approximately 52 percent of carbon in compost (the midpoint of 45 percent and 60 percent),
EPA scaled the Century results by 48 percent to reflect the proportion of carbon that can be classified as
fast or slow (i.e., not passive).
4.2.6.2 WARM Carbon Storage Composting Results
Exhibit 4-11 shows the two carbon storage mechanisms included in WARM'S analysis of the
GHGs associated with composting. The resulting net storage value relies on three main input values: the
direct carbon storage, the carbon stored resulting from humus formation, and the percentage of carbon
in compost assumed to be passive, or resistant to degradation.
Exhibit 4-11: The Soil Carbon Restoration Effect and the Increased Humus Formation Effect for the Typical
Compost Application Rate of 20 Short Tons per Acre
Scenario
Soil Carbon Restoration
Increased
Humus
Formation
Net Carbon Flux
Unweighted
Proportion of C that Is
Not Passive (%)
Weighted
Estimate
Annual application of 20 short
tons of compost per acre
(0.04)
0.48
(0.07)
(0.17)
(0.24)
4.3 LIMITATIONS
Because of data and resource constraints, this chapter does not explore the full range of
conditions under which compost is managed and applied and how these conditions would affect the
results of this analysis. Instead, this study attempts to provide an analysis of GHG emissions and sinks
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associated with centralized composting of organics under a limited set of scenarios. The lack of primary
research on carbon storage associated with composting limited EPA's analysis. The limited availability of
data forced EPA to rely on two modeling approaches, each with its own set of limitations. In addition,
the analysis was limited by the scope of WARM, which is intended to present life-cycle GHG emissions of
waste management practices for selected material types, including food discards and yard trimmings.
4.3.1 Limitations of Modeling Approaches
Because of data and resource constraints, EPA was unable to use Century to evaluate the
variation in carbon storage impacts for a wide range of compost feedstocks (e.g., yard trimmings mixed
with food discards, food discards alone). As noted earlier, resource constraints limited the number of
soil types, climates, and compost applications simulated. The Century results also incorporate the
limitations of the model itself, which have been well documented elsewhere. Perhaps most important,
the model's predictions of soil organic matter levels are driven by four variables: annual precipitation,
temperature, soil texture, and plant lignin content. Beyond these, the model is limited by its sensitivity
to several factors for which data are difficult or impossible to obtain (e.g., pre-settlement grazing
intensity, nitrogen input during soil development) (Parton et al., 1987). The model's monthly simulation
intervals limit its ability to fully address potential interactions between nitrogen supply, plant growth,
soil moisture, and decomposition rates, which may be sensitive to conditions that vary on a shorter time
scale (Paustian et al., 1992). In addition, the model is not designed to capture the hypothesis that,
because of the compost application, soil ecosystem dynamics change and more carbon is stored than is
added to the soil (i.e., the multiplier effect).
Century simulates carbon movement through organic matter pools. Although the model is
designed to evaluate additions of organic matter in general, EPA does not believe that it has been
applied in the past to evaluate the application of organics compost. Century is parameterized to
partition carbon to the various pools based on ratios of lignin to nitrogen and lignin to total carbon, not
on the amount of organic material that has been converted to humus already. EPA addressed this
limitation by developing an add-on analysis to evaluate humus formation in the passive pool, scaling the
Century results, and summing the soil carbon storage values. There is some potential for double
counting, to the extent that Century is routing some carbon to various pools that is also accounted for in
the incremental humus analysis. EPA believes that this effect is likely to be minor.
The bounding analysis used to analyze increased humus formation is limited by the lack of data
specifically dealing with composts composed of yard trimmings or food discards. This analysis is also
limited by the lack of data on carbon in compost that is passive. The approach of taking the average
value from the two scenarios is simplistic, but it appears to be the best available option.
4.3.2 Limitations Related to the Scope of the Emission Factors
As indicated earlier, this chapter describes EPA's estimates of the GHG-related impacts of
composting organics. EPA developed these estimates within the framework of the larger WARM
development effort; therefore, the presentation of results, estimation of emissions and sinks, and
description of ancillary benefits is not comprehensive. The remainder of this section describes specific
limitations of the compost analysis.
As noted in the other documentation chapters, the GHG impacts of composting reported in this
chapter are calculated using a methodology that facilitates comparison between composting and other
possible disposal options for yard trimmings (i.e., landfilling and combustion). To present absolute GHG
emission factors for composted yard trimmings that could be used to compare composting to a baseline
of leaving yard trimmings on the ground where they fall, EPA would need to analyze the home soil. In
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particular, the carbon storage benefits of composting would need to be compared to the impact of
removal of yard trimmings on the home soil.
As mentioned in Section 4.2, the lack of data and resources constrained EPA's analysis and,
therefore, the analysis considers a small sampling of feedstocks and a specific application scenario (i.e.,
degraded agricultural soil). EPA analyzed two types of compost feedstocks—yard trimmings and food
discards—although sewage sludge, animal manure, and several other compost feedstocks also may have
significant GHG implications. Similarly, it was assumed that compost was applied to home gardens,
landscapers, nurseries, and bulk agriculture, despite widespread use of compost in land reclamation,
silviculture, and horticulture,.
This analysis did not consider the full range of soil conservation and management practices that
could be used in combination with compost and the impacts of those practices on carbon storage. Some
research indicates that adding compost to agricultural soils in conjunction with various conservation
practices enhances the generation of soil organic matter to a much greater degree than applying
compost alone. Examples of these conservation practices include conservation tillage, no tillage, residue
management, crop rotation, wintering, and summer fallow elimination. Research also suggests that
allowing crop residues to remain on the soil rather than turning them over helps to protect and sustain
the soil while simultaneously enriching it. Alternatively, conventional tillage techniques accelerate soil
erosion, increase soil aeration, and hence lead to greater GHG emissions (Lai et al., 1998). Compost use
also has been shown to increase soil water retention; moister soil gives a number of ancillary benefits,
including reduced irrigation costs and reduced energy used for pumping water. Compost can also play
an important role in the adaptation strategies that will be necessary as climate zones shift and some
areas become more arid. Additionally, EPA includes a fertilizer offset from the application of compost to
soil (Oregon DEQ 2014). However, this study only examines the offset using food waste as the feedstock
for compost. EPA will continue to research studies that examine the fertilizer offset using yard waste as
feedstock for compost.
As is the case in other chapters, the methodology EPA used to estimate GHG emissions from
composting did not allow for variations in transportation distances. EPA recognizes that the density of
landfills versus composting sites in any given area would have an effect on the extent of transportation
emissions derived from composting. For example, in states that have a higher density of composting
sites, the hauling distance to such a site would be smaller and thus require less fuel than transportation
to a landfill. Alternatively, transporting compost from urban areas, where compost feedstocks may be
collected, to farmlands, where compost is typically applied, could require more fuel because of the large
distance separating the sites.
In addition to the carbon storage benefits of adding compost to agricultural soils, composting
can lead to improved soil quality, improved productivity, and cost savings. For example, nutrients in
compost tend to foster soil fertility (Brady and Weil, 1999). In fact, composts have been used to
establish plant growth on land previously unable to support vegetation.
4.3.3 Ongoing Research to Improve Composting Estimates
EPA is researching several aspects of the composting analysis to improve existing assumptions
based on updated research that is emerging. EPA's literature review focused on the following key topics:
potential end uses and markets for compost, the shares of compost currently used in different
applications in the United States, humus formation, the carbon storage timeframe, the multiplier effect,
and other environmental benefits of composting.
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Research on the potential end uses and markets for compost suggested that the
horticultural/landscaping markets appear to be the most popular markets for compost in the United
States. While data quantifying the size of these markets are limited, this finding suggests that the
assumptions underlying the current WARM modeling may need to be re-examined. Further research
into this subject may be warranted to determine exactly how compost is used in these urban or higher-
end markets.
During EPA's research on carbon storage mechanisms, the agency uncovered new field research
that may provide a basis for using primary data to quantify the carbon storage emission factor. If EPA
decides to calculate a new carbon sequestration value based on field data, both the Century and
bounding analyses will be superseded by this approach. EPA has also conducted extensive research into
potential GHG emissions from composting. Preliminary research indicates that small amounts of both
CH4 and N20 emissions are released during composting, even in well-managed piles.
Addressing the possible GHG emission reductions and other environmental benefits achievable
by applying compost instead of chemical fertilizers, fungicides, and pesticides was beyond the scope of
this documentation. Manufacturing those agricultural products requires energy. To the extent that
compost may replace or reduce the need for these substances, composting may result in reduced
energy-related GHG emissions. Although EPA understands that generally compost is applied for its soil
amendment properties rather than for pest control, compost has been effective in reducing the need for
harmful or toxic pesticides and fungicides.25 Analyses of these benefits, however, are highly sensitive to
assumptions about composting and fertilizer application rates, and information on the typical
applications of these two soil additions is lacking.
4.4 REFERENCES
Barlaz, M.A. (1997). Biodegradative Analysis of Municipal Solid Waste in Laboratory-Scale Landfills.
600/R-97-071. U.S. Environmental Protection Agency. Washington, DC.
Beck-Friis, B., Pell, M., Sonesson, U., Jonsson, H., and Kirchmann, H. (2000). Formation and Emission of
N20 and CH4 from Compost Heaps of Organic Household Waste. Environmental Monitoring and
Assessment. 62: 317-331.
Bernal, M., Sanchez-Mondero, M., Paredes, C., and Roig, A. (1998). Carbon mineralization from organic
wastes at different composting stages during their incubation with soil. Agriculture, Ecosystems
& Environment. 69 (3): 175-189.
Brady, N., & Weil, R. (1999). The Nature and Properties of Soils. Upper Saddle River, NJ: Prentice Hall.
Brown, S. & Subler, S. (2007). Composting and Greenhouse Gas Emissions: A Producer's Perspective.
BioCycle. 48(3): 37-41.
Coker, C. (2006). Environmental Remediation by Composting. BioCycle 47(12):18. Retrieved from
http://www.biocycle.net/2006/12/14/environmental-remediation-bv-composting/
Cornell Waste Management Institute (CWMI). (1998). Master Composter Resource Manual.
http://cwmi.css.cornell.edu/mastercompostermanual.pdf
Davidson, E.A., & Janssens, E.A. (2006). Temperature sensitivity of soil carbon decomposition and
feedbacks to climate change. Nature. 440: 165-173.
25 For example, the use of compost may reduce or eliminate the need for soil fumigation with methyl bromide (an
ozone-depleting substance) to kill plant pests and pathogens.
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December 2023
EPA. (2018). Advancing Sustainable Materials Management: 2015 Fact Sheet. (EPA530-F-18-004).
Washington, DC: U.S. Government Printing Office. Retrieved from
https://www.epa.gov/sites/production/files/2018-
07/documents/2015 smm msw factsheet 07242018 fnl 508 002.pdf.
EPA. (2015). Advancing Sustainable Materials Management: Facts and Figures 2013. (EPA530-R-15-002).
Washington, DC: U.S. Government Printing Office. Retrieved from
http://www.cta.tech/CorporateSite/media/environment/eCvcle/2013 advncng smm rpt.pdf.
EPA. (1994). Composting Yard Trimmings and Municipal Solid Waste (EPA document number EPA530-R-
94-003). Washington, DC: U.S. Environmental Protection Agency, Office of Solid Waste and
Emergency Response.
Francou, C., Lineres, M., Derenne, S., Willio-Poitrenaud, M., and Houot, S. (2008). Influence of green
waste, biowaste and paper-cardboard initial ratios on organic matter transformations during
composting. Bioresource Technology, 99(18): 8926-8934.
FAL. (1994). The Role of Recycling in Integrated Solid Waste Management for the Year 2000. Franklin
Associates, Ltd. (Stamford, CT: Keep America Beautiful, Inc.), September, pp. 1-27, 30, and 31.
Lai, R., et al. (1998). The Potential of U.S. Cropland to Sequester Carbon and Mitigate the Greenhouse
Effect (Ann Arbor, Ml: Sleeping Bear Press, Inc).
Lou, W.F., and J. Nair. 2009. The impact of landfilling and composting on greenhouse gas emissions - A
review. Bioresource Technology. 100 (16): 3792-3798.
Meersmans, J., van Wesemael, B., de Ridder, F., Fallas Dotti, M., de Baets, S., and van Molle, M. (2009).
Changes in organic carbon distribution with depth in agricultural soils in northern Belgium,
1960-2006. Global Change Biology: 1-12.
Metherell, A., Harding, L., Cole, C., and Parton, W. (1993). Century Agroecosystem Version 4.0, Great
Plains System Research Unit Technical Report No. 4, USDA-ARS Global Climate Change Research
Program (Colorado State University: Fort Collins, CO).
National Renewable Energy Laboratory (2015). "U.S. Life Cycle Inventory Database." Retrieved from
https://www.lcacommons.gov/nrel/search
Oregon Department of Environmental Quality. (2014). Evaluation of Climate, Energy, and Soils Impacts
of Selected Food Discards Management Systems (J. Morris Sound Resource Management Group,
Inc., S. Brown University of Washington, H. S. Matthews Avenue C Advisors, LLC, & M. Cotton
Integrated Waste Management Consulting, LLC, Authors). Available online at:
https://www.oregon.gov/deq/FilterDocs/FoodWasteStudyReport.pdf
Parton, W., Schimel, D., Cole, C., and Ojima, D. (1987). Analysis of Factors Controlling Soil Organic Matter
Levels in Great Plains Grasslands. Soil Sci. Soc. Am. J., 51:1173-1179.
Paustian, K., Parton, W., and Persson, J. (1992). Modeling Soil Organic Matter in Organic-Amended and
Nitrogen-Fertilized Long-Term Plots. Soil Sci. Soc. Am. J., 56:476-488.
Williams et al. (2019). Williams, S. R., Zhu-Barker, X., Lew, S., Croze, B. J., Fallan, K. R., & Horwath, W. R.
(2019). Impact of composting food waste with green waste on greenhouse gas emissions from
compost windrows. Compost Science & Utilization, 27(1), 35-45.
Zanker Road Resource Management, Ltd. Undated. Z-Best Compost Facility.
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5 COMBUSTION
This document presents an overview of combustion as a waste management strategy in relation
to the development of material-specific emission factors for EPA's Waste Reduction Model (WARM).
Included are estimates of the net greenhouse gas (GHG) emissions from combustion of most of the
materials considered in WARM and several categories of mixed waste.
5.1 A SUMMARY OF THE GHG IMPLICATIONS OF COMBUSTION
Combustion of municipal solid waste (MSW) results in emissions of C02 and N20. Note that C02
from combustion of biomass (such as paper products and yard trimmings) is not counted because it is
biogenic (as explained in the WARM Background and Overview chapter). WARM estimates emissions
from combustion of MSW in waste-to-energy (WTE) facilities. WARM does not consider any recovery of
materials from the MSW stream that may occur before MSW is delivered to the combustor.
In the United States, about 80 WTE facilities process more than 30 million tons of MSW annually
(ERC, 2014). WTE facilities can be divided into three categories: (1) mass burn, (2) modular, and (3)
refuse-derived fuel (RDF). A mass burn facility generates electricity and/or steam from the combustion
of mixed MSW. Most of the facilities (76 percent) employ mass burn technology. Modular WTE plants
are generally smaller than mass burn plants, and are prefabricated off-site so that they can be
assembled quickly where they are needed. Because of their similarity to mass burn facilities, modular
facilities are treated as part of the mass burn category for the purposes of this analysis.
An RDF facility combusts MSW that has undergone varying degrees of processing, from simple
removal of bulky and noncombustible items to more complex processes (such as shredding and material
recovery) that result in a finely divided fuel. Processing MSW into RDF yields a more uniform fuel that
has a higher heating value than that used by mass burn or modular WTE. MSW processing into RDF
involves both manual and mechanical separation to remove materials such as glass and metals that have
little or no fuel value. In the United States, approximately 14 facilities combust RDF (ERC, 2010).
This study analyzed the net GHG emissions from combustion of all individual and mixed waste
streams in WARM at mass burn and RDF facilities, with the exception of asphalt concrete, drywall, and
fiberglass insulation. These three materials were excluded because EPA determined that they are not
typically combusted at end of life. Note that WARM incorporates only the emission factors for mass
burn facilities, due to (1) the relatively small number of RDF facilities in the United States and (2) the
fact that the RDF emission factors are based on data from only one RDF facility.
Net emissions consist of (1) emissions from the transportation of waste to a combustion facility,
(2) emissions of non-biogenic C02, and (3) emissions of N20 minus (4) avoided GHG emissions from the
electric utility sector and (5) avoided GHG emissions due to the recovery and recycling of ferrous metals
at the combustor. There is some evidence that as combustor ash ages, it absorbs C02 from the
atmosphere. However, EPA did not count absorbed C02 because the quantity is estimated to be less
than 0.02 MTC02E per ton of MSW combusted.26 The results of this analysis for the materials contained
in WARM and the explanations for each of these results are discussed in section 5.3.27
26 Based on data provided by Dr. Jiirgen Vehlow of the Institut fur Technische Chemie in Karlsruhe, Germany, EPA
estimated that the ash from one ton of MSW would absorb roughly 0.004 MTCE of C02.
27 Note that Exhibit 5-1, Exhibit 5-2, and Exhibit 5-6 do not show mixed paper. Mixed paper is shown in the
summary exhibit. The summary values for mixed paper are based on the proportions of the four paper types
(newspaper, office paper, corrugated containers, and magazines/third-class mail) that make up the different
"mixed paper" definitions.
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5.2 CALCULATING THE GHG IMPACTS OF COMBUSTION
This study's general approach was to estimate (1) the gross emissions of C02 and N20 from
MSW combustion (including emissions from transportation of waste to the combustor and ash from the
combustor to a landfill) and (2) the C02 emissions avoided because of displaced electric utility
generation and decreased energy requirements for production processes using recycled inputs. A
comprehensive evaluation would also consider the fate of carbon remaining in combustor ash.
Depending on its chemical form, carbon may be aerobically degraded to C02, anaerobically degraded to
CH4, or remain in a relatively inert form and be stored. Unless the ash carbon is converted to CH4 (which
EPA considers unlikely), the effect on the net GHG emissions will be very small. To obtain an estimate of
the net GHG emissions from MSW combustion, the GHG emissions avoided were subtracted from the
direct GHG emissions. EPA estimated the net GHG emissions from waste combustion per ton of mixed
MSW and per ton of each selected material in MSW. The remainder of this section describes how EPA
developed these estimates.
5.2.1 Emissions of C02 from WTE Facilities
The carbon in MSW has two distinct origins: some of it is derived from sustainably harvested
biomass (i.e., carbon in plant matter that was converted from C02 in the atmosphere through
photosynthesis), and the remainder is from non-biomass sources, e.g., plastic and synthetic rubber
derived from petroleum.
As explained in the WARM Background and Overview chapter, WARM considers only C02 that
derives from fossil sources and does not consider biogenic C02 emissions. Therefore, only C02 emissions
from the combustion of non-biomass components of MSW—plastic, textiles and rubber—were counted.
These components make up a relatively small share of total MSW, so only a small portion of the total
C02 emissions from combustion are considered in WARM.
To estimate the non-biogenic carbon content of the plastics, textiles, rubber and leather
contained in one ton of mixed MSW, EPA first established assumptions for the non-biogenic share of
carbon in these materials. For plastics in products in MSW, EPA assumed that all carbon is non-biogenic
carbon, because biogenic plastics likely make up a small but unknown portion of products. For rubber
and leather products in MSW, EPA assumed that the non-biogenic share of carbon contained in clothing
and footwear is 25 percent; this assumption is based on expert judgment. The non-biogenic share of
carbon in containers, packaging, and other durables is 100 percent; and the non-biogenic share of
carbon in other nondurables is 75 percent (EPA, 2010). For textile products in MSW, EPA assumed that
the non-biogenic share of carbon is 55 percent (DeZan, 2000). EPA then calculated the non-biogenic
carbon content of each of these material groups. For plastics in products in MSW, EPA used the
molecular formula of each resin type to assume that PET is 63 percent carbon; PVC is 38 percent carbon;
polystyrene is 92 percent carbon; HDPE, LDPE, and polypropylene are 86 percent carbon; and a
weighted average of all other resins is 66 percent carbon (by weight). Based on the amount of each
plastic discarded in 2015 (EPA, 2018), EPA calculated a weighted carbon content of 78 percent for
plastics in mixed MSW. For rubber and leather products, EPA used the weighted average carbon content
of rubbers consumed in 2002 to estimate a carbon content of 85 percent (by weight) for rubber and
leather products in mixed MSW. For textiles, EPA used the average carbon content of the four main
synthetic fiber types to estimate a carbon content of 70 percent (by weight) for textiles in mixed MSW.
Next, using data from BioCycle's The State of Garbage in America (Van Haaren et al., 2010), EPA
assumed that seven percent of discards are combusted in the United States. Data from BioCycle is used
instead of EPA's Advancing Sustainable Materials Management: Facts and Figures report (EPA, 2018a),
because it is based off of direct reporting, and provides a more accurate representation of the amount
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of materials discarded at WTE facilities. Additionally, these data are also used in order to maintain
consistency with the data source used in EPA's annual Inventory of U.S. Greenhouse Gas Emissions and
Sinks report. Based on these assumptions, EPA estimated that there are 0.10 tons of non-biogenic
carbon in the plastic, textiles, rubber and leather contained in one ton of mixed MSW (EPA, 2018a; Van
Haaren et al., 2010).
The 10 percent non-biomass carbon content of mixed MSW was then converted to units of
MTC02E per short ton of mixed MSW combusted. The resulting value for mixed MSW is shown in Exhibit
5-1. Note that if EPA had used a best-case assumption for textiles (i.e., assuming that they have no
petrochemical-based fibers), the resulting value for mixed MSW would have been slightly lower. The
values for C02 emissions are shown in column (b) of Exhibit 5-1.
Exhibit 5-1: Gross GHG Emissions from MSW Combustion (MTC02E/Short Ton of Material Combusted)
(a)
(b)
(c)
(d)
(e)
Combustion C02
Combustion N20
Transportation
Gross GHG
Emissions from Non-
Emissions per
C02 Emissions
Emissions per Short
Biomass per Short
Short Ton
per Short Ton
Ton Combusted
Material
Ton Combusted
Combusted
Combusted
(e = b + c + d)
Aluminum Cans
-
-
0.01
0.01
Aluminum Ingot
-
-
0.01
0.01
Steel Cans
-
-
0.01
0.01
Copper Wire
-
-
0.01
0.01
Glass
-
-
0.01
0.01
HDPE
2.79
-
0.01
2.80
LDPE
2.79
-
0.01
2.80
PET
2.04
-
0.01
2.05
LLDPE
2.79
-
0.01
2.80
PP
2.79
-
0.01
2.80
PS
3.01
-
0.01
3.02
PVC
1.25
-
0.01
1.26
PLA
-
-
0.01
0.01
Corrugated Containers
-
0.04
0.01
0.05
Magazines/Third-Class Mail
-
0.04
0.01
0.05
Newspaper
-
0.04
0.01
0.05
Office Paper
-
0.04
0.01
0.05
Phone Books3
-
0.04
0.01
0.05
Textbooks3
-
0.04
0.01
0.05
Dimensional Lumber
-
0.04
0.01
0.05
Medium-Density Fiberboard
-
0.04
0.01
0.05
Food Waste
-
0.04
0.01
0.05
Food Waste (meat only)
-
0.04
0.01
0.05
Food Waste (non-meat)
-
0.04
0.01
0.05
Beef
-
0.04
0.01
0.05
Poultry
-
0.04
0.01
0.05
Grains
-
0.04
0.01
0.05
Bread
-
0.04
0.01
0.05
Fruits and Vegetables
-
0.04
0.01
0.05
Dairy Products
-
0.04
0.01
0.05
Yard Trimmings
-
0.04
0.01
0.05
Grass
-
0.04
0.01
0.05
Leaves
-
0.04
0.01
0.05
Branches
-
0.04
0.01
0.05
Mixed Paper (general)
-
0.04
0.01
0.05
Mixed Paper (primarily residential)
-
0.04
0.01
0.05
Mixed Paper (primarily from offices)
-
0.04
0.01
0.05
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(a)
(b)
(c)
(d)
(e)
Combustion C02
Combustion N20
Transportation
Gross GHG
Emissions from Non-
Emissions per
C02 Emissions
Emissions per Short
Biomass per Short
Short Ton
per Short Ton
Ton Combusted
Material
Ton Combusted
Combusted
Combusted
(e = b + c + d)
Mixed Metals
-
-
0.01
0.01
Mixed Plastics
2.33
-
0.01
2.34
Mixed Recyclables
0.07
0.03
0.01
0.11
Mixed Organics
-
0.04
0.01
0.05
Mixed MSW
0.38
0.04
0.01
0.43
Carpet
1.67
-
0.01
1.68
Desktop CPUs
0.40
-
0.01
0.40
Portable Electronic Devices
0.88
-
0.01
0.89
Flat-panel Displays
0.73
-
0.01
0.74
CRT Displays
0.63
-
0.01
0.64
Electronic Peripheral
2.22
-
0.01
2.23
Hard-copy Devices
1.91
-
0.01
1.92
Mixed Electronics
0.95
-
0.01
0.96
Clay Bricks
NA
NA
NA
NA
Concrete
NA
NA
NA
NA
Fly Ash
NA
NA
NA
NA
Tires
2.20
-
0.01
2.21
Asphalt Concrete
NA
NA
NA
NA
Asphalt Shingles
0.65
0.04
0.01
0.70
Drywall
NA
NA
NA
NA
Fiberglass Insulation
NA
NA
NA
NA
Vinyl Flooring
0.28
-
0.01
0.29
Wood Flooring
-
0.04
0.05
0.08
- = Zero emissions.
Note that totals may not add due to rounding, and more digits may be displayed than are significant.
aThe values for phone books and textbooks are proxies, based on newspaper and office paper, respectively.
5.2.2 Emissions of N20 from WTE Facilities
Studies compiled by the Intergovernmental Panel on Climate Change (IPCC) show that MSW
combustion results in measurable emissions of N20, a GHG with a global warming potential (GWP) 298
times that of C02 (EPA, 2018a; IPCC, 2007; IPCC, 2006). The IPCC compiled reported ranges of N20
emissions, per metric ton of waste combusted, from six classifications of MSW combustors. This study
averaged the midpoints of each range and converted the units to MTC02E of N20 per ton of MSW. The
resulting estimate is 0.04 MTC02E of N20 emissions per ton of mixed MSW combusted. Because the
IPCC did not report N20 values for combustion of individual components of MSW, EPA used the 0.04
value not only for mixed MSW, but also as a proxy for all components of MSW, except for aluminum
cans, steel cans, glass, HDPE, LDPE, and PET. This exception was made because at the relatively low
combustion temperatures found in MSW combustors, most of the nitrogen in N20 emissions is derived
from the waste, not from the combustion air. Because aluminum and steel cans, glass, and plastics do
not contain nitrogen, EPA concluded that running these materials through an MSW combustor would
not result in N20 emissions.
5.2.3 Emissions of C02 from Transportation of Waste and Ash
WARM includes emissions associated with transporting of waste and the subsequent
transportation of the residual waste ash to the landfill. Transportation energy emissions occur when
fossil fuels are combusted to collect and transport material to the combustion facility and then to
operate on-site equipment. Transportation of any individual material in MSW is assumed to use the
5-4
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Combustion
December 2023
same amount of energy as transportation of mixed MSW. To calculate the emissions, WARM relies on
assumptions from FAL (1994) for the equipment emissions and NREL's US Life Cycle Inventory Database
(USLCI) (NREL, 2015). The NREL emission factor assumes a diesel, short-haul truck.
5.2.4 Estimating Utility C02 Emissions Avoided
Most WTE plants in the United States produce electricity. Only a few cogenerate electricity and
steam. In this analysis, EPA assumed that the energy recovered with MSW combustion would be in the
form of electricity, with the exception of two materials that are not assumed to be combusted at WTE
plants. For tires, the avoided utility C02 emissions per ton of tires combusted is based on the weighted
average of three tire combustion pathways: combustion at cement kilns, power plants, and pulp and
paper mills. For asphalt shingles, the avoided utility C02 emissions per ton of shingles combusted is
equal to the amount of avoided refinery gas combusted at cement kilns where asphalt shingles are
combusted. The avoided utility C02 emissions analysis is shown in Exhibit 5-2. EPA used three data
elements to estimate the avoided electric utility C02 emissions associated with combustion of waste in a
WTE plant: (1) the energy content of mixed MSW and of each separate waste material considered, (2)
the combustion system efficiency in converting energy in MSW to delivered electricity, and (3) the
electric utility C02 emissions avoided per kilowatt-hour (kWh) of electricity delivered by WTE plants.
Exhibit 5-2: Avoided Utility GHG Emissions from Combustion at WTE Facilities
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Emission
Factor for
Avoided Utility
Utility-
GHG Emissions
Generated
per Ton
Avoided Utility
RDF
Electricity3
Combusted at
C02 per Ton
Energy
Mass Burn
Combus-
(MTCOzE/
Mass Burn
Combusted at
Content
Combustion
tion System
Million Btu of
Facilities3
RDF Facilities
Material
(Million Btu
System
Efficiency
Electricity
(MTCOzE)
(MTCOzE)
Combusted
Per Ton)
Efficiency (%)
(%)
Delivered)
(f = b x c x e)
(g = b x d x e)
Aluminum Cans
(0.67)b
17.8%
16.3%
0.21
(0.03)
(0.02)
Aluminum Ingot
(0.67)
17.8%
16.3%
0.21
(0.03)
(0.02)
Steel Cans
(0.42)b
17.8%
16.3%
0.21
(0.02)
(0.01)
Copper Wire
(0.55)c
17.8%
16.3%
0.21
(0.02)
(0.02)
Glass
(0.47)b
17.8%
16.3%
0.21
(0.02)
(0.02)
HDPE
39.97d
17.8%
16.3%
0.21
1.52
1.38
LDPE
39.75d
17.8%
16.3%
0.21
1.51
1.38
PET
21.20
17.8%
16.3%
0.21
0.80
0.73
LLDPE
39.89
17.8%
16.3%
0.21
1.51
1.38
PP
39.90
17.8%
16.3%
0.21
1.51
1.38
PS
36.00
17.8%
16.3%
0.21
1.37
1.25
PVC
15.75
17.8%
16.3%
0.21
0.60
0.55
PLA
16.74
17.8%
16.3%
0.21
0.64
0.58
Corrugated
Containers
14.09d
17.8%
16.3%
0.21
0.53
0.49
Magazines/Third-
Class Mail
10.52d
17.8%
16.3%
0.21
0.40
0.36
Newspaper
15.90d
17.8%
16.3%
0.21
0.60
0.55
Office Paper
13.60d
17.8%
16.3%
0.21
0.52
0.47
Phone Books
15.90d
17.8%
16.3%
0.21
0.60
0.55
Textbooks
13.60d
17.8%
16.3%
0.21
0.52
0.47
Dimensional
Lumber
16.60f
17.8%
16.3%
0.21
0.63
0.58
Medium-Density
Fiberboard
16.60f
17.8%
16.3%
0.21
0.63
0.58
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Combustion
December 2023
(a)
(b)
(c)
(d)
(e)
Emission
Factor for
Utility-
Generated
(f)
Avoided Utility
GHG Emissions
per Ton
(g)
Avoided Utility
RDF
Electricity3
Combusted at
C02 per Ton
Energy
Mass Burn
Combus-
(MTCOzE/
Mass Burn
Combusted at
Content
Combustion
tion System
Million Btu of
Facilities3
RDF Facilities
Material
(Million Btu
System
Efficiency
Electricity
(MTCOzE)
(MTCOzE)
Combusted
Per Ton)
Efficiency (%)
(%)
Delivered)
(f = b x c x e)
(g = b x d x e)
Food Waste
4.74d
17.8%
16.3%
0.21
0.18
0.16
Food Waste (meat
only)
4.74d
17.8%
16.3%
0.21
0.18
0.16
Food Waste (non-
meat)
4.74d
17.8%
16.3%
0.21
0.18
0.16
Beef
4.74d
17.8%
16.3%
0.21
0.18
0.16
Poultry
4.74d
17.8%
16.3%
0.21
0.18
0.16
Grains
4.74d
17.8%
16.3%
0.21
0.18
0.16
Bread
4.74d
17.8%
16.3%
0.21
0.18
0.16
Fruits and
Vegetables
4.74d
17.8%
16.3%
0.21
0.18
0.16
Dairy Products
4.74d
17.8%
16.3%
0.21
0.18
0.16
Yard Trimmings
5.60s
17.8%
16.3%
0.21
0.21
0.19
Grass
5.60s
17.8%
16.3%
0.21
0.21
0.19
Leaves
5.60s
17.8%
16.3%
0.21
0.21
0.19
Branches
5.60s
17.8%
16.3%
0.21
0.21
0.19
Mixed Paper
(general)
NA
17.8%
16.3%
0.21
0.54
NA
Mixed Paper
(primarily
residential)
NA
17.8%
16.3%
0.21
0.53
NA
Mixed Paper
(primarily from
offices)
NA
17.8%
16.3%
0.21
0.49
NA
Mixed Metals
NA
17.8%
16.3%
0.21
-0.02
NA
Mixed Plastics
NA
17.8%
16.3%
0.21
1.09
NA
Mixed Recyclables
NA
17.8%
16.3%
0.21
0.50
NA
Mixed Organics
NA
17.8%
16.3%
0.21
0.20
NA
Mixed MSW
10.00h
17.8%
16.3%
0.21
0.38
0.35
Carpet
is^o1
17.8%
16.3%
0.21
0.58
0.53
Desktop CPUs
3.07
17.8%
16.3%
0.21
0.12
0.11
Portable Electronic
3.07
Devices
17.8%
16.3%
0.21
0.12
0.11
Flat-panel Displays
3.07
17.8%
16.3%
0.21
0.12
0.11
CRT Displays
3.07
17.8%
16.3%
0.21
0.12
0.11
Electronic
3.07
Peripherals
17.8%
16.3%
0.21
0.12
0.11
Hard-copy Devices
3.07
17.8%
16.3%
0.21
0.12
0.11
Mixed Electronics
3.07
17.8%
16.3%
0.21
0.12
0.11
Clay Bricks
NA
NA
NA
NA
NA
NA
Concrete
NA
NA
NA
NA
NA
NA
Fly Ash
NA
NA
NA
NA
NA
NA
Tires
27.78>
NA
NA
NA
1.57
1.57
Asphalt Concrete
NA
NA
NA
NA
NA
NA
Asphalt Shingles
8.80
NAk
NAk
NAk
1.051
1.051
Drywall
NA
NA
NA
NA
NA
NA
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(a)
(b)
(c)
(d)
(e)
(f)
(g)
Emission
Factor for
Avoided Utility
Utility-
GHG Emissions
Generated
per Ton
Avoided Utility
RDF
Electricity3
Combusted at
C02 per Ton
Energy
Mass Burn
Combus-
(MTCOzE/
Mass Burn
Combusted at
Content
Combustion
tion System
Million Btu of
Facilities3
RDF Facilities
Material
(Million Btu
System
Efficiency
Electricity
(MTCOzE)
(MTCOzE)
Combusted
Per Ton)
Efficiency (%)
(%)
Delivered)
(f = b x c x e)
(g = b x d x e)
Fiberglass
Insulation
NA
NA
NA
NA
NA
NA
Vinyl Flooring
15.75
17.8%
16.3%
0.21
0.60
0.55
Wood Flooring
17.99m
21.5%"
16.3%
0.21
0.82
0.62
NA = Not applicable.
Note that totals may not add due to rounding, and more digits may be displayed than are significant.
a The values in this column are based on national average emissions from utility-generated electricity. The Excel version of WARM also allows
users to choose region-specific utility-generated factors, which are contained in Exhibit 5-4.
b EPA developed these estimates based on data on the specific heat of aluminum, steel, and glass and calculated the energy required to raise
the temperature of aluminum, steel, and glass from ambient temperature to the temperature found in a combustor (about 750° Celsius), based
on Incropera and DeWitt (1990).
c Average of aluminum and steel.
d Source: EPA (1995). "Magazines" used as proxy for magazines/third-class mail; "mixed paper" used as a proxy for the value for office paper
and textbooks; "newspapers" used as a proxy for phone books.
e Source: Gaines and Stodolsky (1993).
f EPA used the higher end of the MMBtu factor for basswood from the USDA-FS. Basswood is a relatively soft wood, so its high-end MMBtu
content should be similar to an average factor for all wood types (Fons et al., 1962).
8 Proctor and Redfern, Ltd. and ORTECH International (1993).
h Source: IWSA and American Ref-Fuel (personal communication, October 28, 1997). Mixed MSW represents the entire waste stream as
disposed of.
'Source: Realff, M. (2010).
j Tires used as tire-derived fuel substitute for coal in cement kilns and electric utilities; used as a substitute for natural gas in pulp and paper
facilities. Therefore, columns (d) through (h) are a weighted average of multiple tire combustion pathways, and are not calculated in the same
manner as the other materials and products in the table.
kThe avoided utility GHG emissions are assumed to equal avoided cement kiln refinery gas combustion, so this factor is not used.
'Assumes avoided cement kiln refinery gas combustion.
m Bergman and Bowe (2008), Table 3, p. 454. Note that this is in agreement with values already in WARM for lumber and medium-density
fiberboard.
n Based on average heat rate of U.S. dedicated biomass electricity plants.
5.2.4.1 Energy Content
The energy content of each of the combustible materials in WARM is contained in column (b) of
Exhibit 5-2. For the energy content of mixed MSW, EPA used a value of 10.0 million Btu (MMBtu) per
short ton of mixed MSW combusted, which is a value commonly used in the WTE industry (IWSA and
American Ref-Fuel, 1997). This estimate is within the range of values (9.0 to 13.0 MMBtu per ton)
reported by FAL (1994) and is slightly higher than the 9.6 MMBtu per ton value reported in EPA's MSW
Fact Book (EPA, 1995). For the energy content of RDF, a value of 11.4 MMBtu per ton of RDF combusted
was used (Harrington, 1997). This estimate is within the range of values (9.6 to 12.8 MMBtu per ton)
reported by the DOE's National Renewable Energy Laboratory (NREL, 1992). For the energy content of
specific materials in MSW, EPA consulted three sources: (1) EPA's MSW Fact Book (1995), a compilation
of data from primary sources, (2) a report by Environment Canada (Procter and Redfern, Ltd. and
ORTECH International, 1993), and (3) a report by Argonne National Laboratories (Gaines and Stodolsky,
1993). EPA assumed that the energy contents reported in the first two of these sources were for
materials with moisture contents typically found for the materials in MSW (the sources imply this but do
not explicitly state it). The Argonne study reports energy content on a dry weight basis.
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5.2.4.2 Combustion System Efficiency
To estimate the combustion system efficiency of mass burn plants, EPA used a net value of 550
kWh generated by mass burn plants per ton of mixed MSW combusted (Zannes, 1997).
To estimate the combustion system efficiency of RDF plants, EPA evaluated three sources: (1)
data supplied by an RDF processing facility located in Newport, MN (Harrington, 1997); (2) the
Integrated Waste Services Association report, The 2000 Waste-to-Energy Directory: Year 2000 (IWSA,
2000); and (3) the National Renewable Energy Laboratory (NREL, 1992). EPA used the Newport
Processing Facility's reported net value of 572 kWh generated per ton of RDF for two reasons. First, this
value is within the range of values reported by the other sources. Second, the Newport Processing
Facility provides a complete set of data for evaluating the overall system efficiency of an RDF plant. The
net energy value reported accounts for the estimated energy required to process MSW into RDF and the
estimated energy consumed by the RDF combustion facility. The dataset includes estimates on the
composition and amount of MSW delivered to the processing facility, as well as estimates for the heat
value of RDF, the amount of energy required to process MSW into RDF, and the amount of energy used
to operate the RDF facility.
Next, EPA considered losses in transmission and distribution of electricity specific to WTE
combustion facilities. The U.S. average transmission and distribution ("line") loss rate is about nine
percent, although for some facilities or cities, this rate may be lower. According to IWSA and American
Ref-Fuel (1997), this rate could be as low as four percent. IWSA supports a five percent line loss rate,
and for purposes of this analysis, we assume this value. Using the five percent loss rate, EPA estimated
that 523 kWh are delivered per ton of waste combusted at mass burn facilities, and 544 kWh are
delivered per ton of waste input at RDF facilities.
EPA then used the value for the delivered kWh per ton of waste combusted to derive the
implicit combustion system efficiency (i.e., the percentage of energy in the waste that is ultimately
delivered in the form of electricity). To determine this efficiency, we estimate the MMBtu of MSW
needed to deliver one kWh of electricity. EPA divided the MMBtu per ton of waste by the delivered kWh
per ton of waste to obtain the MMBtu of waste per delivered kWh. The result is 0.0191 MMBtu per kWh
for mass burn and 0.0210 MMBtu per kWh for RDF. The physical constant for the energy in one kWh
(0.0034 MMBtu) is then divided by the MMBtu of MSW and RDF needed to deliver one kWh, to estimate
the total system efficiency at 17.8 percent for mass burn and 16.3 percent for RDF (see Exhibit 5-2,
columns (d) and (e)). Note that the total system efficiency is the efficiency of translating the energy
content of the fuel into the energy content of delivered electricity. The estimated system efficiencies of
17.8 and 16.3 percent reflect losses in (1) converting energy in the fuel into steam, (2) converting energy
in steam into electricity, and (3) delivering electricity.
5.2.4.3 Electric Utility Carbon Emissions Avoided
To estimate the avoided utility GHG emissions from waste combustion, EPA used "non-
baseload" emission factors from EPA's Emissions and Generation Resource Integrated Database (eGRID).
EPA made the decision to use non-baseload factors rather than a national average of only fossil-fuel
plants28 because the non-baseload emission rates provide a more accurate estimate of the marginal
emissions rate. The non-baseload rates scale emissions from generating units based on their capacity
28 While coal accounts for 33 percent of U.S. primary energy consumption—and 56 percent of fossil-fuel
consumption—in the electricity sector, these plants may serve as baseload power with marginal changes in
electricity supply met by natural gas plants in some areas (EIA, 2018). Natural gas plants have a much lower
emissions rate than the coal-dominated national average of fossil-fuel plants.
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factor. Plants that run at more than 80 percent capacity are considered "baseioad" generation and not
included in the "non-baseload" emission factor; a share of generation from plants that run between 80
percent and 20 percent capacity is included in the emission factor based on a "linear relationship/' and
all plants with capacity factors below 20 percent are included (E.H. Pechan & Associates, 2006).
In order to capture the regional differences in the emissions rate due to the variation in sources
of electricity generation, WARM first uses state-level eGRID non-baseload emission factors and
aggregates them into weighted average regional emission factors based on fossil-fuel-only state
electricity generation. The geographic regions are based on U.S. Census Bureau-designated areas.
Exhibit 5-3 contains a map, prepared by the U.S. Census Bureau, of the nine regions. Exhibit 5-4 shows
the national average eGRID emission factor and the factors for each of the nine geographic regions. In
addition to the calculated regional non-baseload emission factors, EPA also utilized eGRID's national
non-baseload emission factor to represent the national average non-baseload avoided utility emission
factor. The resulting non-baseload regional and national average estimates for utility carbon emissions
avoided for each material at mass burn facilities are shown in Exhibit 5-5. Columns (g) and (h),
respectively, of Exhibit 5-2 show the national average estimates for mass burn and RDF facilities.
Exhibit 5-3: Electric Utility Regions Used in WARM
Census Regions and Divisions of the United States
Source: U.S. Census Bureau (2009).
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Exhibit 5-4: Avoided Utility Emission Factors by Region
Region
Emission Factors for Utility-Generated Electricity3
(MTC02E/Million Btu of Electricity Delivered)
National Average
0.213
Pacific
0.143
Mountain
0.233
West-North Central
0.272
West-South Central
0.199
East-North Central
0.273
East-South Central
0.220
New England
0.141
Middle Atlantic
0.194
South Atlantic
0.206
a Includes transmission and distributions losses, which are assumed to be 5.8% (ElA, 2018).
Exhibit 5-5: Avoided Utility GHG Emissions at Mass Burn Facilities by Region (MTC02E/Short Ton of Material
Combusted)
Material Combusted
National
Average
Pacific
Moun
tain
West-
North
Central
West-
South
Central
East-
North
Central
East-
South
Central
New
England
Middle
Atlantic
South
Atlantic
Aluminum Cans
(0.03)
(0.02)
(0.03)
(0.03)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
Aluminum Ingot
(0.03)
(0.02)
(0.03)
(0.03)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
Steel Cans
(0.02)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.01)
(0.01)
(0.02)
Copper Wire
(0.02)
(0.01)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
(0.01)
(0.02)
(0.02)
Glass
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
HDPE
1.52
1.02
1.66
21.94
1.42
1.94
1.57
1.01
1.38
1.47
LDPE
1.51
1.02
1.65
1.93
1.41
1.93
1.56
1.00
1.38
1.46
PET
0.80
0.54
0.88
1.03
0.75
1.03
0.83
0.53
0.73
0.78
LLDPE
1.51
1.02
1.66
1.93
1.41
1.94
1.57
1.00
1.38
1.47
PP
1.51
1.02
1.66
1.93
1.41
1.94
1.57
1.00
1.38
1.47
PS
1.37
0.92
1.50
1.74
1.27
1.75
1.41
0.91
1.25
1.432
PVC
0.60
0.40
0.66
0.76
0.56
0.77
0.62
0.40
0.54
0.58
PLA
0.64
0.43
0.70
0.81
0.59
0.81
0.66
0.42
0.58
0.61
Corrugated
Containers
0.53
0.36
0.59
0.68
0.50
0.68
0.55
0.35
0.49
0.52
Magazines/Third-
Class Mail
0.40
0.27
0.44
0.51
0.37
0.51
0.41
0.26
0.36
0.39
Newspaper
0.60
0.41
0.66
0.77
0.56
0.77
0.62
0.40
0.55
0.58
Office Paper
0.52
0.35
0.57
0.66
0.48
0.66
0.53
0.34
0.47
0.50
Phone Books
0.60
0.41
0.66
0.77
0.56
0.77
0.62
0.40
0.55
0.58
Textbooks
0.52
0.35
0.57
0.66
0.48
0.66
0.53
0.34
0.47
0.50
Dimensional Lumber
0.63
0.42
0.69
0.80
0.59
0.81
0.65
0.42
0.57
0.61
Medium-Density
Fiberboard
0.63
0.42
0.69
0.80
0.59
0.81
0.65
0.42
0.57
0.61
Food Waste
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
Food Waste (meat
only)
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
Food Waste (non-
meat)
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
Beef
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
Poultry
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
Grains
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
Bread
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.217
Fruits and Vegetables
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
Dairy Products
0.18
0.12
0.20
0.23
0.17
0.23
0.19
0.12
0.16
0.17
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West-
West-
East-
East-
National
Moun
North
South
North
South
New
Middle
South
Material Combusted
Average
Pacific
tain
Central
Central
Central
Central
England
Atlantic
Atlantic
Yard Trimmings
0.21
0.14
0.23
0.27
0.20
0.27
0.22
0.14
0.19
0.21
Mixed MSW
0.38
0.26
0.42
0.48
0.35
0.49
0.39
0.25
0.35
0.37
Carpet
0.58
0.39
0.63
0.74
0.54
0.74
0.60
0.38
0.53
0.56
Desktop CPUs
0.12
0.08
0.13
0.15
0.11
0.15
0.12
0.08
0.11
0.11
Portable Electronic
0.12
0.08
0.13
0.15
0.11
0.15
0.12
0.08
0.11
0.11
Devices
Flat-panel Displays
0.12
0.08
0.13
0.15
0.11
0.15
0.12
0.08
0.11
0.11
CRT Displays
0.12
0.08
0.13
0.15
0.11
0.15
0.12
0.08
0.11
0.11
Electronic
0.12
0.08
0.13
0.15
0.11
0.15
0.12
0.08
0.11
0.11
Peripherals
Hard-copy Devices
0.12
0.08
0.13
0.15
0.11
0.15
0.12
0.08
0.11
0.11
Mixed Electronics
0.12
0.08
0.13
0.15
0.11
0.15
0.12
0.08
0.11
0.11
Tires3
1.57
1.57
1.57
1.57
1.57
1.57
1.57
1.57
1.57
1.57
Asphalt Shingles'5
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
Vinyl Flooring
0.60
0.40
0.66
0.76
0.56
0.77
0.62
0.40
0.54
0.58
Wood Flooring
0.82
0.56
0.90
1.05
0.77
1.06
0.85
0.55
0.75
0.80
Note that the "National Average" column is also represented in column (g) of Exhibit 5-2.
a Assumes weighted average avoided utility GHG emissions for multiple tire combustion pathways.
b Assumes avoided cement kiln refinery gas combustion.
5.2.5 Avoided C02 Emissions Due to Steel Recycling
WARM estimates the avoided C02 emissions from increased steel recycling made possible by
steel recovery from WTE plants for steel cans, mixed MSW, electronics, and tires. Most MSW combusted
with energy recovery in the United States is combusted at WTE plants that recover ferrous metals (e.g.,
iron and steel).29 Note that EPA does not credit increased recycling of nonferrous materials due to a lack
of data on the proportions of those materials being recovered. Therefore, the result tends to
overestimate net GHG emissions from combustion.
For mixed MSW, EPA estimated the amount of steel recovered per ton of mixed MSW
combusted, based on (1) the amount of MSW combusted in the United States, and (2) the amount of
steel recovered, post-combustion. Ferrous metals are recovered at approximately 98 percent of WTE
facilities in the United States (Bahor, 2010) and at five RDF processing facilities that do not generate
power on-site. These facilities recovered a total of nearly 706,000 short tons per year of ferrous metals
in 2004 (IWSA, 2004). By dividing 706,000 short tons (total U.S. steel recovery at combustors) by total
U.S. combustion of MSW, which is 28.5 million tons (Van Haaren al., 2010), EPA estimated that 0.02
short tons of steel are recovered per short ton of mixed MSW combusted (as a national average).
For steel cans, EPA first estimated the national average proportion of steel cans entering WTE
plants that would be recovered. As noted above, approximately 98 percent of MSW destined for
combustion goes to facilities with a ferrous recovery system. At these plants, approximately 90 percent
of steel is recovered (Bahor, 2010). EPA multiplied these percentages to estimate the weight of steel
cans recovered per ton of MSW combusted—about 0.88 tons recovered per ton combusted.
Finally, to estimate the avoided C02 emissions due to increased recycling of steel, EPA multiplied
(1) the weight of steel recovered by (2) the avoided C02 emissions per ton of steel recovered. The
29 EPA did not consider any recovery of materials from the MSW stream that might occur before MSW is delivered
to the combustor. EPA considered such prior recovery to be unrelated to the combustion operation—unlike the
recovery of steel from combustor ash, an activity that is an integral part of the operation of many combustors.
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estimated avoided C02 emissions results are in column (d) of Exhibit 5-6. For more information on the
GHG benefits of recycling, see the Recycling and Metals chapters.
Exhibit 5-6: Avoided GHG Emissions Due to Increased Steel Recovery from MSW at WTE Facilities
(a)
(b)
(c)
(d)
Short Tons of Steel
Avoided C02 Emissions
Avoided C02 Emissions
Recovered per Short Ton
per Short Ton of Steel
per Short Ton of Waste
of Waste Combusted
Recovered
Combusted
Material Combusted
(Short Tons)
(MTC02E/Short Ton)
(MTC02E/Short Ton)3
Aluminum Cans
-
-
-
Aluminum Ingot
-
-
-
Steel Cans
0.88
1.83
1.62
Copper Wire
-
-
-
Glass
-
-
-
HDPE
-
-
-
LDPE
-
-
-
PET
-
-
-
LLDPE
-
-
-
PP
-
-
-
PS
-
-
-
PVC
-
-
-
PLA
-
-
-
Corrugated Containers
-
-
-
Magazines/Third-Class Mail
-
-
-
Newspaper
-
-
-
Office Paper
-
-
-
Phone Books
-
-
-
Textbooks
-
-
-
Dimensional Lumber
-
-
-
Medium-Density Fiberboard
-
-
-
Food Waste
-
-
-
Food Waste (meat only)
-
-
-
Food Waste (non-meat)
-
-
-
Beef
-
-
-
Poultry
-
-
-
Grains
-
-
-
Bread
-
-
-
Fruits and Vegetables
-
-
-
Dairy Products
-
-
-
Yard Trimmings
-
-
-
Mixed Paper (general)
-
-
-
Mixed Paper (primarily residential)
-
-
-
Mixed Paper (primarily from offices)
-
-
-
Mixed Metals
-
-
-1.04
Mixed Plastics
-
-
-
Mixed Recyclables
-
-
-0.04
Mixed Organics
-
-
-
Mixed MSW
0.02
1.83
0.04
Carpet
-
-
-
Desktop CPUs
0.52
1.83
0.95
Portable Electronic Devices
0.06
1.83
0.12
Flat-panel Displays
0.33
1.83
0.60
CRT Displays
0.04
1.83
0.08
Electronic Peripherals
0.02
1.83
0.03
Hard-copy Devices
0.33
1.83
0.60
Mixed Electronics
0.27
1.83
0.50
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(a)
(b)
(c)
(d)
Short Tons of Steel
Avoided C02 Emissions
Avoided C02 Emissions
Recovered per Short Ton
per Short Ton of Steel
per Short Ton of Waste
of Waste Combusted
Recovered
Combusted
Material Combusted
(Short Tons)
(MTC02E/Short Ton)
(MTC02E/Short Ton)3
Clay Bricks
-
-
-
Concrete
-
-
-
Fly Ash
-
-
-
Tires
0.06
1.80
0.10
Asphalt Concrete
-
-
-
Asphalt Shingles
-
-
-
Drywall
-
-
-
Fiberglass Insulation
-
-
-
Vinyl Flooring
-
-
-
Wood Flooring
-
-
-
- = Zero emissions.
Note that totals may not sum due to independent rounding, and more digits may be displayed than are significant.
aThe value in column (d) is a national average and is weighted to reflect 90 percent recovery at the 98 percent of facilities that recover ferrous
metals.
b Assumes that only 68 percent of facilities that use TDF recover ferrous metals.
5.3 RESULTS
The national average results of this analysis are shown in Exhibit 5-7. The results from the last
column of Exhibit 5-1, the last two columns of Exhibit 5-2, and the last column of Exhibit 5-6 are shown
in columns (b) through (e) in Exhibit 5-7. The net GHG emissions from combustion of each material at
mass burn and RDF facilities are shown in columns (f) and (g), respectively. These net values represent
the gross GHG emissions (column (b)), minus the avoided GHG emissions (columns (c), (d), and (e)). As
stated earlier, these estimates of net GHG emissions are expressed for combustion in absolute terms,
and are not values relative to another waste management option, although they must be used
comparatively, as all WARM emission factors must be. They are expressed in terms of short tons of
waste input (i.e., tons of waste prior to processing).
Exhibit 5-7: Net National Average GHG Emissions from Combustion at WTE Facilities
(a)
(b)
(c)
(d)
(e = b - c - d)
Gross GHG
Avoided Utility GHG
Avoided C02
Net GHG Emissions
Emissions per
Emissions per Ton
Emissions per Ton
from Combustion at
Ton Combusted
Combusted at Mass
Combusted Due to
Mass Burn Facilities
(MTC02E/ Short
Burn Facilities (MTC02E
Steel Recovery
(MTC02E/ Short
Material Combusted
Ton)
/ Short Ton)3
(MTC02E/ Short Ton)
Ton)
Aluminum Cans
0.01
(0.03)
-
0.03
Aluminum Ingot
0.01
(0.03)
-
0.03
Steel Cans
0.01
(0.02)
1.62
(1.59)
Copper Wire
0.01
(0.02)
-
0.03
Glass
0.01
(0.02)
-
0.03
HDPE
2.80
1.52
-
1.29
LDPE
2.80
1.51
-
1.29
PET
2.05
0.80
-
1.24
LLDPE
2.80
1.51
-
1.29
PP
2.80
1.51
-
1.29
PS
3.02
1.37
-
1.65
PVC
1.26
0.60
-
0.66
PLA
0.01
0.64
-
(0.63)
Corrugated Containers
0.05
0.53
-
(0.49)
Magazines/Third-Class Mail
0.05
0.40
-
(0.35)
Newspaper
0.05
0.60
-
(0.56)
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(a)
(b)
(c)
(d)
(e = b - c - d)
Gross GHG
Avoided Utility GHG
Avoided C02
Net GHG Emissions
Emissions per
Emissions per Ton
Emissions per Ton
from Combustion at
Ton Combusted
Combusted at Mass
Combusted Due to
Mass Burn Facilities
(MTCOzE/ Short
Burn Facilities (MTC02E
Steel Recovery
(MTCOzE/ Short
Material Combusted
Ton)
/ Short Ton)3
(MTCOzE/ Short Ton)
Ton)
Office Paper
0.05
0.52
-
(0.47)
Phone Books
0.05
0.60
-
(0.56)
Textbooks
0.05
0.52
-
(0.47)
Dimensional Lumber
0.05
0.63
-
(0.58)
Medium-Density Fiberboard
0.05
0.63
-
(0.58)
Food Waste
0.05
0.18
-
(0.13)
Food Waste (meat only)
0.05
0.18
-
(0.13)
Food Waste (non-meat)
0.05
0.18
-
(0.13)
Beef
0.05
0.18
-
(0.13)
Poultry
0.05
0.18
-
(0.13)
Grains
0.05
0.18
-
(0.13)
Bread
0.05
0.18
-
(0.13)
Fruits and Vegetables
0.05
0.18
-
(0.13)
Dairy Products
0.05
0.18
-
(0.13)
Yard Trimmings
0.05
0.21
-
(0.17)
Grass
0.05
0.21
-
(0.17)
Leaves
0.05
0.21
-
(0.17)
Branches
0.05
0.21
-
(0.17)
Mixed Paper (general)15
0.05
0.54
-
(0.49)
Mixed Paper (primarily
residential)15
0.05
0.53
-
(0.49)
Mixed Paper (primarily from
offices)15
0.05
0.49
-
(0.45)
Mixed Metals
0.01
-0.02
1.05
(1.02)
Mixed Plastics
2.35
1.09
-
1.26
Mixed Recyclables
0.11
0.50
0.04
(0.42)
Mixed Organics
0.05
0.20
-
(0.15)
Mixed MSW
0.43
0.38
0.04
0.01
Carpet
1.68
0.58
-
1.10
Desktop CPUs
0.40
0.12
0.95
(0.66)
Portable Electronic Device
0.89
0.12
0.12
0.65
Flat-panel Displays
0.74
0.12
0.60
0.03
CRT Displays
0.64
0.12
0.08
0.45
Electronic Peripherals
2.23
0.12
0.03
2.08
Hard-copy Devices
1.92
0.12
0.60
1.20
Mixed Electronics
0.96
0.12
0.50
0.34
Clay Bricks
NA
NA
NA
NA
Concrete
NA
NA
NA
NA
Fly Ash
NA
NA
NA
NA
Tiresc
2.21
1.57
0.13
0.50
Asphalt Concrete
NA
NA
NA
NA
Asphalt Shingles
0.70
1.05m
-
(0.35)
Drywall
NA
NA
-
NA
Fiberglass Insulation
NA
NA
-
NA
Vinyl Flooring
0.29
0.60
-
(0.31)
Wood Flooring
0.08
0.82
-
(0.74)
Note that totals may not sum due to independent rounding, and more digits may be displayed than are significant.
a The values in this column represent the national average avoided utility GHG emissions. WARM also allows users to use region-specific
avoided utility emissions, which are contained in Exhibit 5-5.
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b The summary values for mixed paper are based on the proportions of the four paper types (corrugated containers, magazines/third-class mail,
newspaper, and office paper) that constitute the different "mixed paper" definitions.
c Tires used as TDF substitute for coal in cement kilns and utility boilers and as a substitute for natural gas, coal, and biomass in pulp and paper
facilities.
In the Excel version of WARM, the user can select the state where the waste is being disposed of
to determine the combustion emissions based on regional avoided utility emission factors. This
functionality is not available in the online version of WARM, which only allows for national average
emissions calculations.
Net GHG emissions are estimated to be negative for all biogenic sources of carbon (paper and
wood products, organics) because C02 emissions from these sources are not counted, as discussed
earlier.
As shown in Exhibit 5-7, combustion of plastics results in substantial net GHG emissions. This
result is primarily because of the high content of non-biomass carbon in plastics. Also, when combustion
of plastics results in electricity generation, the utility carbon emissions avoided (due to displaced utility
fossil fuel combustion) are much lower than the carbon emissions from the combustion of plastics. This
result is largely due to the lower system efficiency of WTE plants compared with electric utility plants.
Recovery of ferrous metals at combustors results in negative net GHG emissions for steel cans, due to
the increased steel recycling made possible by ferrous metal recovery at WTE plants. Combustion of
mixed MSW results in slightly negative GHG emissions because of the high proportion of biogenic
carbon and steel.
5.4 LIMITATIONS
The certainty of the analysis presented in this chapter is limited by the reliability of the various
data elements used. The most significant limitations are as follows:
• Combustion system efficiency of WTE plants may be improving. If efficiency improves, more
utility C02 will be displaced per ton of waste combusted (assuming no change in utility emissions
per kWh), and the net GHG emissions from combustion of MSW will decrease.
• Data for the RDF analysis were provided by the Minnesota Office of Environmental Assistance
and were obtained from a single RDF processing facility and a separate RDF combustion facility.
Research indicates that each RDF processing and combustion facility is different. For example,
some RDF combustion facilities may generate steam for sale off-site, which can affect overall
system efficiency. In addition, the amount of energy required to process MSW into RDF and the
amount of energy used to operate RDF combustion facilities can be difficult to quantify and can
vary among facilities on daily, seasonal and annual bases. This is one of the reasons that RDF
factors are not included in WARM.
• The reported ranges for N20 emissions were broad. In some cases, the high end of the range
was 10 times the low end of the range. Research has indicated that N20 emissions vary with the
type of waste burned. Thus, the average value used for mixed MSW and for all MSW
components should be interpreted as approximate values.
• For mixed MSW, the study assumed that all carbon in textiles is from synthetic fibers derived
from petrochemicals (whereas, in fact, some textiles are made from cotton, wool and other
natural fibers). Because EPA assumed that all carbon in textiles is non-biogenic, all of the C02
emissions from combustion of textiles as GHG emissions were counted. This assumption will
slightly overstate the net GHG emissions from combustion of mixed MSW, but the magnitude of
the error is small because textiles represent only a small fraction of the MSW stream. Similarly,
the MSW category of "rubber and leather" contains some biogenic carbon from leather and
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natural rubber. By not considering this small amount of biogenic carbon, the analysis slightly
overstates the GHG emissions from MSW combustion.
• Because the makeup of a given community's mixed MSW may vary from the national average,
the energy content also may vary from the national average energy content used in this analysis.
For example, MSW from communities with a higher- or lower-than-average recycling rate may
have a different energy content, and MSW with more than the average proportion of dry leaves
and branches will have a higher energy content.
• In this analysis, EPA used the national average recovery rate for steel. Where waste is sent to a
WTE plant with steel recovery, the net GHG emissions for steel cans will be slightly lower (i.e.,
more negative). Where waste is sent to a WTE plant without steel recovery, the net GHG
emissions for steel cans will be the same as for aluminum cans (i.e., close to zero). EPA did not
credit increased recycling of nonferrous materials, because of a lack of information on the
proportions of those materials. This assumption tends to result in overstated net GHG emissions
from combustion.
• This analysis uses the "non-baseload" emission factors for electricity as the proxy for fuel
displaced at the margin when WTE plants displace utility electricity. These non-baseload
emission factors vary depending on the state where the waste is assumed to be combusted. If
some other fuel or mix of fuels is displaced at the margin (e.g., a more coal-heavy fuel mix), the
avoided utility C02 would be different.
5.5 REFERENCES
Bahor, B. (2010). Personal communications between Victoria Thompson, ICF International, and Brian
Bahor, Covanta Energy. May 24, 2010; June 7, 2010; and July 14, 2010.
Bergman, R., & Bowe, S. A. (2008). Environmental impact of producing hardwood lumber using life-cycle
inventory. Wood and Fiber Science, 40(3), 448-458. Retrieved October 20, 2009 from
http://www.treesearch.fs.fed.us/pubs/31113.
DeZan, D. (2000). Personal communication between Diane DeZan, Fiber Economics Bureau and Joe
Casola, ICF Consulting. 4 August 2000.
EIA. (2018). U.S. Electricity Flow, 2017.Retrieved from
https://www.eia.gov/totalenergy/data/monthlv/pdf/flow/electricitv.pdf.
EPA. (1995). The EPA Municipal Solid Waste Fact Book, Version 2.0. Washington, D.C.: U.S.
Environmental Protection Agency, Office of Solid Waste.
EPA. (2018a). Advancing Sustainable Materials Management: 2015 Fact Sheet. (EPA530-F-18-004).
Washington, DC: U.S. Government Printing Office. Retrieved from
https://www.epa.gov/sites/production/files/2018-
07/documents/2015 smm msw factsheet 07242018 fnl 508 002.pdf.
EPA. (2018b). Emissions & Generation Resource Integrated Database (eGRID). Available from EPA at
http://www.epa.gov/energy/egrid.
EPA. (2017). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015. (EPA 430-R-15-004).
Washington, DC: U.S. Government Printing Office. Retrieved from
http://www3.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-lnventory-2015-Main-
Text.pdf. EPA.
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ERC. (2014). The 2014 ERC Directory of Waste-to-Energy Plants. Washington, DC. Energy Recovery
Council.
FAL. (2002a). Energy and Greenhouse Gas Factors for Nylon Broadloom Residential Carpet Prairie
Village, KS: Franklin Associates, Ltd., July 3, 2002.
FAL. (1994). The Role of Recycling in Integrated Solid Waste Management to the Year 2000. Franklin
Associates, Ltd. (Stamford, CT: Keep America Beautiful, Inc.), September, pp. 1-24.
Fons, W. L., Clements, H. B., Elliott, E. R., & George, P. M. (1962). Project Fire Model. Summary Progress
Report-ll. Period May 1,1960 to April 30, 1962. Macon, GA: U.S. Department of Agriculture,
Forest Service, Southeastern Forest Experiment Station, Southern Forest Fire Laboratory. 58 pp.
[16824],
Gaines, L., and Stodolsky, F. (1993). Mandated Recycling Rates: Impacts on Energy Consumption and
Municipal Solid Waste Volume. Argonne, IL: Argonne National Laboratory, pp. 11 and 85.
Harrington, K. (1997). Personal communication by facsimile with Karen Harrington, principal planner for
the Minnesota Office of Environmental Assistance. October 1997.
ICF Consulting. (1995). Memorandum for Work Assignment 239, Task 2: Carbon Sequestration in
Landfills. April 28, 1995. Exhibit 2-A, column (o).
Incropera, F. P., & DeWitt, D. P. (1990J. Introduction to Heat Transfer, Second Edition. New York: John
Wiley & Sons, pp. A3-A4.
IPCC. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA.
IPCC. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Hayama, Japan:
Intergovernmental Panel on Climate Change.
IWSA. (2007). The 2007IWSA Waste-To-Energy Directory of United States Facilities. Washington DC:
Integrated Waste Services Association.
IWSA. (2004). The 2004 IWSA Waste-To-Energy Directory of United States Facilities. Washington, DC:
Integrated Waste Services Association.
IWSA. (2000). The 2000 IWSA Waste-To-Energy Directory of United States Facilities. Washington, DC:
Integrated Waste Services Association.
IWSA & American Ref-Fuel (1997). Telephone conversation among representatives of Integrated Waste
Services Association, American Ref-Fuel, and ICF Consulting, October 28, 1997.
National Renewable Energy Laboratory (2015). "U.S. Life Cycle Inventory Database." Retrieved from
https://www.lcacommons.gov/nrel/search
NREL. (1992). Data Summary of Municipal Solid Waste Management Alternatives, Volume IV: Appendix B
- RDF Technologies. Springfield, VA: National Technical Information Service, National Renewable
Energy Laboratory/TP-431-4988D), p. B-5.
Procter and Redfern, Ltd. & ORTECH International. (1993). Estimation of the Effects of Various Municipal
Waste Management Strategies on Greenhouse Gas Emissions, Part II. Ottawa, Canada:
Environment Canada, Solid Waste Management Division, and Natural Resources Canada,
Alternative Energy Division.
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Realff, M. (2010). "The role of using carpet as a fuel in carpet recovery system development." Delivered
to ICF International via email on September 9, 2010.
U.S. Census Bureau. (2009). Census Regions and Divisions of the United States. Washington, DC: U.S.
Census Bureau, Geography Division.
Van Haaren, R., Goldstein, N., & Themelis, N.J. (2008). The State of Garbage in America. BioCycle, 51
(10), 16.
Zannes, M. (1997). Personal communication with Maria Zannes of Integrated Waste Services
Association, Washington, DC. August 25, 1997.
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6 LANDFILLING
This chapter presents an overview of landfilling as a waste management strategy in relation to
the development of material-specific emission factors for EPA's Waste Reduction Model (WARM).
Estimates of the net greenhouse gas (GHG) emissions from landfilling most of the materials considered
in WARM and several categories of mixed waste streams (e.g., mixed paper, mixed recyclables, and
mixed municipal solid waste (MSW)) are included in the chapter.
6.1 A SUMMARY OF THE GHG IMPLICATIONS OF LANDFILLING
When food waste, yard trimmings, paper, and wood are landfilled, anaerobic bacteria degrade
the materials, producing methane (CH4) and carbon dioxide (C02). CH4 is counted as an anthropogenic
GHG because, even if it is derived from sustainably harvested biogenic sources, degradation would not
result in CH4 emissions if not for deposition in landfills. The C02 produced after landfilling is not counted
as a GHG because it is considered part of the natural carbon cycle of growth and decomposition; for
more information, see the text box on biogenic carbon in the WARM Background and Overview chapter.
The other materials in WARM either do not contain carbon or do not biodegrade measurably in
anaerobic conditions, and therefore do not generate any CH4.
In addition to carbon emissions, some of the carbon in these materials (i.e., food waste, yard
trimmings, paper, and wood) is stored in the landfill because these materials are not completely
decomposed by anaerobic bacteria. Because this carbon storage would not normally occur under
natural conditions (virtually all of the biodegradable material would degrade to C02, completing the
photosynthesis/respiration cycle), this is counted as an anthropogenic sink. However, carbon in plastics
and rubber that remains in the landfill is not counted as stored carbon because it is of fossil origin. Fossil
carbon (e.g., petroleum, coal) is already considered "stored" in its natural state; converting it to plastic
or rubber and putting it in a landfill only moves the carbon from one storage site to another.
EPA developed separate estimates of emissions from (1) landfills without gas recovery systems,
(2) those that flare CH4, (3) those that combust CH4 for energy recovery, and (4) the national average
mix of these three categories. The national average emission estimate accounts for the extent to which
CH4 will not be managed at some landfills, flared at some landfills, and combusted onsite for energy
recovery at others.30 The assumed mix of the three landfill categories that make up the national average
for construction and demolition (C&D) materials and all other material types in WARM are presented in
Exhibit 6-1. These estimates are based on the amount of CH4 generated by U.S. landfills, as reported in
Subpart HH and TT from EPA's Greenhouse Gas Reporting Program (EPA, 2018a), and the type of
collection system from EPA's Landfill Methane Outreach Program (LMOP) (EPA, 2018b), as summarized
in Exhibit 6-2. For C&D materials, EPA assumes that roughly 3% of waste is landfilled in a municipal
landfill, while the remaining 97% waste is landfilled in a C&D landfill, which recovers landfill gas (LFG) at
a rate that is consistent with industrial landfills (EPA, 2019; EPA, 2017). For all other materials, EPA
assumes LFG recovery rates that are consistent with municipal landfills.
30 Although gas from some landfills is piped to an offsite power plant and combusted there, for the purposes of
WARM, the simplifying assumption was that all gas for energy recovery was combusted onsite. This assumption
was made due to the lack of information about the frequency of offsite power generation, piping distances, and
losses from pipelines.
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Exhibit 6-1: Percentage of CH4 Generated under the National Average
Material Type
Percentage of CH4 from
Landfills without LFG Recovery
Percentage of CH4 from
Landfills with LFG Recovery
and Flaring only
CH4 from Landfills with LFG
Recovery and Electricity
Generation (%)
C&D Material
96%
2%
2%
MSW Materials
8%
26%
66%
Exhibit 6-2: Percentage of CH4 Generated from Each Type of Landfill
Landfill Type
Percentage of CH4 from
Landfills without LFG Recovery
Percentage of CH4 from
Landfills with LFG Recovery
and Flaring only
CH4 from Landfills with LFG
Recovery and Electricity
Generation (%)a
Industrial Landfill
98%
2%
-
Municipal Landfill
8%
26%
66%
a The LMOP database indicates landfills that have active landfill-gas-to-energy (LFGTE) systems. However, it does not report the
percentage of LFG recovered at these facilities for energy generation versus the percentage of LFG recovered for flaring. In
WARM, all LFG generation at landfills with LFGTE systems is assumed to be recovered for energy. Therefore, this approach likely
underestimates the total percentage of LFG generation that is flared in the U.S. by not accounting for LFG flaring at landfills
with LFGTE systems.
6.2 CALCULATING THE GHG IMPACTS OF LANDFILLING
The landfilling emission factors are made up of the following components:
1. CH4 emissions from anaerobic decomposition of biogenic carbon compounds;
2. Transportation C02 emissions from landfilling equipment;
3. Biogenic carbon stored in the landfill; and
4. C02 emissions avoided through landfill gas-to-energy projects.
As mentioned above, WARM does not calculate CH4 emissions, stored carbon, or C02 avoided
for materials containing only fossil carbon (e.g., plastics, rubber). These materials have net landfilling
emissions that are very low because they include only the transportation-related emissions from
landfilling equipment. Some materials (e.g., newspaper, dimensional lumber) result in net storage (i.e.,
carbon storage exceeds CH4 plus transportation energy emissions) at all landfills, regardless of whether
gas recovery is present, while others (e.g., food waste) result in net emissions regardless of landfill gas
collection and recovery practices. Whether the remaining materials result in net storage or net
emissions depends on the landfill gas recovery scenario.
6.2.1 Carbon Stocks and Flows in Landfills
Exhibit 6-3 shows the carbon flows within a landfill system. Carbon entering the landfill can have
one of several fates: exit as CH4, exit as C02, exit as volatile organic compounds (VOCs), exit dissolved in
leachate, or remain stored in the landfill.31
After entering landfills, a portion of the biodegradable material decomposes and eventually is
transformed into landfill gas and/or leachate. Aerobic bacteria initially decompose the waste until the
available oxygen is consumed. This stage usually lasts less than a week and is followed by the anaerobic
acid state, in which carboxylic acids accumulate, the pH decreases, and some cellulose and
hemicellulose decomposition occurs. Finally, during the methanogenic state, bacteria further
decompose the biodegradable material into CH4 and C02.
31 The exhibit and much of the ensuing discussion are taken directly from Freed et al. (2004).
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The rate of decomposition in landfills is affected by a number of factors, including: (1) waste
composition; (2) factors influencing microbial growth (moisture, available nutrients, pH, temperature);
and (3) whether the operation of the landfill retards or enhances waste decomposition. Most studies
have shown that the amount of moisture in the waste, which can vary widely within a single landfill, is a
critical factor in the rate of decomposition (Barlaz et al., 1990). Due to this fact, the emission factors
presented in WARM are per wet ton of waste.
Among the research conducted on the various components of the landfill carbon system, much
to date has focused on the transformation of landfill carbon into CH4. This interest has been spurred by a
number of factors, including EPA's 1996 rule requiring large landfills to control landfill gas emissions (40
Code of Federal Regulations Part 60, Subparts Cc and WWW), the importance of CH4 emissions in GHG
inventories, and the market for CH4 as an energy source. CH4 production occurs in the methanogenic
stage of decomposition, as methanogenic bacteria break down the fermentation products from earlier
decomposition processes. Since CH4 emissions result from waste decomposition, the quantity and
duration of the emissions is dependent on the same factors that influence waste degradability (e.g.,
waste composition, moisture). The CH4 portion of each material type's emission factor is discussed
further in section 6.2.2.
Carbon dioxide is produced in the initial aerobic stage and in the anaerobic acid stage of
decomposition. However, relatively little research has been conducted to quantify CQ2 emissions during
these stages. Emissions during the aerobic stage are generally assumed to be a small proportion of total
organic carbon inputs, and a screening-level analysis indicates that less than one percent of carbon is
likely to be emitted through this pathway (Freed et al., 2004). Once the methanogenic stage of
decomposition begins, landfill gas as generated is composed of approximately 50 percent CH4 and 50
percent C02 (Bingemer and Crutzen, 1987). However, landfill gas as collected generally has a higher CH4
concentration than C02 concentration (sometimes as much as a 60 percent: 40 percent ratio), because
some of the C02 is dissolved in the leachate as part of the carbonate system (C02 <—> H2C03 <-> HC03~
o co32).
Exhibit 6-3: Landfill Carbon Mass Balance
Source: Freed etal. (2004).
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To date, very little research has been conducted on the role of VOC emissions in the landfill
carbon mass balance. Given the thousands of compounds entering the landfill environment, tracking the
biochemistry by which these compounds ultimately are converted to VOC is a complex undertaking.
Existing research indicates that ethane, limonene, /7-decane, p-dichlorobenzene, and toluene may be
among the most abundant landfill VOCs (Eklund et al., 1998). Hartog (2003) reported non-CH4 volatile
organic compound concentrations in landfill gas at a bioreactor site in Iowa, averaging 1,700 parts per
million (ppm) carbon by volume in 2001 and 925 ppm carbon by volume in 2002. If the VOC
concentrations in landfill gas are generally of the order of magnitude of 1,000 ppm, VOCs would have a
small role in the overall carbon balance, as concentrations of CH4 and C02 will both be hundreds of times
larger.
Leachate is produced as water percolates through landfills. Factors affecting leachate formation
include the quantity of water entering the landfill, waste composition, and the degree of decomposition.
Because it may contain materials capable of contaminating groundwater, leachate (and the carbon it
contains) is typically collected and treated before being released to the environment, where it
eventually degrades into C02. However, leachate is increasingly being recycled into the landfill as a
means of inexpensive disposal and to promote decomposition, increasing the mass of biodegradable
materials collected by the system and consequently enhancing aqueous degradation (Chan et al., 2002;
Warith et al., 1999). Although a significant body of literature exists on landfill leachate formation, little
research is available on the carbon implications of this process. Based on a screening analysis, Freed et
al. (2004) found that loss as leachate may occur for less than one percent of total carbon inputs to
landfills.
In mass balance terms, carbon storage can be characterized as the carbon that remains after
accounting for the carbon exiting the system as landfill gas or dissolved in leachate. On a dry weight
basis, municipal refuse contains 30-50 percent cellulose, 7-12 percent hemicellulose and 15-28 percent
lignin (Hilger and Barlaz, 2001). Although the degradation of cellulose and hemicellulose in landfills is
well documented, lignin does not degrade to a significant extent under anaerobic conditions (Colberg,
1988). Landfills in effect store some of carbon from the cellulose and hemicellulose and all of the carbon
from the lignin that is buried initially. The amount of storage will vary with environmental conditions in
the landfill; pH and moisture content have been identified as the two most important variables
controlling decomposition (Barlaz et al., 1990). These variables and their effects on each material type's
emission factor are discussed further below.
6.2.2 Estimating Emissions from Landfills
As discussed in section 6.2.1, when biodegradable materials such as wood products, food
wastes, and yard trimmings are placed into a landfill, a fraction of the carbon within these materials
degrades into CH4emissions. The quantity and timing of CH4 emissions released from the landfill
depends upon three factors: (1) how much of the original material decays into CH4, (2) how readily the
material decays under different landfill moisture conditions, and (3) landfill gas collection practices. This
section describes how these three factors are addressed in WARM.
6.2.2.1 Methane Generation and Landfill Carbon Storage
The first step is to determine the amount of carbon contained in degradable materials that is
emitted from the landfill as CH4, and the amount that remains in long-term storage within the landfill.
Although a large body of research exists on CH4 generation from mixed solid wastes, only a few
investigators—most notably Dr. Morton Barlaz and colleagues at North Carolina State University—have
measured the behavior of specific waste wood, paper, food waste, and yard trimming components. The
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results of their experiments yield data on the inputs—specifically the initial carbon contents, CH4
generation, and carbon stored—that are required for calculating material-specific emission factors for
WARM.
Barlaz (1998) developed a series of laboratory experiments designed to measure biodegradation
of these materials in a simulated landfill environment, in conditions designed to promote decomposition
(i.e., by providing ample moisture and nutrients). Each waste component (e.g., grass, branches, leaves,
paper) was dried; analyzed for cellulose, hemicellulose, and lignin content; weighed; placed in two-liter
plastic containers (i.e., reactors); and allowed to decompose anaerobically under moist conditions
(Eleazer et al., 1997). At the end of the experiment, the contents of the reactors were dried, weighed,
and analyzed for cellulose, hemicellulose, lignin, and (in the case of food waste only) protein content.
The carbon in these residual components is assumed to represent carbon that would remain
undegraded over the long term in landfills: that is, it would be stored.
Based on these components, Dr. Barlaz estimated the initial biogenic carbon content of each
waste material as a percent of dry matter. For some materials, the carbon content estimates have been
updated to reflect more recent studies or to better reflect changes in material composition in recent
years. Exhibit 6-4 shows the initial carbon contents of the wastes analyzed by Barlaz (1998) and Wang et
al. (2011).
Exhibit 6-4: Initial Biogenic Carbon Content of Materials Tested in Barlaz (1998) and Wang et al. (2011)
Material
Initial Biogenic Carbon
Content, % of Dry
Matter
Source
Corrugated Containers
47%
Barlaz (1998)
Newspaper
49%
Barlaz (1998)
Office Paper
32%
Barlaz (1998)a
Coated Paper
26%
Barlaz (1998)
Food Waste - Vegetable
48%
Barlaz (1998)
Food Waste - Non-Vegetable
57%
Barlaz (1998)
Grass
45%
Barlaz (1998)
Leaves
46%
Barlaz (1998)
Branches
49%
Barlaz (1998)
Mixed MSW
42%
Barlaz (1998)
Gypsum Board
5%
Barlaz (1998)
Dimensional Lumber
49%
Wang et al. (2011)
Medium-density Fiberboard
44%
Wang et al. (2011)
Wood Flooring15
46%
Wang et al. (2011)
a Based on 2014 discussions with Dr. Morton Barlaz, the carbon content of office paper has been updated to account for an
average calcium carbonate (CaCOs) content of 20 percent in office paper in recent years.
b Based on an average of carbon content values for red oak and plywood in Wang et al. (2011).
The principal stocks and flows in the landfill carbon balance are:
• Initial carbon content (Initial C);
• Carbon output as CH4 (CH4);
• Carbon output as C02 (CO2); and
• Residual carbon (i.e., landfill carbon storage, LFC).
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The initial carbon content, along with the other results from the Barlaz (1998), Wang et al.
(2013), Wang et al. (2011), and Levis et al. (2013) experiments are used to estimate each material type's
emission factor in WARM. The Barlaz (1998), Wang et al. (2013), Wang et al. (2011), and Levis et al.
(2013) experiments did not capture C02 emissions in the carbon balance; however, in a simple system
where the only carbon fates are CH4, C02 and carbon storage, the carbon balance can be described as
CH4+C02+LFc=lnitial C
If the only decomposition is anaerobic, then CH4 = CO2.32 Thus, the carbon balance can be
expressed as
= Initial C2xCH4+LFc=lnitial C
Exhibit 6-5 shows the measured experimental values, in terms of the percentage of initial
carbon for each of the materials analyzed, the implied landfill gas yield, and the sum of outputs as a
percentage of initial carbon (Barlaz, 1998; Wang et al., 2013; Wang et al., 2011; Levis et al., 2013). As the
sum of the outputs shows, the balance between carbon outputs and carbon inputs generally was not
perfect. This imbalance is attributable to measurement uncertainty in the analytic techniques.
Exhibit 6-5: Experimental Values for CH4 Yield and Carbon Storage3
(a)
(b)
(c)
(d)
(e)
Implied Yield of Landfill Gas
Measured
Measured CH4
(CH4+C02) as a Proportion of
Proportion of
Output as % of
Yield as a % of
Initial Carbon
Initial Carbon
Initial Carbon
Material
Initial Carbon
(c = 2 x b)
Stored
(e = c + d)
Corrugated Containers
22%
45%
55%
100%
Newspaper
8%
16%
86%
102%
Office Paper
44%
88%
124%
212%
Coated Paper
13%
26%
79%
100%
Food Waste - Non-Vegetable
35%
70%
30%
100%
Grass
23%
46%
53%
100%
Leaves
8%
15%
85%
100%
Branches
12%
23%
77%
100%
Mixed MSW
16%
32%
19%
50%
Gypsum Board
0%
0%
55%
55%
Dimensional Lumber
1%
3%
88%
91%
Medium-density Fiberboard
1%
1%
84%
85%
Wood Flooring15
2%
4%
96%
100%
a The CH4, C02, and carbon stored from these experiments represents only the biogenic carbon in each material type.
b Based on an average of carbon content values for red oak and plywood in Wang et al. (2011).
To calculate the WARM emission factors, adjustments were made to the measured values so
that exactly 100 percent of the initial carbon would be accounted for. After consultation with Dr. Barlaz,
the following approach was adopted to account for exactly 100 percent of the initial carbon:
• For most materials where the total carbon output is less than the total carbon input (e.g.,
corrugated containers, office paper, food waste, grass, leaves), the "missing" carbon was
32 The emissions ratio of CH4 to C02 is 1:1 for carbohydrates (e.g., cellulose, hemicellulose). For proteins, the ratio
is 1.65 CH4 per 1.55 C02; for protein, it is C3.2H5ONo.86 (Barlaz et al., 1989). Given the predominance of
carbohydrates, for all practical purposes, the overall ratio is 1:1.
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assumed to be emitted as equal quantities of CH4 and CO2. In these cases (corrugated
containers, office paper, food waste, grass, leaves), the CH4 was increased with respect to the
measured values as follows:
Initial C-LFc ^
2 =CH4
This calculation assumes that CO2 =CH4 . In essence, the adjustment approach was to increase
landfill gas production, as suggested by Dr. Barlaz.
For coated paper, newspaper, and wood flooring, where carbon outputs were greater than
initial carbon, the measurements of initial carbon content and CH4 mass were assumed to be accurate.
Here, the adjustment approach was to decrease carbon storage. Thus, landfill carbon storage was
calculated as the residual of initial carbon content minus (2 x CH4). The resulting adjusted CH4 yields and
carbon storage are presented in Exhibit 6-6.
For branches, dimensional lumber, medium-density fiberboard, and mixed MSW, the measured
CH4yield as a percentage of initial carbon was considered to be the most realistic estimate for methane
yield, based on consultation with Dr. Barlaz. Therefore, no adjustment was made for these materials.
For gypsum board, the sulfate in wallboard is estimated to reduce methane generation, as
bacteria use sulfate preferentially to the pathway that results in methane, as suggested by Dr. Barlaz. As
such, methane yield from gypsum board is likely to be negligible and is therefore adjusted to 0% in
WARM.
Exhibit 6-6: Adjusted CH4 Yield and Carbon Storage by Material Type
Material
Adjusted Yield of CH4 as
Proportion of Initial Carbon
Adjusted Carbon Storage as
Proportion of Initial Carbon
Corrugated Containers3
22%
55%
Newspaper15
8%
84%
Office Paper3
44%
12%
Coated Paperb
17%
66%
Food Waste - Vegetable3
42%
17%
Food Waste - Non-Vegetable3
35%
30%
Grass3
23%
53%
Leaves3
8%
85%
Branches0
12%
77%
Mixed MSWC
16%
19%
Gypsum Boardd
0%
55%
Dimensional Lumberc
1%
88%
Medium-density Fiberboardc
1%
84%
Wood Flooring15
2%
96%
a CH4 yield is adjusted to account for measurement uncertainty in the analytic techniques to measure these quantities. For
corrugated containers, office paper, food waste, grass, and leaves, the yield of CH4 was increased such that the proportion of
initial carbon emitted as landfill gas (i.e., 2 x CH4) plus the proportion that remains stored in the landfill is equal to 100% of the
initial carbon.
b For coated paper, newspaper, and wood flooring, the proportion of initial carbon that is stored in the landfill is decreased such
that the proportion of initial carbon emitted as landfill gas (i.e., 2 x CH4) plus the proportion that remains stored in the landfill is
equal to 100% of the initial carbon.
c For branches, dimensional lumber, medium-density fiberboard, and mixed MSW, the measured CH4 yield as a percentage of
initial carbon and measured proportion of initial carbon stored shown in columns b and d, respectively of Exhibit 6-5 was
considered to be the most realistic estimate for methane yield. Therefore, these values were not adjusted.
d For gypsum board, the sulfate in wallboard is estimated to reduce methane generation; thus, the methane yield from gypsum
board is likely to be negligible and is therefore adjusted to 0%.
Dr. Barlaz's experiment did not test all of the biodegradable material types in WARM. EPA
identified proxies for the remaining material types for which there were no experimental data.
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Magazines and third-class mail placed in a landfill were assumed to contain a mix of coated paper and
office paper and were therefore assumed to behave like an average of those two materials. Similarly,
phone books and textbooks were assumed to behave in the same way as newspaper and office paper,
respectively. Fruits and vegetables were assumed to behave like vegetable food waste, while beef,
poultry, grains, bread, and dairy products were assumed to behave like non-vegetable food waste. In
addition, the ratio of dry-to-wet weight was tailored to each food-type based on data obtained from a
Clemson study (Clemson 2021) and the USDA Agriculture Research Service (USDA 2015). Results from
two studies by Wang et al. were used for dimensional lumber, medium-density fiberboard, and wood
flooring (2011; 2013). For wood flooring, the ratio of dry-to-wet weight was adjusted to more accurately
represent the moisture content of wood lumber (Staley and Barlaz, 2009). Exhibit 6-7 shows the landfill
CH4 emission factors and the final carbon storage factors for all applicable material types.
Exhibit 6-7: CH4 Yield for Solid Waste Components
Final (Adjusted)
Final (Adjusted)
Adjusted Yield of
CH4 Generation,
CH4 Generation
Initial Biogenic
CH4 as Proportion
MTCOzE/Dry
(MTCOzE /Wet
Material
Carbon Content
of Initial Carbon
Metric Ton3
Short Ton)b
Corrugated Containers
47%
22%
3.48
2.62
Magazines/Third-class Mail
36%
12%
1.43
1.19
Newspaper
49%
8%
1.33
1.05
Office Paper
32%
44%
4.71
3.89
Phonebooks
49%
8%
1.33
1.05
Textbooks
32%
44%
4.71
3.89
Dimensional Lumber
49%
1%
0.24
0.17
Medium-density Fiberboard
44%
1%
0.08
0.06
Food Waste
52%
38%
6.65
1.72
Food Waste (meat only)
57%
35%
6.60
1.75
Food Waste (non-meat)
51%
39%
6.60
1.70
Beef
57%
35%
6.60
1.62
Poultry
57%
35%
6.60
1.86
Grains
57%
35%
6.60
5.33
Bread
57%
35%
6.60
3.83
Fruits and Vegetables
48%
42%
6.60
0.69
Dairy Products
57%
35%
6.60
1.82
Yard Trimmings
Grass
45%
23%
3.48
0.57
Leaves
46%
8%
1.17
0.65
Branches
49%
12%
1.90
1.45
Wood Flooring
43%
2%
0.27
0.18
Drywallc
5%
0%
0
0
a Final adjusted CH4 generation per dry metric ton is the product of the initial carbon content and the final percent carbon emitted as CH4
multiplied by the molecular ratio of carbon to CH4 (12/16).
b CH4 generation is converted from per dry metric ton to per wet short ton by multiplying the CH4 generation on a dry metric ton basis by (1 -
the material's moisture content) and by converting from metric tons to short tons of material.
c Drywall was assumed to have characteristics similar to gypsum board.
6.2.2.2 Component-Specific Decay Rates
The second factor in estimating material-specific landfill emissions is the rate at which a material
decays under anaerobic conditions in the landfill. The decay rate is an important factor that influences
the landfill collection efficiency described further in the next section. Although the final adjusted CH4
yield shown in Exhibit 6-7 will eventually occur no matter what the decay rate, the rate at which the
material decays influences how much of the CH4 yield will eventually be captured for landfills with
collection systems.
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Recent studies by De la Cruz and Barlaz (2010) found that different materials degrade at
different rates relative to bulk MSW rates of decay. For example, one short ton of a relatively inert wood
material—such as lumber—will degrade slowly and produce a smaller amount of methane than food
waste, which readily decays over a much shorter timeframe. Materials will also degrade faster under
wetter landfill conditions. Consequently, the rate at which CH4 emissions are generated from decaying
material in a landfill depends upon: (1) the type of material placed in the landfill, and (2) the moisture
conditions of the landfill.
De la Cruz and Barlaz (2010) measured component-specific decay rates in laboratory
experiments that were then scaled to field-level, component-specific decay rates based on mixed MSW
field-scale decay rates published in EPA (1998) guidance.
To scale the laboratory-scale, component-specific decay rate measurements to field-scale
values, De la Cruz and Barlaz (2010) assumed that the weighted average decay rate for a waste mixture
of the same composition as MSW would be equal to the bulk MSW decay rate. They also related a lab-
scale decay rate for mixed MSW to the field-scale decay rate using a scaling factor. Using these two
relationships, the authors were able to estimate field-scale decay rates for different materials based on
the laboratory data. The following equations were used to estimate the component-specific decay rates:
Equation 1
/ x Ef=i^(ab,i x (wt. fraction)i = decay rate
Equation 2
kfield.,i f X ^lab,i
where,
/ = a correction factor to force the left side of the equation to equal the overall MSW decay
rate
khb,i = the component-specific decay rate calculated from lab experiments
kfieidj = the component-specific decay rate determined for the field
/' = the waste component
Based on the results from De la Cruz and Barlaz (2010), the Excel version of WARM allows users
to select different component-specific decay rates based on different assumed moisture contents of the
landfill to estimate the rate at which CH4 is emitted for each material type (or "component"). The five
MSW decay rates used are:
1. k = 0.02/year ("Dry"), corresponding to landfills receiving fewer than 20 inches of annual
precipitation: based values reported in EPA (2010)
2. k = 0.04/year ("Moderate"), corresponding to landfills receiving between 20 and 40 inches of
annual precipitation: based values reported in EPA (2010)
3. k = 0.06/year ("Wet"), corresponding to landfills receiving greater than 40 inches of annual
precipitation: based values reported in EPA (2010)
4. k = 0.12/year ("Bioreactor"), corresponding to landfills operating as bioreactors where water is
added until the moisture content reaches 40 percent moisture on a wet-weight basis: based on
expert judgment using values reported in Barlaz et al. (2010) and Tolaymat et al. (2010)
5. k = 0.052/year ("National Average"), corresponding to a weighted average based on the share of
waste received at each landfill type: based on expert judgment using values reported in EPA
(2010)
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The final waste component-specific decay rates as a function of landfill moisture conditions are
provided in Exhibit 6-8.
Exhibit 6-8: Component-Specific Decay Rates (yr_1) by Landfill Moisture Scenario
Material
Landfill Moisture Conditions
Dry
Moderate
Wet
Bioreactor
National
Average
Corrugated Containers
0.01
0.02
0.04
0.06
0.03
Magazines/Third-Class Mail
0.06
0.12
0.24
0.37
0.16
Newspaper
0.02
0.03
0.07
0.10
0.04
Office Paper
0.02
0.03
0.06
0.09
0.04
Phonebooks
0.02
0.03
0.07
0.10
0.04
Textbooks
0.02
0.03
0.06
0.09
0.04
Dimensional Lumber
0.04
0.08
0.16
0.25
0.11
Medium-Density Fiberboard
0.03
0.06
0.13
0.19
0.08
Food Waste
0.07
0.14
0.29
0.43
0.19
Yard Trimmings
0.10
0.20
0.39
0.59
0.26
Grass
0.15
0.30
0.60
0.89
0.39
Leaves
0.09
0.17
0.34
0.51
0.22
Branches
0.01
0.02
0.03
0.05
0.02
Mixed MSW
0.02
0.04
0.08
0.12
0.05
Drywall3
-
-
-
-
-
Wood Flooring3
-
-
-
-
-
- = Zero Emissions.
aDecay rates were not estimated since WARM assumes that the construction and demolition landfills where these materials are
disposed of do not collect landfill gas.
The profile of methane emissions as materials decay in landfills overtime is commonly
approximated using a first-order decay methodology summarized in De la Cruz and Barlaz (2010). The
CH4 generation potential of landfilled waste decreases gradually throughout time and can be estimated
using first order decomposition mathematics. The profile of methane emissions from landfills over time
for mixed MSW is shown in Exhibit 6-9 as a graphic representation of the methane emissions
approximated using a first-order decay equation. As Exhibit 6-9 shows, materials will degrade faster
under wetter conditions in landfills (i.e., landfills whose conditions imply higher decay rates for
materials).
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Exhibit 6-9. Rate of Methane Generation for Mixed MSW as a Function of Decay Rate
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material in WARM. The landfill collection efficiency scenarios are described below and the assumptions
for each are shown in Exhibit 6-10:
1. Typical collection - phased-in collection with an improved cover; judged to represent the
average U.S. landfill, although every landfill is unique and a typical landfill is an approximation of
reality.
2. Worst-case collection - the minimum collection requirements under EPA's New Source
Performance Standards.
3. Aggressive collection - landfills where the operator is aggressive in gas collection relative to a
typical landfill; bioreactor landfills are assumed to collect gas aggressively.
4. California regulatory scenario34 - equivalent to landfill management practices based on
California regulatory requirements.
Exhibit 6-10: WARM Gas Collection Scenario Assumptions and Efficiencies Compared to EPA AP-42 (1998) with
Landfill Gas Recovery for Energy
Landfill Gas Collection Efficiency (%)
for Mixed MSWa
MSW Decay Rate (yr1)
Gas Collection Scenario
National
Scenario
Description
Gas Collection Scenario
0.02
0.04
0.06
0.12
Average
AP-42
EPA default gas
All years: 75%
collection assumption
(EPA 1998 AP-42) (not
75.0
75.0
75.0
75.0
75.0
modeled in WARM)
1
"Typical collection",
judged to represent the
Years 0-1: 0%
Years 2-4: 50%
average U.S. landfill
Years 5-14: 75%
Years 15 to 1 year before final cover: 82.5%
Final cover: 90%
68.2
65.0
64.1
60.6
64.8
2
"Worst-case collection"
under EPA New Source
Years 0-4: 0%
Years 5-9: 50%
Performance Standards
Years 10-14: 75%
66.2
61.3
59.2
50.6
60.3
(NSPS)
Years 15 to 1 year before final cover: 82.5%
Final cover: 90%
3
"Aggressive gas
collection," typical
Year 0: 0%
Years 0.5-2: 50%
bioreactor operation
Years 3-14: 75%
Years 15 to 1 year before final cover: 82.5%
Final Cover: 90%
68.6
65.8
66.3
63.9
66.4
4
"California regulatory
scenario", landfill
Year 0: 0%
Year 1: 50%
management based on
Years 2-7: 80%
83.6
79.5
77.4
72.9
78.8
California regulatory
Years 8 to 1 year before final cover: 85%
requirements
Final cover: 90%
a The values in this table are for landfills that recover gas for energy. In reality, a small share of gas recovered is eventually
flared. The values provided in this table include both the gas recovered for energy and the small portion recovered for flaring.
The landfill gas collection efficiencies by material type for each of the four landfill collection
efficiency scenarios and each of the five moisture conditions are provided in Exhibit 6-11. In addition to
the gas collected, EPA also took into account the percentage of gas that is flared, oxidized, and emitted
for landfills that recover gas for energy, as described in Levis and Barlaz (2014). Some of the uncollected
34 This additional landfill gas collection scenario was incorporated in June 2014 into WARM Version 13 to allow
WARM users to estimate and view landfill management results based on California regulatory requirements.
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methane is oxidized to C02 as it passes through the landfill cover; Levis and Barlaz (2014) adapted EPA
recommendations for methane oxidation (71 FR 230, 2013) to develop the following oxidation rates at
various stages of landfill gas collection:
• Without gas collection or final cover: 10 percent
• With gas collection before final cover: 20 percent
• After final cover installation: 35 percent
In the EPA recommendations, the fraction of uncollected methane that is oxidized varies with
the methane flux (mass per area per time) and ranges from 10 percent to 35 percent (71 FR 230, 2013).
Measurement or estimation of the methane flux is possible on a site-specific basis but requires
assumptions on landfill geometry and waste density to estimate flux for a generic landfill as is
represented by WARM. As such, the methane oxidation values published by EPA were used as guidance
for the values listed above. Landfills with a final cover and a gas collection system in place will have a
relatively low flux through the cover, which justifies the upper end of the range (35 percent) given by
EPA. Similarly, landfills without a gas collection system in place will have a relatively high flux, suggesting
that an oxidation rate of 10 percent is most appropriate. Landfills with a gas collection system in place
but prior to final cover placement were assigned an oxidation rate of 20 percent. Based on preliminary
calculations for a variety of landfill geometries and waste densities, Levis and Barlaz (2014) determined
that the methane flux would justify an oxidation rate of 25 percent most but not all of the time. As such,
an oxidation rate of 20 percent was adopted in WARM for landfills with gas collection before final cover
(Levis and Barlaz, 2014).
For landfill gas that is not collected for energy use, EPA took into account the percentage of
landfill CH4 that is flared (when recovery for flaring is assumed), oxidized near the surface of the landfill,
and emitted. Based on analysis by Levis and Barlaz, EPA estimated the percentage of the landfill CH4
generated that are either flared, chemically oxidized or converted by bacteria to C02, and emitted for
each material type for each of the four landfill collection efficiency scenarios and each of the five
moisture conditions (Levis and Barlaz, 2014).
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Exhibit 6-11: Waste Component-Specific Collection Efficiencies by Landfill Moisture Condition with Landfill Gas Recovery for Energy
Material
Typical Landfill Scenario
Worst-Case Landfill Scenario
Aggressive Collection Landfill
Scenario
California Regulations Collection
Scenario
Dry
Mod
erate
Wet
Bio-
react
or
Natio
nal
Avg.
Dry
Mode
rate
Wet
Bio-
react
or
Natio
nal
Avg.
Dry
Mode
rate
Wet
Bio-
react
or
Natio
nal
Avg.
Dry
Mode
rate
Wet
Bio-
react
or
Natio
nal
Avg.
Corrugated Containers
58%
53%
54%
55%
56%
53%
43%
53%
50%
54%
59%
56%
56%
58%
57%
65%
61%
60%
62%
61%
Magazines/Third-class
Mail
61%
55%
52%
45%
54%
60%
54%
40%
26%
43%
61%
56%
57%
51%
57%
66%
59%
61%
54%
62%
Newspaper
59%
55%
59%
57%
59%
55%
46%
55%
49%
56%
61%
58%
61%
60%
61%
67%
63%
65%
65%
65%
Office Paper
62%
59%
58%
57%
59%
61%
56%
55%
50%
56%
62%
59%
60%
60%
60%
67%
64%
64%
65%
64%
Phonebooks
62%
58%
59%
57%
59%
61%
56%
55%
49%
56%
62%
59%
61%
60%
61%
67%
63%
65%
65%
65%
Textbooks
62%
59%
58%
57%
59%
61%
56%
55%
50%
56%
62%
59%
60%
60%
60%
67%
64%
64%
65%
64%
Dimensional Lumber
62%
60%
57%
50%
58%
61%
56%
48%
35%
50%
63%
61%
60%
55%
60%
67%
65%
65%
60%
65%
Medium-Density
Fiberboard
62%
59%
59%
53%
59%
59%
52%
51%
40%
53%
63%
61%
62%
58%
62%
68%
66%
67%
62%
67%
Food Waste
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Food Waste (meat only)
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Food Waste (non-meat)
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Beef
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Poultry
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Grains
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Bread
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Fruits and Vegetables
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Dairy Products
56%
51%
50%
42%
52%
50%
40%
36%
22%
40%
58%
54%
55%
49%
55%
64%
59%
59%
51%
60%
Yard Trimmings
43%
37%
44%
39%
47%
40%
33%
31%
21%
35%
43%
39%
49%
44%
50%
47%
41%
52%
45%
54%
Grass
61%
53%
39%
33%
41%
60%
52%
20%
9%
25%
61%
54%
45%
39%
46%
65%
57%
48%
38%
50%
Leaves
49%
43%
47%
40%
49%
39%
27%
33%
19%
37%
51%
47%
52%
46%
53%
57%
51%
57%
48%
58%
Branches
-
-
51%
52%
54%
-
-
51%
49%
53%
-
-
53%
54%
55%
-
-
57%
58%
59%
Mixed MSW
62%
59%
60%
57%
60%
61%
56%
55%
47%
56%
62%
59%
62%
60%
62%
67%
64%
67%
65%
66%
Gypsum3
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Wood Flooring3
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- = Zero Emissions.
aWARM assumes that construction and demolition landfills do not collect landfill gas.
6-14
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Landfilling
December 2023
6.2.3 Emissions from Transportation to Landfills and Landfill Operation
WARM includes emissions associated with transportation and landfilling the material.
Transportation energy emissions occur when fossil fuels are combusted to collect and transport material
to the landfill facility and then to operate landfill operational equipment. To calculate the emissions,
WARM relies on assumptions from FAL (1994) for the equipment emissions and NREL's US Life Cycle
Inventory Database (USLCI) (NREL, 2015). The NREL emission factor assumes a diesel, short-haul truck.
Exhibit 6-12 provides the transportation emission factor calculation.
Exhibit 6-12: Transportation C02 Emissions Assumptions and Calculation
Equipment
Total
(MTCOzE/Short
Ton)
Collection Vehicles
0.00
Landfill Equipment
0.02
Total
0.02
6.2.4 Estimating Landfill Carbon Storage
The other anthropogenic fate of carbon in landfills is storage. As described in section 6.2.1, a
portion of the carbon in biodegradable materials (i.e., food waste, yard trimmings, paper, and wood)
that is not completely decomposed by anaerobic bacteria remains stored in the landfill. This carbon
storage would not normally occur under natural conditions, so it is counted as an anthropogenic sink
(IPCC, 2006; Bogner et al., 2007).
The discussion in section 6.2.2 on initial carbon contents and CH4 generation includes the
measured carbon stored from the Barlaz (1998), Wang et al. (2013), Wang et al. (2011), and Levis et al.
(2013) experiments. For the most part, the amount of stored carbon measured as the output during
these experiments is considered the final ratio of carbon stored to total initial dry weight of each
material type. For newspaper, wood flooring, and coated paper—which is used to estimate landfill
characteristics for magazines and third-class mail—the amount of carbon stored is reduced because
carbon outputs were greater than initial carbon.
To estimate the final carbon storage factor, the proportion of initial carbon stored found in
Exhibit 6-6 is multiplied by the initial carbon contents in Exhibit 6-4 to obtain the ratio of carbon storage
to dry weight for each material type found in Exhibit 6-13. These estimates are then converted from dry
weight to wet weight and from grams to metric tons of C02 per wet short ton of material. The last
column of Exhibit 6-13 provides the final carbon storage factors for the biodegradable solid waste
components modeled in WARM.
6-15
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WARM Version 16 Landfilling December 2023
Exhibit 6-13: Carbon Storage for Solid Waste Components
Ratio of Carbon
Ratio of Carbon
Storage to Dry
Ratio of Dry
Storage to Wet
Amount of Carbon
Weight (gram
Weight to Wet
Weight (gram
Stored (MTC02E
Material
C/dry gram)
Weight
C/wet gram)
per Wet Short Ton)
Corrugated Containers
0.26
0.83
0.22
0.72
Magazines/Third-class Mail
0.28
0.92
0.25
0.85
Newspaper
0.41
0.87
0.36
1.19
Office Paper
0.04
0.91
0.04
0.12
Phonebooks
0.41
0.87
0.36
1.19
Textbooks
0.04
0.91
0.04
0.12
Dimensional Lumber
0.44
0.75
0.33
1.09
Medium-density Fiberboard
0.37
0.75
0.28
0.92
Food Waste
0.12
0.29
0.04
0.12
Food Waste (meat only)
0.17
0.29
0.05
0.16
Food Waste (non-meat)
0.11
0.28
0.03
0.11
Beef
0.17
0.27
0.05
0.15
Poultry
0.17
0.31
0.05
0.17
Grains
0.17
0.89
0.15
0.50
Bread
0.17
0.64
0.11
0.36
Fruits and Vegetables
0.08
0.12
0.01
0.03
Dairy Products
0.17
0.30
0.05
0.17
Yard Trimmings
0.31
0.45
0.16
0.54
Grass
0.24
0.18
0.04
0.14
Leaves
0.39
0.62
0.24
0.79
Branches
0.38
0.84
0.32
1.06
Mixed MSW
0.08
0.80
0.06
0.21
Drywall
0.03
0.94
0.02
0.08
Wood Flooring
0.42
0.75
0.31
1.04
6.2.5 Electric Utility GHG Emissions Avoided
The CH4 component of landfill gas that is collected from landfills can be combusted to produce
heat and electricity, and recovery of heat and electricity from landfill gas offsets the combustion of
other fossil fuel inputs. WARM models the recovery of landfill gas for electricity generation and assumes
that this electricity offsets non-baseload electricity generation in the power sector.
WARM applies non-baseload electricity emission rates to calculate the emissions offset from
landfill gas energy recovery because the model assumes that incremental increases in landfill energy
recovery will affect non-baseload power plants (i.e., power plants that are "demand-following" and
adjust to marginal changes in the supply and demand of electricity). EPA calculated non-baseload
emission rates as the average emissions rate from power plants that combust fuel and have capacity
factors less than 0.8 (EPA, 2015a).
EPA estimated the avoided GHG emissions per MTC02E of CH4 combusted using several physical
constants and data from EPA's Landfill Methane Outreach Program and eGRID (EPA, 2013; EPA, 2018c).
The mix of fuels used to produce electricity varies regionally in the United States; consequently, EPA
applied a different C02-intensity for electricity generation depending upon where the electricity is
offset. The Excel version of WARM includes C02-intensity emission factors for non-baseload electricity
generated in nine different U.S. regions as well as a U.S.-average C02-intensity (EPA, 2015a). The
formula used to calculate the quantity of electricity generation emissions avoided per MTC02E of CH4
combusted is as follows:
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WARM Version 16
Landfilling
December 2023
BTU,
CH4
H,
X CL X R
LFGTE
Where:
BtuCH4 = Energy content of CH4 per MTC02E CH4 combusted; assumed to be 1,012 Btu per cubic foot
of CH4 (EPA, 2013), converted into Btu per MTC02E CH4 assuming 20 grams per cubic foot of
CH4 at standard temperature and pressure and a global warming potential of CH4 of 21
Hlfgte = Heat rate of landfill gas to energy conversion; assumed to be 11,700 Btu per kWh generated
(EPA, 2013)
a = Net capacity factor of electricity generation; assumed to be 85 percent (EPA, 2013)
Egnd = Non-baseload C02-equivalent GHG emissions intensity of electricity produced at the
regional or national electricity grid; values assumed for each region and U.S. average are
shown in Exhibit 6-15
R = Ratio of GHG emissions avoided from electricity generation per MTC02E of CH4 combusted
for landfill gas to energy recovery
Exhibit 6-14 shows variables in the GHG emissions offset for the national average fuel mix. The
final ratio is the product of columns (a) through (h). Exhibit 6-15 shows the amount of carbon avoided
per kilowatt-hour of generated electricity and the final ratio of MTC02E avoided of utility carbon per
MTC02E of CH4 combusted (column (g) and resulting column (i)).
Exhibit 6-14: Calculation to Estimate Utility GHGs Avoided Through Combustion of Landfill CH4 for Electricity
Based on National Average Electricity Grid Mix
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
Kg Utility
Metric
Ratio of
co2
Tons
mtco2e
Metric Tons
Cubic Ft.
kWh
Avoided/
Avoided
Avoided Utility
CH4/MTCO2E
Grams
ch4/
Electricity
Electricity
kWh
Utility
C02 per
ch4
CH4/Metric
Gram
Btu/Cubic
Generated/
Generation
Generated
C02/Kg
mtco2e ch4
Combusted
Ton CH4
ch4
Ft. CH4
Btu
Efficiency
Electricity
Utility C02
Combusted
0.04
1,000,000
0.05
1,012
0.00009
0.85
0.73
0.001
0.11
Exhibit 6-15: Ratio of MTC02E Avoided Utility Carbon per MTC02E CH4 Combusted by Region
Region
Kg Utility C02 Avoided/kWh
Generated Electricity
Ratio of MTC02E Avoided Utility C
per MTC02E CH4
Pacific
0.49
0.07
Mountain
0.80
0.12
West-North Central
0.93
0.14
West-South Central
0.68
0.10
East-North Central
0.93
0.14
East-South Central
0.75
0.11
New England
0.48
0.07
Mid Atlantic
0.66
0.10
South Atlantic
0.70
0.10
National Average
0.73
0.11
If regional avoided utility emission factors are not employed, WARM calculates U.S.-average
avoided utility emission factors based on the percent of CH4 generated at landfills in the nation with
landfill gas recovery and electricity production found in Exhibit 6-2, and assuming U.S.-average, non-
baseload electricity GHG emission intensity. Exhibit 6-16 shows this calculation for each material type
for the national average fuel mix.
6-17
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WARM Version 16 Landfilling December 2023
Exhibit 6-16: Overall Avoided Utility C02 Emissions per Short Ton of Waste Material (National Average Grid Mix)
Methane from Landfills With LFG
Recovery and Electricity Generation
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Percentage of CH4
Utility GHG
Recovered for
Percentage of CH4
Net Avoided C02
Emissions Avoided
Electricity
Utility GHG
From Landfills with
Emissions from
CH4 Generation
per MTCOzE CH4
Generation Not
Emissions Avoided
LFG Recovery and
Energy Recovery
(MTC02E/ Wet
Percentage of CH4
Combusted
Utilized Due to LFG
(MTC02E/ Wet
Electricity
(MTC02E/Wet Short
Short Ton)
Recovered
(MTCOzE)
System "Down
Short Ton)
Generation
Ton)
Material
(Exhibit 6-7)
(Exhibit 6-11)
(Exhibit 6-15)
Time"
(f = bxcxdx (1-e))
(Exhibit 6-2)
(h = f x g)
Corrugated
2.62
56%
0.11
3%
(0.15)
63%
(0.10)
Containers
Magazines/Third-
1.19
54%
(0.11)
3%
(0.07)
66%
(0.04)
class Mail
Newspaper
1.05
59%
(0.11)
3%
(0.06)
66%
(0.04)
Office Paper
3.89
59%
(0.11)
3%
(0.24)
66%
(0.16)
Phonebooks
1.05
59%
(0.11)
3%
(0.06)
66%
(0.04)
Textbooks
3.89
59%
(0.11)
3%
(0.24)
66%
(0.16)
Dimensional Lumber
0.17
58%
(0.11)
3%
(0.01)
2%
(0.00)
Medium-density
0.06
59%
(0.11)
3%
(0.00)
2%
(0.00)
Fiberboard
Food Waste
1.72
52%
(0.11)
3%
(0.09)
66%
(0.06)
Food Waste (meat
1.75
52%
(0.11)
3%
(0.09)
66%
(0.06)
only)
Food Waste (non-
1.70
52%
(0.11)
3%
(0.09)
66%
(0.06)
meat)
Beef
1.62
52%
(0.11)
3%
(0.09)
66%
(0.06)
Poultry
1.86
52%
(0.11)
3%
(0.10)
66%
(0.07)
Grains
5.33
52%
(0.11)
3%
(0.28)
66%
(0.19)
Bread
3.83
52%
(0.11)
3%
(0.20)
66%
(0.14)
Fruits and
0.69
52%
(0.11)
3%
(0.04)
66%
(0.02)
Vegetables
Dairy Products
1.82
52%
(0.11)
3%
(0.10)
66%
(0.06)
Yard Trimmings
0.81
47%
(0.11)
3%
(0.04)
66%
(0.03)
Grass
0.57
41%
(0.11)
3%
(0.02)
66%
(0.02)
Leaves
0.65
49%
(0.11)
3%
(0.03)
66%
(0.02)
Branches
1.45
54%
(0.11)
3%
(0.08)
66%
(0.05)
Mixed Paper
2.38
57%
(0.11)
3%
(0.14)
66%
(0.09)
(general)
6-18
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WARM Version 16
Landfilling
December 2023
Methane from Landfills With LFG
Recovery and Electricity Generation
(a)
(b)
(c)
(d)
Utility GHG
(e)
Percentage of CH4
Recovered for
(f)
(g)
Percentage of CH4
(h)
Net Avoided C02
Emissions Avoided
Electricity
Utility GHG
From Landfills with
Emissions from
CH4 Generation
per MTCOzE CH4
Generation Not
Emissions Avoided
LFG Recovery and
Energy Recovery
(MTC02E/ Wet
Percentage of CH4
Combusted
Utilized Due to LFG
(MTC02E/ Wet
Electricity
(MTC02E/Wet Short
Short Ton)
Recovered
(MTC02E)
System "Down
Short Ton)
Generation
Ton)
Material
(Exhibit 6-7)
(Exhibit 6-11)
(Exhibit 6-15)
Time"
(f = bxcxdx (1-e))
(Exhibit 6-2)
(h = f x g)
Mixed Paper
2.29
57%
(0.11)
3%
(0.14)
66%
(0.09)
(primarily
residential)
Mixed Paper
2.26
57%
(0.11)
3%
(0.13)
66%
(0.09)
(primarily from
offices)
Mixed Recyclables
2.01
49%
(0.11)
3%
(0.10)
66%
(0.07)
Mixed Organics
1.30
49%
(0.11)
3%
(0.07)
66%
(0.04)
Mixed MSW
1.62
60%
(0.11)
3%
(0.10)
66%
(0.07)
Wood Flooring3
0.18
0%
(0.11)
3%
-
2%
-
Drywall3
-
0%
(0.11)
3%
-
2%
-
- = Zero Emissions.
a WARM assumes that construction and demolition landfills do not collect landfill gas.
6-19
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WARM Version 16
Landfilling
December 2023
6.2.6 Net GHG Emissions from Landfilling
CH4 emissions, transportation C02 emissions, carbon storage, and avoided utility GHG emissions
are then summed to estimate the net GHG emissions from landfilling each material type. Exhibit 6-17
shows the net emission factors for landfilling each material based on typical landfill gas collection
practices, average landfill moisture conditions (i.e., for landfills receiving between 20 and 40 inches of
precipitation annually), and U.S.-average non-baseload electricity grid mix.
Exhibit 6-17: Net GHG Emissions from Landfilling (MTC02E/Short Ton)
Net
Avoided C02
Landfill
Emissions
Material
Transportation
Emissions from
Carbon
(Post-
to Landfill
Landfill CH4
Energy Recovery
Sequestration
Consumer)
Aluminum Cans
0.02
-
-
-
0.02
Aluminum Ingot
0.02
-
-
-
0.02
Steel Cans
0.02
-
-
-
0.02
Copper Wire
0.02
-
-
-
0.02
Glass
0.02
-
-
-
0.02
HDPE
0.02
-
-
-
0.02
LDPE
0.02
-
-
-
0.02
PET
0.02
-
-
-
0.02
LLDPE
0.02
-
-
-
0.02
PP
0.02
-
-
-
0.02
PS
0.02
-
-
-
0.02
PVC
0.02
-
-
-
0.02
PLA
0.02
-
-
(1.66)
(1.64)
Corrugated Containers
0.02
0.98
(0.10)
(0.72)
0.18
Magazines/Third-Class Mail
0.02
0.44
(0.04)
(0.85)
(0.43)
Newspaper
0.02
0.37
(0.04)
(1.19)
(0.85)
Office Paper
0.02
1.39
(0.16)
(0.12)
1.13
Phonebooks
0.02
0.37
(0.04)
(1.19)
(0.85)
Textbooks
0.02
1.39
(0.16)
(0.12)
1.13
Dimensional Lumber
0.02
0.15
0.00
(1.09)
(0.92)
Medium-density Fiberboard
0.02
0.05
0.00
(0.92)
(0.85)
Food Waste
0.02
0.66
(0.06)
(0.12)
0.50
Food Waste (meat only)
0.02
0.67
(0.06)
(0.16)
0.46
Food Waste (non-meat)
0.02
0.65
(0.06)
(0.11)
0.50
Beef
0.02
0.62
(0.06)
(0.15)
0.43
Poultry
0.02
0.71
(0.07)
(0.17)
0.49
Grains
0.02
2.04
(0.19)
(0.50)
1.37
Bread
0.02
1.47
(0.14)
(0.36)
0.99
Fruits and Vegetables
0.02
0.26
(0.02)
(0.03)
0.23
Dairy Products
0.02
0.70
(0.06)
(0.17)
0.48
Yard Trimmings
0.02
0.34
(0.03)
(0.54)
(0.20)
Grass
0.02
0.26
(0.02)
(0.14)
0.12
Leaves
0.02
0.26
(0.02)
(0.79)
(0.53)
Branches
0.02
0.56
(0.05)
(1.06)
(0.54)
Mixed Paper (general)
0.02
0.87
(0.09)
(0.72)
0.07
Mixed Paper (primarily residential)
0.02
0.84
(0.09)
(0.76)
0.02
Mixed Paper (primarily from offices)
0.02
0.82
(0.09)
(0.64)
0.11
Mixed Metals
0.02
-
-
-
0.02
Mixed Plastics
0.02
-
-
-
0.02
Mixed Recyclables
0.02
0.73
(0.07)
(0.65)
0.03
Mixed Organics
0.02
0.52
(0.04)
(0.30)
0.18
Mixed MSW
0.02
0.56
(0.07)
(0.21)
0.31
6-20
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WARM Version 16
Landfilling
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Net
Avoided C02
Landfill
Emissions
Material
Transportation
Emissions from
Carbon
(Post-
to Landfill
Landfill CH4
Energy Recovery
Sequestration
Consumer)
Carpet
0.02
-
-
-
0.02
Desktop CPUs
0.02
-
-
-
0.02
Portable Electronic Devices
0.02
-
-
-
0.02
Flat-panel Displays
0.02
-
-
-
0.02
CRT Displays
0.02
-
-
-
0.02
Electronic Peripherals
0.02
-
-
-
0.02
Hard-copy Devices
0.02
-
-
-
0.02
Mixed Electronics
0.02
-
-
-
0.02
Clay Bricks
0.02
-
-
-
0.02
Concrete
0.02
-
-
-
0.02
Fly Ash
0.02
-
-
-
0.02
Tires
0.02
-
-
-
0.02
Asphalt Concrete
0.02
-
-
-
0.02
Asphalt Shingles
0.02
-
-
-
0.02
Drywall
0.02
-
-
(0.08)
(0.06)
Fiberglass Insulation
0.02
-
-
-
0.02
Structural Steel
0.02
-
-
-
0.02
Vinyl Flooring
0.02
-
-
-
0.02
Wood Flooring3
0.02
0.16
-
(1.04)
(0.86)
- = Zero Emissions.
a WARM assumes that construction and demolition landfills do not collect landfill gas
In WARM, emissions from landfills are dependent on the user selection of one of four different
landfill scenarios (i.e., "Landfills: National Average/' "Landfills Without LFG Recovery/' "Landfills With
LFG Recovery and Flaring/' and "Landfills With LFG Recovery and Electric Generation") as described in
section 1. The net landfilling emission factors for landfilling each material based on the default options
in WARM (i.e., typical landfill gas collection practices, average landfill moisture conditions, and U.S.-
average non-baseload electricity grid mix) are shown in Exhibit 6-18.
Exhibit 6-18: Landfilling Net Emission Factors in WARM Using Default Options (MTC02E/Ton)
Landfills:
Landfills
Landfills with
Landfills with LFG
National Average
without LFG
LFG Recovery
Recovery and
Material
(Exhibit 6-17)
Recovery
and Flaring
Electricity Generation
Aluminum Cans
0.02
0.02
0.02
0.02
Aluminum Ingot
0.02
0.02
0.02
0.02
Steel Cans
0.02
0.02
0.02
0.02
Copper Wire
0.02
0.02
0.02
0.02
Glass
0.02
0.02
0.02
0.02
HDPE
0.02
0.02
0.02
0.02
LDPE
0.02
0.02
0.02
0.02
PET
0.02
0.02
0.02
0.02
LLDPE
0.02
0.02
0.02
0.02
PP
0.02
0.02
0.02
0.02
PS
0.02
0.02
0.02
0.02
PVC
0.02
0.02
0.02
0.02
PLA
(1.64)
(1.64)
(1.64)
(1.64)
Corrugated Containers
0.18
1.66
0.45
0.06
Magazines/Third-Class Mail
(0.43)
0.25
(0.37)
(0.46)
Newspaper
(0.85)
(0.23)
(0.75)
(0.89)
Office Paper
1.13
3.40
1.51
0.95
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Landfills:
Landfills
Landfills with
Landfills with LFG
National Average
without LFG
LFG Recovery
Recovery and
Material
(Exhibit 6-17)
Recovery
and Flaring
Electricity Generation
Phonebooks
(0.85)
(0.23)
(0.75)
(0.96)
Textbooks
1.13
3.40
1.51
0.72
Dimensional Lumber
(0.92)
(0.92)
(1.01)
(1.02)
Medium-density Fiberboard
(0.85)
(0.85)
(0.88)
(0.89)
Food Waste
0.50
1.45
0.57
0.45
Food Waste (meat only)
0.46
1.44
0.57
0.41
Food Waste (non-meat)
0.50
1.43
0.53
0.45
Beef
0.43
1.32
0.50
0.38
Poultry
0.49
1.52
0.57
0.44
Grains
1.37
4.32
1.59
1.22
Bread
0.99
3.11
1.15
0.88
Fruits and Vegetables
0.23
0.61
0.26
0.21
Dairy Products
0.48
1.48
0.55
0.43
Yard Trimmings
(0.20)
0.21
(0.17)
(0.22)
Grass
0.12
0.39
0.12
0.10
Leaves
(0.53)
(0.18)
(0.51)
(0.55)
Branches
(0.54)
0.26
(0.39)
(0.61)
Mixed Paper (general)
0.07
1.44
0.30
(0.03)
Mixed Paper (primarily residential)
0.02
1.33
0.23
(0.09)
Mixed Paper (primarily from offices)
0.11
1.42
0.29
0.02
Mixed Metals
0.02
0.02
0.02
0.02
Mixed Plastics
0.02
0.02
0.02
0.02
Mixed Recyclables
0.03
1.18
0.37
(0.13)
Mixed Organics
0.18
0.85
0.21
0.12
Mixed MSW
0.31
1.27
0.46
0.24
Carpet
0.02
0.02
0.02
0.02
Desktop CPUs
0.02
0.02
0.02
0.02
Portable Electronic Devices
0.02
0.02
0.02
0.02
Flat-panel Displays
0.02
0.02
0.02
0.02
CRT Displays
0.02
0.02
0.02
0.02
Electronic Peripherals
0.02
0.02
0.02
0.02
Hard-copy Devices
0.02
0.02
0.02
0.02
Mixed Electronics
0.02
0.02
0.02
0.02
Clay Bricks
0.02
0.02
0.02
0.02
Concrete
0.02
0.02
0.02
0.02
Fly Ash
0.02
0.02
0.02
0.02
Tires
0.02
0.02
0.02
0.02
Asphalt Concrete
0.02
0.02
0.02
0.02
Asphalt Shingles
0.02
0.02
0.02
0.02
Drywall
(0.06)
(1.04)
(0.06)
(0.06)
Fiberglass Insulation
0.02
0.02
0.02
0.02
Structural Steel
0.02
0.02
0.02
0.02
Vinyl Flooring
0.02
(0.86)
(0.86)
(0.86)
Wood Flooring
(0.86)
0.02
0.02
0.02
6.3 LIMITATIONS
The landfilling analysis has several limitations, outlined below.
• The net GHG emissions from landfilling each material are quite sensitive to the LFG recovery
rate. Because of the high global warming potential of CH4, small changes in the LFG recovery
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rate (for the national average landfill) could have a large effect on the net GHG impacts of
landfilling each material and the ranking of landfilling relative to other MSW management
options.
• The distribution of waste in place is not a perfect proxy for the distribution of ongoing waste
generation destined for landfill.
• Ongoing shifts in the use of landfill cover and liner systems are likely to influence the rate of CH4
generation and collection. As more landfills install effective covers and implement controls to
keep water and other liquids out, conditions will be less favorable for degradation of
biodegradable wastes. Over the long term, these improvements may result in a decrease in CH4
generation and an increase in carbon storage. Moreover, Dr. Barlaz believes that the CH4 yields
from his laboratory experiments are likely to be higher than CH4 yields in a landfill, because the
laboratory experiments were designed to generate the maximum amount of CH4 possible. If the
CH4 yields from the laboratory experiments were higher than yields in a landfill, the net GHG
emissions from landfilling biodegradable materials would be lower than estimated here.
• EPA assumed that once wastes are disposed in a landfill, they are never removed. In other
words, it was assumed that landfills are never "mined." A number of communities have mined
their landfills—removing and combusting the waste—in order to create more space for
continued disposal of waste in the landfill. To the extent that landfills are mined in the future, it
is incorrect to assume that carbon stored in a landfill will remain stored. For example, if
landfilled wastes are later combusted, the carbon that was stored in the landfill will be oxidized
to C02 in the combustor.
• The estimate of avoided utility GHG emissions per unit of CH4 combusted assumes that all
landfill gas-to-energy projects produce electricity. In reality, some projects are "direct gas"
projects, in which CH4 is piped directly to the end user for use as fuel. In these cases, the CH4
typically replaces natural gas as a fuel source. Because natural gas use is less GHG-intensive than
average electricity production, direct gas projects will tend to offset fewer GHG emissions than
electricity projects will—a fact not reflected in the analysis.
• For landfilling of yard trimmings (and other organic materials), EPA assumed that all carbon
storage in a landfill environment is incremental to the storage that occurs in a non-landfill
environment. In other words, it was assumed that in a baseline where yard trimmings are
returned to the soil (i.e., in a non-landfill environment), all of the carbon is decomposed
relatively rapidly (i.e., within several years) to C02, and there is no long-term carbon storage. To
the extent that long-term carbon storage occurs in the baseline, the estimates of carbon storage
reported here are overstated, and the net postconsumer GHG emissions are understated.
• Another limitation is the assumptions used in developing "corrected" CH4 yields for
biodegradable materials in MSW. Because of the high GWP of CH4, a small difference between
estimated and actual CH4 generation values would have a large effect on the GHG impacts of
landfilling and the ranking of landfilling relative to other MSW management options.
6.4 REFERENCES
61 FR 49. (1996). Standards of Performance for New Stationary Sources and Guidelines for Controls of
Existing Sources, Municipal Solid Waste Landfills. 61 Federal Register 49 (12 March 1996), pp.
9905-9944.
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71 FR 230. (2013). Revisions to the Greenhouse Gas Reporting Rule and Final Confidentiality
Determinations for New or Substantially Revised Data Elements; Final Rule. 78 Federal Register
230(29 November 2013), pp. 71904-71981.
Barlaz, M.A., Bareither, C.A., Hossain, A., Saquing, J., Mezzari, I., Benson, C.H., Tolaymat, T.M., Yazdani,
R. (2010). Performance of North American bioreactor landfills. II: Chemical and biological
characteristics. Journal of Environmental Engineering-ASCE 2010 (136), 839-853.
Barlaz, M.A. (2008). Memorandum to Parties Interested in Carbon Sequestration from Municipal Solid
Waste: "Corrections to Previously Published Carbon Storage Factors." February 27, 2008.
Barlaz, M.A. (2005). Letter to Randy Freed, ICF International: "Decomposition of Leaves." June 29, 2005.
Barlaz, M.A. (1998). Carbon storage during biodegradation of municipal solid waste components in
laboratory-scale landfills. Global Biogeochemical Cycles, 12 (2), 373-380.
Barlaz, M. A., Chanton, J. P., & Green, R. B. (2009). Controls on Landfill Gas Collection Efficiency:
Instantaneous and Lifetime Performance. Journal of the Air & Waste Management Association,
59 (12).
Barlaz, M. A., Ham, R. K., & Schaefer, D. M. (1990). Methane Production from Municipal Refuse: A
Review of Enhancement Techniques and Microbial Dynamics. Critical Reviews in Environmental
Control, 19 (6), 557.
Barlaz, M. A., Ham, R. K., & Schaefer, D. M. (1989). Mass Balance Analysis of Decomposed Refuse in
Laboratory Scale Lysimeters. Journal of Environmental Engineering, ASCE, 115 (6), 1088-1102.
Bogner, J., Ahmed, M. A., Diaz, C., Faaij, A., Gao, Q., Hashimoto, S., Mareckova, K., Pipatti, R., & Zhang, T.
(2007). Chapter 10L: Waste Management. In B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, & L.A.
Meyer (Eds.), Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and New
York: Cambridge University Press.
Bingemer, H G., & Crutzen, P. J. (1987). The Production of Methane from Solid Wastes. Journal of
Geophysical Research, 90 (D2), 2181-2187.
Chan G., Chu, L., & Wong, M. (2002). Effects of leachate recirculation on biogas production from landfill
co-disposal of municipal solid waste, sewage sludge and marine sediment. Environmental
Pollution 118 (3), 393-399.
Clemson University. (2021). "Available Moisture in Foods: What Is It Anyway?". College of Agriculture,
Forestry and Life Sciences. Clemson University, South Carolina. Available online at:
www.clemson.edu/extension/food/canning/canning-tips/39available-moisture.html.
Colberg, P.J. (1988). Anaerobic microbial degradation of cellulose lignin, oligolignols, and monoaromatic
lignin derivatives. In A. J .B. Zehnder (Ed.), Biology of anaerobic microorganisms. New York:
Wiley, pp. 333-372.
Czepiel, P. M., Mosher, B., Crill, P. M., & Harriss, R. C. (1996). Quantifying the effects of oxidation on
landfill methane emissions. Journal of Geophysical Research, 101, 16721-16729.
De la Cruz, F. B., & Barlaz, M. A. (2010). Estimation of Waste Component-Specific Landfill Decay Rates
Using Laboratory-Scale Decomposition Data. Environmental Science & Technology, 44 (12),
4722-4728. doi:10.1021/esl00240r.
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Eklund B., Anderson, E., Walker, B., & Burrows, D. (1998). Characterization of landfill gas composition at
the Fresh Kills municipal solid-waste landfill. Environmental Science & Technology, 32, 15, 2233-
2237.
Eleazer, W.E., Odle, W. S. Ill, Wang, Y. S., & Barlaz, M. A. (1997). "Biodegradability of municipal solid
waste components in laboratory-scale landfills." Env. Sci. Tech, 31 (3), 911-917.
EPA. (2019). Advancing Sustainable Materials Management: 2017 Fact Sheet. (EPA 530-F-19-007).
Washington, DC: U.S. Retrieved from https://www.epa.gov/sites/production/files/2019-
ll/documents/2017 facts and figures fact sheet final.pdf.
EPA (2018a) Greenhouse Gas Reporting Program (GHGRP). 2018 Envirofacts. Subpart HH: Municipal
Solid Waste Landfills and Subpart TT: Industrial Waste Landfills. Available online at:
https://www.epa.gov/enviro/greenhouse-gas-customized-search
EPA (2018b) Landfill Methane Outreach Program (LMOP). 2018 Landfill and Project Level Data.
September 2018. Available online at: https://www.epa.gov/lmop/landfill-technical-data
EPA. (2018c). Emissions & Generation Resource Integrated Database (eGRID). Available from EPA at
http://www.epa.gov/energy/egrid.
EPA. (2017). Methodology to Estimate the Quantity, Composition, and Management of Construction and
Demolition Debris in the United States. (EPA 600-R-15-111). Washington, DC: U.S. Retrieved
from https://nepis.epa.gov/Adobe/PDF/P100NDZ0.pdf.
EPA. (2015). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013. (EPA 430-R-15-004).
Washington, DC: U.S. Government Printing Office. Retrieved from
http://www3.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-lnventory-2015-Main-
Text.pdf
EPA. (2013). The Landfill Methane Outreach Program (LMOP) LFGE Benefits Calculator. Available online
at: http://www.epa.gov/lmop/proiects-candidates/lfge-calculator.html.
EPA. (2010). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2008. (EPA publication no. EPA
430-R-10-006.) Washington, DC: U.S. Environmental Protection Agency, Office of Atmospheric
Programs, April. Retrieved from:
http://www.epa.gov/climatechange/ghgemissions/usinventorvreport/archive.html.
EPA. (1998). AP-42 Emission factors for municipal solid waste landfills - Supplement E. Washington, DC:
U.S. Environmental Protection Agency.
FAL (1994). The Role of Recycling in Integrated Solid Waste Management to the Year 2000. Franklin
Associates, Ltd. (Stamford, CT: Keep America Beautiful, Inc.), pp. 1-16.
Freed, J., Skog, K., Mintz, C., & Glick, N. (2004). Carbon Storage due to Disposal of Biogenic Materials in
U.S. Landfills. Proceedings of the Third Annual Conference on Carbon Sequestration. U.S.
Department of Energy.
Hartog, C. L. (2003). The Bluestem Bioreactor. Briefing presented at the Bioreactor Workshop,
sponsored by USEPA, February 27-28, 2003, Arlington, VA.
Hilger, H., & Barlaz, M. (2001). Anaerobic decomposition of refuse in landfills and methane oxidation in
landfill cover soils, Manual of Environmental Microbiology, 2nd Ed., Washington, DC: Am. Soc.
Microbiol., pp. 696-718.
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IPCC. (2006). Chapter 3: Solid Waste Disposal. In 2006IPCC Guidelines for National Greenhouse Gas
Inventories, Volume 5: Waste (Vol. 5). Intergovernmental Panel on Climate Change (IPCC).
Retrieved from http://www.ipcc-nggip.iges.or.ip/public/2006gl/vol5.html.
Levis, J. and Barlaz, M.A. (2014). Landfill Gas Monte Carlo Model Documentation and Results. June 18,
2014.
Levis, J. W., Barlaz, M. A., DeCarolis, J. F., Ranjithan, S. R. (2013). What is the optimal way for a suburban
U.S. city to sustainably manage future solid waste? Perspectives from a Solid Waste
Optimization Life-cycle Framework (SWOLF). Environ. Sci. Technol., submitted.
Liptay, K., Chanton, J., Czepiel, P., & Mosher, B. (1998). Use of stable isotopes to determine methane
oxidation in landfill cover soils. Journal of Geophysical Research, 103 (D7), 8243-8250.
National Renewable Energy Laboratory (2015). "U.S. Life Cycle Inventory Database." Retrieved from
https://www.lcacommons.gov/nrel/search
Staley, B. F., & Barlaz, M. A. (2009). Composition of Municipal Solid Waste in the United States and
Implications for Carbon Sequestration and Methane Yield. Journal of Environmental Engineering,
135 (10), 901-909. doi: 10.1061/(ASCE)EE.1943-7870.0000032
Tolaymat, T.M., Green, R.B., Hater, G.R., Barlaz, M.A., Black, P., Bronson, D., Powell, J. (2010). Evaluation
of landfill gas decay constant for municipal solid waste landfills operated as bioreactors. Journal
of the Air & Waste Management Association, 2010 (60), 91-97.
US Department of Agriculture. Agricultural Research Service. (2015). USDA National Nutrient Database
for Standard Reference. Nutrient Data Laboratory Home Page, Available online at
http://www.ars.usda.gov/ba/bhnrc/nd.
Wang, X., Padgett, J. M., Powell, J. S., Barlaz, M. A. (2013). Decomposition of Forest Products Buried in
Landfills. Waste Management, 33 (11), 2267-2276.
Wang, X., Padgett, J.M., De la Cruz, F.B., and Barlaz, M.A. (2011). Wood Biodegradation in Laboratory-
Scale Landfills. Environmental Science & Technology, 2011 (45), 6864-6871.
Warith, M. A., Zekry, M., & Gawri, N. (1999). Effect of leachate recirculation on municipal solid waste
biodegradation." Water Quality Research Journal of Canada, 34 (2), pp. 267-280.
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7 ENERGY IMPACTS
Other chapters in EPA's Waste Reduction Model (WARM) focus on the effects of materials
management decisions on greenhouse gases (GHG). Generally, a large portion of GHG emissions is
related to energy use in resource acquisition, manufacturing, transportation, and end-of-life life-cycle
stages. Not all GHG emissions are related to energy, however, and the effects of GHG are not directly
translatable to energy impacts. One of the benefits of WARM is to help users see results in terms of both
GHG emissions (metric tons of carbon dioxide equivalent or carbon equivalent) and energy (millions of
Btu). For additional background, see the WARM Background and Overview chapter.
The energy effects of materials occur in each life-cycle stage—source reduction, recycling, reuse,
manufacturing—and knowledge of those effects can reduce the demand for raw materials and energy.
Energy savings can also result from some waste disposal practices, including waste-to-energy
combustors and landfill gas-to-energy systems.
To better understand the relationship between materials management and energy use, WARM
provides energy factors for five management scenarios (source reduction, recycling, combustion,
landfilling, and anaerobic digestion). This chapter discusses how these energy factors affect the
relationship between energy savings and GHG benefits.
7.1 METHODOLOGY FOR DEVELOPING ENERGY FACTORS
The WARM methodology described in the other chapters focuses on GHG emissions; the
methodology in this chapter focuses on all life-cycle components as they appear through the lens of
energy consumption or savings, rather than GHG emissions. Components such as forest carbon storage
and landfill carbon sequestration are not components in the energy life cycle, and thus we have not
included them as energy factors. We base energy factors primarily on the amount of energy required to
produce one ton of a given material. The total energy consumed is a result of direct fuel and electricity
consumption associated with raw material acquisition and manufacturing, fuel consumption for
transportation, and embedded energy. The other WARM chapters on specific materials describe the
energy required for processing and transporting virgin and recycled materials. Although the GHG
emission factors are a product of the electricity fuel mix and the carbon coefficients of fuels, the
methodology in this chapter is based only on energy consumption; therefore, the energy required for
the total process to make one ton of a particular material is the sum of energy consumed across all fuel
types.
The total energy, or embodied energy, required to manufacture each material comprises two
components: (1) process and transportation energy, and (2) embedded energy (i.e., energy content of
the raw material). The first component, to process and transport a material, is conceptually
straightforward; but the second component, embedded energy, is more complex. Embedded energy is
the energy inherently contained in the raw materials used to manufacture a product. For example, the
embedded energy of plastics comes from the petroleum used to make them. Because petroleum has an
inherent energy value, the amount of energy that is saved through plastic recycling and source
reduction is directly related to the energy that could have been produced if the petroleum had been
used as an energy source rather than as a raw material input. Another example is aluminum, which
includes an embedded energy component. The aluminum smelting process requires a carbon anode,
which is consumed during the electrolytic reduction process; carbon anodes are made from coal, itself
an energy source. Additional examples are carpet and electronics that contain embedded energy in their
plastic and aluminum components. Total energy values also include both nonrenewable and renewable
sources. For example, some aspects of the paper life-cycle include renewable fuel sources that have
little effect on GHG emissions.
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7.2 ENERGY IMPLICATIONS FOR WASTE MANAGEMENT OPTIONS
This chapter discusses the life-cycle energy implications for four management scenarios. As with
the GHG emission factors discussed in other chapters, negative values indicate net energy savings.
Waste reduction efforts, such as source reduction and recycling, can result in significant energy
savings. Source reduction techniques, such as double-sided copying, reducing the weight of products
(light-weighting), and reducing generation of food waste are, in most cases, more effective at reducing
energy than recycling because source reduction significantly lowers energy consumption associated with
raw material extraction and manufacturing processes.
In relating recycling to landfill disposal, the greatest energy savings per ton come from
aluminum cans, as shown in Exhibit 7-1. The savings reflect the nature of aluminum production-
manufacturing aluminum cans from virgin inputs is very energy intensive, whereas relatively little
energy is required to manufacture cans from recycled aluminum. Significant energy savings also result
from recycling carpet because the recycled material can be used to produce secondary goods, and thus
avoiding the energy-intensive processes required to manufacture those secondary goods.
Exhibit 7-1: Energy Savings per Short Ton of Recycled Material (Relative to Landfilling)
Concrete
0.4
Textbooks
0.6
Magazines/Third-class Mail
0.7
Asphalt Concrete
1 1.5
Glass
I 2.4
Asphalt Shingles
1 2.7
Dry wall
¦ 2.9
Tires
¦ 3.9
Fly Ash
¦ 5.0
Dimensional Lumber
¦ 5.7
Electronic Peripherals
H 8.1
Hard-Copy Devices
™ 8.2
CRT Displays
" 8.2
Structural Steel
H 9.5
Office Paper
9.6
Mixed Electronics
12.0
Phonebooks
12.0
Mixed Recyclables
14.9
Corrugated Containers
Flat-Panel Displays
14.9
15.3
Newspaper
16.6
Steel Cans
Mixed Paper (general)
Mixed Paper (primarily residential)
Mixed Paper (primarily from offices)
20.2
20.4
20.4
20.7
Portable Electronic Devices
21.2
Desktop CPUs
21.5
Carpet
21.7
PET
Mixed Plastics
28.9
DD
rr
unDC
HUrt
Mixed Metals
Copper Wire
Aluminum Ingot
Aluminum Cans
Million Btu/ShortTon Waste
Note: This figure excludes materials in WARM for which recycling is not a viable end-of-life management option.
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7.3 APPLYING ENERGY FACTORS
Fuels and energy are limited and expensive resources, and it is increasingly important to
examine the effects of waste management practices on energy. Organizations can use the energy factors
presented in Exhibit 7-6 through Exhibit 7-12 to quantify energy savings associated with waste
management practices. Organizations can use these comparisons to weigh the benefits of switching
from landfilling to another waste management option. For example, researchers used the comparisons
to evaluate the benefits of voluntary programs aimed at source reduction and recycling, such as EPA's
WasteWise and Pay-as-You-Throw programs. Additional information about the methodology of deriving
and applying these factors is available in the chapters on individual materials.
To apply the WARM energy factors, two scenarios are necessary: (1) a baseline scenario that
represents current management practices (e.g., disposing of one ton of steel cans in a landfill), and (2)
an alternative scenario that represents the alternative management practice (e.g., recycling a ton of
steel cans).35 With these scenarios, it is possible to calculate the amount of energy consumed or avoided
in the baseline and alternative management practices and then to calculate the difference between the
alternative scenario and the baseline scenario. The result represents the energy consumed or avoided
that is attributable to the alternative management scenario.
Exhibit 7-2 illustrates the application of these factors. The baseline management scenario in the
example uses disposal in a landfill that has national average conditions. The Btu number represents the
amount of energy required to transport and process the ton. The alternate scenario is based on
recycling the ton of cans. The difference, shown as a negative number, indicates that recycling one ton
of steel cans rather than landfilling them reduces the energy consumed by 20.23 million Btu.
Exhibit 7-2: Comparison of Waste Reduction Scenarios
Baseline: landfill 1 ton of steel cans 1 ton x 0.27 million Btu/ton = 0.27 million Btu
Alternate: recycle 1 ton of steel cans 1 ton x 19.97 million Btu/ton = 19.97 million Btu
Energy Impacts: 19.97 million Btu - 0.53 million Btu = - 20.23 million Btu
Note: Negative numbers indicate avoided emissions or energy savings.
7.4 RELATING ENERGY SAVINGS TO GHG BENEFITS
Because it can be difficult to conceptualize energy savings in Btu and GHG emissions reductions
in metric ton carbon dioxide equivalent (MTC02E), the common way to express the amount, the results
can be converted to common equivalents such as barrels of crude oil or gallons of gasoline, as shown in
Exhibit 7-3. These interpreted results produce important nuances, particularly when applied to convert
MTC02E savings to equivalent energy savings. The conversion is complicated for two reasons: (1) GHG
reductions reflect both energy and non-energy savings, and (2) the energy savings reflect savings across
a range of fossil fuels. Thus, conversions from total GHG reductions to an equivalency for barrels of oil
must be done with caution.
35 The energy factors are expressed in terms of million Btu of energy per short ton of material managed. In the case
of recycling, EPA defines one ton of material managed as one ton collected for recycling.
7-3
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WARM Version 16 Energy Impacts December 2023
Exhibit 7-3: Common Energy Conversion Factors and Emissions Equivalencies
Common Energy Conversion Factors
Emissions Equivalencies
Fuel:
Propane Cylinders Used for Home BBQs:
• Million Btu per Barrel of Oil: 5.8
• C02 emissions per cylinder (metric tons): 0.02
• Gallons per Barrel of Oil: 42
Railroad Cars Worth of Coal:
• Million Btu per Gallon of Gasoline: 0.12
• C02 emissions per Railcar (metric tons): 183.29
Cars (average passenger car over one year):
Cars (average passenger car over one year):
• Fuel Consumption (gallons of gas): 529
• C02 Emissions (metric tons): 4.71
• Fuel Consumption (Million Btu/year): 66
Household (average household per year):
• Million Btu per day: 0.32
Source: EPA, 2018
Source: EPA, 2018
Although energy savings are often associated with GHG emissions savings, it is inaccurate to
directly convert overall GHG emission benefits into energy savings equivalents. Equivalencies must
remain consistent within the energy category or the GHG emission context in which they were created.
Exhibit 7-4 illustrates GHG benefits derived from energy savings achieved through recycling relative to
landfilling. For example, for asphalt shingles, 100 percent of the GHG savings associated with recycling
rather than landfilling are energy-related, whereas for glass, only about half of the GHG savings are
energy-related. Because the GHG benefits of glass recycling consist of some energy and some non-
energy-related savings, this material type demonstrates the difficulties of converting GHG savings to
energy equivalents.
7-4
-------
WARM Version 16 Energy Impacts December 2023
Exhibit 7-4: Recycling GHG Benefits Attributable to Energy Savings (Relative to Landfilling)
Ly"j|\LLJ^J LrU j
OldSS
IVI lACU rAtLy Lid U1 c j
Magazines/Third-class Mail
Dimensional Lumber
Drywall
—
0
% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Note: This figure excludes materials in WARM for which recycling is not a viable end-of-life management option.
7-5
-------
WARM Version 16
Energy Impacts
December 2023
Exhibit 7-5 shows how energy savings and GHG savings can differ for a single scenario. The
example is for total derived GHG benefits from recycling glass and the conversion of energy savings is to
barrels of oil. Using the common equivalency factors, the GHG emission benefits are equivalent to GHG
emissions from the combustion of 70 barrels of oil. In contrast, the energy savings associated with
recycling glass are equivalent to the energy content of 41 barrels of oil.
Exhibit 7-5: Comparison of Emissions and Energy Benefits from Recycling
Recycling 100 Short Tons of Glass Compared to Landfilling
GHG Emission Benefits: 30 MTCO E Equivalent to the combustion emissions from 70 barrels of oil.
Energy Savings: 239 Million Btu Equivalent to the energy contained in 41 barrels of oil.
The difference between the benefits and the conversions has important implications. The term
"energy savings" covers a diverse mix of fuels (petroleum, electricity, natural gas, coal). In reality, glass
manufacturing depends mainly on energy produced from electricity, coal, and natural gas, not from
petroleum. The equivalency, stated as "barrels of oil," is only a simplified and recognizable energy
equivalent; little or no petroleum is actually saved.
Exhibit 7-6, Exhibit 1-1, Exhibit 7-8,
Exhibit 7-9, Exhibit 7-10, and Exhibit 7-11 show the components of the energy impact factors for
source reduction, recycling, combustion, composting, anaerobic digestion, and landfilling, respectively.
Exhibit 7-12 shows the net energy impacts of the six materials management options.
Exhibit 7-6: Energy Impacts for Source Reduction (Million Btu/Ton of Material Source Reduced)
(a)
(b)
(c)
(d)
Raw Materials Acquisition
Raw Materials Acquisition
and Manufacturing
and Manufacturing
Net Energy
Process Energy
Transport Energy
(d = b + c)
Displace
Displace
Displace
Current Mix
Current Mix
Current Mix
of Virgin and
Displace
of Virgin and
Displace
of Virgin and
Displace
Recycled
Virgin
Recycled
Virgin
Recycled
Virgin
Material
Inputs
Inputs
Inputs
Inputs
Inputs
Inputs
Aluminum Cans
(88.74)
(199.30)
(0.95)
(1.27)
(89.69)
(200.57)
Aluminum Ingot
(126.03)
(126.03)
(0.92)
(0.92)
(126.95)
(126.95)
Steel Cans
(25.11)
(31.58)
(4.78)
(4.96)
(29.88)
(36.54)
Copper Wire
(121.45)
(122.52)
(0.91)
(0.82)
(122.36)
(123.35)
Glass
(5.99)
(6.49)
(0.91)
(0.97)
(6.90)
(7.46)
HDPE
(57.93)
(63.71)
(3.19)
(3.28)
(61.12)
(66.99)
LDPE
(67.59)
(67.59)
(3.33)
(3.33)
(70.92)
(70.92)
PET
(48.46)
(49.59)
(1.56)
(1.54)
(50.02)
(51.13)
LLDPE
(62.98)
(62.98)
(3.30)
(3.30)
(66.29)
(66.29)
PP
(63.01)
(63.59)
(2.90)
(2.90)
(65.91)
(66.49)
PS
(71.95)
(71.95)
(2.90)
(2.90)
(74.85)
(74.85)
PVC
(46.14)
(46.14)
(2.00)
(2.00)
(48.14)
(48.14)
PLA
(29.38)
(29.38)
(0.71)
(0.71)
(30.09)
(30.09)
Corrugated Containers
(20.45)
(25.13)
(1.87)
(2.05)
(22.32)
(27.18)
Magazines/Third-class Mail
(32.95)
(32.99)
(0.28)
(0.28)
(33.23)
(33.27)
Newspaper
(35.80)
(39.92)
(0.67)
(0.78)
(36.46)
(40.70)
Office Paper
(36.32)
(37.01)
(0.28)
(0.28)
(36.60)
(37.29)
Phonebooks
(39.61)
(39.61)
(0.59)
(0.59)
(40.20)
(40.20)
Textbooks
(35.01)
(35.07)
(0.59)
(0.59)
(35.60)
(35.66)
Dimensional Lumber
(7.06)
(7.06)
NA
NA
(7.06)
(7.06)
Medium-density Fiberboard
(21.66)
(21.66)
(0.73)
(0.73)
(22.39)
(22.39)
7-6
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WARM Version 16
Energy Impacts
December 2023
(a)
(b)
(c)
(d)
Raw Materials Acquisition
Raw Materials Acquisition
and Manufacturing
and Manufacturing
Net Energy
Process Energy
Transport Energy
(d = b + c)
Displace
Displace
Displace
Current Mix
Current Mix
Current Mix
of Virgin and
Displace
of Virgin and
Displace
of Virgin and
Displace
Recycled
Virgin
Recycled
Virgin
Recycled
Virgin
Material
Inputs
Inputs
Inputs
Inputs
Inputs
Inputs
Food Waste
(12.81)
(12.81)
(1.75)
(1.75)
(14.56)
(14.56)
Food Waste (meat only)
(40.86)
(40.86)
(2.74)
(2.74)
(43.60)
(43.60)
Food Waste (non-meat)
(5.71)
(5.71)
(1.50)
(1.50)
(7.20)
(7.20)
Beef
(62.25)
(62.25)
(1.63)
(1.63)
(63.88)
(63.88)
Poultry
(22.80)
(22.80)
(3.68)
(3.68)
(26.48)
(26.48)
Grains
(5.35)
(5.35)
(0.29)
(0.29)
(5.64)
(5.64)
Bread
(6.34)
(6.34)
(0.18)
(0.18)
(6.52)
(6.52)
Fruits and Vegetables
(2.95)
(2.95)
(2.12)
(2.12)
(5.07)
(5.07)
Dairy Products
(13.61)
(13.61)
(0.65)
(0.65)
(14.27)
(14.27)
Yard Trimmings
NA
NA
NA
NA
NA
NA
Grass
NA
NA
NA
NA
NA
NA
Leaves
NA
NA
NA
NA
NA
NA
Branches
NA
NA
NA
NA
NA
NA
Mixed Paper (general)
(28.31)
(31.68)
(1.14)
(1.25)
(29.44)
(32.93)
Mixed Paper (primarily residential)
(27.45)
(30.98)
(1.21)
(1.33)
(28.66)
(32.31)
Mixed Paper (primarily from offices)
(34.20)
(35.58)
(0.44)
(0.47)
(34.64)
(36.05)
Mixed Metals
(47.43)
(90.41)
(3.43)
(3.67)
(50.86)
(94.08)
Mixed Plastics
(52.22)
(55.20)
(2.21)
(2.23)
(54.43)
(57.43)
Mixed Recyclables
(23.37)
(28.34)
(1.42)
(1.56)
NA
NA
Mixed Organics
NA
NA
NA
NA
NA
NA
Mixed MSW
NA
NA
NA
NA
NA
NA
Carpet
(89.70)
(89.70)
(1.36)
(1.36)
(91.06)
(91.06)
Desktop CPUs
(130.53)
(130.53)
(0.10)
(0.10)
(130.63)
(130.63)
Portable Electronic Devices
(163.16)
(163.16)
(0.19)
(0.19)
(163.36)
(163.36)
Flat-panel Displays
(125.51)
(125.51)
(0.16)
(0.16)
(125.66)
(125.66)
CRT Displays
NA
NA
NA
NA
NA
NA
Electronic Peripherals
(105.42)
(105.42)
(0.59)
(0.59)
(106.00)
(106.00)
Hard-copy Devices
(72.12)
(72.12)
(0.41)
(0.41)
(72.53)
(72.53)
Mixed Electronics
(119.49)
(119.49)
(0.22)
(0.22)
(119.70)
(119.70)
Clay Bricks
(5.10)
(5.10)
(0.03)
(0.03)
(5.13)
(5.13)
Concrete
NA
(0.05)
NA
(0.19)
NA
NA
Fly Ash
NA
(4.77)
NA
(0.10)
NA
NA
Tires
(71.14)
(73.79)
(0.57)
(0.54)
(71.71)
(74.33)
Asphalt Concrete
(0.94)
(0.94)
(0.73)
(0.73)
(1.68)
(1.68)
Asphalt Shingles
(2.15)
(2.15)
(0.96)
(0.96)
(3.11)
(3.11)
Drywall
(3.08)
(3.08)
(0.48)
(0.48)
(3.56)
(3.56)
Fiberglass Insulation
(3.96)
(4.73)
(0.77)
(0.83)
(4.73)
(5.56)
Structural Steel
(15.30)
(23.34)
(0.60)
(1.80)
(15.90)
(25.15)
Vinyl Flooring
(9.34)
(9.34)
(1.26)
(1.26)
(10.60)
(10.60)
Wood Flooring
(7.55)
(7.55)
(2.59)
(2.59)
(10.14)
(10.14)
Note: Negative numbers = Energy savings. NA = Not applicable.
7-7
-------
WARM Version 16 Energy Impacts December 2023
Exhibit 7-7: Energy Impacts for Recycling (Million Btu/Ton of Material Recycled)
(d)
(b)
(c)
Net Energy
(a)
Recycled Input Credit -
Recycled Input Credit -
(Post-Consumer)
Material
Process Energy
Transportation Energy
(d = b + c)
Aluminum Cans
(152.32)
(0.44)
(152.76)
Aluminum Ingot
(113.53)
(0.32)
(113.85)
Steel Cans
(19.40)
(0.56)
(19.97)
Copper Wire
(81.64)
(0.95)
(82.59)
Glass
(1.91)
(0.21)
(2.13)
HDPE
(44.11)
(0.67)
(44.78)
LDPE
NA
NA
NA
PET
(29.01)
0.42
(28.59)
LLDPE
NA
NA
NA
PP
(44.33)
(0.17)
(44.50)
PS
NA
NA
NA
PVC
NA
NA
NA
PLA
NA
NA
NA
Corrugated Containers
(14.37)
(0.73)
(15.10)
Magazines/Third-Class Mail
(0.69)
-
(0.69)
Newspaper
(16.07)
(0.42)
(16.49)
Office Paper
(10.08)
-
(10.08)
Phonebooks
(11.93)
-
(11.93)
Textbooks
(1.03)
-
(1.03)
Dimensional Lumber3
(5.21)
-
(5.21)
Medium-density Fiberboard
NA
NA
NA
Food Waste
NA
NA
NA
Food Waste (meat only)
NA
NA
NA
Food Waste (non-meat)
NA
NA
NA
Beef
NA
NA
NA
Poultry
NA
NA
NA
Grains
NA
NA
NA
Bread
NA
NA
NA
Fruits and Vegetables
NA
NA
NA
Dairy Products
NA
NA
NA
Yard Trimmings
NA
NA
NA
Grass
NA
NA
NA
Leaves
NA
NA
NA
Branches
NA
NA
NA
Mixed Paper (general)
(19.14)
(1.43)
(20.56)
Mixed Paper (primarily residential)
(19.14)
(1.43)
(20.56)
Mixed Paper (primarily from offices)
(19.39)
(1.46)
(20.85)
Mixed Metals
(66.03)
(0.52)
(66.55)
Mixed Plastics
(35.01)
(0.01)
(35.02)
Mixed Recyclables
(14.46)
(0.49)
(14.94)
Mixed Organics
NA
NA
NA
Mixed MSW
NA
NA
NA
Carpet
(21.83)
0.36
(21.46)
Desktop CPUs
(21.27)
0.06
(21.21)
Portable Electronic Devices
(21.08)
0.13
(20.95)
Flat-panel Displays
(15.24)
0.17
(15.07)
CRT Displays
(7.98)
0.04
(7.95)
Electronic Peripherals
(8.10)
0.22
(7.88)
Hard-Copy Devices
(7.90)
(0.02)
(7.91)
Mixed Electronics
(14.12)
0.11
(14.02)
Clay Bricks
NA
NA
NA
7-8
-------
WARM Version 16
Energy Impacts
December 2023
(d)
(b)
(c)
Net Energy
(a)
Recycled Input Credit -
Recycled Input Credit -
(Post-Consumer)
Material
Process Energy
Transportation Energy
(d = b + c)
Concrete
(0.01)
(0.09)
(0.11)
Fly Ash
(4.77)
-
(4.77)
Tires
(4.99)
1.39
(3.60)
Asphalt Concrete
(0.53)
(0.69)
(1.22)
Asphalt Shingles
(1.96)
(0.45)
(2.41)
Drywall
(2.11)
(0.49)
(2.60)
Fiberglass Insulation
NA
NA
NA
Structural Steel
(8.05)
(1.20)
(9.25)
Vinyl Flooring
NA
NA
NA
Wood Flooring3
(5.69)
(2.31)
(7.99)
Note: Negative energy impacts = Energy savings. NA = Not applicable.
a Modeled as Reuse in WARM.
Exhibit 7-8: Energy Impacts for Combustion (Million Btu/Ton of Material Combusted)
(c)
(e)
(b)
Energy Impacts
(d)
Net Energy
(a)
Electric Utility
due to Steel
Transportation to
(Post-Consumer)
Material
Fuel Consumption
Recovery
Combustion Facility
(e = b + c + d)
Aluminum Cans
0.32
NA
0.00
0.32
Aluminum Ingot
0.32
NA
0.00
0.32
Steel Cans
0.20
(17.61)
0.00
(17.41)
Copper Wire
0.26
NA
0.00
0.26
Glass
0.22
NA
0.00
0.22
HDPE
(18.83)
NA
0.00
(18.83)
LDPE
(18.72)
NA
0.00
(18.72)
PET
(9.99)
NA
0.00
(9.99)
LLDPE
(18.79)
NA
0.00
(18.79)
PP
(18.79)
NA
0.00
(18.79)
PS
(16.96)
NA
0.00
(16.96)
PVC
(7.42)
NA
0.00
(7.42)
PLA
(7.88)
NA
0.00
(7.88)
Corrugated Containers
(6.63)
NA
0.00
(6.63)
Magazines/Third-class Mail
(4.95)
NA
0.00
(4.95)
Newspaper
(7.49)
NA
0.00
(7.49)
Office Paper
(6.41)
NA
0.00
(6.41)
Phonebooks
(7.49)
NA
0.00
(7.49)
Textbooks
(6.41)
NA
0.00
(6.41)
Dimensional Lumber
(7.82)
NA
0.00
(7.82)
Medium-density Fiberboard
(7.82)
NA
0.00
(7.82)
Food Waste
(2.23)
NA
0.00
(2.23)
Food Waste (meat only)
(2.23)
NA
0.00
(2.23)
Food Waste (non-meat)
(2.23)
NA
0.00
(2.23)
Beef
(2.23)
NA
0.00
(2.23)
Poultry
(2.23)
NA
0.00
(2.23)
Grains
(2.23)
NA
0.00
(2.23)
Bread
(2.23)
NA
0.00
(2.23)
Fruits and Vegetables
(2.23)
NA
0.00
(2.23)
Dairy Products
(2.23)
NA
0.00
(2.23)
Yard Trimmings
(2.64)
NA
0.00
(2.64)
Grass
(2.64)
NA
0.00
(2.64)
Leaves
(2.64)
NA
0.00
(2.64)
7-9
-------
WARM Version 16
Energy Impacts
December 2023
(c)
(e)
(b)
Energy Impacts
(d)
Net Energy
(a)
Electric Utility
due to Steel
Transportation to
(Post-Consumer)
Material
Fuel Consumption
Recovery
Combustion Facility
(e = b + c + d)
Branches
(2.64)
NA
0.00
(2.64)
Mixed Paper (general)
(6.66)
NA
0.00
(6.66)
Mixed Paper (primarily residential)
(6.63)
NA
0.00
(6.63)
Mixed Paper (primarily from offices)
(6.12)
NA
0.00
(6.12)
Mixed Metals
0.24
(11.43)
0.00
(11.19)
Mixed Plastics
(13.50)
NA
0.00
(13.50)
Mixed Recyclables
(6.18)
(0.43)
0.00
(6.61)
Mixed Organics
(2.42)
NA
0.00
(2.42)
Mixed MSW
(4.71)
NA
0.00
(4.71)
Carpet
(7.16)
NA
0.00
(7.16)
Desktop CPUs
(1.44)
(10.32)
0.00
(11.76)
Portable Electronic Devices
(1.44)
(1.29)
0.00
(2.73)
Flat-panel Displays
(1.44)
(6.51)
0.00
(7.96)
CRT Displays
(1.44)
(0.85)
0.00
(2.30)
Electronic Peripherals
(1.44)
(0.37)
0.00
(1.82)
Hard-copy Devices
(1.44)
(6.58)
0.00
(8.03)
Mixed Electronics
(1.44)
(5.45)
0.00
(6.89)
Clay Bricks
NA
NA
NA
NA
Concrete
NA
NA
NA
NA
Fly Ash
NA
NA
NA
NA
Tires
(27.78)
(1.01)
0.00
(28.79)
Asphalt Concrete
NA
NA
NA
NA
Asphalt Shingles
(8.80)
NA
0.00
(8.80)
Drywall
NA
NA
NA
NA
Fiberglass Insulation
NA
NA
NA
NA
Structural Steel
NA
NA
NA
NA
Vinyl Flooring
(7.42)
NA
0.00
(7.42)
Wood Flooring
(10.23)
NA
0.00
(10.23)
Note: Negative energy impacts = Energy savings. NA = Not applicable.
Exhibit 7-9: Energy Impacts for Composting (Million Btu/Ton of Material Composted)
Material
Transportation and Turning Energy (Post-Consumer)
Aluminum Cans
NA
Aluminum Ingot
NA
Steel Cans
NA
Copper Wire
NA
Glass
NA
HDPE
NA
LDPE
NA
PET
NA
LLDPE
NA
PP
NA
PS
NA
PVC
NA
PLA
0.51
Corrugated Containers
NA
Magazines/Third-class Mail
NA
Newspaper
NA
Office Paper
NA
Phonebooks
NA
Textbooks
NA
7-10
-------
WARM Version 16
Energy Impacts
December 2023
Material
Transportation and Turning Energy (Post-Consumer)
Dimensional Lumber
NA
Medium-density Fiberboard
NA
Food Waste
0.73
Food Waste (meat only)
0.73
Food Waste (non-meat)
0.73
Beef
0.73
Poultry
0.73
Grains
0.73
Bread
0.73
Fruits and Vegetables
0.73
Dairy Products
0.73
Yard Trimmings
0.26
Grass
0.26
Leaves
0.26
Branches
0.26
Mixed Paper (general)
NA
Mixed Paper (primarily residential)
NA
Mixed Paper (primarily from offices)
NA
Mixed Metals
NA
Mixed Plastics
NA
Mixed Recyclables
NA
Mixed Organics
0.51
Mixed MSW
NA
Carpet
NA
Desktop CPUs
NA
Portable Electronic Devices
NA
Flat-panel Displays
NA
CRT Displays
NA
Electronic Peripherals
NA
Hard-copy Devices
NA
Mixed Electronics
NA
Clay Bricks
NA
Concrete
NA
Fly Ash
NA
Tires
NA
Asphalt Concrete
NA
Asphalt Shingles
NA
Drywall
NA
Fiberglass Insulation
NA
Structural Steel
NA
Vinyl Flooring
NA
Wood Flooring
NA
Note: Negative energy impacts = Energy savings. NA = Not applicable.
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WARM Version 16 Energy Impacts December 2023
Exhibit 7-10: Energy Impacts for Anaerobic Digestion
Million Btu/Ton of Material Digested
(e)
(c)
(d)
Net Energy
(a)
(b)
Transportation
Avoided Utility
(Post-Consumer)
Material
Process Energy
Energy to Digester
Energy
(e = b + c + d)
Aluminum Cans
NA
NA
NA
NA
Aluminum Ingot
NA
NA
NA
NA
Steel Cans
NA
NA
NA
NA
Copper Wire
NA
NA
NA
NA
Glass
NA
NA
NA
NA
HDPE
NA
NA
NA
NA
LDPE
NA
NA
NA
NA
PET
NA
NA
NA
NA
LLDPE
NA
NA
NA
NA
PP
NA
NA
NA
NA
PS
NA
NA
NA
NA
PVC
NA
NA
NA
NA
PLA
NA
NA
NA
NA
Corrugated Containers
NA
NA
NA
NA
Magazines/Third-class Mail
NA
NA
NA
NA
Newspaper
NA
NA
NA
NA
Office Paper
NA
NA
NA
NA
Phonebooks
NA
NA
NA
NA
Textbooks
NA
NA
NA
NA
Dimensional Lumber
NA
NA
NA
NA
Medium-density Fiberboard
NA
NA
NA
NA
Food Waste
0.26
0.04
(0.66)
(0.36)
Food Waste (meat only)
0.26
0.04
(0.66)
(0.36)
Food Waste (non-meat)
0.26
0.04
(0.66)
(0.36)
Beef
0.26
0.04
(0.66)
(0.36)
Poultry
0.26
0.04
(0.66)
(0.36)
Grains
0.26
0.04
(0.66)
(0.36)
Bread
0.26
0.04
(0.66)
(0.36)
Fruits and Vegetables
0.26
0.04
(0.66)
(0.36)
Dairy Products
0.26
0.04
(0.66)
(0.36)
Yard Trimmings
0.29
0.04
(0.19)
0.14
Grass
0.26
0.04
(0.18)
0.12
Leaves
0.31
0.04
(0.10)
0.24
Branches
0.32
0.04
(0.28)
0.08
Mixed Paper (general)
NA
NA
NA
NA
Mixed Paper (primarily residential)
NA
NA
NA
NA
Mixed Paper (primarily from offices)
NA
NA
NA
NA
Mixed Metals
NA
NA
NA
NA
Mixed Plastics
NA
NA
NA
NA
Mixed Recyclables
NA
NA
NA
NA
Mixed Organics
0.27
0.04
(0.44)
(0.13)
Mixed MSW
NA
NA
NA
NA
Carpet
NA
NA
NA
NA
Desktop CPUs
NA
NA
NA
NA
Portable Electronic Devices
NA
NA
NA
NA
Flat-panel Displays
NA
NA
NA
NA
CRT Displays
NA
NA
NA
NA
Electronic Peripherals
NA
NA
NA
NA
Hard-copy Devices
NA
NA
NA
NA
Mixed Electronics
NA
NA
NA
NA
Clay Bricks
NA
NA
NA
NA
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WARM Version 16
Energy Impacts
December 2023
(e)
(c)
(d)
Net Energy
(a)
(b)
Transportation
Avoided Utility
(Post-Consumer)
Material
Process Energy
Energy to Digester
Energy
(e = b + c + d)
Concrete
NA
NA
NA
NA
Fly Ash
NA
NA
NA
NA
Tires
NA
NA
NA
NA
Asphalt Concrete
NA
NA
NA
NA
Asphalt Shingles
NA
NA
NA
NA
Drywall
NA
NA
NA
NA
Fiberglass Insulation
NA
NA
NA
NA
Structural Steel
NA
NA
NA
NA
Vinyl Flooring
NA
NA
NA
NA
Wood Flooring
NA
NA
NA
NA
Note: Negative energy impacts = Energy savings. NA = Not applicable.
Assumes dry digestion with digestate curing and national average utility grid mix.
Exhibit 7-11: Energy Impacts for Landfilling (Million Btu/Ton of Material Landfilled)
(d)
(b)
(c)
Net Energy
(a)
Transportation to
Electric Utility Fuel
(Post-Consumer)
Material
Landfill
Consumption
(d = b + c)
Aluminum Cans
0.27
NA
0.27
Aluminum Ingot
0.27
NA
0.27
Steel Cans
0.27
NA
0.27
Copper Wire
0.27
NA
0.27
Glass
0.27
NA
0.27
HDPE
0.27
NA
0.27
LDPE
0.27
NA
0.27
PET
0.27
NA
0.27
LLDPE
0.27
NA
0.27
PP
0.27
NA
0.27
PS
0.27
NA
0.27
PVC
0.27
NA
0.27
PLA
0.27
NA
0.27
Corrugated Containers
0.27
(0.48)
(0.21)
Magazines/Third-class Mail
0.27
(0.21)
0.06
Newspaper
0.27
(0.20)
0.07
Office Paper
0.27
(0.74)
(0.47)
Phonebooks
0.27
(0.20)
0.07
Textbooks
0.27
(0.74)
(0.47)
Dimensional Lumber
0.27
(0.00)
0.27
Medium-density Fiberboard
0.27
(0.00)
0.27
Food Waste
0.27
(0.29)
(0.02)
Food Waste (meat only)
0.27
(0.29)
(0.02)
Food Waste (non-meat)
0.27
(0.28)
(0.02)
Beef
0.27
(0.27)
(0.00)
Poultry
0.27
(0.31)
(0.04)
Grains
0.27
(0.89)
(0.62)
Bread
0.27
(0.64)
(0.37)
Fruits and Vegetables
0.27
(0.11)
0.15
Dairy Products
0.27
(0.30)
(0.03)
Yard Trimmings
0.27
(0.12)
0.15
Grass
0.27
(0.08)
0.19
Leaves
0.27
(0.10)
0.16
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WARM Version 16
Energy Impacts
December 2023
(d)
(b)
(c)
Net Energy
(a)
Transportation to
Electric Utility Fuel
(Post-Consumer)
Material
Landfill
Consumption
(d = b + c)
Branches
0.27
(0.25)
0.01
Mixed Paper (general)
0.27
(0.44)
(0.17)
Mixed Paper (primarily residential)
0.27
(0.42)
(0.15)
Mixed Paper (primarily from offices)
0.27
(0.42)
(0.15)
Mixed Metals
0.27
NA
0.27
Mixed Plastics
0.27
NA
0.27
Mixed Recyclables
0.27
(0.32)
(0.05)
Mixed Organics
0.27
(0.21)
0.06
Mixed MSW
0.27
(0.31)
(0.05)
Carpet
0.27
NA
0.27
Desktop CPUs
0.27
NA
0.27
Portable Electronic Devices
0.27
NA
0.27
Flat-panel Displays
0.27
NA
0.27
CRT Displays
0.27
NA
0.27
Electronic Peripherals
0.27
NA
0.27
Hard-copy Devices
0.27
NA
0.27
Mixed Electronics
0.27
NA
0.27
Clay Bricks
0.27
NA
0.27
Concrete
0.27
NA
0.27
Fly Ash
0.27
NA
0.27
Tires
0.27
NA
0.27
Asphalt Concrete
0.27
NA
0.27
Asphalt Shingles
0.27
NA
0.27
Drywall
0.27
NA
0.27
Fiberglass Insulation
0.27
NA
0.27
Structural Steel
0.27
NA
0.27
Vinyl Flooring
0.27
NA
0.27
Wood Flooring
0.27
NA
0.27
Note: Negative energy impacts = Energy savings. NA = Not applicable.
Exhibit 7-12: Net Energy Impacts from Source Reduction and MSW Management Options (Million Btu/Ton)
Source
Reduction for
Current Mix
Anaerobic
Material
of Inputs
Recycling
Combustion
Composting
Digestion
Landfilling
Aluminum Cans
(89.69)
(152.76)
0.32
NA
NA
0.27
Aluminum Ingot
(126.95)
(113.85)
0.32
NA
NA
0.27
Steel Cans
(29.88)
(19.97)
(17.41)
NA
NA
0.27
Copper Wire
(122.36)
(82.59)
0.26
NA
NA
0.27
Glass
(6.90)
(2.13)
0.22
NA
NA
0.27
HDPE
(61.12)
(44.78)
(18.83)
NA
NA
0.27
LDPE
(70.92)
NA
(18.72)
NA
NA
0.27
PET
(50.02)
(28.59)
(9.99)
NA
NA
0.27
LLDPE
(66.29)
NA
(18.79)
NA
NA
0.27
PP
(65.91)
(44.50)
(18.79)
NA
NA
0.27
PS
(74.85)
NA
(16.96)
NA
NA
0.27
PVC
(48.14)
NA
(7.42)
NA
NA
0.27
PLA
(30.09)
NA
(7.88)
0.51
NA
0.27
Corrugated Containers
(22.32)
(15.10)
(6.63)
NA
NA
(0.21)
Magazines/Third-class Mail
(33.23)
(0.69)
(4.95)
NA
NA
0.06
Newspaper
(36.46)
(16.49)
(7.49)
NA
NA
0.07
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Energy Impacts
December 2023
Source
Reduction for
Current Mix
Anaerobic
Material
of Inputs
Recycling
Combustion
Composting
Digestion
Landfilling
Office Paper
(36.60)
(10.08)
(6.41)
NA
NA
(0.47)
Phonebooks
(40.20)
(11.93)
(7.49)
NA
NA
0.07
Textbooks
(35.60)
(1.03)
(6.41)
NA
NA
(0.47)
Dimensional Lumber3
(7.06)
(5.21)
(7.82)
NA
NA
0.27
Medium-density Fiberboard
(22.39)
NA
(7.82)
NA
NA
0.27
Food Waste
(14.56)
NA
(2.23)
0.73
(0.36)
(0.02)
Food Waste (meat only)
(43.60)
NA
(2.23)
0.73
(0.36)
(0.02)
Food Waste (non-meat)
(7.20)
NA
(2.23)
0.73
(0.36)
(0.02)
Beef
(63.88)
NA
(2.23)
0.73
(0.36)
(0.00)
Poultry
(26.48)
NA
(2.23)
0.73
(0.36)
(0.04)
Grains
(5.64)
NA
(2.23)
0.73
(0.36)
(0.62)
Bread
(6.52)
NA
(2.23)
0.73
(0.36)
(0.37)
Fruits and Vegetables
(5.07)
NA
(2.23)
0.73
(0.36)
0.15
Dairy Products
(14.27)
NA
(2.23)
0.73
(0.36)
(0.03)
Yard Trimmings
NA
NA
(2.64)
0.26
0.14
0.15
Grass
NA
NA
(2.64)
0.26
0.12
0.19
Leaves
NA
NA
(2.64)
0.26
0.24
0.16
Branches
NA
NA
(2.64)
0.26
0.08
0.01
Mixed Paper (general)
(29.44)
(20.56)
(6.66)
NA
NA
(0.17)
Mixed Paper (primarily residential)
(28.66)
(20.56)
(6.63)
NA
NA
(0.15)
Mixed Paper (primarily from offices)
(34.64)
(20.85)
(6.12)
NA
NA
(0.15)
Mixed Metals
(50.86)
(66.55)
(11.19)
NA
NA
0.27
Mixed Plastics
(54.43)
(35.02)
(13.50)
NA
NA
0.27
Mixed Recyclables
NA
(14.94)
(6.61)
NA
NA
(0.05)
Mixed Organics
NA
NA
(2.42)
0.51
(0.13)
0.06
Mixed MSW
NA
NA
(4.71)
NA
NA
(0.05)
Carpet
(91.06)
(21.46)
(7.16)
NA
NA
0.27
Desktop CPUs
(130.63)
(21.21)
(11.76)
NA
NA
0.27
Portable Electronic Devices
(163.36)
(20.95)
(2.73)
NA
NA
0.27
Flat-panel Displays
(125.66)
(15.07)
(7.96)
NA
NA
0.27
CRT Displays
NA
(7.95)
(2.30)
NA
NA
0.27
Electronic Peripherals
(106.00)
(7.88)
(1.82)
NA
NA
0.27
Hard-copy Devices
(72.53)
(7.91)
(8.03)
NA
NA
0.27
Mixed Electronics
(119.70)
(14.02)
(6.89)
NA
NA
0.27
Clay Bricks
(5.13)
NA
NA
NA
NA
0.27
Concrete
NA
(0.11)
NA
NA
NA
0.27
Fly Ash
NA
(4.77)
NA
NA
NA
0.27
Tires
(71.71)
(3.60)
(28.79)
NA
NA
0.27
Asphalt Concrete
(1.68)
(1.22)
NA
NA
NA
0.27
Asphalt Shingles
(3.11)
(2.41)
(8.80)
NA
NA
0.27
Drywall
(3.56)
(2.60)
NA
NA
NA
0.27
Fiberglass Insulation
(4.73)
NA
NA
NA
NA
0.27
Structural Steel
(15.90)
(9.25)
NA
NA
NA
0.27
Vinyl Flooring
(10.60)
NA
(7.42)
NA
NA
0.27
Wood Flooring3
(10.14)
(7.99)
(10.23)
NA
NA
0.27
Note: Negative energy impacts = Energy savings. NA = Not applicable.
a Modeled as Reuse in WARM.
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7.5 REFERENCES
EPA. (2018). Greenhouse Gas Equivalencies Calculator. U.S. Environmental Protection Agency,
Washington, DC.
EPA. (2006). Solid Waste Management and Greenhouse Gases: A Life-Cycle Assessment of Emissions and
Sinks. U.S. Environmental Protection Agency (EPA).
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Economic Impacts
December 2023
8 ECONOMIC IMPACTS
This chapter describes the development of the economic impact factors for EPA's Waste
Reduction Model (WARM). The chapter includes a summary of the economic implications of materials
management (Section 8.1); a summary of the types of economic impacts considered (Section 8.20); a
summary of the economic impact modeling methodology and output metrics incorporated into the tool
(Section 8.3); detailed methodology for calculating the economic factors associated with waste
management processes (i.e., waste diversion and waste disposal) and energy reduction and generation
(Section 8.4); a summary of the results (Section 8.5); and a summary of the limitations (Section 8.6). To
view the consolidated economic impact factor results for materials management activity and energy
savings/generation, please access Section 8.5.3 of this chapter.
8.1 A SUMMARY OF THE ECONOMIC IMPLICATIONS OF WASTE REDUCTION OR ALTERNATIVE WASTE
MANAGEMENT PRACTICES
Prior to the release of WARM Version 15, WARM did not include potential economic benefits
from waste reduction or alternative waste management practices. These various economic impacts can
be a main driver in materials management decisions and are an important consideration for users
hoping to manage materials more sustainably.
In recent years WARM stakeholders have acknowledged the benefit of including economic
impacts as a consideration for their waste management practices as they have to assess materials
management options in the context of budget considerations. For these organizations, having a better
understanding of the ways in which waste management activities support jobs, wages, and tax revenue
in addition to the GHG impacts could provide additional support for moving towards more sustainable
management practices.
8.2 SUMMARY OF ECONOMIC IMPACTS
The U.S. Recycling Economic Information (REI) study—first published in 2001 (R.W. Beck 2001)
and updated in 2016 (U.S. EPA 2016a) and in 2020 (U.S. EPA 2020)—provides an approach for measuring
economic activity associated with recycling. This approach uses a Waste Input-Output (WIO) model built
on the official Input-Output (10) tables maintained by the U.S. Bureau of Economic Analysis (BEA). The
BEA 10 tables describe the economic transactions between industries and were used in the 2020 REI
report to estimate the economic activity attributable to recycling processes and activities for nine
material sectors.36 EPA used the estimates from the REI report to develop economic impact factors
associated with the waste management practices themselves (i.e., landfilling, combustion, recycling,
composting, and anaerobic digestion) for WARM.
One limitation of the 2020 REI study is that it does not account for the impact of reduced input
costs and energy usage for recycled material by end users or energy generation that are otherwise
captured as part of the GHG emissions and energy impacts estimated in WARM. Economic factors
incorporated into the most recent version of WARM build on the findings in the 2020 REI report by
quantifying the economic impacts associated with both waste management as well as the broader
36 Currently, the U.S. official 10 table shows flows of transactions between industries but does not distinguish
between recycling operations and recyclable material flows. Separating out the recycling activities is complicated
because they are either embedded in the broader activities of a manufacturing sector or aggregated within the
waste management and remediation services industry. To isolate the impact of recycling in the 2020 REI study, EPA
developed a methodology to determine the presence of recycled content in final goods and the upstream impacts
of the recycled content. Recycling material flows from the WIO model are cross walked with separate data tables
of taxes, wages and jobs to assess the impact of recycling activities within the U.S. economy. More information on
the REI factors is included in Section 8.4.1.1.
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Economic Impacts
December 2023
economic benefits associated with energy savings and generation (i.e., product manufacturing using
recycled inputs or energy production from anaerobic digestion).
The WARM tool provides a consolidated economic benefit factor for various waste management
practices and end user energy savings. The factor for each component was calculated and is discussed
separately below given the different methodological approaches employed.
8.2.1 Economic Impacts Associated with Waste Management Processes
Waste diversion management processes—which refer to all activity associated with recycling,
composting, and anaerobic digestion—encompass a wide range of activities ranging from material
collection, separation, cleaning and/or other processing (e.g., baling plastic bottles); transformation of
recyclable materials into marketable products, distribution, storage and service delivery (e.g.,
distribution of food to and from food banks), and transportation between each stage. The waste
diversion factors incorporated into WARM account for both the direct economic activity associated with
the actual transformation of recyclable materials into marketable products, as well as the economic
activities involved in the value chain of the direct processes such as the collection, sorting, and
transportation of materials.
Activity associated with waste disposal includes the economic activity related to landfilling and
combustion. These impacts are generally smaller than the impacts related to waste diversion, as waste
disposal activities are not as labor intensive. Waste disposal and materials collection relies on large
capital-intensive equipment to handle large waste tonnages with few employees.
8.2.2 Economic Impacts Associated with Energy Savings and Energy Generation
Energy savings are typically realized when products are produced/manufactured with recycled
materials rather than virgin materials. These energy savings, which lower the cost of production, are
then reinvested into the manufacturing processing thereby increasing production. Similarly, when
energy is produced through the process of anaerobic digestion, the electricity that is generated and
used on-site at the anaerobic digestion facility, and excess electricity that is exported to the grid has a
positive economic benefit.
8.3 SUMMARY OF ECONOMIC IMPACT MODELING METHODOLOGY, ASSUMPTIONS AND
OUTPUT METRICS INCORPORATED INTO MATERIALS MANAGEMENT FOR WARM
Regional economic modeling is founded on the principle that industry sectors are
interdependent: one industry purchases inputs from other industries and households (e.g., labor) and
then sells outputs to other industries, households, and government entities. Therefore, economic
activity in one sector can cause an increased flow of money throughout the economy. The effect of
these multiple rounds of spending constitutes the total impact.
The economic impacts of materials management practices that provide the basis for the WARM
waste diversion and disposal economic factors are captured at two levels of impact—direct and
indirect—as defined in the U.S. REI study published in 2001 by the National Recycling Coalition and the
U.S. EPA (R.W. Beck 2001) and updated in 2016 (U.S. EPA 2016a) and in 2020 (U.S. EPA 2020). The
impacts are defined below:
• Direct impacts are associated with the "actual transformation of recyclable materials into
marketable products."
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• Indirect impacts include "upstream supply chain economic activity that supports recycling
processes/' such as the number of employees who work for the suppliers of recycling materials
and employees of other suppliers throughout the upstream supply chain (U.S. EPA 2020).
The economic impacts of energy savings and energy production are inclusive of direct, indirect,
and induced impacts. Specifically, in addition to the direct impacts in primary industries where spending
occurs and indirect impacts of industries that supply or interact with primary industries, the economic
impacts of energy savings and energy production also include induced impacts, which represent
increased spending of workers who earn income as a result of the proposed project.
These economic impacts are characterized by WARM using three commonly used output
metrics: employment, wage, and tax output. Each output metric is described below in further detail:
• Employment represents the jobs supported by materials management of each ton of a material,
based on the output per worker and output for each industry. For the purposes of this analysis,
employment is characterized in terms of labor hours, as described in Section 8.4.1 below.
• Labor income (wages) includes all forms of employment income, including Employee
Compensation (wages and benefits) and Proprietor Income.
• Tax impact is the breakdown of taxes collected by the federal, state and local government,
including corporate taxes, household income taxes, and other business taxes.
A number of underlying assumptions were made as the basis for incorporating economic factors
into WARM. These assumptions apply to both the factors that capture the economic activity of waste
management activity as well as related energy savings and generation.
• EPA relied on two conventions that are common in economic analysis: that (1) economic
impacts of waste management practices are consistent across the country and (2) historic
impacts will hold true in the present.
• While employment factors are typically presented in terms of number of jobs (full-time
employment), EPA determined that the scale of employment impacts of one ton of waste are
more meaningfully represented in terms of labor hours. To convert the employment factors for
jobs from the underlying data sources to labor hours, EPA assumed that one job is equivalent to
one annual, full-time employment and therefore equal to 2,080 labor hours.37
8.4 DETAILED METHODOLOGY FOR CALCULATING ECONOMIC FACTORS
This section describes the detailed methodology EPA used to calculate the economic factors
associated with waste diversion (recycling, composting and anaerobic digestion), waste disposal
(landfilling and combustion), energy reduction, and energy generation.
8.4.1 Economic Factors for Waste Diversion
8.4.1.1 Sources for Calculating Factors for Waste Diversion
All activity associated with recycling, composting, and anaerobic digestion were considered
collectively as "waste diversion" for the purposes of developing the economic factors for relevant
materials in WARM. The primary source for determining economic factors for waste diversion was the
U.S. REI study published in 2001 (R.W. Beck 2001) and updated in 2016 (U.S. EPA 2016) and in 2020 (U.S.
37 Congressional Research Service. 2021. Federal Workforce Statistics Sources: OPM and OMB. Retrieved from:
https://sgp.fas.org/crs/misc/R43590.pdf.
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Economic Impacts
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EPA 2020). The REI study focused on estimating the economic impacts of waste diversion activities
associated with nine waste stream categories, including a range of materials that are diverted and used
to make new products. The nine material categories include: ferrous metals, aluminum, glass, plastics,
rubber, construction and demolition (C&D) material, electronics, food and organics (donated food), and
food and organics (recyclable organics). In the REI study, recycling is defined as "the recovery of
discarded materials from the waste stream (e.g., municipal solid waste [MSW]) and the processing of
that material into new products," resulting in the displacement of "the use (and demand) of virgin
materials" (U.S. EPA 2020).
Based on this definition of "recycling," the REI study includes materials management practices
that are captured in WARM (e.g., composting of food waste) as well as management practices not
explicitly modeled in WARM (e.g., recovery and refurbishment of end-of-life products and materials,
food donation). The REI study includes an analysis of economic impacts for the weighted average mix of
organics management practices as well as economic impact data for a range of detailed organics
diversion practices, including animal waste processing, animal feed, biodiesel, biogas, compost, mulch
and wood chips, and community food services.
EPA assumed that the composting and biogas generation practices for organics correspond to
the composting and anaerobic digestion material management processes, respectively, for organics in
WARM. However, the economic impact factors for biogas generation calculated from data in the REI
study were approximately three orders of magnitude lower than the economic impacts for all other
organic diversion processes assessed in the study. Because the economic data mapped to anaerobic
digestion is only available at an aggregated industry level, EPA assumed that the biogas factors were an
outlier and applied the weighted average organics economic impact factors for anaerobic digestion. The
REI study relied on recycling process data about quantity and price, major consumers and processes
consuming recyclable material, and recyclable material proportion to allocate economic data in the
waste input-output (WIO) framework. These data were obtained from publicly-available databases
including the Census Bureau Statistics of U.S. Businesses (SUSB), the U.S. Agricultural Census, and the
U.S. Census of Governments. The recycling process of anaerobic digestion was mapped to the six-digit
NAICS code for "Other electric power generation, transmission, and distribution". The activity in this
industry is extremely variable and includes solar, wind, and tidal electric power generation.
The REI economic impact metrics include direct and indirect economic activity associated with
recycling, composting and anaerobic digestion. Direct recycling activities are those associated with the
actual transformation of recyclable materials into marketable products such as the transformation of
aluminum scrap into semi-fabricated products (e.g., ingots) in a secondary smelter. Indirect recycling
activities include upstream supply chain economics, such as employees who work in material recovery
facilities that separate steel scrap, employees who work for suppliers of steel recycling facilities (e.g.,
electric utilities), and employees of other suppliers through the upstream supply chain. Indirect activities
associated with recycling, composting, and anaerobic digestion, include the activities involved in the
value chain of the direct processes such as the collection, sorting, and transportation of aluminum scrap
to the smelter and the transportation of finished compost to a farm for land application. Under the REI
methodology, both impacts relating to the industries that supply or interact with primary industries, as
well as impacts that result from increased spending by workers who earn money in waste disposal and
management industries (typically referred to as induced impacts) are not included. Impacts focus only
on the primary industries that engage with waste collection, hauling, and processing. Because many
goods go on to create economic value after being recycling, composted, or anaerobically digested, there
are life-cycle benefits of these activities. However, these benefits are not included in the REI economic
impacts resulting in REI undervaluing the total economic impact of these activities. For example, the REI
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economic metrics for steel does not include the value of the recycled steel being used as an input for
new products.
The 2001 REI study (R.W. Beck 2001), the 2016 update (U.S. EPA 2016a), and the 2020 update
(U.S. EPA 2020) served as a foundation for the analytical approach used to derive the economic factors,
and as such EPA was required to make assumptions to align the studies to WARM. Because there is not a
direct match between the materials addressed by the REI study and WARM, EPA had to make
assumptions on the relationships between material categories to map REI factors to WARM materials.
Materials not modeled explicitly in the REI study include copper wire, fly ash, and PLA. Based on
material descriptions in WARM, EPA mapped these materials to the most appropriate categories. There
were no appropriate REI categories for carpet, so factors for carpet waste were instead developed using
data from the Carpet America Recovery Effort Annual Report (CARE 2017). This imputed metric was then
compared to a carpet diversion jobs metric drawn from the Tellus report, "More Jobs, Less Pollution"
(Tellus Institute 2011), which augments previous research captured in the 2001 REI study to address a
more diverse set of materials and waste management practices. EPA assumed that factors in the 2020
REI study and the 2011 Tellus report are comparable because the Tellus report is based largely on the
methodology of the 2001 REI study.
Exhibit 8-1 shows the alignment of the WARM materials with the corresponding REI materials
categories and subcategories with the exception of carpet, as discussed above. For three WARM
materials - mixed plastics, mixed recyclables, and mixed metals - the composition is made up of a
weighted mix of REI material categories, as shown in the "REI Material Category" column.
Exhibit 8-1: WARM Materials and Corresponding REI Categories and Subcategories
WARM Material
REI Material Category
REI Material Subcategories
Aluminum Cans
Nonferrous Metals
Aluminum, copper, lead, nickel, tin,
titanium, zinc
Aluminum Ingot
Copper Wire
Steel Cans
Ferrous Metals
Iron, steel
Structural Steel
Mixed Metals
Calculated based on weighting of
Aluminum and Ferrous Metals
categories based on default weightings
for Mixed Metals in WARM
N/A
Glass
Glass
No subcategory
HDPE
Plastics
PET, HDPE, PP
PET
PP
Mixed Plastics
Corrugated Containers
Paper
Paper and newsprint, paperboard
Magazines/Third-class Mail
Newspaper
Office Paper
Phonebooks
Textbooks
Mixed Paper-(general)
Mixed Paper - (primarily residential)
Mixed Paper-(primarily from offices)
Food Waste
Organics
Animal by-products, crop residue, dairy
by-products, deceased animal stock,
grease/fats, oil, grease, plate waste,
produce, oilseed, and grain residues,
spoiled food, trim and other cooking
waste, yard trimmings
Food Waste (non-meat)
Food Waste (meat-only)
Beef
Poultry
Grains
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WARM Material
REI Material Category
REI Material Subcategories
Bread
Fruits and Vegetables
Dairy Products
Mixed Organics
Yard Trimmings
Grass
Leaves
Branches
PLA
Tires
Rubber
Rubber crumb, tire-derived fuel, other
recyclable rubber
Dimensional Lumber
Concrete
Asphalt Concrete
Asphalt Shingles
Drywall
Fly Ash
Wood Flooring
Construction & Demolition (C&D)
Materials
Concrete, asphalt pavement, asphalt
shingles, gypsum wallboard, wood
Desktop CPUs
Portable Electronic Devices
Flat-panel Displays
CRT Displays
Electronic Peripherals
Hard-copy Devices
Mixed Electronics
Electronics
Computers, computer displays,
hardcopy devices, keyboards & mice,
televisions, mobile devices
Mixed Recyclables
Calculated based on weighting of
Aluminum, Ferrous Metals, Glass,
Plastics, Paper, and C&D Materials
categories based on default weightings
for Mixed Recyclables in WARM
N/A
Source: U.S. EPA 2020
8.4.1.2 Method for Calculating Economic Factors for Waste Diversion
Economic factors for employment, wage, and taxes were calculated based on the 2020 REI study
and the employment factor for carpet were derived from the CARE 2017 report.
The 2020 REI study was the main source for the economic factors incorporated into WARM
because the study quantifies the economic impact associated with waste diversion activities in terms of
traditional economic metrics (jobs, wages, and industry activity). Using a waste input-output model, the
2020 REI study captures the flow of goods and flow of waste, allowing clearer definitions of a material's
lifecycle boundaries. Furthermore, the 2020 REI study specifically addresses the need to capture not
only the recycling process but also the related impacts that occur during the material transformation or
remanufacturing processes. These broader life-cycle benefits align more closely with the life-cycle and
GHG emissions impacts captured in WARM and thus is it critical that any economic impact approach
proposed for use in WARM accounts for them as well.
The factors from the "Total (Direct and Indirect) Impacts" (D&l) approach in the 2020 REI study
were used in developing the employment factors in WARM. The D&l approach uses a waste input-
output model to measure the multiplier effect, or "ripple effect" of final demand on direct recycling
activity and the upstream supply chain. Official U.S. input-output tables are maintained by the Bureau of
Economic Analysis (BEA). However, in order to use this approach, REI developed a specific waste input-
output model (WIO) that distinguished flows and outputs of wastes and recycling from ordinary
activities. In order to develop waste and recycling specific process allocation assumptions for the new
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model, REI compiled data on recycled material quantity and price, major consumers of and processes
consuming recycled material, and recyclable proportion for each of the nine material categories.
Background data from the U.S Internal Revenue Service (IRS), U.S. Census of Governments and U.S.
Economic Census was then used to attribute proportions of personal and corporate tax revenues to
different sectors of the economy and tie the new model more closely with the national 10 framework.
Using the new WIO model, REI developed material factors for total employment, wages, and
taxes. The economic factor for employment is presented in the REI study as the number of jobs per
metric ton (and converted to labor hours for use in WARM), while wage and tax factors are presented as
thousand dollars per thousand metric tons. Because the WARM model uses short tons, EPA converted
these factors to represent the impact per short ton.
As previously described, the REI study does not include economic factors for carpet, and the
corresponding Tellus material category for textiles does not offer wage or tax factors. Therefore, EPA
estimated a jobs per ton factor for carpet using annual tonnage and employment data from the Carpet
America Recovery Effort Annual Report (CARE 2017). In order to confirm the reasonableness of this
estimate, the imputed jobs per ton factor was compared to the Tellus jobs per ton metric for textiles. In
order to develop wage and tax metrics for carpet, EPA assumed that the relationship between the
economic factors for carpet recycling were most similar to the REI metrics for plastics. According to
respondents from an annual survey conducted by Carpet America Recovery Effort (CARE 2017),
approximately 80 percent of recycled post-consumer carpet is manufactured into engineered resins.
These resins are plastic materials that are likely have better mechanical and or thermal properties than
commodity plastics and can be used in a multitude of industries.
8.4.2 Economic Factors for Waste Disposal
Employment impacts, and subsequently wage and tax impacts, related to landfilling and
combustion are generally smaller than the impacts related to waste diversion, so different economic
factors must be generated to accurately capture waste disposal economic impacts. To calculate
employment factors for landfilling and combusting waste (collectively referred to here as waste
disposal), EPA used the 2011 Tellus Institute study, "More Jobs, Less Pollution: Growing the Recycling
Economy in the U.S." This study incorporates material job factors for collection, reuse, and re-
manufacturing from the 2001 REI study in addition to adding baseline job factors for disposed waste.
The Tellus study uses ISLR estimates and CM Consulting data to develop material factor estimates for
landfilling and combustion.
Tellus did not include factors for wages and taxes associated with landfilling and combustion,
thus EPA calculated these factors using the IMPLAN model.38 EPA created a sample run of the waste and
remediation sector in a national IMPLAN model and was thus able to determine the impact on wages
and taxes associated with each job. Using that wage and tax impact factor per employee and the Tellus
jobs per ton factor for disposed waste, EPA determined that a total of $43 in wages and $16 in taxes are
generated from one ton of waste disposal (landfilling and combustion). In order to confirm the accuracy
of these factors, EPA calculated the wages and taxes generated per employee using the REI diverted
waste factors, and applied the average of each to the Tellus disposed waste employment factor to
determine wage and tax per ton of waste disposed factors. These calculated factors were of similar
magnitude to the factors determined by IMPLAN.
Employment factor estimates do not vary by material type mainly because landfilling and
combustion processes are similar across the material subcategories included in the analysis. The
38 IMPLAN is a static input-output economic model, described further in Section 8.4.4.2.
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employment impacts for waste disposal are smaller than those calculated for waste diversion (recycling,
composting, and anaerobic digestion) as waste disposal activities are not as labor intensive. The Tellus
study suggests that this finding is explained by the fact that waste disposal relies on large capital-
intensive equipment to handle large waste tonnages with few employees. This explanation also applies
to materials collection, where large trucks are equipped to handle large tonnage volumes with fewer
employees.
The Tellus study provided employment factors for waste disposal collection, landfilling, and
combustion. For all material types, 0.56 collection jobs and 0.1 landfilling or combustion jobs are
supported for every thousand tons disposed. Because collection occurs for both landfilling and
combustion, final employment factors for each management practice were calculated as the sum of the
landfill/combustion factor and the collection factor (0.66 jobs per thousand tons). These factors were
the same across all material types. Factors are presented in labor hours per metric ton, $1,000 wages
per metric ton, and $1,000 taxes per metric ton. Because the WARM model uses short tons, EPA
converted these factors to represent the impact per short ton.
8.4.3 Economic Impacts of End User Energy Savings
EPA determined that energy savings typically derived from the of use of recycled materials to
produce/manufacture products rather than from virgin materials or from the use of a mix of virgin
materials. Thus, EPA focused the modeling of the economic impacts of energy savings to end users
based on recycled materials.
For each material type, EPA considered three manufacturing mixes to determine energy savings:
product manufacturing using 100% virgin material, product manufacturing using 100% recycled
material, and product manufacturing using the current mix. EPA then compared the energy
consumption of 100% virgin material manufacturing to the energy consumption using 100% recycled
materials, and the energy consumption of manufacturing using the current mix of inputs to 100%
recycled materials to calculate energy savings.
8.4.4 Energy Savings by Native Fuels
EPA first determined the energy consumption for recycled/mixed input and virgin production by
fuel type for each material, referred to as native fuels.39 Internal WARM documentation was used to
determine the manufacturing energy use by native fuel type for each material. For each material type,
total fuel consumption by fuel type in millions of British Thermal Units (MMBtu) was calculated using
the native fuel mixes for the energy consumption for manufacturing with 100% virgin material and 100%
recycled material. Because the native fuels mix was not available for the current mix, EPA used an
average of the virgin and recycled current mixes by fuel type, based on the percentage of virgin and
recycled inputs in the current mix. EPA then subtracted fuel consumption with 100% recycled inputs
from fuel consumption with the current mix and from 100% virgin to determine the two energy savings
values by fuel type for each material type.
8.4.4.1 Regional Fuel Savings
Because fuel costs can vary across the United States, EPA used regional fuel prices. Regions
include: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East
South Central, West South Central, Mountain, and Pacific. ElA's Annual Energy Outlook 2021 (EIA 2021a)
report was used for regional fuel prices for the following fuel types: propane, distillate oil, residual oil,
39 Native fuels refer to the specific fuel type, such as diesel, propane, gasoline, etc., that is used in a manufacturing
process.
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natural gas, coal, electricity, gasoline, and nuclear (uranium). The EIA report includes the average energy
prices paid in different sectors, such as residential, commercial, industrial, transportation, and electric
power. Because energy savings primarily impact manufacturers, EPA used the average annual energy
prices for the industrial sector for all fuels that have an average price available in that sector. Because
the EIA report does not include fuel prices for diesel and biomass/wood/hydropower, EPA used the
annual diesel fuel price for 2020 from ElA's 2020 Weekly Retail Gasoline and Diesel Prices (EIA 2021c).
For biomass/wood/hydropower, EPA used data from ElA's December 2020 Monthly Densified Biomass
Fuel Report (EIA Dec 2020) and the U.S. Office of Energy Efficiency & Renewable Energy 2021
Hydropower Market Report (U.S. Office of Energy Efficiency & Renewable Energy 2021) and used a
weighted average of hydropower and densified biomass fuel in each region based on ElA's Net
Electricity Generation by Type of Producer (EIA 2021b) for 2019. To value energy savings, EPA multiplied
energy prices by net savings for each fuel type and summed across all fuels to calculate total savings for
each region.
8.4.4.2 Modeling Economic Impact
To determine which industries had energy savings from using recycled materials as inputs in
manufacturing, EPA researched the major consumers of recycled materials for each material type, using
REI's "Allocation of Sector Economic Impacts" waste 10 model NAICS codes mapping as a primary source
(U.S. EPA 2020).
To model the economic impact of energy savings, EPA used the IMPLAN model. The IMPLAN
data set is constructed of data from the U.S. National Income and Product Accounts (NIPA) and the
Bureau of Economic Analysis, among a variety of other data sources. The model includes 546 industry
sectors based on the North American Industry Classification System (NAICS). The model uses region-
specific multipliers to trace and calculate the flow of dollars from the industries that originate the
impact to supplier industries.
Using NAICS codes, EPA mapped impacted industries to IMPLAN sectors. For all material types,
at least two IMPLAN sectors experience energy savings. To apportion the value of energy savings
between industries, EPA utilized IMPLAN's commodity balance sheets. Commodity balance sheets
provide a detailed breakdown of all the industries/IMPLAN sectors that demand or use a selected
commodity in their production, and therefore allowed EPA to determine for each material the regional
demand of the major consuming industries and apportion energy savings accordingly.
Additionally, EPA used the allocation values from REI's "Allocation of Sector Economic Impacts,"
which estimates the recycled portion of the commodity used in each industry (U.S. EPA 2020).
Multiplying the regional input values from IMPLAN's commodity balance sheets by the allocation of the
inputs that are recycled materials allows EPA to determine for each material the regional demand of the
major consuming industries more accurately and apportion energy savings accordingly.
When considering production cost savings, such as energy savings, industries may reinvest some
of the savings into increasing production. An individual industry and a firm within an industry may use a
different percentage of savings to reinvest based on a number of industry-specific factors. Since this
information was not available, EPA modeled the impact of 25 percent, 50 percent, and 75 percent of
savings being used to increase output and thus increase industry sales. The results of 50 percent saving
reinvestment scenario were selected to develop the next version of WARM factors while the results of
25 percent and 75 percent scenarios were used as a sensitivity analysis to understand the range of
potential impacts. Under the 25 percent reinvestment scenario, the impacts would be 25 percent lower,
and under 75 percent reinvestment impacts would be 25 percent higher. The 50 percent reinvestment
modeling results were selected because it showed the median of the potential impacts.
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8.4.5 Economic Impacts from Energy Production
Energy is produced through the process of anaerobic digestion. EPA modeled the impacts by
quantifying the electricity that is generated and used on-site at the anaerobic digestion facility, and
excess electricity that is exported to the grid.
8.4.5.1 Electricity Generation by Anaerobic Digestion Type
There are two different types of anaerobic digesters, wet and dry digesters. WARM assumes
that the wet digester is only used for food waste, and dry digester is used for food waste, branches,
grass, leaves, mixed yard trimmings, and mixed organics. The digestate, after being removed for the
digester, can either be cured before application to land as a fertilizer, or can be directly applied to
agricultural lands. The WARM internal documentation developed by EPA includes factors of kilowatt
hours (kWh) of electricity generated and net electricity exported to the grid per milligram (Mg) of waste
anaerobically digested for the four anaerobic digestion scenarios:
1. Wet digestion, cured and then applied to land (Wet - Cured)
2. Wet digestion, directly applied to land (Wet - Direct Apply)
3. Dry digestion, cured and then applied to land (Dry - Cured)
4. Dry digestion, directly applied to land (Dry - Directly Apply)
By subtracting the net electricity exported to the grid from the total electricity generation, EPA
was able to determine the amount of electricity that is used on-site. Therefore, EPA developed the
factors for electricity used on-site and electricity exported to the grid for the materials and anaerobic
digestion types included in Exhibit 8-2.
Exhibit 8-2: WARM Materials and Applicable Anaerobic Digestion Types
Material
Anaerobic Digestion Type
Food waste
Wet - Direct Applied
Wet-Cured
Dry - Direct Applied
Dry - Cured
Branches
Dry - Direct Applied
Dry - Cured
Grass
Dry - Direct Applied
Dry - Cured
Leaves
Dry - Direct Applied
Dry - Cured
Mixed yard trimmings
Dry - Direct Applied
Dry - Cured
Mixed organics
Dry - Direct Applied
Dry - Cured
8.4.5.2 Valuing Regional Electricity Generation
EPA monetized the electricity produced through anaerobic digestion using the ElA's Annual
Energy Outlook 2021 report regional industrial prices of electricity (EIA 2021a). Using the IMPLAN
model, EPA modeled the energy used on-site as savings to the anaerobic digestion facility and energy
sold to the grid as income to the anaerobic digestion facility using the same methodology as described in
Section 8.4.3, assuming that 25 percent, 50 percent, and 75 percent of savings will be used to increase
output.
EPA assumed that there are three primary industries involved in anaerobic digestion: stand-
alone digesters, on-farm co-digesters, and co-digestion systems at water resource recovery facilities.
Using the findings of EPA's Anaerobic Digestion Facilities Process Food Waste in the United States (EPA
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2016b) survey results, EPA proportioned the value of the savings between industries based on their
relative capacities for processing food waste in 2018.
8.5 RESULTS
The national average results of this analysis are shown in Exhibit 8-3, Exhibit 8-4, Exhibit 8-5,
Exhibit 8-6, and Exhibit 8-7 for each of the waste management practices in WARM. The factors reflect
consolidated impacts from both waste management processes and energy savings/generation. Factors
for composting, landfilling, and combustion are only associated with waste management activities. For
recycling and anaerobic digestion, the results for the economic impacts associated with waste
management activities and energy savings/generation are presented separately in Exhibit 8-8, Exhibit
8-9, Exhibit 8-10, and Exhibit 8-11. The energy savings/generation results assume a 50 percent
reinvestment of energy saved/generated.
These estimates of economic impacts are expressed for waste management in absolute terms
and are not values relative to another waste management option, although they must be used
comparatively, as all WARM economic and emissions factors must be. They are expressed in terms of
short tons of waste input (i.e., tons of waste prior to processing).
8.5.1 Consolidated Results of Economic Impacts
Exhibit 8-3: Economic Factors for Waste Diversion and Energy Savings from Recycling (2020 dollars)a
Material
Employment
(Labor Hours/Short Ton)
Wages
($1,000/Short Ton)
Tax
($1,000/Short Ton)
Aluminum Cans
61.60
1.62
0.34
Aluminum Ingot
61.60
1.62
0.34
Copper Wire
61.60
1.62
0.34
Steel Cans
8.21
0.24
0.04
Mixed Metals
26.38
0.70
0.14
Glass
19.33
0.52
0.08
HDPE
48.82
1.11
0.19
PET
48.82
1.11
0.19
PP
48.82
1.11
0.19
Mixed Plastics
47.75
1.07
0.17
Corrugated Containers
6.79
0.22
0.06
Magazines/Third-class Mail
6.79
0.22
0.06
Newspaper
6.79
0.22
0.06
Office Paper
6.79
0.22
0.06
Phonebooks
6.79
0.22
0.06
Textbooks
6.79
0.22
0.06
Mixed Paper-(general)
6.79
0.22
0.06
Mixed Paper - (primarily residential)
6.79
0.22
0.06
Mixed Paper-(primarily from offices)
6.79
0.22
0.06
Tires
23.79
0.57
0.08
Dimensional Lumberb
1.03
0.03
<0.01
Concrete
1.03
0.03
<0.01
Asphalt Concrete
1.03
0.03
<0.01
Asphalt Shingles
1.03
0.03
<0.01
Drywall
1.03
0.03
<0.01
Fly Ash
1.03
0.03
<0.01
Wood Flooring15
1.03
0.03
<0.01
Carpet
23.18
0.50
0.07
Structural Steel
8.11
0.24
0.04
Desktop CPUs
67.84
2.55
0.58
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Portable Electronic Devices
67.84
2.55
0.58
Flat-panel Displays
67.84
2.55
0.58
CRT Displays
67.84
2.55
0.58
Electronic Peripherals
67.84
2.55
0.58
Hard-copy Devices
67.84
2.55
0.58
Mixed Electronics
67.84
2.55
0.58
Mixed Recyclables
7.16
0.20
0.04
a Factors reflect economic impacts associated with waste diversion activities and energy savings when using 100% recycled
material relative to material produced with the current mix of recycled inputs.
b Modeled as Reuse in WARM.
Exhibit 8-4: Economic Factors for Composting (2020 dollars)3'b
Material Composted
Employment
(Labor Hours/Short Ton)
Wages
($1,000/Short Ton)
Tax
($1,000/Short Ton)
PLA
0.51
0.01
0.004
Food Waste (non-meat)
0.51
0.01
0.004
Food Waste (meat only)
0.51
0.01
0.004
Beef
0.51
0.01
0.004
Poultry
0.51
0.01
0.004
Grains
0.51
0.01
0.004
Bread
0.51
0.01
0.004
Fruits and Vegetables
0.51
0.01
0.004
Dairy Products
0.51
0.01
0.004
Yard Trimmings
0.51
0.01
0.004
Grass
0.51
0.01
0.004
Leaves
0.51
0.01
0.004
Branches
0.51
0.01
0.004
Food Waste
0.51
0.01
0.004
Mixed Organics
0.51
0.01
0.004
a Factors reflect economic impacts associated with waste diversion activities.
b EPA acknowledges that composting economic impacts are undervalued in the REI metrics because they do not include life-
cycle impacts. See Sections 8.3 and 8.6 for further explanation.
Exhibit 8-5: Economic Factors for Waste Diversion and Energy Generation from Anaerobic Digestion (2020
dollars)3-13
Material
Type c
Employment
(Labor Hours/Short Ton)
Wages
($1,000/Short Ton)
Tax
($1,000/Short Ton)
Food Waste
All Types
2.33
4.38
2.53
Food Waste (non-meat)
All Types
2.44
8.71
5.06
Food Waste (meat only)
All Types
2.44
8.71
5.06
Beef
All Types
2.44
8.71
5.06
Poultry
All Types
2.44
8.71
5.06
Grains
All Types
2.44
8.71
5.06
Bread
All Types
2.44
8.71
5.06
Fruits and Vegetables
All Types
2.44
8.71
5.06
Dairy Products
All Types
2.44
8.71
5.06
Yard Trimmings
Dry - Direct Applied
2.25
1.19
0.67
Dry - Cured
2.26
1.58
0.90
Grass
All Types
2.26
1.54
0.87
Leaves
All Types
2.24
1.09
0.62
Branches
All Types
2.27
2.15
1.23
a Factors reflect economic impacts associated with waste diversion activities and energy generation.
b EPA acknowledges that anaerobic digestion economic impacts are undervalued in the REI metrics because they do not include
life-cycle impacts. See Sections 8.3 and 8.6 for further explanation.
c All types include Wet-Direct Applied, Wet-Cured, Dry-Direct Applied, and Dry-Cured.
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Exhibit 8-6: Economic Factors for Landfilling (2020 dollars)3
Employment
Wages
Tax
Material Landfilled
(Labor Hours/Short Ton)
($1,000/ Short Ton)
($1,000/Short Ton)
All Materials
1.25
0.04
0.02
a Factors reflect economic impacts associated with waste disposal activities.
Exhibit 8-7: Economic Factors for Combustion (2020 dollars)3
Employment
Wages
Tax
Material Combusted
(Labor Hours/Short Ton)
($1,000/Short Ton)
($1,000/Short Ton)
Aluminum Cans
1.25
0.04
0.02
Aluminum Ingot
1.25
0.04
0.02
Steel Cans
1.25
0.04
0.02
Copper Wire
1.25
0.04
0.02
Glass
1.25
0.04
0.02
HDPE
1.25
0.04
0.02
LDPE
1.25
0.04
0.02
PET
1.25
0.04
0.02
LLDPE
1.25
0.04
0.02
PP
1.25
0.04
0.02
PS
1.25
0.04
0.02
PVC
1.25
0.04
0.02
PLA
1.25
0.04
0.02
Corrugated Containers
1.25
0.04
0.02
Magazines/Third-class Mail
1.25
0.04
0.02
Newspaper
1.25
0.04
0.02
Office Paper
1.25
0.04
0.02
Phonebooks
1.25
0.04
0.02
Textbooks
1.25
0.04
0.02
Dimensional Lumber
1.25
0.04
0.02
Medium-density Fiberboard
1.25
0.04
0.02
Food Waste (non-meat)
1.25
0.04
0.02
Food Waste (meat only)
1.25
0.04
0.02
Beef
1.25
0.04
0.02
Poultry
1.25
0.04
0.02
Grains
1.25
0.04
0.02
Bread
1.25
0.04
0.02
Fruits and Vegetables
1.25
0.04
0.02
Dairy Products
1.25
0.04
0.02
Yard Trimmings
1.25
0.04
0.02
Grass
1.25
0.04
0.02
Leaves
1.25
0.04
0.02
Branches
1.25
0.04
0.02
Mixed Paper (general)
1.25
0.04
0.02
Mixed Paper (primarily residential)
1.25
0.04
0.02
Mixed Paper (primarily from offices)
1.25
0.04
0.02
Mixed Metals
1.25
0.04
0.02
Mixed Plastics
1.25
0.04
0.02
Mixed Recyclables
1.25
0.04
0.02
Food Waste
1.25
0.04
0.02
Mixed Organics
1.25
0.04
0.02
Mixed MSW
1.25
0.04
0.02
Carpet
1.25
0.04
0.02
Desktop CPUs
1.25
0.04
0.02
Portable Electronic Devices
1.25
0.04
0.02
Flat-panel Displays
1.25
0.04
0.02
CRT Displays
1.25
0.04
0.02
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Material Combusted
Employment
(Labor Hours/Short Ton)
Wages
($1,000/Short Ton)
Tax
($1,000/Short Ton)
Electronic Peripherals
1.25
0.04
0.02
Hard-copy Devices
1.25
0.04
0.02
Mixed Electronics
1.25
0.04
0.02
Tires
1.25
0.04
0.02
Asphalt Shingles
1.25
0.04
0.02
Vinyl Flooring
1.25
0.04
0.02
Wood Flooring
1.25
0.04
0.02
a Factors reflect economic impacts associated with waste disposal activities.
8.5.2 Economic Impacts Associated with Waste Management Activities
Exhibit 8-8 and Exhibit 8-9 below detail the economic impacts associated with waste diversion processes
for recycling and anaerobic digestion, respectively. These results are consolidated with the economic
impacts from energy savings to develop the economic factors for recycling and anaerobic digestion
presented in WARM (see Exhibit 8-3 and Exhibit 8-5). The results below may be used to analyze the
economic impacts that are felt specifically in the waste management industries.
Exhibit 8-8: Economic Factors from Waste Diversion for Recycling (2020 dollars)
Material Recycled
Employment
(Labor Hours/Short Ton)
Wages
($1,000/Short Ton)
Tax
($1,000/Short Ton)
Aluminum Cans
53.76
1.35
0.24
Aluminum Ingot
53.76
1.35
0.24
Copper Wire
53.76
1.35
0.24
Steel Cans
7.76
0.22
0.04
Mixed Metals
23.90
0.62
0.11
Glass
19.21
0.51
0.08
HDPE
44.27
0.95
0.13
PET
44.27
0.95
0.13
PP
44.27
0.95
0.13
Mixed Plastics
44.27
0.95
0.13
Corrugated Containers
3.19
0.09
0.01
Magazines/Third-class Mail
3.19
0.09
0.01
Newspaper
3.19
0.09
0.01
Office Paper
3.19
0.09
0.01
Phonebooks
3.19
0.09
0.01
Textbooks
3.19
0.09
0.01
Mixed Paper (general)
3.19
0.09
0.01
Mixed Paper (primarily residential)
3.19
0.09
0.01
Mixed Paper (primarily from offices)
3.19
0.09
0.01
Tires
22.38
0.53
0.07
Dimensional Lumber3
0.89
0.02
<0.01
Concrete
0.89
0.02
<0.01
Asphalt Concrete
0.89
0.02
<0.01
Asphalt Shingles
0.89
0.02
<0.01
Drywall
0.89
0.02
<0.01
Fly Ash
0.89
0.02
<0.01
Wood Flooring3
0.89
0.02
<0.01
Carpet
23.04
0.49
0.07
Structural Steel
7.76
0.22
0.04
Desktop CPUs
62.27
2.29
0.50
Portable Electronic Devices
62.27
2.29
0.50
Flat-panel Displays
62.27
2.29
0.50
CRT Displays
62.27
2.29
0.50
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Material Recycled
Employment
(Labor Hours/Short Ton)
Wages
($1,000/Short Ton)
Tax
($1,000/Short Ton)
Electronic Peripherals
62.27
2.29
0.50
Hard-copy Devices
62.27
2.29
0.50
Mixed Electronics
62.27
2.29
0.50
Mixed Recyclables
6.06
0.16
0.02
a Modeled as Reuse in WARM.
Exhibit 8-9: Economic Factors from Waste Diversion for Anaerobic Digestion (2020 dollars)3
Material Anaerobically Digested
Employment
(Labor Hours/Short Ton)
Wages
($1,000/Short Ton)
Tax
($1,000/Short Ton)
Food Waste (non-meat)
2.22
0.06
0.01
Food Waste (meat only)
2.22
0.06
0.01
Beef
2.22
0.06
0.01
Poultry
2.22
0.06
0.01
Grains
2.22
0.06
0.01
Bread
2.22
0.06
0.01
Fruits and Vegetables
2.22
0.06
0.01
Dairy Products
2.22
0.06
0.01
Yard Trimmings
2.22
0.06
0.01
Grass
2.22
0.06
0.01
Leaves
2.22
0.06
0.01
Branches
2.22
0.06
0.01
Food Waste
2.22
0.06
0.01
Mixed Organics
2.22
0.06
0.01
a EPA acknowledges that anaerobic digestion economic impacts are undervalued in the REI metrics because they do not include
life-cycle impacts. See Sections 8.3 and 8.6 for further explanation.
8.5.3 Results of Economic Impacts Associated with Energy Savings and Energy Generation
Exhibit 8-10, and Exhibit 8-11 below detail the economic impacts associated with energy savings from
recycling and energy generation from anaerobic digestion, respectively. These results are consolidated
with the economic impacts from waste diversion activities to develop the economic factors for recycling
and anaerobic digestion presented in WARM (see Exhibit 8-3 and Exhibit 8-5). The results below may be
used to analyze the economic impacts that are specifically attributable to the energy savings from
remanufacturing with recycled material and energy generated through anaerobic digestion.
Exhibit 8-10: Economic Factors for Energy Savings from Recycling (2020 dollars)
WARM Material
Mixed vs 100% Recycled
100% Virgin vs 100% Recycled
Employment
(Labor
Hours/Short
Ton)
Wages
($1,000/
Short Ton)
Tax
($1,000/
Short Ton)
Employment
(Labor
Hours/
Short Ton)
Wages
($1,000/
Short Ton)
Tax
($1,000/
Short Ton)
Aluminum Cans
7.84
0.27
0.10
26.71
0.90
0.32
Aluminum Ingot
7.84
0.27
0.10
26.71
0.90
0.32
Copper Wire
7.84
0.27
0.10
26.71
0.90
0.32
Steel Cans
0.45
0.02
0.01
0.32
0.01
<0.01
Mixed Metals
2.49
0.09
0.03
5.81
0.20
0.07
Glass
0.12
<0.01
<0.01
0.16
0.01
<0.01
HDPE
4.55
0.16
0.06
4.60
0.16
0.06
PET
4.55
0.16
0.06
4.60
0.16
0.06
PP
4.55
0.16
0.06
4.60
0.16
0.06
Mixed Plastics
3.48
0.12
0.05
3.42
0.12
0.04
Corrugated Containers
3.60
0.13
0.05
4.25
0.15
0.05
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Magazines/Third-class Mail
3.60
0.13
0.05
4.25
0.15
0.05
Newspaper
3.60
0.13
0.05
4.25
0.15
0.05
Office Paper
3.60
0.13
0.05
4.25
0.15
0.05
Phonebooks
3.60
0.13
0.05
4.25
0.15
0.05
Textbooks
3.60
0.13
0.05
4.25
0.15
0.05
Mixed Paper - Broad
3.60
0.13
0.05
4.25
0.15
0.05
Mixed Paper-Residential
3.60
0.13
0.05
4.25
0.15
0.05
Mixed Paper-Office
3.60
0.13
0.05
4.25
0.15
0.05
Tires
1.41
0.04
0.02
5.15
0.16
0.06
Dimensional Lumber3
0.14
<0.01
<0.01
1.03
0.03
0.01
Concrete
0.14
<0.01
<0.01
1.03
0.03
0.01
Asphalt Concrete
0.14
<0.01
<0.01
1.03
0.03
0.01
Asphalt Shingles
0.14
<0.01
<0.01
1.03
0.03
0.01
Drywall
0.14
<0.01
<0.01
1.03
0.03
0.01
Fly Ash
0.14
<0.01
<0.01
1.03
0.03
0.01
Wood Flooring3
0.14
<0.01
<0.01
1.03
0.03
0.01
Carpet
0.14
<0.01
<0.01
1.03
0.03
0.01
Structural Steel
0.36
0.01
<0.01
2.00
0.07
0.03
Desktop CPUs
5.57
0.26
0.09
8.75
0.40
0.14
Portable Electronic Devices
5.57
0.26
0.09
8.75
0.40
0.14
Flat-panel Displays
5.57
0.26
0.09
8.75
0.40
0.14
CRT Displays
5.57
0.26
0.09
8.75
0.40
0.14
Electronic Peripherals
5.57
0.26
0.09
8.75
0.40
0.14
Hard-copy Devices
5.57
0.26
0.09
8.75
0.40
0.14
Mixed Electronics
5.57
0.26
0.09
8.75
0.40
0.14
Mixed Recyclables
1.10
0.04
0.01
5.26
0.18
0.07
a Modeled as Reuse in WARM.
Exhibit 8-11: Economic Factors for Energy Generation from Anaerobic Digestion (2020 dollars)
Material
Type3
Employment
Wages
Tax
(Labor Hours/Short Ton)
($1,000/Short Ton)
($1,000/Short Ton)
Food Waste
All types
0.11
4.32
2.52
Food Waste (non-meat)
All types
0.23
8.65
5.05
Food Waste (meat only)
All types
0.23
8.65
5.05
Beef
All types
0.23
8.65
5.05
Poultry
All types
0.23
8.65
5.05
Grains
All types
0.23
8.65
5.05
Bread
All types
0.23
8.65
5.05
Fruits and Vegetables
All types
0.23
8.65
5.05
Dairy Products
All types
0.23
8.65
5.05
Yard Trimmings
Dry - Direct Applied
0.03
1.13
0.66
Dry - Cured
0.04
1.52
0.89
Grass
All types
0.04
1.48
0.86
Leaves
All types
0.03
1.04
0.60
Branches
All types
0.06
2.09
1.22
a All types include Wet-Direct Applied, Wet-Cured, Dry-Direct Applied, and Dry-Cured.
8.6 LIMITATIONS
8.6.1 Limitations Associated with Analysis of Economic Impacts from Waste Management Processes
The certainty of the analysis associated with waste management activity presented in this
chapter is limited by the reliability of the various data elements used. The most significant limitations
are as follows:
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• The key limitation of using the economic factors produced in the 2020 REI study to develop
economic impact factors associated with materials management activity for WARM is that EPA
was not able to disaggregate the individual components of economic activity that comprise the
factors (e.g., jobs from energy inputs to recycling processes). This means that EPA was unable to
update any aspects of the underlying activity that drives the factors or fully document the
assumptions that were used by the REI project team to derive each factor.
• The REI study does not cover all material types or management practices that are addressed in
WARM, such as carpet, landfilling, and combustion. To fill these gaps, EPA utilized other
secondary studies. EPA relied on data from Tellus Institute (2011) to incorporate economic
factors for recycling of carpet as well as employment factors for landfilling and combustion.
Additionally, the Tellus study is based on the original REI report from 2001, which uses a dated
methodology compared to what is used in the 2020 REI report.
• While source reduction is a priority for EPA, the associated economic impacts of source
reduction were not analyzed by REI or in any other sources reviewed by EPA; therefore, WARM
currently does not quantify the economic impacts from source reduction.
• While the limited definition of economic impacts used in the 2020 REI study limits the total
economic impacts captured in the factors. The 2020 REI D&l approach was chosen to assess the
impact of materials management activity because it most closely resembles a traditional
economic impact analysis. However, the approach that the study uses to characterizes direct
and indirect impacts differs from the way EPA would optimally capture total economic impact.
In the REI approach, direct impact is characterized as only the activity associated with the actual
transformation of recyclable materials into marketable products, while the indirect activity
includes upstream supply chain economic activity that supports recycling processes. Economic
impacts determined by modeling programs, such as IMPLAN, capture three levels of impacts:
direct, indirect, and induced. Typically, direct impacts are impacts in the primary industries that
engage with transformation or remanufacturing, waste collection, hauling, and processing. This
is encompassing of what is included as direct and indirect impacts in the REI D&l approach.
Indirect impacts, as defined by IMPLAN, are impacts in the industries that supply or interact with
the primary industries, and induced activity represents the increased spending of workers who
earn money due to the increased economic activity. Therefore, the limited definition of
economic impacts used in the REI study limits the total economic impacts captured in the
factors.
• The waste disposal factors from the Tellus Institute (2011) assume a constant value for both
waste disposal processes, combustion and landfilling, across all material types. Tellus
determined these factors based on tonnage and employment data from surveys of various
recycling and reuse businesses completed by the ILSR in the 1990s. Because EPA did not develop
these calculations, the exact process Tellus used to arrive at these factors is uncertain.
Additionally, the age of the data used in the Tellus report raises concerns about potential
inaccuracy of the data and its relevance today. This also raises some concerns about making
comparisons between the economic factors for waste diversion and disposal practices.
• Few studies have attempted to determine the economic impacts of waste management
practices to the extent presented here. Thus, EPA had to primarily rely on a single source, the
2020 REI study, for metrics and had limited validation opportunities. Many other state and
regional studies explored the impacts of recycling in terms of total employment or total industry
activity, but few went as far as to determine factors for specific materials. EPA reviewed many
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such studies, as listed below, to determine potential analytical approaches and to evaluate the
calculated factors. Exhibit 8-12 lists several of these sources.
Exhibit 8-12: State and Regional Studies
Study Region
Study Year
Study Name
Delaware
2007
A Scenario for Resource Management in the State of Delaware
Iowa
2007
Economic Impacts of Recycling in Iowa
Connecticut
2012
The Economic Impact on Connecticut from Recycling Activity
Pennsylvania
2013
The Economic Impacts of the Municipal Waste Collection, Transportation,
Recycling, and Disposal Industry in Pennsylvania
Texas
2015
Study on the Economic Impacts of Recycling
Indiana
2013
The Untapped Job Potential of Indiana's Recycling Industry
Multi-Region
2018
The Economics of Recycling: Reports from States and Others
National
1989
Salvaging the Future: Waste Based Production
National
1995
Manufacturing from Recyclables: 24 Case Studies of Successful Enterprises
8.6.2 Limitations Associated with Analysis of Economic Impacts from Energy Savings and Generation
Because EPA's analysis is specific to energy savings for the end user of the recycled product, EPA
is only able to monetize economic impacts of end user energy savings for recycling, and not for other
materials management practices. If a material is landfilled, combusted, or not used at all (source
reduction) then it cannot be used as an input to production once that process is complete.
EPA does estimate the economic impacts from energy production for anaerobic digestion. A
note for future consideration and improvement is the possibility of estimating economic impacts from
other energy production processes such as landfill gas to energy or waste to energy.
EPA recognizes that fertilizer offsets are another potential impact of anaerobic digestion.
However, because the amount of fertilizer produced per Mg of waste anaerobically digested is relatively
minor (max 3.9 kg), the price of fertilizer is already low (approximately $0.40 per pound),40 and it is
difficult to determine the price differential between conventional fertilizer and fertilizer produced
through anaerobic digestion, EPA chose to focus the analysis of the economic impact of anaerobic
digestion on only the impact of electricity generation.
8.7 REFERENCES
Carpet America Recovery Effort. 2017. "CARE 2017 Annual Report." Retrieved from:
https://carpetrecoverv.org/wp-content/uploads/2018/05/CARE-Annual-Report-2017-FINAL.pdf.
CM Consulting. 2010. On behalf of the Container Recycling Institute for a forthcoming report on job
creation from recycling.
Congressional Research Service. 2021. Federal Workforce Statistics Sources: OPM and OMB. Retrieved
from: https://sgp.fas.org/crs/misc/R43590.pdf
DTN. 2019. DTN Retail Fertilizer Trends. Accessible at,
https://www.dtnpf.com/agriculture/web/ag/crops/article/2019/Q9/ll/nitrogen-phosphate-
fertilizers-lead-3.
EIA. 2021a. "Annual Energy Outlook 2021." Retrieved from:
https://www.eia.gov/outlooks/aeo/data/browser/#/?id=3-AEQ2021®ion=l-
40 DTN. DTN Retail Fertilizer Trends. 2019. Accessible at,
https://www.dtnpf.com/agriculture/web/ag/crops/article/2019/09/ll/nitrogen-phosphate-fertilizers-lead-3.
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l&cases=ref2021&start=2019&end=2021&f=A&linechart=ref2021-dll3020a.3-3-AE02021.1-
l&map=ref2021-dll3020a.4-3-AE02021.1-l&sourcekev=0
EIA. 2021b. "Net Generation by State by Type of Producer Energy Source." Retrieved from:
https://www.eia.gov/electricity/data/state/.
EIA. 2021c. Petroleum & Other Liquids. "Weekly Retail Gasoline and Diesel Prices." Retrieved from:
https://www.eia.gov/dnav/pet/pet pri end a epd2d pte dpgal a.htm.
EIA. Dec 2020. "Monthly Densified Biomass Fuel Report. Retrieved from:
https://www.eia.gOv/biofuels/biomass/#table data.
R.W Beck. 2001. "U.S. Recycling Economic Information Study: Final Report." Prepared for the National
Recycling Coalition.
Tellus Institute. 2011. "More Jobs, Less Pollution: Growing the Recycling Economy in the U.S." Prepared
byTellus Institute with Sound Resources Management. Retrieved from:
https://www.tellus.org/pub/More%20Jobs.%20Less%20Pollution%20%20Growing%20the%20Re
cycling%20Economv%20in%20the%20US.pdf.
The Institute for Local Self-Reliance. 1993. "The Economic Benefits of Recycling." Retrieved from:
https://ilsr.org/wp-content/uploads/2015/12/the-economic-benefits-of-recycling.pdf.
U.S. EPA 2020. "Recycling Economic Information (REI) Report". November 2020.
U.S. EPA. 2018. "Advancing Sustainable Materials Management: 2015 Tables and Figures." July 2018.
U.S. EPA. 2016a. "2016 Recycling Economic Information (REI) Report". October 2016. EPA530-R-17-002.
U.S. EPA. 2016b. "Anaerobic Digestion Facilities Process Food Waste in the United States (2016)."
Retrieved from: https://www.epa.gov/sites/production/files/2019-
09/documents/ad data report vlO - 508 comp vl.pdf.
U.S. BLS. 2017. "May 2017 National Industry-Specific Occupational Employment and Wage Estimates."
May 2017.
U.S. Office of Energy Efficiency & Renewable Energy. Jan 2021. "U.S. Hydropower Market Report."
Retrieved from: https://www.energy.gov/sites/prod/files/2021/01/f82/us-hvdropower-market-
report-full-2021.pdf.
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