A Life-Cycle Assessment of 

Emissions and Sinks

Solid Waste Management 

And Greenhouse Gases

A Life-Cycle Assessment of 

Emissions and Sinks
SOLID WASTE MANAGEMENT AND GREENHOUSE GASES

A Life-Cycle Assessment of Emissions and Sinks

2nd EDITION

EPA530-R-02-006

May 2002
TABLE OF CONTENTS 

EXECUTIVE SUMMARY: BACKGROUND AND FINDINGS......................................................... ES-1 

ES.1 GREENHOUSE GASES AND CLIMATE CHANGE................................................ ES-1 

ES.2 WHAT IS THE UNITED STATES DOING ABOUT CLIMATE CHANGE?........... ES-2 

ES.3 WHAT IS THE RELATIONSHIP OF MUNICIPAL SOLID WASTE TO 

GREENHOUSE GAS EMISSIONS? .......................................................................... ES-3 

ES.4 WHY EPA PREPARED THIS REPORT AND HOW IT HAS BEEN USED............ ES-4 

ES.5 HOW WE ANALYZED THE IMPACT OF MUNICIPAL SOLID WASTE ON 

GREENHOUSE GAS EMISSIONS ............................................................................ ES-5 

ES.6 RESULTS OF THE ANALYSIS ................................................................................. ES-9 

ES.7 OTHER LIFE-CYCLE GHG ANALYSES AND TOOLS....................................... ES-18 

ES.8 LIMITATIONS OF THE ANALYSIS....................................................................... ES-20 

1. METHODOLOGY................................................................................................................................... 1 

1.1 THE OVERALL FRAMEWORK: A STREAMLINED LIFE-CYCLE 

INVENTORY..................................................................................................................... 2

1.2 MSW MATERIALS CONSIDERED IN THE STREAMLINED LIFE-CYCLE 

INVENTORY..................................................................................................................... 2

1.3 KEY INPUTS AND BASELINES FOR THE STREAMLINED LIFE-CYCLE 

INVENTORY..................................................................................................................... 4

1.4 SUMMARY OF THE LIFE-CYCLE STAGES................................................................. 7 

1.5 ESTIMATING AND COMPARING NET GHG EMISSIONS....................................... 13 

2. RAW MATERIALS ACQUISITION AND MANUFACTURING....................................................... 15 

2.1 GHG EMISSIONS FROM ENERGY USE IN RAW MATERIALS 

ACQUISITION AND MANUFACTURING................................................................... 15 

2.2 NON-ENERGY GHG EMISSIONS FROM MANUFACTURING AND RAW 

MATERIALS ACQUISITION ........................................................................................ 18 

2.3 RESULTS......................................................................................................................... 19 

2.4 LIMITATIONS ................................................................................................................ 19 

3. FOREST CARBON SEQUESTRATION.............................................................................................. 29 

3.1 MODELING FRAMEWORK.......................................................................................... 30 

3.2 THE NORTH AMERICAN PULP AND PAPER (NAPAP) MODEL ........................... 32 

3.3 THE TIMBER ASSESSMENT MARKET MODEL (TAMM) AND THE 

AGGREGATE TIMBERLAND ASSESSMENT SYSTEM (ATLAS)........................... 38 

3.4 THE FOREST CARBON MODEL (FORCARB) ........................................................... 42 

3.5 THE HARVESTED CARBON MODEL (WOODCARB).............................................. 44 

3.6 APPLYING THE MODELS FOR WOOD PRODUCTS ................................................ 46 

3.7 RESULTS......................................................................................................................... 48 

3.8 LIMITATIONS ................................................................................................................ 52 

4. SOURCE REDUCTION AND RECYCLING....................................................................................... 55 

4.1 GHG IMPLICATIONS OF SOURCE REDUCTION ..................................................... 55 

4.2 GHG IMPLICATIONS OF RECYCLING ...................................................................... 57 

4.3 SOURCE REDUCTION WITH MATERIAL SUBSTITUTION.................................... 60 

4.4 LIMITATIONS ................................................................................................................ 61 

5. COMPOSTING ...................................................................................................................................... 65 

5.1 POTENTIAL GREENHOUSE GAS EMISSIONS ......................................................... 66 

5.2 POTENTIAL CARBON STORAGE............................................................................... 66 

5.3 NET GHG EMISSIONS FROM COMPOSTING ........................................................... 76 

5.4 LIMITATIONS ................................................................................................................ 78 

6. COMBUSTION...................................................................................................................................... 81 
6.1 METHODOLOGY........................................................................................................... 83 

6.2 RESULTS......................................................................................................................... 93 

6.3 LIMITATIONS OF THE ANALYSIS............................................................................. 93 

7. LANDFILLING ..................................................................................................................................... 97 

7.1 EXPERIMENTAL VALUES FOR METHANE GENERATION AND CARBON 

STORAGE ....................................................................................................................... 98 

7.2 FATES OF LANDFILL METHANE: CONVERSION TO CO2, EMISSIONS, 

AND FLARING OR COMBUSTION WITH ENERGY RECOVERY........................ 101 

7.3 UTILITY CO2 EMISSIONS AVOIDED ....................................................................... 103 

7.4 NET GHG EMISSIONS FROM LANDFILLING......................................................... 103 

7.5 LIMITATIONS .............................................................................................................. 103 

8. ACCOUNTING FOR EMISSION REDUCTIONS............................................................................. 113 

8.1 NET GHG EMISSIONS FOR EACH WASTE MANAGEMENT OPTION................ 113 

8.2 APPLYING EMISSION FACTORS ............................................................................. 115 

8.3 OTHER LIFE-CYCLE GHG ANALYSES AND TOOLS............................................ 118 

8.4 OPPORTUNITIES FOR GHG REDUCTIONS ............................................................ 119 

 
ES-1 

EXECUTIVE SUMMARY: BACKGROUND AND FINDINGS

In the 21st century, management of municipal solid waste (MSW) continues to be an 

important environmental challenge facing the United States. In 2000, the United States generated 

232 million tons of MSW, an increase of 13 percent over 1990 generation levels and 53 percent 

over 1980 levels.

1

 Climate change is also a serious issue, and the United States is embarking on a 

number of voluntary actions to reduce the emissions of greenhouse gases (GHGs) that can 

intensify climate change. By presenting material-specific GHG emission factors for various waste 

management options, this report examines how the two issues—MSW management and climate 

change—are related. 

Among the efforts to slow the potential for climate change are measures to reduce 

emissions of carbon dioxide (CO2) from energy use, decrease emissions of methane (CH4) and 

other non-carbon dioxide GHGs, and promote long-term storage of carbon in forests and soil. 

Management options for MSW provide many opportunities to affect these processes, directly or 

indirectly. This report integrates information on the GHG implications of various management 

options for some of the most common materials in MSW. To our knowledge, this work represents 

the most complete national study on climate change emissions and sinks from solid waste 

management practices. The report’s findings may be used to support a variety of programs and 

activities, including voluntary reporting of emission reductions from waste management 

practices. 

ES.1 GREENHOUSE GASES AND CLIMATE CHANGE 

Climate change is a serious international environmental concern and the subject of much 

research and debate. Many, if not most, of the readers of this report will have a general 

understanding of the greenhouse effect and climate change. However, for those who are not 

familiar with the topic, a brief explanation follows.

2

A naturally occurring shield of “greenhouse gases” (primarily water vapor, carbon 

dioxide, methane, and, nitrous oxide), comprising 1 to 2 percent of the Earth’s atmosphere, 

absorbs some of the solar radiation that would otherwise be radiated to space and helps warm the 

planet to a comfortable, livable temperature range. Without this natural “greenhouse effect,” the 

average temperature on Earth would be approximately -2 degrees Fahrenheit, rather than the 

current 57 degrees Fahrenheit.

3

Many scientists are alarmed by a significant increase in the concentration of CO2 and 

other GHGs in the atmosphere. Since the pre-industrial era, atmospheric concentrations of CO2

have increased by nearly 30 percent and CH4 concentrations have more than doubled. There is a 

growing international scientific consensus that this increase has been caused, at least in part, by 

 1

 U.S. EPA Office of Solid Waste, Municipal Solid Waste in the United States: 2000 Facts and 

Figures, EPA (2002), p. 2.

2

 For more detailed information on climate change, please see The 2001 Inventory of U.S.

Greenhouse Gas Emissions and Sinks: 1990-1999, 

(http://www.epa.gov/globalwarming/publications/emissions/us2001/index.html) (April 2001); and Climate 

Change 2001: The Scientific Basis (J.T. Houghton, et al., eds. Intergovernmental Panel on Climate Change 

[IPCC]; published by Cambridge University Press, 2001). To obtain a list of additional documents 

addressing climate change, access EPA’s global warming Web site at www.epa.gov/globalwarming. 

3 Climate Change 2001: The Scientific Basis, op. cit., pp. 89-90. 
ES-2 

human activity, primarily the burning of fossil fuels (coal, oil, and natural gas) for such activities 

as generating electricity and driving cars.4

Moreover, in international scientific circles a consensus is growing that the buildup of 

CO2 and other GHGs in the atmosphere will lead to major environmental changes such as (1) 

rising sea levels that may flood coastal and river delta communities; (2) shrinking mountain 

glaciers and reduced snow cover that may diminish fresh water resources; (3) the spread of 

infectious diseases and increased heat-related mortality; (4) possible loss in biological diversity 

and other impacts on ecosystems; and (5) agricultural shifts such as impacts on crop yields and 

productivity.5

 Although reliably detecting the trends in climate due to natural variability is 

difficult, the most accepted current projections suggest that the rate of climate change attributable 

to GHGs will far exceed any natural climate changes that have occurred during the last 1,000 

years.6

Many of these changes appear to be occurring already. Global mean surface temperatures 

already have increased by about 1 degree Fahrenheit over the past century. A reduction in the 

northern hemisphere’s snow cover, a decrease in Arctic sea ice, a rise in sea level, and an increase 

in the frequency of extreme rainfall events all have been documented.7

Such important environmental changes pose potentially significant risks to humans, 

social systems, and the natural world. Many uncertainties remain regarding the precise timing, 

magnitude, and regional patterns of climate change and the extent to which mankind and nature 

can adapt to any changes. It is clear, however, that changes will not be easily reversed for many 

decades or even centuries because of the long atmospheric lifetimes of GHGs and the inertia of 

the climate system. 

ES.2 WHAT IS THE UNITED STATES DOING ABOUT CLIMATE CHANGE? 

In 1992, world leaders and citizens from some 200 countries met in Rio de Janeiro, 

Brazil, to confront global ecological concerns. At this “Earth Summit,” 154 nations, including the 

United States, signed the Framework Convention on Climate Change, an international agreement 

to address the danger of global climate change. The objective of the Convention was to stabilize 

GHG concentrations in the atmosphere over time at a level at which man-made climate 

disruptions would be minimized. 

By signing the Convention, countries made a voluntary commitment to reduce GHGs or 

take other actions to stabilize emissions of GHGs. All Parties to the Convention were required to 

develop and periodically update national inventories of their GHG emissions. The United States 

ratified the Convention in October 1992. One year later, the United States issued its Climate 

Change Action Plan (CCAP), which calls for cost-effective domestic actions and voluntary 

cooperation with states, local governments, industry, and citizens to reduce GHG emissions. 

 In order to achieve the goals outlined in the Climate Change Action Plan, EPA initiated 

several new voluntary programs to realize the most cost-effective opportunities for reducing 

emissions. For example, in 1994 EPA created the Landfill Methane Outreach Program, which 

aims to reduce landfill CH4 emissions by facilitating the development of projects that use landfill 

 4 Ibid., p. 7. 

5

 J.J. McCarthy, et al., eds. 2001. Climate Change 2001: Impacts, Adaptation, and Vulnerability. 

IPCC. Cambridge University Press. pp. 9-13. 

6 Climate Change 2001: The Scientific Basis , op. cit., p. 2. 

7

 Ibid., p. 4. 
ES-3 

gas to produce energy.

8

 In that same year, EPA introduced the Climate and Waste Program, with

its focus on a broader set of waste management practices and climate protection. 

To date, EPA’s voluntary partnership programs for climate protection have achieved 

substantial environmental results. In 2000 alone, these programs reduced GHG emissions by 35 

million metric tons of carbon equivalent (MMTCE)—the equivalent of eliminating the emissions 

from approximately 25 million cars. In addition, substantial CH4 emission reductions—estimated 

at more than 1 MMTCE for the period from 1999-2000—are being obtained as an ancillary

benefit of Clean Air Act (CAA) regulatory requirements that were promulgated in 1996. These 

reductions are expected to rise to nearly 47 MMTCE by 2004.

Meanwhile, an increasing number of states have been instituting their own voluntary

actions to reduce emissions. Thirty-nine states and Puerto Rico have created GHG Inventories for 

their own emissions. Twenty-five states and Puerto Rico have completed or initiated state action 

plans, which list steps to reduce emissions. At least six of these states—Delaware, Iowa, 

Minnesota, Montana, New Jersey, and Oregon—have incorporated the reduction of waste into 

their GHG mitigation strategies. Finally, a few states—including California, Maine, New 

Hampshire, and Wisconsin—are in the process of establishing GHG registries, which enable 

companies and other entities to report voluntary emission reductions. 

ES.3 WHAT IS THE RELATIONSHIP OF MUNICIPAL SOLID WASTE TO

GREENHOUSE GAS EMISSIONS?

What does MSW have to do with rising sea levels, higher temperatures, and GHG 

emissions? For many wastes, the materials in MSW represent what is left over after a long series 

of steps: (1) extraction and processing of raw materials; (2) manufacture of products; (3) 

transportation of materials and products to markets; (4) use by consumers; and (5) waste 

management. 

Virtually every step along this “life cycle” impacts GHG emissions. Waste management 

decisions can reduce GHGs by affecting one or more of the following: 

(1) Energy consumption (specifically, combustion of fossil fuels) associated with making, 

transporting, using, and disposing the product or material that becomes a waste. 

(2) Non-energy-related manufacturing emissions, such as the CO2 released when 

limestone is converted to lime (which is needed for use in aluminum and steel 

manufacturing). 

(3) CH4 emissions from landfills where the waste is disposed. 

(4) Carbon sequestration, which refers to natural or man-made processes that remove

carbon from the atmosphere and store it for long periods or permanently. 

The first three mechanisms add GHGs to the atmosphere and contribute to global 

warming. The fourth—carbon sequestration—reduces GHG concentrations by removing CO2

from the atmosphere. Forest growth is one mechanism for sequestering carbon; if more biomass 

is grown than is removed (through harvest or decay), the amount of carbon stored in trees 

increases, and thus carbon is sequestered. 

 8 The Landfill Methane Outreach Program (LMOP) is a voluntary assistance and partnership

program that helps facilitate and promote the use of landfill gas as a renewable energy source. By 

controlling landfill gas instead of allowing it to migrate into the air, the LMOP helps businesses, states, and

communities protect the environment and build a sustainable future. The program has an Internet home 

page (http://www.epa.gov/landfill.html) and can be reached via a toll-free hotline number (800-782-7937). 
ES-4 

Different wastes and waste management options have different implications for energy

consumption, CH4 emissions, and carbon sequestration. Source reduction and recycling of paper 

products, for example, reduce energy consumption, decrease combustion and landfill emissions, 

and increase forest carbon sequestration. 

ES.4 WHY EPA PREPARED THIS REPORT AND HOW IT HAS BEEN USED 

Recognizing the potential for source reduction and recycling of municipal solid waste to 

reduce GHG emissions, EPA included a source reduction and recycling initiative in the original 

1994 Climate Change Action Plan and set an emission reduction goal based on a preliminary 

analysis of the potential benefits of these activities. It was clear that a rigorous analysis would be 

needed to gauge more accurately the total GHG emission reductions achievable through source 

reduction and recycling. That all of the options for managing MSW should be considered also 

became clear. By addressing a broader set of MSW management options, a more comprehensive 

picture of the GHG benefits of voluntary actions in the waste sector could be determined and the 

relative GHG impacts of various waste management approaches could be assessed. To this end, 

EPA launched a major research effort, the results of which were published in the first edition of 

this report in September 1998. This edition of the report includes additional materials and 

incorporates updated data affecting many of the material-specific results. The emission factors 

presented will continue to be updated and improved as more data become available. The latest 

emission factors, reflecting these ongoing revisions, can be found on the EPA Global Warming 

Web site <http://www.epa.gov/globalwarming/actions/waste/w-online.htm>. 

The primary application of the GHG emission factors in this report is to support 

mitigation accounting for waste management practices that mitigate climate change. In recent 

years, the emission factors have been applied for this purpose in a number of ways. In 

conjunction with the U.S. Department of Energy, EPA has used these estimates to develop 

guidance for voluntary reporting of GHG reductions, as authorized by Congress in Section 

1605(b) of the Energy Policy Act of 1992. 

Other applications have included quantifying the GHG reductions from voluntary 

programs aimed at source reduction and recycling, such as EPA’s WasteWise and Pay-As-YouThrow programs. EPA also has worked with the Climate Neutral Network to develop companyspecific GHG “footprints” for the network’s member companies, who have pledged to become 

GHG “neutral” through emission reductions or offset activities. 

The international community has shown considerable interest in using the emission 

factors—or adapted versions—to develop GHG emissions estimates for non-U.S. solid waste 

streams.9

 For example, Environment Canada recently employed our life-cycle methodology and 

components of our analysis to develop a set of Canada-specific GHG emission factors to support 

analysis of waste-related mitigation opportunities.10 

Additionally, EPA worked with the International Council for Local Environmental 

Initiatives (ICLEI) to incorporate GHG emission factors into its municipal GHG accounting 

software. Currently, 350 communities participate in ICLEI’s Cities for Climate Protection 

Campaign, which helps them establish a GHG emission reduction target and implement a 

 9

 Note that waste composition and product life cycles vary significantly among countries. This 

report may assist other countries by providing a methodologic framework and benchmark data for 

developing GHG emission estimates for their solid waste streams. 

10 Environment Canada. 2001. Determination of the Impact of Waste Management Activities on 

Greenhouse Gas Emissions. Prepared by ICF Consulting, Torrie-Smith Associates, and Enviros-RIS. 
ES-5 

comprehensive local action plan designed to achieve that target. Currently, we are exploring other 

options for broadening the use of our research internationally.

To make it easier for organizations to use these emission factors, EPA created the Waste 

Reduction Model (WARM) spreadsheet tool.

11 WARM enables waste managers and other users 

to calculate changes in total GHG emissions quickly by entering in information on baseline and 

alternative waste management practices. By applying the appropriate material-specific emission 

factors for each practice, the tool generates an estimate of the net GHG impact from

implementing the alternative waste management practice as compared to the baseline practice. 

ES.5 HOW WE ANALYZED THE IMPACT OF MUNICIPAL SOLID WASTE ON 

GREENHOUSE GAS EMISSIONS

To measure the GHG impacts of MSW, one must first decide which wastes to analyze. 

We surveyed the universe of materials and products found in MSW and determined those that are 

most likely to have the greatest impact on GHGs. These determinations were based on (1) the 

quantity generated; (2) the differences in energy use for manufacturing a product from virgin 

versus recycled inputs; and (3) the potential contribution of materials to CH4 generation in 

landfills. By this process, we limited the analysis to the following 16 items: 

• Aluminum Cans; 

• Steel Cans; 

• Glass; 

• HDPE (high-density polyethylene) Plastic; 

• LDPE (low-density polyethylene) Plastic; 

• PET (polyethylene terephthalate) Plastic; 

• Corrugated Cardboard; 

• Magazines/Third-class Mail; 

• Newspaper; 

• Office Paper;

• Phonebooks; 

• Textbooks; 

• Dimensional Lumber; 

• Medium-density Fiberboard; 

• Food Discards; and 

• Yard Trimmings. 

The foregoing materials constitute 64.4 percent, by weight, of MSW, as shown in Exhibit 

ES-1.

12 

 11 WARM is available on the EPA Web site: 

http://www.epa.gov/globalwarming/actions/waste/warm.htm.

12 Note that these data are based on national averages. The composition of solid waste varies 

locally and regionally; local or state-level data should be used when available. 
ES-6 

We also examined the GHG implications of managing mixed paper, mixed plastics, 

mixed organics, mixed recyclables, and mixed MSW. 

• Mixed paper is recycled in large quantities 

and is an important class of scrap material in 

many recycling programs. Presenting a single 

definition of mixed paper is difficult, 

however, because recovered paper varies 

considerably, depending on the source. For 

purposes of this report, we identified three 

categories of mixed paper according to the 

dominant source—broad (general sources), 

office, and residential. 

• Mixed plastics is comprised of HDPE, LDPE, 

and PET, and is estimated by taking a 

weighted average of the 2000 recovery rates 

for these three plastic types. 

• Mixed organics is a weighted average of food 

discards and yard trimmings, using generation 

rates for 2000. 

• Mixed recyclables are materials that are

typically recycled. As used in this report, the 

term includes the items listed in Exhibit ES-1, 

except food discards and yard trimmings. The 

emission factors reported for mixed 

recyclables represent the average GHG 

emissions for these materials, weighted by the 

tonnages at which they were recycled in 2000. 

• Mixed MSW is comprised of the waste 

material typically discarded by households 

and collected by curbside collection vehicles; 

it does not include white goods (e.g., 

refrigerators, toasters) or industrial waste. 

This report analyzes mixed MSW on an “asdisposed” (rather than “as-generated”) basis. 

We developed a streamlined life-cycle inventory for each of the selected materials. Our 

analysis is streamlined in the sense that it examines GHG emissions only and is not a more 

comprehensive environmental analysis of all emissions from municipal solid waste management 

options.

13

 13 EPA’s Office of Research and Development (ORD) performed a more extensive application of

life-cycle assessment for various waste management options for MSW. A decision support tool (DST) and 

life-cycle inventory (LCI) database for North America have been developed with funding by ORD through 

a cooperative agreement with the Research Triangle Institute (RTI) (CR823052). This methodology is 

based on a multi-media, multi-pollutant approach and includes analysis of GHG emissions as well as a 

broader set of emissions (air, water, and waste) associated with MSW operations. At the time of publication 

of this report, the MSW-DST is available for site-specific applications. For further information, contact

Keith Weitz at rti.org or (919) 541-6973. The LCI database is expected to be released in 2002. The Web 

site address for further information is: <http://www.rti.org/units/ese/p2/lca.cfm#life>. 

Exhibit ES-1 

Percentage of 2000 U.S. Generation of MSW for 

Materials in This Report 

Material 

Percentage of 

MSW Generation 

(by Weight) 

Aluminum Cans 0.7% 

Steel Cans 1.1% 

Glass 5.5% 

HDPE 1.6% 

LDPE 1.3% 

PET 0.8% 

Corrugated Cardboard 13.0% 

Magazines/Third-class Mail 3.3% 

Newspaper 6.5% 

Office Paper 3.2% 

Phonebooks 0.3% 

Textbooks 0.5% 

Dimensional Lumber (listed 

as “Wood – Containers and 

Packaging) 

3.4% 

Medium-density Fiberboard NA 

Food Discards 11.2% 

Yard Trimmings 12.0% 

TOTAL 64.4% 

Source: U.S. EPA. 2002. Municipal Solid Waste in 

the United States: 2000 Facts and Figures, EPA 

530-R-02-001. 
ES-7 

Improvements to the First Edition 

This report is the second edition of Greenhouse Gas 

Emissions from Management of Selected Materials in 

Municipal Solid Waste. This edition includes the following 

improvements: 

• Incorporates new data on energy and recycling loss rates 

from EPA’s Office of Research and Development; 

• Expands the analysis of the GHG benefits of composting, 

including results of CENTURY model runs; 

• Develops emission factors for five new material types: 

magazines/third-class mail, phonebooks, textbooks, 

dimensional lumber, and medium-density fiberboard; 

• Develops emission factors for two new categories of 

mixed materials: mixed plastics and mixed organics; 

• Incorporates new energy data into calculations of utility 

offsets; 

• Revises carbon coefficients and fuel use for national 

average electricity generation; 

• Updates information on landfill gas recovery rates; 

• Adds a discussion of emerging issues in the area of 

climate change and waste management; and 

• Provides a list of suggested proxy values for voluntary 

reporting of GHG emission reductions. 

These changes and/or revisions are described in more detail 

throughout the report. 

We focused on those aspects of the life cycle that have the potential to emit GHGs as 

materials change from their raw states to products and then to waste. Exhibit ES-2 shows the 

steps in the life cycle at which GHGs are emitted, carbon sequestration is affected, and utility 

energy is displaced. As shown, we examined the potential for these effects at the following points 

in a product’s life cycle: 

• Raw material acquisition (fossil fuel energy and other emissions, and changes in forest 

carbon sequestration); 

• Manufacturing (fossil fuel energy emissions); and 

• Waste management (CO2 emissions associated with composting, non-biogenic CO2 and 

nitrous oxide (N2O) emissions from combustion, and CH4 emissions from landfills); these 

emissions are offset to some degree by carbon storage in soil and landfills, as well as 

avoided utility emissions from energy recovery at combustors and landfills. 

At each of these points, we also 

considered transportation-related energy 

emissions. Estimates of GHG emissions 

associated with electricity used in the raw 

materials acquisition and manufacturing steps 

are based on the nation’s current mix of energy 

sources,14 including fossil fuels, hydropower, and 

nuclear power. Estimates of GHG emission 

reductions attributable to utility emissions 

avoided from waste management practices, 

however, are based solely on the reduction of 

fossil fuel use.15

We did not analyze the GHG emissions 

associated with consumer use of products 

because energy use for the selected materials is 

small (or zero) at this point in the life cycle. In 

addition, the energy consumed during use would 

be approximately the same whether the product 

was made from virgin or recycled inputs. 

To apply the GHG estimates developed 

in this report, one must compare a baseline 

scenario with an alternative scenario, on a lifecycle basis. For example, we could compare a 

baseline scenario, where 10 tons of office paper 

are manufactured, used, and landfilled, to an 

alternative scenario, where 10 tons are 

manufactured, used, and recycled. 

 14 The emissions are based on the current national grid mix, as opposed to regional grids. 

15 We adopted this approach based on suggestions from several reviewers who argued that fossil 

fuels should be regarded as the marginal fuel displaced by waste-to-energy and landfill gas recovery 

systems. 
ES-8 

Exhibit ES-2 Greenhouse Gas Sources and Sinks Associated with the Material Life Cycle 
ES-9 

Exhibit ES-3 shows how GHG sources and sinks are affected by each waste management 

strategy. For example, the top row of the exhibit shows that source reduction16 (1) reduces GHG 

emissions from raw materials acquisition and manufacturing; (2) results in an increase in forest 

carbon sequestration; and (3) does not result in GHG emissions from waste management. The 

sum of emissions (and sinks) across all steps in the life cycle represents net emissions. 

Exhibit ES-3 Components of Net Emissions for Various MSW Management Strategies 

GHG Sources and Sinks 

MSW 

Management 

Strategy Raw Materials Acquisition and 

Manufacturing 

Changes in Forest or 

Soil Carbon Storage Waste Management

Source Reduction Decrease in GHG emissions, 

relative to the baseline of 

manufacturing 

Increase in forest carbon 

sequestration (for organic 

materials) 

No emissions/sinks 

Recycling Decrease in GHG emissions due to 

lower energy requirements 

(compared to manufacture from 

virgin inputs) and avoided process 

non-energy GHGs 

Increase in forest carbon 

sequestration (for organic 

materials) 

Process and transportation 

emissions associated with 

recycling are counted in the 

manufacturing stage 

Composting (food 

discards, yard 

trimmings) 

No emissions/sinks Increase in soil carbon 

storage 

Compost machinery emissions 

and transportation emissions 

Combustion No change No change Non-biogenic CO2, N2O 

emissions, avoided utility 

emissions, and transportation 

emissions 

Landfilling No change No change CH4 emissions, long-term 

carbon storage, avoided utility 

emissions, and transportation 

emissions 

ES.6 RESULTS OF THE ANALYSIS 

Management of municipal solid waste presents many opportunities for GHG emission 

reductions. Source reduction and recycling can reduce GHG emissions at the manufacturing 

stage, increase forest carbon sequestration, and avoid landfill CH4 emissions. When waste is 

combusted, energy recovery displaces electricity generated by utilities by burning fossil fuels 

(thus reducing GHG emissions from the utility sector), and landfill CH4 emissions are avoided. 

Landfill CH4 emissions can be reduced by using gas recovery systems and by diverting organic 

materials from landfills. Landfill CH4 can be flared or utilized for its energy potential. When used 

for its energy potential, landfill CH4 displaces fossil fuels, as with MSW combustion. 

 16 In this analysis, the source reduction techniques we analyze involve using less of a given 

product without using more of some other product—e.g., making aluminum cans with less aluminum 

(“lightweighting”); double-sided rather than single-sided photocopying; or reuse of a product. We did not 

consider source reduction of one product that would be associated with substitution by another product—

e.g., substituting plastic boxes for corrugated paper boxes. Nor did we estimate the potential for source 

reduction of chemical fertilizers and pesticides with increased production and use of compost. For a 

discussion of source reduction with material substitution, see Section 4.3. 
ES-10 

 In order to support a broad portfolio of climate change mitigation activities covering a 

range of GHGs, many different methodologies for estimating emissions will be needed. The 

primary result of this research is the development of material-specific GHG emission factors that 

can be used to account for the climate change benefits of waste management practices. 

Exhibits ES-4 and ES-5 present the GHG impacts of source reduction, recycling, 

composting, combustion, and landfilling. The impacts are presented on a per-ton managed basis 

for the individual and mixed materials, using the waste generation reference point. Exhibit ES-4 

presents these values in MTCE/ton, and Exhibit ES-5 presents the values in metric tons of carbon 

dioxide equivalent/ton (MTCO2E/ton). For comparison, Exhibits ES-6 and ES-7 show the same 

results (in MTCE/ton and MTCO2E/ton, respectively) using the raw material extraction reference 

point. In these tables, emissions for 1 ton of a given material are presented across different 

management options.17 The life-cycle GHG emissions for each of the first four waste 

management strategies—source reduction, recycling, composting, and combustion—are 

compared to the GHG emissions from landfilling in Exhibits ES-8 and ES-9. These exhibits show 

the GHG values for each of the first four management strategies, minus the GHG values for 

landfilling. With these exhibits, one may compare the GHG emissions of changing management 

of 1 ton of each material from landfilling (often viewed as the baseline waste management 

strategy) to one of the other waste management options. 

All values shown in Exhibits ES-4 through ES-9 are for national average conditions (e.g., 

average fuel mix for raw material acquisition and manufacturing using recycled inputs; typical 

efficiency of a mass burn combustion unit; national average landfill gas collection rates). GHG 

emissions are sensitive to some factors that vary on a local basis, and thus site-specific emissions 

will differ from those summarized here. 

Following is a discussion of the principal GHG emissions and sinks for each waste 

management practice and the effect that they have on the emission factors: 

• Source reduction, in general, represents an opportunity to reduce GHG emissions in a 

significant way.18 For many materials, the reduction in energy-related CO2 emissions from the 

raw material acquisition and manufacturing process, and the absence of emissions from waste 

management, combine to reduce GHG emissions more than other options. 

• For most materials, recycling has the second lowest GHG emissions. For these materials, 

recycling reduces energy-related CO2 emissions in the manufacturing process (although not 

as dramatically as source reduction) and avoids emissions from waste management. Paper 

recycling increases the sequestration of forest carbon. 

• Composting is a management option for food discards and yard trimmings. The net GHG 

emissions from composting are lower than landfilling for food discards (composting avoids 

CH4 emissions), and higher than landfilling for yard trimmings (landfilling is credited with 

the carbon storage that results from incomplete decomposition of yard trimmings). Overall, 

given the uncertainty in the analysis, the emission factors for composting or combusting these 

materials are similar. 

 17 Note that the difference between any two values for a given material in Exhibit ES-4 (i.e., 

emissions for the same material in two waste management options) is the same as the difference between 

the two corresponding values in Exhibit ES-5. 

18 As noted above, the only source reduction strategy analyzed in this study is lightweighting. 

Consequently, the results shown here do not directly apply to material substitution. 
ES-11 

Material

Source 

Reduction2 Recycling Composting3 Combustion4 Landfilling

Aluminum Cans -2.49 -4.11 NA 0.02 Steel Cans -0.79 -0.49 NA -0.42 Glass -0.14 -0.08 NA 0.01 HDPE -0.49 -0.38 NA 0.23 LDPE -0.61 -0.47 NA 0.23 PET -0.49 -0.42 NA 0.28 Corrugated Cardboard -0.51 -0.71 NA -0.19 Magazines/Third-class Mail -1.04 -0.74 NA -0.13 Newspaper -0.81 -0.95 NA -0.21 Office Paper -0.80 -0.68 NA -0.18 Phonebooks -1.28 -0.91 NA -0.21 Textbooks -1.23 -0.75 NA -0.18 Dimensional Lumber -0.55 -0.67 NA -0.22 Medium-density Fiberboard -0.60 -0.67 NA -0.22 Food Discards NA NA -0.05 -0.05 Yard Trimmings NA NA -0.05 -0.06 Mixed Paper Broad Definition NA -0.67 NA -0.19  Residential Definition NA -0.67 NA -0.18  Office Paper Definition NA -0.83 NA -0.17 Mixed Plastics NA -0.41 NA 0.25 Mixed Recyclables NA -0.76 NA -0.17 Mixed Organics NA NA -0.05 -0.06 Mixed MSW as Disposed NA NA NA -0.04 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed. 1MTCE/ton: Metric tons of carbon equivalent per short ton of material. Material tonnages are on an as-managed (wet weight) basis.

2Source reduction assumes initial production using the current mix of virgin and recycled inputs.3There is considerable uncertainty in our estimate of net GHG emissions from composting; 

the values of zero are plausible values based on assumptions and a bounding analysis. 

4Values are for mass burn facilities with national average rate of ferrous recovery.5Values reflect estimated national average methane recovery in year 2000.

Exhibit ES-4

Net GHG Emissions from Source Reduction and MSW Management Options - Emissions Counted from a Wast

Generation Reference Point (MTCE/Ton)1
ES-12 

Material Source Reduction2 Recycling Composting3 Combustion4 Landfillin

Aluminum Cans -9.15 -15.07 NA 0.06 Steel Cans -2.89 -1.79 NA -1.53 Glass -0.50 -0.28 NA 0.05 HDPE -1.79 -1.40 NA 0.85 LDPE -2.25 -1.71 NA 0.85 PET -1.78 -1.55 NA 1.04 Corrugated Cardboard -1.89 -2.60 NA -0.68 Magazines/Third-class Mail -3.80 -2.70 NA -0.49 Newspaper -2.97 -3.48 NA -0.77 Office Paper -2.95 -2.48 NA -0.65 Phonebooks -4.70 -3.34 NA -0.77 Textbooks -4.49 -2.74 NA -0.65 Dimensional Lumber -2.01 -2.45 NA -0.81 Medium-density Fiberboard -2.20 -2.47 NA -0.81 Food Discards NA NA -0.20 -0.19 Yard Trimmings NA NA -0.20 -0.23 Mixed Paper Broad Definition NA -2.47 NA -0.68  Residential Definition NA -2.47 NA -0.68  Office Paper Definition NA -3.05 NA -0.62 Mixed Plastics NA -1.51 NA 0.93 Mixed Recyclables NA -2.80 NA -0.61 Mixed Organics NA NA -0.20 -0.21 Mixed MSW as Disposed NA NA NA -0.13 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1MTCO2E/ton: Metric tons of carbon dioxide equivalent per short ton of material. Material tonnages are on an as-managed (wet weight) basis.2Source reduction assumes initial production using the current mix of virgin and recycled inputs.3There is considerable uncertainty in our estimate of net GHG emissions from composting; 

the values of zero are plausible values based on assumptions and a bounding analysis. 

4Values are for mass burn facilities with national average rate of ferrous recovery.5Values reflect estimated national average methane recovery in year 2000.

Exhibit ES-5

Net GHG Emissions from Source Reduction and MSW Management Options - Emissions Counted from a Waste Gener

Reference Point (MTCO2E/Ton)1
ES-13 

Material

Source 

Reduction1 Recycling2 Composting2 Combustion2 Landfilling2

Aluminum Cans 0.00 -1.61 NA 2.51 Steel Cans 0.00 0.30 NA 0.37 Glass 0.00 0.06 NA 0.15 HDPE 0.00 0.10 NA 0.72 LDPE 0.00 0.15 NA 0.85 PET 0.00 0.06 NA 0.77 Corrugated Cardboard -0.28 -0.47 NA 0.05 Magazines/Third-class Mail -0.58 -0.28 NA 0.33 Newspaper -0.35 -0.49 NA 0.25 Office Paper -0.50 -0.37 NA 0.13 Phonebooks -0.65 -0.27 NA 0.42 Textbooks -0.64 -0.16 NA 0.41 Dimensional Lumber -0.50 -0.62 NA -0.17 -Medium-density Fiberboard -0.50 -0.58 NA -0.12 -Food Discards NA NA -0.05 -0.05 Yard Trimmings NA NA -0.05 -0.06 -Mixed Paper Broad Definition NA -0.30 NA 0.19  Residential Definition NA -0.30 NA 0.19  Office Paper Definition NA 0.02 NA 0.68 Mixed Plastics NA 0.09 NA 0.76 Mixed Recyclables NA -0.40 NA 0.19 Mixed Organics NA NA -0.05 -0.06 Mixed MSW as Disposed NA NA NA -0.04 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed. 1Source reduction assumes initial production using the current mix of virgin and recycled inputs.

2Includes emissions from the initial production of the material being managed, except for foodwaste, yard waste, and mixed MSW. 

Exhibit ES-6

Net GHG Emissions from Source Reduction and MSW Management Options - Emissions Counted from a Raw 

Materials Extraction Reference Point (MTCE/Ton)
ES-14 

Material

Source 

Reduction1 Recycling2 Composting2 Combustion2 Landfilling2

Aluminum Cans 0.00 -5.92 NA 9.21 9.Steel Cans 0.00 1.09 NA 1.35 2.Glass 0.00 0.22 NA 0.55 0.HDPE 0.00 0.38 NA 2.64 1.LDPE 0.00 0.54 NA 3.11 2.PET 0.00 0.23 NA 2.82 1.Corrugated Cardboard -1.01 -1.72 NA 0.20 1.Magazines/Third-class Mail -2.11 -1.02 NA 1.20 1.Newspaper -1.29 -1.79 NA 0.91 0.Office Paper -1.82 -1.36 NA 0.47 3.Phonebooks -2.37 -1.01 NA 1.56 1.Textbooks -2.35 -0.60 NA 1.49 4.Dimensional Lumber -1.84 -2.28 NA -0.64 -0.Medium-density Fiberboard -1.84 -2.11 NA -0.45 -0.Food Discards NA NA -0.20 -0.19 0.Yard Trimmings NA NA -0.20 -0.23 -0.Mixed Paper Broad Definition NA -1.09 NA 0.70 1. Residential Definition NA -1.08 NA 0.71 1. Office Paper Definition NA 0.07 NA 2.50 3.Mixed Plastics NA 0.34 NA 2.79 1.Mixed Recyclables NA -1.48 NA 0.71 1.Mixed Organics NA NA -0.20 -0.21 0.Mixed MSW as Disposed NA NA NA -0.13 0.

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed. 1Source reduction assumes initial production using the current mix of virgin and recycled inputs.

2Includes emissions from the initial production of the material being managed, except for foodwaste, yard waste, and mixed MSW. 

Exhibit ES-7

Net GHG Emissions from Source Reduction and MSW Management Options - Emissions Counted from a Raw 

Materials Extraction Reference Point (MTCO2E/Ton)
ES-15 

Material

Source Reduction2 

Net Emissions Minus 

Landfilling Net 

Emissions (Current 

Mix)

Source Reduction 

Net Emissions 

Minus Landfilling 

Net Emissions 

(100% Virgin 

Inputs)

Recycling Net 

Emissions Minus 

Landfilling Net 

Emissions

Composting3 Net 

Emissions Minus 

Landfilling Net 

Emissions

Combustio

Emissions 

Landfillin

Emissi

Aluminum Cans -2.50 -4.68 -4.12 NA Steel Cans -0.80 -1.02 -0.50 NA Glass -0.15 -0.17 -0.09 NA HDPE -0.50 -0.54 -0.39 NA LDPE -0.63 -0.65 -0.48 NA PET -0.50 -0.59 -0.43 NA Corrugated Cardboard -0.59 -1.03 -0.79 NA Magazines/Third-class Mail -0.92 -1.07 -0.62 NA Newspaper -0.60 -1.11 -0.74 NA Office Paper -1.43 -1.63 -1.30 NA Phonebooks -1.07 -1.19 -0.70 NA Textbooks -1.85 -1.94 -1.37 NA Dimensional Lumber -0.44 NA -0.56 NA Medium-density Fiberboard -0.50 NA -0.57 NA Food Discards NA NA NA -0.22 Yard Trimmings NA NA NA 0.04 Mixed Paper Broad Definition NA NA -0.78 NA  Residential Definition NA NA -0.74 NA  Office Paper Definition NA NA -0.99 NA Mixed Plastics NA NA -0.42 NA Mixed Recyclables NA NA -0.82 NA Mixed Organics NA NA NA -0.09 Mixed MSW as Disposed NA NA NA NA 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed. 1Values for landfilling reflect projected national average methane recovery in year 2000.

2Source reduction assumes initial production using the current mix of virgin and recycled inputs.3Calculation is based on assuming zero net emissions for composting.4Values are for mass burn facilities with national average rate of ferrous recovery.

GHG Emissions of MSW Management Options Compared to Landfilling1 (MTCE/Ton)

Exhibit ES-8
ES-16 

Material

Source Reduction2 

Net Emissions Minus 

Landfilling Net 

Emissions (Current 

Mix)

Source Reduction 

Net Emissions 

Minus Landfilling 

Net Emissions 

(100% Virgin 

Inputs)

Recycling Net 

Emissions Minus 

Landfilling Net 

Emissions

Composting3 Net 

Emissions Minus 

Landfilling Net 

Emissions

Comb

Emiss

Land

Em

Aluminum Cans -9.18 -17.15 -15.11 NA Steel Cans -2.92 -3.72 -1.83 NA Glass -0.54 -0.61 -0.32 NA HDPE -1.82 -1.99 -1.44 NA LDPE -2.29 -2.38 -1.75 NA PET -1.82 -2.18 -1.59 NA Corrugated Cardboard -2.17 -3.79 -2.88 NA Magazines/Third-class Mail -3.36 -3.94 -2.26 NA Newspaper -2.21 -4.07 -2.72 NA Office Paper -5.23 -5.99 -4.77 NA Phonebooks -3.94 -4.37 -2.57 NA Textbooks -6.78 -7.13 -5.03 NA Dimensional Lumber -1.63 NA -2.07 NA Medium-density Fiberboard -1.82 NA -2.09 NA Food Discards NA NA NA -0.82 Yard Trimmings NA NA NA 0.15 Mixed Paper Broad Definition NA NA -2.84 NA  Residential Definition NA NA -2.72 NA  Office Paper Definition NA NA -3.62 NA Mixed Plastics NA NA -1.55 NA Mixed Recyclables NA NA -2.99 NA Mixed Organics NA NA NA -0.32 Mixed MSW as Disposed NA NA NA NA 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed. 1Values for landfilling reflect projected national average methane recovery in year 2000.

2Source reduction assumes initial production using the current mix of virgin and recycled inputs.3Calculation is based on assuming zero net emissions for composting.4Values are for mass burn facilities with national average rate of ferrous recovery.

Exhibit ES-9

GHG Emissions of MSW Management Options Compared to Landfilling1 (MTCO2E/Ton)
ES-17 

• The net GHG emissions from combustion of mixed MSW are lower than landfilling 

mixed MSW (under national average conditions for landfill gas recovery). Because, in 

practice, combustors and landfills manage a mixed waste stream, net emissions are 

determined more by technology factors (e.g., the efficiency of landfill gas collection 

systems and combustion energy conversion) than by material specificity. Materialspecific emissions for landfills and combustors provide a basis for comparing these 

options with source reduction, recycling, and composting. 

The ordering of combustion, landfilling, and composting is affected by (1) the GHG 

inventory accounting methods, which do not count CO2 emissions from sustainable biogenic 

sources,19 but do count emissions from sources such as plastics; and (2) a series of assumptions 

on sequestration, future use of CH4 recovery systems, system efficiency for landfill gas recovery, 

ferrous metals recovery, and avoided utility fossil fuels. On a site-specific basis, the ordering of 

results between a combustor and a landfill could be different from the ordering provided here, 

which is based on national average conditions. 

We conducted sensitivity analyses to examine the GHG emissions from landfilling under 

varying assumptions about (1) the percentage of landfilled waste sent to landfills with gas 

recovery, and (2) CH4 oxidation rate and gas collection system efficiency. The sensitivity 

analyses demonstrate that the results for landfills are very sensitive to these factors, which are 

site-specific.20 Thus, using a national average value when making generalizations about emissions 

from landfills masks some of the variability that exists from site to site. 

The scope of this report is limited to developing emission factors that can be used to 

evaluate GHG implications of solid waste decisions. We do not analyze policy options in this 

report. Nevertheless, the differences in emission factors across various waste management 

options are sufficiently large as to imply that GHG mitigation policies in the waste sector can 

make a significant contribution to U.S. emission reductions. A number of examples, using the 

emission factors in this report, bear this out. 

• At the firm level, targeted recycling programs can reduce GHGs. For example, a 

commercial facility that shifts from (a) a baseline practice of landfilling (in a landfill with 

no gas collection system) 50 tons office paper and 4 tons of aluminum cans to (b) 

recycling the same materials can reduce GHG emissions by more than 100 MTCE. 

• At the community level, a city of 100,000 with average waste generation (4.5 lbs/day per 

capita), recycling (30 percent), and baseline disposal in a landfill with no gas collection 

system could increase its recycling rate to 40 percent—for example, by implementing a 

pay-as-you-throw program—and reduce emissions by about 10,000 MTCE per year. 

(Note that further growth in recycling would be possible; some communities already are 

exceeding recycling rates of 50 percent). 

• A city of 1 million, disposing of 650,000 tons per year in a landfill without gas collection, 

could reduce its GHG emissions by nearly 138,000 MTCE per year by managing waste in 

a mass burn combustor unit. 

 19 Sustainable biogenic sources include paper and wood products from sustainably managed 

forests. When these materials are burned or aerobically decomposed to CO2, the CO2 emissions are not 

counted. Our approach to measuring GHG emissions from biogenic sources is described in detail in 

Chapter 1. 

20 For details on the sensitivity analyses, see section 7.5 and Exhibits 7-7 and 7-8.
ES-18 

• A town of 50,000 landfilling 30,000 tons per year could install a landfill gas recovery

system and reduce emissions by about 6,000 MTCE per year. 

• At the national level, if the United States attains the goal of a 35 percent recycling rate by

2005, emissions will be reduced by nearly 10 million MTCE per year compared to a 

baseline where we maintain the current 30 percent recycling rate and use the “national 

average” landfill for disposal. 

ES.7 OTHER LIFE-CYCLE GHG ANALYSES AND TOOLS 

Life-cycle analysis is being used increasingly to quantify the GHG impacts of private and 

public sector decisions. In addition to the life-cycle analyses that underpin the emission factors in 

this report, Environmental Defense,

21 ICLEI, Ecobilan, and others have analyzed the life-cycle 

environmental impacts of various industry processes (e.g., manufacturing) and private and public 

sector practices (e.g., waste management). In many cases, the results of life-cycle analyses are 

packaged into software tools that distill the information according to a specific user’s needs. 

As mentioned earlier, the WARM model was designed as a tool for waste managers to 

weigh the GHG impacts of their waste management practices. As a result, the model focuses 

exclusively on waste sector GHG emissions, and the methodology used to estimate emissions is 

consistent with international and domestic GHG accounting guidelines. Life-cycle tools designed 

for broader audiences necessarily include other sectors and/or other environmental impacts, and 

are not necessarily tied to the Intergovernmental Panel on Climate Change (IPCC) guidelines for 

GHG accounting or the methods used in the Inventory of U.S. Greenhouse Gas Emissions and 

Sinks. 

• WARM, developed by ICF Consulting for EPA, allows users to input several key 

variables (e.g., landfill gas collection system information, electric utility fuel mix, 

transportation distances).

22 The model covers 21 types of materials and 5 waste 

management options: source reduction, recycling, combustion, composting, and 

landfilling. WARM accounts for upstream energy and non-energy emissions, 

transportation distances to disposal and recycling facilities, carbon sequestration, and 

utility offsets that result from landfill gas collection and combustion. The tool provides 

participants in the U.S. Department of Energy’s 1605b program with the option to report 

results by year, by gas, and by year and gas. WARM software is available free of charge 

in both a Web-based calculator format and a Microsoft Excel

 spreadsheet. The tool is 

ideal for waste planners interested in tracking and reporting voluntary GHG emission 

reductions from waste management practices and for comparing the climate change 

impacts of different approaches. To access the tool, visit: 

<http://www.epa.gov/globalwarming/actions/waste/warm.htm>. 

• ICLEI Cities for Climate Protection (CCP) Campaign Greenhouse Gas Emission 

Software was developed by Torrie Smith Associates for ICLEI. This Windows-based 

tool, targeted for use by local governments, can analyze emissions and emission 

reductions on a community-wide basis and for municipal operations alone. The 

 21 Blum, L., Denison, R.A., and Ruston, V.F. 1997. A Life-Cycle Approach to Purchasing and 

Using Environmentally Preferable Paper: A Summary of the Paper Task Force Report,” Journal of

Industrial Ecology. I:3:15-46. Denison, R.A. 1996. “Environmental Life-Cycle Comparison of Recycling, 

Landfilling, and Incineration: A Review of Recent Studies;” Annual Review of Energy and the

Environment 21:6:191-237. 

22 Microsoft Excel and Web-based versions of this tool are available online at the following Web 

site: http://www.epa.gov/globalwarming/actions/waste/tools.html. 
ES-19 

community-wide module looks at residential, commercial, and industrial buildings, 

transportation activity, and community-generated waste. The municipal operations 

module looks at municipal buildings, municipal fleets, and waste from municipal inhouse operations. In addition to computing GHG emissions, the CCP software estimates 

reductions in criteria air pollutants, changes in energy consumption, and financial costs 

and savings associated with energy use and other emission reduction initiatives. A 

version of the software program was made available for use by private businesses and 

institutions during the summer of 2001. CCP software subscriptions, including technical 

support, are available to governments participating in ICLEI for a subsidized price of 

$240. The full retail price of the software in the United States is $2,000. For more 

information, visit: <http://www.iclei.org/us/ccpsoftware.html> or contact the U.S. ICLEI 

office at (510)-540-8843, iclei_usa@iclei.org. 

• The MSW Decision Support Tool (DST) and life-cycle inventory database for North 

America have been developed through funding by ORD through a cooperative agreement 

with the Research Triangle Institute (CR823052). The methodology is based on a multimedia, multi-pollutant approach and includes analysis of GHG emissions as well as a 

broader set of emissions (air, water, and waste) associated with MSW operations. The 

MSW-DST is available for site-specific applications and has been used to conduct 

analyses in several states and 15 communities including use by the U.S. Navy in the 

Pacific Northwest. The tool is intended for use by solid waste planners at state and local 

levels to analyze and compare alternative MSW management strategies with respect to 

cost, energy consumption, and environmental releases to the air, land, and water. The 

costs are based on full cost accounting principles and account for capital and operating 

costs using an engineering economics analysis. The MSW-DST calculates not only

projected emissions of GHGs and criteria air pollutants, but also emissions of more than 

30 air- and water-borne pollutants. The DST models emissions associated with all MSW 

management activities, including waste collection and transportation, transfer stations, 

materials recovery facilities, compost facilities, landfills, combustion and refuse-derived 

fuel facilities, utility offsets, material offsets, and source reduction. The differences in 

residential, multi-family, and commercial sectors can be evaluated individually. The 

software has optimization capabilities that enable one to identify options that evaluate 

minimum costs as well as solutions that can maximize environmental benefits, including 

energy conservation and GHG reductions. 

At the time of the publication of this report, the life-cycle inventory (LCI) database for 

North America was to be released in 2002. Plans to develop a Web-based version are 

being considered. The MSW-DST provides extensive default data for the full range of 

MSW process models and requires minimum input data. However, these defaults can be 

tailored to the specific communities using site-specific information. The MSW-DST also 

includes a calculator for source reduction and carbon sequestration using a methodology

that is consistent with the IPCC in terms of the treatment of biogenic CO2 emissions. For 

more information, refer to the project Web site: 

<http://www.rti.org/units/ese/p2/lca.cfm#life> or contact Susan Thornloe, U.S. EPA, 

(919)-541-2709, thornloe.susan@epamail.epa.gov, or Keith Weitz, Research Triangle 

Institute, (919)-541-6973, kaw@rti.org. 

• The Tool for Environmental Analysis and Management (TEAM), developed by Ecobilan, 

simulates operations associated with product design, processes, and activities associated 

with several industrial sectors. The model considers energy consumption, material 

consumption, transportation, waste management, and other factors in its evaluation of 

environmental impacts. Many private firms and some government agencies have used the 

model. Users pay a licensing fee of $3,000 and an annual maintenance contract of $3,000. 
ES-20 

This model is intended for use in Europe and was not developed for use in North 

America. For more information, visit: 

<http://www.ecobalance.com/software/gb_software.html>. 

ES.8 LIMITATIONS OF THE ANALYSIS 

When conducting this analysis, we used a number of analytical approaches and numerous 

data sources, each with its own limitations. In addition, we made and applied assumptions 

throughout the analysis. Although these limitations would be troublesome if used in the context 

of a regulatory framework, we believe that the results are sufficiently accurate to support their use 

in voluntary programs. Some of the major limitations include the following: 

• The manufacturing GHG analysis is based on estimated industry averages for energy 

usage, and in some cases the estimates are based on limited data. In addition, we used 

values for the average GHG emissions per ton of material produced, not the marginal 

emission rates per incremental ton produced. In some cases, the marginal emission rates 

may be significantly different. 

• The forest carbon sequestration analysis deals with a very complicated set of interrelated 

ecological and economic processes. Although the models used represent the state-of-theart in forest resource planning, their geographic scope is limited. Because of the global 

market for forest products, the actual effects of paper recycling would occur not only in 

the United States but in Canada and other countries. Other important limitations include: 

(1) the estimate does not include changes in carbon storage in forest soils and forest 

floors; (2) the model assumes that no forested lands will be converted to non-forest uses 

as a result of increased paper recycling; and (3) we use a point estimate for forest carbon 

sequestration, whereas the system of models predicts changing net sequestration over 

time. 

• The composting analysis considers a small sampling of feedstocks and a single compost 

application (i.e., agricultural soil). The analysis did not consider the full range of soil 

conservation and management practices that could be used in combination with compost 

and their impacts on carbon storage. 

• The combustion analysis uses national average values for several parameters; variability 

from site to site is not reflected in our estimate. 

• The landfill analysis (1) incorporates considerable uncertainty on CH4 generation and 

carbon sequestration, due to limited data availability; and (2) uses landfill estimated CH4

recovery levels for the year 2000 as a baseline. 

Finally, throughout most of the report, we express analytical inputs and outputs as point 

estimates. We recognize that a rigorous treatment of uncertainty and variability would be useful, 

but in most cases the information needed to treat these in statistical terms is not available. The 

report includes some sensitivity analyses to illustrate the importance of selected parameters and 

expresses ranges for a few other factors such as GHG emissions from manufacturing. We 

encourage readers to provide more accurate information where it is available; perhaps with 

additional information, future versions of this report will be able to shed more light on uncertainty 

and variability. Meanwhile, we caution that the emission factors reported here should be 

evaluated and applied with an appreciation for the limitations in the data and methods, as 

described at the end of each chapter. 
1 

1. METHODOLOGY

This report is the second edition of Greenhouse Gas Emissions from Management of Selected 

Materials in Municipal Solid Waste. We made the following improvements to the first edition of the 

report: 

• Incorporated new energy data and information on recycling loss rates from EPA’s Office of 

Research and Development (ORD); 

• Expanded analysis of the GHG benefits of composting, including results of CENTURY 

model runs; 

• Developed emission factors for five new material types: magazines/third-class mail, 

phonebooks, textbooks, dimensional lumber, and medium-density fiberboard; 

• Developed emission factors for two new categories of mixed materials: mixed plastics and 

mixed organics; 

• Updated national recovery and generation rates to include 2000 data; 

• Incorporated new energy data into calculations of utility offsets; 

• Revised carbon coefficients and fuel use for national average electricity generation; 

• Updated information on landfill gas recovery rates; 

• Added a discussion of emerging issues in the area of climate change and waste management; 

and 

• Provided a list of suggested proxy values for voluntary reporting of GHG emission 

reductions. 

All of these changes and/or revisions are described in more detail throughout the body of the report. 

Because this is the second edition, we have moved some of the background information from the 

body of the report to background documents, which are available in the docket in the Resource 

Conservation and Recovery Act (RCRA) Information Center. Background Document A provides data on 

life-cycle energy intensity and fuel mix, provided by Franklin Associates, Ltd. (FAL) (All other 

background documents, and this report, were written by ICF Consulting.) Background Document B 

provides a discussion of the review cycles leading up to the first and second editions of the report. 

Background Document C includes a discussion of how we screened materials for the first edition of the 

report. 

The remainder of this chapter provides an overview of the methodology used to calculate the 

GHG emissions associated with various management strategies for MSW. The first section briefly 

describes the life-cycle framework used for the analysis. Next is a discussion of the materials included in 

the analysis. The final three sections present a description of key inputs and baselines, a summary of the 

life-cycle stages, and an explanation of how to estimate and compare net GHG emissions and sinks. 
2 

1.1 THE OVERALL FRAMEWORK: A STREAMLINED LIFE-CYCLE INVENTORY 

Early in this analysis of the GHG benefits of specific waste management practices, it became 

clear that all waste management options provide opportunities for reducing GHG emissions, depending on 

individual circumstances. Although source reduction and recycling are often the most advantageous waste 

management practices from a GHG perspective, a material-specific comparison of all available waste 

management options would clarify where the greatest GHG benefits can be obtained for particular 

materials in MSW. A material-specific comparison can help policymakers identify the best options for 

GHG reductions. 

This study determined that the best way to conduct such a comparative analysis is a streamlined 

application of a life-cycle assessment (LCA). A full LCA is an analytical framework for understanding 

the material inputs, energy inputs, and environmental releases associated with manufacturing, using, and 

disposing of a given material. A full LCA generally consists of four parts: (1) goal definition and scoping; 

(2) an inventory of the materials and energy used during all stages in the life of a product or process, and 

an inventory of environmental releases throughout the product life cycle; (3) an impact assessment that 

examines potential and actual human health effects related to the use of resources and environmental 

releases; and (4) an assessment of the change that is needed to bring about environmental improvements 

in the product or processes. 

A full LCA is beyond the scope of this analysis. Rather, the streamlined LCA described in this 

report is limited to an inventory of the emissions and other environmental impacts related to global 

warming. This study did not assess human health impacts, necessary environmental improvements, and 

air, water, or environmental impacts that do not have a direct bearing on climate change. 

1.2 MSW MATERIALS CONSIDERED IN THE STREAMLINED LIFE-CYCLE 

INVENTORY 

Each material in MSW has different GHG impacts depending on how it is manufactured and 

disposed of. We began our research by performing a screening analysis of 37 of the most common 

materials and products found in MSW.1

 The materials included in screening analysis then were ranked by 

their potential for GHG reductions.2

 The first edition of the report included 12 materials: aluminum cans, 

steel cans,3

 glass, high-density polyethylene (HDPE) plastic blow-molded containers, low-density 

polyethylene (LDPE) plastic blow-molded containers, polyethylene terephthalate (PET) plastic blowmolded containers, corrugated cardboard, newspaper, office paper,4

 and three grades of mixed paper 

(broad, residential, and office). In addition to these materials, we examined the GHG implications of 

various management strategies for food discards, yard trimmings, mixed MSW, and mixed recyclables. 

 1 In addition to the materials and products covered in the first edition of the report, the screening analysis 

included the following materials and products: other paper materials (bags and sacks, other paper packaging, books, 

other paperboard packaging, wrapping papers, paper plates and cups, folding cartons, other nonpackaging paper, 

and tissue paper and towels), other plastic materials (plastic wraps, plastic bags and sacks, other plastic containers, 

and other plastic packing), other metal materials (aluminum foil/closures, other steel packaging), and other 

miscellaneous materials (miscellaneous durable goods, wood packaging, furniture and furnishings, carpet and rugs, 

and other miscellaneous packaging). 

2

 For more information on the screening analysis used to identify materials for the first edition of the 

report, see Background Document C. 

3 Other steel materials also may be recycled, but this analysis was limited to steel cans from households. 

4

 Office paper refers to the type of paper used in computer printers and photocopiers. 
3 

Most of the changes from the first edition of this report reflect additions of new or updated data. 

This second edition features an expanded list of material types, including magazines and third-class mail, 

phonebooks, textbooks, dimensional lumber, medium-density fiberboard, and several additional 

categories of mixed recycled materials (e.g. mixed plastics, mixed organics). This edition also 

incorporates updated data developed by ORD through its work on life-cycle management of MSW. 

ORD’s data set on energy, fuel mix, and loss rates has been thoroughly reviewed by industry and other 

stakeholders, and is likely to be more up-to-date than some of the information in the first edition of this 

report. Thus, where a complete set of energy intensity and fuel mix data was available from ORD, that 

information was incorporated in this report. For other materials—steel cans and mixed paper (broad, 

residential, and office definitions)—we retained the original data set developed by FAL. This edition 

includes new data (also developed by FAL) on dimensional lumber and medium-density fiberboard. 

Exhibit 1-1 lists the materials that were analyzed for this report and the energy-related data sources 

underlying the estimates. All of the material types listed in Exhibit 1-1 are discussed in subsequent 

chapters and included in exhibits throughout the report, with the exception of three mixed waste 

categories. Mixed plastics, mixed recyclables, and mixed organics are included only in Chapter 8 because 

emission factors for these materials simply reflect the weighted average emissions of other material types. 

Comparison of EPA/ORD and EPA/OSW Emission Factors 

Efforts to harmonize our previous life-cycle emission factors with the results of recent work by EPA’s 

Office of Research and Development (ORD) began in October 2000. Noticing significant differences in our 

bottom line emission factors, we compared a range of assumptions, including energy consumption, fuel mix, loss 

rates, landfill oxidation rate, timing of landfill methane emissions, fraction of landfill gas collected, electricity 

mix, transportation distances, and carbon storage. Our comparison of energy intensities and fuel mixes included 

process and transportation energy for virgin and recycled production of each material type. Because the previous 

Office of Solid Waste (OSW) energy values were based on an average of Franklin Associates, Ltd. (FAL) and 

Tellus data, we compared the ORD values to the FAL data, Tellus data, and average of FAL and Tellus data. 

This comparison revealed that the differences between the OSW and ORD emission factors are mostly 

attributable to the different assumptions about energy consumption (i.e., the sum of precombustion, process, and 

transportation energy), fuel mix, and loss rates. In general, we found that ORD’s total energy values are lower 

than OSW’s energy values for both virgin and recycled materials. Comparing fuel mix, we found the most 

significant differences occurring for electricity, coal, natural gas, and “other” fuel types comprising process 

energy. The fractions of diesel fuel, residual fuel, and natural gas exhibited the greatest disparities for 

transportation energy. Our comparison of loss rates, which are used to develop the recycling emission factors, 

showed significant variation for office paper, steel cans, and, to a lesser extent, newspaper. 

In an effort to reconcile the remaining differences between ORD and OSW estimates of GHG emissions 

from the acquisition of raw materials and their manufacture into products, we identified additional 

methodological differences that could be affecting the recycling numbers. In particular, we found that ORD 

simulates closed-loop recycling for all materials, while OSW assumes open-loop recycling for office paper and 

corrugated cardboard. We also found that ORD’s estimates do not include non-energy process emissions from 

perfluorocarbons (PFCs). To isolate any remaining differences between the two analyses, we substituted ORD 

energy intensities, fuel mixes, and loss rates into the OSW model. 

Once we had identified and resolved all methodological differences between ORD and OSW estimates 

for raw materials acquisition and manufacturing, we selected the material types for which we could substitute 

ORD data for the existing OSW data: aluminum, glass, HDPE, LDPE, PET, corrugated cardboard, 

magazines/third-class mail, newspaper, office paper, phonebooks, and textbooks. For wood products, ORD did 

not develop emission factors, while for steel its data was not sufficiently disaggregated to replace the existing 

OSW data. 
4 

Comparing GHGs 

 Carbon dioxide (CO2), methane (CH4), and nitrous 

oxide (N2O) are very different gases in terms of their heattrapping potential. An international protocol has established 

CO2 as the reference gas for measurement of heat-trapping 

potential (also known as global warming potential or GWP). 

By definition, the GWP of 1kilogram (kg) of CO2 is 1. 

 CH4 has a GWP of 21, which means that 1 kg of 

methane has the same heat-trapping potential as 21 kg of CO2. 

 N2O has a GWP of 310. 

 PFCs are the most potent GHG included in this 

analysis; GWPs are 6,500 for CF4 and 9,200 for C2F6. 

 In this report, emissions of CO2, CH4, N2O, and PFCs 

have been converted to their “carbon equivalents.” Because 

CO2 is 12/44 carbon by weight, 1 metric ton of CO2 is equal to 

12/44 or 0.27 metric tons of carbon equivalent (MTCE). The 

MTCE value for 1 metric ton of each of the other gases is 

determined by multiplying its GWP by a factor of 12/44. (All 

data provided here are from The Intergovernmental Panel on 

Climate Change (IPCC), Climate Change 1995: The Science of 

Climate Change, 1996, p. 121.)

Exhibit 1-1 Materials Analyzed and Energy-related Data Sources 

Material Energy Data Source Material Energy Data Source 

Aluminum Cans ORD Medium-Density 

Fiberboard 

FAL 

Steel Cans FAL Food Discards NA 

Glass ORD Yard Trimmings NA 

Corrugated Cardboard ORD Mixed Paper – Broad 

Definition 

FAL 

Magazines/Third-class 

Mail 

ORD Mixed Paper – 

Residential Definition 

FAL 

Newspaper ORD Mixed Paper – Office 

Paper Definition 

FAL 

Office Paper ORD Mixed Plastics Weighted Average 

Phonebooks ORD Mixed Recyclables Weighted Average 

Textbooks ORD Mixed Organics NA 

Dimensional Lumber FAL Mixed MSW NA 

NA = Not applicable (data not energy-related) 

1.3 KEY INPUTS AND BASELINES FOR THE STREAMLINED LIFE-CYCLE 

INVENTORY 

Evaluating the GHG emissions of waste 

management requires analysis of three factors: 

(1) GHG emissions throughout the life cycle of 

the material (including the chosen disposal 

option); (2) the extent to which carbon sinks are 

affected by manufacturing and disposing of the 

material; and (3) the extent to which the 

management option recovers energy that can be 

used to replace electric utility energy, thus 

reducing utility GHG emissions. In addition, to 

provide a consistent basis for comparison, we 

made several choices in our GHG accounting 

framework in terms of timing and levels of 

production. Each of these factors warrants 

further discussion. 

GHG Emissions Relevant to Waste: 

The most important GHGs for purposes of 

analyzing MSW management options are CO2, 

CH4, N2O, and perfluorocarbons (PFCs). Of 

these, CO2 is by far the most common GHG 

emitted in the United States. Most CO2 

emissions result from energy use, particularly fossil fuel combustion. A great deal of energy is consumed 

when a product is manufactured and then discarded. This energy is used in the following stages: (1) 

extracting and processing raw materials; (2) manufacturing products; (3) managing products at the end of 

their useful lives; and (4) transporting materials and products between each stage of their life cycles. This 

study estimated energy-related GHG emissions during all of these stages, except for transportation of 

products to consumers (because GHG emissions resulting from transportation to consumers will vary 

little among the options considered). Much of this report is devoted to explaining the methodology 
5 

point in the life cycle. In addition, the energy consumed during use would be about the same whether the 

product is made from virgin or recycled inputs. 

CH4, a more potent GHG, is produced when organic waste decomposes in an oxygen-free 

(anaerobic) environment, such as a landfill. CH4 from landfills is the largest source of CH4 in the United 

States;5

 these emissions are addressed in Chapter 7. CH4 is also emitted when natural gas is released to the 

atmosphere during production of coal or oil, production or use of natural gas, and agricultural activities. 

N2O results from the use of commercial and organic fertilizers and fossil fuel combustion, as well 

as other sources. This analysis estimated N2O emissions from waste combustion. 

Perfluorocarbons (CF4 and C2F6) are emitted during the reduction of alumina to aluminum in the 

primary smelting process. The source of fluorine for CF4 and C2F6 is the molten cryolite (Na3AlF6) where 

the reduction of alumina occurs. Perfluorocarbons are formed when the fluorine in cryolite reacts with the 

carbon in the anode (a carbon mass of paste, coke briquettes, or prebaked carbon blocks) and in the 

carbon lining that serves as the cathode. Although the quantities of perfluorocarbons emitted are small, 

these gases are significant because of their high global warming potential. 

Carbon Stocks, Carbon Storage, and Carbon Sequestration: This analysis includes carbon storage 

to the extent that it is due to waste management practices. For example, landfilled organic materials result 

in landfill carbon storage, as carbon is moved from a product pool (e.g., furniture) to the landfill pool. The 

same is true for composted organics that lead to carbon storage in soil. 

Carbon sequestration differs from carbon storage because it represents a transfer of carbon from 

the atmosphere to a carbon pool. For example, trees in a forest undergo photosynthesis, converting CO2 in 

the atmosphere to carbon in biomass. In this analysis, we consider the impact of waste management on 

forest carbon sequestration. 

The baseline against which changes in carbon stocks are measured is a projection by the U.S. 

Forest Service of forest growth, mortality, harvests, and other removals under anticipated market 

conditions for forest products. One of the assumptions for the projections is that U.S. forests will be 

harvested on a sustainable basis (i.e., trees will be grown at a rate at least equal to the rate at which they 

are cut).6

 Thus, the baseline assumes that harvesting trees at current levels results in no diminution of the 

forest carbon stock and no additional CO2 in the atmosphere. On the other hand, forest carbon 

sequestration increases as a result of source reduction or recycling of paper products because both source 

reduction and recycling cause annual tree harvests to drop below otherwise anticipated levels (resulting in 

additional accumulation of carbon in forests). Consequently, source reduction and recycling “get credit” 

for increasing the forest carbon stock, whereas other waste management options (combustion and 

landfilling) do not. 

Although source reduction and recycling are associated with forest carbon sequestration, 

composting—in particular, application of compost to degraded soils—enhances soil carbon storage. Four 

mechanisms of increased carbon storage are hypothesized in Chapter 5; a modeling approach is used to 

estimate the magnitude of carbon storage associated with three of these mechanisms. 

 5

 U.S. EPA. 2001.Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1999. U.S. Environmental 

Protection Agency, Office of Policy, Planning and Evaluation, Washington, DC. EPA-236-R-01-001. 

6

 Assuming a sustainable harvest in the United States is reasonable because from 1952 to 1997 U.S. forest 

carbon stocks steadily increased. In the early part of this period, the increases were mostly due to reversion of 

agricultural land to forest land. More recently, improved forest management practices and the regeneration of 

previously cleared forest areas have resulted in a net annual uptake (sequestration) of carbon. The steady increase in 

forest carbon stocks implies sustainable harvests, and it is reasonable to assume that the trend of sustainable harvests 

will continue. 
6 

Finally, landfills are another means by which carbon is removed from the atmosphere. Landfill 

carbon stocks increase over time because much of the organic matter placed in landfills does not 

decompose, especially if the landfill is located in an arid area. However, not all carbon in landfills is 

counted in determining the extent to which landfills are carbon stocks. For example, the analysis does not 

count plastic in landfills toward carbon storage. Plastic in a landfill represents simply a transfer from one 

carbon stock (the oil field containing the petroleum or natural gas from which the plastic was made) to 

another carbon stock (the landfill); thus, no change has occurred in the overall amount of carbon stored. 

On the other hand, the portion of organic matter (such as yard trimmings) that does not decompose in a 

landfill represents an addition to a carbon stock, because it would have largely decomposed into CO2 if 

left to deteriorate on the ground. 

Although changes in fossil fuel carbon stocks (i.e., reductions in oil field stores that result from 

the extraction and burning of oil resources) are not measured directly in this analysis, the reduction in 

fossil fuel carbon stocks is indirectly captured by counting the CO2 emissions from fossil fuel combustion 

in calculating GHG emissions. 

Avoided Electric Utility GHG Emissions Related to Waste: Waste that is used to generate 

electricity (either through waste combustion or recovery of CH4 from landfills) displaces fossil fuels that 

utilities would otherwise use to produce electricity. Fossil fuel combustion is the single largest source of 

GHG emissions in the United States. When waste is substituted for fossil fuel to generate electricity, the 

GHG emissions from burning the waste are offset by the avoided electric utility GHG emissions. 

Baseline Year: The baseline year selected for most parts of the analysis is the most recent year for 

which data are available. However, for the system efficiency and ferrous recovery rate at waste 

combustors, this study uses values previously projected for the year 2000. For paper recycling, annual 

projections through 2010 were used to develop an average forest carbon storage value for the period from 

1996 through 2010.7

 The compost analysis relied on model simulations of compost application, beginning 

in 1996 and ending in 2005. The carbon storage estimates resulting from these model runs correspond to 

model outputs in 2010 in order to maintain consistency with forest carbon storage estimates. We 

developed “future”8

 scenarios for paper recycling, composting, and carbon storage analyses because some 

of the underlying factors that affect GHG emissions are changing rapidly, and this study seeks to define 

relationships (e.g., between tonnage of waste landfilled and CH4 emissions) that represent an average over 

the next several years. 

• Although the existing U.S. municipal waste combustors include a few small facilities that do 

not recover energy, the study assumes that those facilities will be closed in the near future. 

Thus, the report assumes that all combustors recover energy. The study used an estimate 

provided by the combustion industry for anticipated levels of ferrous recovery. 

• For paper recycling, earlier analyses indicated that the marginal impact of increased paper 

recycling on forest carbon sequestration changes over time. The impact also differs 

depending on the initial paper recycling rate and how that rate changes over time. To estimate 

the impact of increased paper recycling on forest carbon sequestration, the study needed to 

account for these influences. First, we used the American Forest and Paper Association’s 

baseline projection that paper recycling rates will continue to increase from about 35 percent 

 7

 The models we used simulated carbon sequestration through 2040, but we selected a value based on 

average conditions through 2010. 

8

 In the case of system efficiency and ferrous recovery at waste combustors, the year 2000 represented a 

future value when the first edition of this report was published. This edition of the report does not reflect these 

updated values because more recent data are not available. 
7 

in 1994 to 50 percent by 2000.9 The trajectory for a baseline scenario for paper recycling 

passes through 50 percent in 2000, with continued modest increases in the following years. 

Because of the need to estimate the effect of efforts (e.g., by EPA) to enhance recycling 

beyond the baseline projected rate, we developed a plausible scenario for enhanced paper 

recycling rates and then compared the projected forest carbon sequestration under the 

baseline and increased recycling scenarios.10 (This approach is fully described in Chapter 3.) 

• The baseline for our landfill recovery scenario is based on estimated recovery rates and 

percentages of waste disposed in landfills with no recovery, landfills with flaring, and 

landfills with landfill-gas-to-energy projects for the year 2000. According to our estimates, 49 

percent of all landfill CH4 was generated at landfills with recovery systems, and the 

remaining 51 percent was generated at landfills without landfill gas (LFG) recovery.11 Of the 

49 percent of all CH4 generated at landfills with LFG recovery, 49 percent (or 24 percent of 

all CH4) was generated at landfills that use LFG to generate electricity, and 51 percent (or 25 

percent of all CH4) at landfills that flare LFG.12,13 

1.4 SUMMARY OF THE LIFE-CYCLE STAGES 

Exhibit 1-2 shows the GHG sources and carbon sinks associated with the manufacture of various 

materials and the post-consumer management of these materials as wastes. As shown in the exhibit, 

GHGs are emitted from (1) the pre-consumer stages of raw materials acquisition and manufacturing, and 

(2) the post-consumer stage of waste management. No GHG emissions are attributed to the consumer’s 

use of any product. 

 9

 Actual paper recovery in 2000 (taken from EPA’s Municipal Solid Waste in the United States: 2000 

Facts and Figures) averaged about 53%, confirming that 50 percent is a reasonable estimate for 2000. 

10 Note that this estimate is necessary for analyzing the scenarios; however, it does not represent a plan of 

action by EPA. 

11 Based on data on (1) year 2000 MSW landfill methane generation of 72.7 million MTCE (from draft 

U.S. Climate Action Report – 2001), (2) year 2000 landfill methane recovery of 26.7 million MTCE (also from draft 

U.S. Climate Action Report – 2001), and (3) estimated landfill methane recovery efficiency of 75 percent (from U.S. 

Methane Emissions 1990-2020: Inventories, Projections, and Opportunities for Reductions). 

12 Draft U.S. Climate Action Report – 2001. 

13 The assumption that 49 percent of landfills recovering methane will use it to generate electricity is 

subject to change over time based upon changes in the cost of recovery, and the potential payback. Additionally, 

new technologies may arise that use recovered methane for purposes other than generating electricity. 
8 

Exhibit 1-2 Greenhouse Gas Sources and Sinks Associated with the Material Life Cycle 
9 

The remainder of this chapter describes how this study analyzed each of the upstream (raw 

materials acquisition, manufacturing, and forest carbon sequestration) and downstream (source reduction, 

recycling, composting, combustion, and landfilling) stages in the life cycle. The following sections 

explain stages of the life cycle and the corresponding emission factor components presented in Exhibit 1-

3, and outline the GHG emissions and carbon sinks at each stage of the product life cycle. These GHG 

emissions and carbon sinks are described in detail and quantified for each material in Chapters 2 through 

7. 

Exhibit 1-3 Components of Net Emissions for Various MSW Management Strategies 

MSW GHG Sources and Sinks 

Management 

Strategy

Process and Transportation 

GHGs from Raw Materials 

Acquisition and Manufacturing 

Forest Carbon 

Sequestration or Soil 

Carbon Storage 

Waste Management 

GHGs

Source Reduction Decrease in GHG emissions, 

relative to the baseline of 

manufacturing 

Increase in forest carbon 

sequestration 

No emissions/sinks 

Recycling Decrease in GHG emissions due to 

lower energy requirements 

(compared to manufacture from 

virgin inputs) and avoided process 

non-energy GHGs 

Increase in forest carbon 

sequestration 

Process and transportation 

emissions are counted in the 

manufacturing stage 

Composting No emissions/sinks* Increase in soil carbon 

storage 

Compost machinery 

emissions and transportation 

emissions 

Combustion Baseline process and transportation 

emissions due to manufacture from 

the current mix of virgin and 

recycled inputs 

No change Nonbiogenic CO2, N2O 

emissions, avoided utility 

emissions, and 

transportation emissions 

Landfilling Baseline process and transportation 

emissions due to manufacture from 

the current mix of virgin and 

recycled inputs 

No change CH4 emissions, long-term 

carbon storage, avoided 

utility emissions, and 

transportation emissions 

* No manufacturing transportation GHG emissions are considered for composting of food discards and yard 

trimmings because these materials are not considered to be manufactured. 

1.4.1 GHG Emissions and Carbon Sinks Associated with Raw Materials Acquisition and 

Manufacturing 

The top left of Exhibit 1-2 shows inputs for raw materials acquisition. These virgin inputs are 

used to make various materials, including ore for manufacturing metal products, trees for making paper 

products, and petroleum or natural gas for producing plastic products. Fuel energy also is used to obtain 

or extract these material inputs. 

The inputs used in manufacturing are (1) energy and (2) either virgin raw materials or recycled 

materials. In the exhibit these inputs are identified with arrows that point to the icon labeled 

“Manufacturing.” 
10 

For source reduction, the “baseline” GHG emissions from raw materials acquisition and 

manufacturing are avoided. This analysis thus estimates, for source reduction, the GHG reductions

(relative to a baseline of initial manufacture) at the raw materials acquisition and manufacturing stages. 

Source reduction is assumed to entail more efficient use of a given material. Examples are lightweighting 

(reducing the quantity of raw material in a product), double-sided photocopying, and extension of a 

product’s useful life. No other material substitutions are assumed for source reduction; therefore, this 

report does not analyze any corresponding increases in production and disposal of other materials (which 

could result in GHG emissions).14

The GHG emissions associated with raw materials acquisition and manufacturing are (1) GHG 

emissions from energy used during the acquisition and manufacturing processes, (2) GHG emissions from 

energy used to transport materials,15 and (3) non-energy GHG emissions resulting from manufacturing 

processes (for aluminum, steel, plastics, office paper, and medium-density fiberboard). Each type of 

emission is described below. Changes in carbon sequestration in forests also are associated with raw 

materials acquisition for paper products. 

Process Energy GHG Emissions: Process energy GHG emissions consist primarily of CO2

emissions from the combustion of fuels used in raw materials acquisition and manufacturing. CO2 

emissions from combustion of biomass are not counted as GHG emissions. (See “CO2 Emissions from 

Biogenic Sources” on page 12.) 

The majority of process energy CO2 emissions are from combustion of fuels used directly, e.g., to 

operate ore mining equipment or to fuel a blast furnace. Fuel also is needed to extract the oil or mine the 

coal that is ultimately used to produce energy and transport these fuels to the place where they are used. 

Thus CO2 emissions from this “pre-combustion energy” are counted in this category as well. When 

electricity generated by combustion of fossil fuels is used in manufacturing, the CO2 emissions from the 

fossil fuels also are counted. 

To estimate process energy GHG emissions, the study first obtained estimates of both the total 

amount of process energy used per ton of product (measured in British thermal units or Btu’s), and the 

fuel mix (e.g., diesel oil, natural gas, fuel oil, etc.). Next, emissions factors for each type of fuel were used 

to convert the amount of each type of fuel used to GHG emissions. As noted earlier, making a material 

from recycled inputs generally requires less process energy (and uses a different fuel mix) than making 

the material from virgin inputs. 

Details of the methodology for estimating process energy GHG emissions are provided in 

Chapter 2. 

Transportation Energy GHG Emissions: Transportation energy GHG emissions consist of CO2

emissions from the combustion of fuels used to transport raw materials and intermediate products to the 

final manufacturing or fabrication facility. The estimates of transportation energy emissions are based on: 

(1) the amounts of raw material inputs and intermediate products used in manufacturing 1 ton of each 

material; (2) the average distance that each raw material input or intermediate product is transported; and 

(3) the transportation modes and fuels used. For the amounts of fuel used, the study used data on the 

average fuel consumption per ton-mile for each mode of transportation.16 Then an emission factor for 

 14 Although material substitution is not quantitatively addressed in the report, it is discussed from a 

methodological standpoint in Chapter 2 and also is discussed briefly in Chapter 4, Section 4.3. 

15 For some materials (plastics, magazines/third-class mail, office paper, phonebooks, and textbooks), the 

transportation data we received were included in the process energy data. For these materials, we report total GHG 

emissions associated with process and transportation in the “process energy” estimate. 

16 These data are found in Background Document A. 
11 

each type of fuel was used to convert the amount of each type of fuel consumed to the GHG emissions 

produced. 

More detail on the methodology to estimate transportation energy GHG emissions is provided in 

Chapter 2. 

Process Non-Energy GHG Emissions: Some GHG emissions occur directly in the manufacture of 

certain materials and are not associated with energy consumption. In this analysis, these emissions are 

referred to as process non-energy emissions. For example, the production of steel or aluminum requires 

lime (calcium oxide, or CaO), which is produced from limestone (calcium carbonate, or CaCO3), and the 

manufacture of lime results in CO2 emissions. Other process non-energy GHG emissions are associated 

with the manufacture of plastics, office paper, and medium-density fiberboard. In some cases, process 

non-energy GHG emissions are associated only with production using virgin inputs; in other cases, these 

emissions result when either virgin or recycled inputs are used. These emissions are described in Chapter 

2. 

Carbon Sinks: The only carbon sink during the stages of raw materials acquisition and 

manufacturing is the additional carbon sequestration in trees associated with source reduction or recycling 

of paper products. The methodology for estimating forest carbon sequestration is described in Chapter 3. 

1.4.2 GHG Emissions and Carbon Sinks Associated with Waste Management 

As shown in Exhibit 1-3, there are up to five post-consumer waste management options, 

depending on the material: recycling, composting, combustion, and landfilling. This section describes the 

GHG emissions and carbon sinks associated with these five options. 

Source Reduction: In this analysis, source reduction is measured by the amount of material that 

would otherwise be produced but is not generated due to a program promoting source reduction. Thus, 

with source reduction there are no emissions from MSW management. 

Recycling: When a material is recycled, it is used in place of virgin inputs in the manufacturing 

process. The avoided GHG emissions from remanufacture using recycled inputs is calculated as the 

difference between (1) the GHG emissions from manufacturing a material from 100 percent recycled 

inputs, and (2) the GHG emissions from manufacturing an equivalent amount of the material (accounting 

for loss rates) from 100 percent virgin inputs (including the process of collecting and transporting the 

recyclables). No GHG emissions occur at the MSW management stage because the recycled material is 

diverted from waste management facilities.17 (If the product made from the recycled material is later 

composted, combusted, or landfilled, the GHG emissions at that point would be attributed to the product 

that was made from the recycled material.) Chapter 4 details GHG emissions from recycling. 

All of the materials considered in this analysis are modeled as being recycled in a “closed loop” 

(e.g., newspaper is recycled into new newspaper). However, a variety of paper types are recycled under 

the general heading of “mixed paper.” Mixed paper can be remanufactured, via an open loop, into 

boxboard or paper towels. Other materials are recycled in open-loop processes, but due to limited 

resources, this study could not analyze all open-loop processes.18

 17 We do not include GHG emissions from managing residues (e.g., wastewater treatment sludges) from the 

manufacturing process for either virgin or recycled inputs. 

18 For example, not all steel cans are recycled into more steel cans; not all aluminum cans are recycled into 

more aluminum cans. 
12 

CO2 Emissions from Biogenic Sources 

The United States and all other parties to the U.N. Framework Convention on Climate Change 

(UNFCCC) agreed to develop inventories of GHGs for purposes of (1) developing mitigation strategies and (2) 

monitoring the progress of those strategies. The Intergovernmental Panel on Climate Change (IPCC) developed a 

set of inventory methods to be used as the international standard. (IPCC 1997. IPCC Guidelines for National 

Greenhouse Gas Inventories, three volumes.) The methodologies used in this report to evaluate emissions and 

sinks of GHGs are consistent with the IPCC guidance. 

One of the elements of the IPCC guidance that deserves special mention is the approach used to address 

CO2 emissions from biogenic sources. For many countries, the treatment of CO2 releases from biogenic sources 

is most important when addressing releases from energy derived from biomass (e.g., burning wood), but this 

element is also important when evaluating waste management emissions (for example, the decomposition or 

combustion of grass clippings or paper). The carbon in paper and grass trimmings was originally removed from 

the atmosphere by photosynthesis, and under natural conditions, it would cycle back to the atmosphere 

eventually as CO2 due to degradation processes. The quantity of carbon that these natural processes cycle 

through the Earth’s atmosphere, waters, soils, and biota is much greater than the quantity added by 

anthropogenic GHG sources. But the focus of the UNFCCC is on anthropogenic emissions—those resulting from 

human activities and subject to human control. Those emissions have the potential to alter the climate by 

disrupting the natural balances in carbon’s biogeochemical cycle and altering the atmosphere’s heat-trapping 

ability. For processes with CO2 emissions, if the emissions are from biogenic materials and the materials are 

grown on a sustainable basis, then those emissions are considered simply to close the loop in the natural carbon 

cycle. They return to the atmosphere CO2 that was originally removed by photosynthesis. In this case, the CO2

emissions are not counted. (For purposes of this analysis, biogenic materials are paper, yard trimmings, and food 

discards.) On the other hand, CO2 emissions from burning fossil fuels are counted because these emissions 

would not enter the cycle were it not for human activity. Likewise, CH4 emissions from landfills are counted. 

Even though the source of carbon is primarily biogenic, CH4 would not be emitted were it not for the human 

activity of landfilling the waste, which creates anaerobic conditions conducive to CH4 formation. Note that this 

approach does not distinguish between the timing of CO2 emissions, provided that they occur in a reasonably 

short time scale relative to the speed of the processes that affect global climate change. In other words, as long as 

the biogenic carbon would eventually be released as CO2, it does not matter whether it is released virtually 

instantaneously (e.g., from combustion) or over a period of a few decades (e.g., decomposition on the forest 

floor)

Composting: When organic materials are composted, the anaerobic decomposition of materials 

produces CH4. Similarly, the collection and transportation of organics produces non-biogenic emissions. 

During the composting process and after the compost is added to the soil, the decomposition of plants 

produces biogenic CO2 emissions. Carbon compounds that do not decompose, however, result in longterm carbon storage. All of the materials that may be composted (e.g., leaves, brush, grass, food waste, 

newspaper) originally are produced by trees or other plants. As described in the above in “CO2 Emissions 

from Biogenic Sources,” the biogenic CO2 emitted from these materials during composting is not counted 

in GHG emissions. However, composting does result in increased soil carbon storage due to increased 

production of humic material (natural organic polymers, which degrade at a slow rate) and several other 

factors, which are described in Chapter 5. 

Although composting may result in some production of CH4 (due to anaerobic decomposition in 

the center of the compost pile), compost researchers believe that the CH4 is almost always oxidized to 

CO2 before it escapes from the compost pile. 

Because the CO2 emissions from composting are biogenic—generally producing no CH4

emissions—the only GHG emissions from composting result from transportation of compostable 

materials to composting facilities and mechanical turning of the compost piles. GHG emissions associated 

with compost application are discussed in Chapter 5. 
13 

Combustion: When waste is combusted, two GHGs are emitted: CO2 and N2O. Non-biogenic CO2

emitted during combustion (i.e., CO2 from plastics) is counted toward the GHG emissions associated with 

combustion, but biogenic CO2 is not. Because most waste combustors produce electricity that substitutes 

for utility-generated electricity, the net GHG emissions are calculated by subtracting the utility GHG 

emissions avoided from the gross GHG emissions. GHG emissions from combustion are described in 

Chapter 6. 

Landfilling: When organic matter is landfilled, some of this matter decomposes anaerobically and 

releases CH4, a GHG. Some of the organic matter never decomposes at all; instead, it becomes stored 

carbon. (Landfilling of metals and plastics does not result in either CH4 emissions or carbon storage.) 

At some landfills, virtually all of the CH4 produced is released to the atmosphere. At others, CH4

is captured for flaring or combustion with energy recovery (i.e., electricity production). Most of the 

captured CH4 is converted to CO2, but that CO2 is not counted in this study as a GHG because it is 

biogenic. With combustion of CH4 for energy recovery, credit is given for the electric utility GHG 

emissions avoided. Regardless of the fate of the CH4, credit is given for the landfill carbon storage 

associated with landfilling of some organic materials. GHG emissions and carbon sinks from landfilling 

are described in Chapter 7. 

1.5 ESTIMATING AND COMPARING NET GHG EMISSIONS 

To calculate the net GHG implications of a waste management strategy for a given material, a 

baseline and alternative scenarios must be established. For example, a baseline scenario in which 10 tons 

of office paper are manufactured, used, and landfilled could be compared with an alternative scenario in 

which 10 tons are manufactured, used, and recycled. For each scenario, net GHG emissions are estimated 

based on (1) the GHG emissions associated with that material, and (2) any increases in carbon stocks 

and/or displaced fossil fuel combustion that offset those emissions. The formula for net GHG emissions is 

as follows: 

Net GHG emissions = Gross GHG emissions - (Increase in carbon stocks + Avoided utility GHG 

emissions) 

Comparing net GHG emissions for the two scenarios enables the lowest net GHG emissions to be 

identified. For example, when a material is source reduced (i.e., some or all of it is not produced), GHG 

emissions throughout the life cycle are avoided. In addition, when paper products are source reduced, 

additional carbon may be sequestered in forests. 

Similarly, when a material is recycled, the GHG emissions from making an equivalent amount of 

material from virgin inputs are reduced. In most cases, recycling reduces GHG emissions because 

manufacturing a product from recycled inputs requires less fossil energy than making the product from 

virgin inputs and thus reduces energy-related GHG emissions. 

If a waste is not source reduced or recycled, it may be either composted (if it is organic matter), 

combusted, or landfilled. In any of these cases, GHG emissions are produced during acquisition and 

manufacture. These GHG emissions may be augmented by CH4 emissions from landfills (which 

themselves may be offset to some degree by energy recovery at landfills or landfill carbon storage). If the 

wastes are combusted, there may be an offset for avoided utility emissions. 

In calculating emissions for the life-cycle scenarios, we can use the following two reference 

points: 

• In a “raw material extraction” approach (i.e., cradle-to-grave perspective), raw material 

acquisition can be used as the “zero point” for emissions, with all emissions being added (and 

sinks deducted) from that point on through the life cycle. 
14 

• In a “waste-generation” approach (solid waste manager’s perspective), accounting for GHG 

emissions can start at the point of waste generation. All subsequent emissions and sinks from

waste management practices then are accounted for. Changes in emissions and sinks from

raw material acquisition and manufacturing processes are captured to the extent that certain 

waste management practices (i.e., source reduction and recycling) affect these processes. 

Because it is the difference in emissions between the baseline and alternate scenarios that is 

meaningful, using either of these reference points yields the same results. The March 1997 draft working 

paper used the cradle-to-grave method to display GHG emissions because it is most consistent with 

standard accounting techniques for life-cycle inventories. Several reviewers pointed out that solid waste 

decision-makers tend to view raw materials acquisition and manufacturing as beyond their control and 

suggested that a waste generation GHG accounting approach would provide increased clarity for 

evaluating waste management options. Thus, both editions of this report use the waste generation 

approach and define the “standard” raw material acquisition and manufacturing step for each material as 

consisting of average GHG emissions based on the current mix of virgin and recycled inputs.

19 

Exhibit 1-3 indicates how GHG sources and sinks have been counted for each MSW management 

strategy in order to estimate net GHG emissions using the post-consumer reference point. For example, 

the top row of the exhibit shows that source reduction (1) reduces GHG emissions from raw materials 

acquisition and manufacturing, (2) results in an increase in forest carbon sequestration, and (3) does not 

result in GHG emissions from waste management. The sum of emissions (and sinks) across all steps in the 

life cycle represents net emissions. Section 8.2, “Applying Emission Factors,” describes how waste 

managers and companies have used these emission factors to estimate GHG emissions and potential GHG 

emission reductions associated with integrated waste management. In addition, EPA used these emission 

factors to develop the Waste Reduction Model (WARM). WARM enables users to analyze the GHG 

savings associated with changing their waste management practices. WARM is available on EPA’s Web 

site at <http://www.epa.gov/globalwarming/actions/waste/warm.htm>. 

 19 Changes in the mix of production (i.e., higher proportions of either virgin or recycled inputs) result in 

incremental emissions (or reductions) with respect to this reference point. 
15 

2. RAW MATERIALS ACQUISITION AND MANUFACTURING 

The GHG emissions associated with raw materials acquisition and manufacturing are a key 

element of a life-cycle GHG analysis. This chapter describes how we estimated these emissions for 15 

materials: aluminum cans, steel cans, glass, three types of plastic (HDPE, LDPE, and PET), corrugated 

cardboard, magazines/third-class mail, newspaper, office paper, phonebooks, textbooks, dimensional 

lumber, medium-density fiberboard, and mixed paper. 

In manufacturing, substantial amounts of energy are used both in the acquisition of raw materials 

and in the manufacturing process itself. In general, the majority of energy used for these activities is 

derived from fossil fuels. Combustion of fossil fuels results in emissions of CO2, a GHG. In addition, 

manufacturing of some materials also results in GHG emissions that are not associated with energy 

consumption. Section 2.1 addresses energy-related CO2 emissions, and Section 2.2 covers non-energy 

GHG emissions. Sections 2.3 and 2.4 discuss results and limitations of the analysis, respectively. 

2.1 GHG EMISSIONS FROM ENERGY USE IN RAW MATERIALS ACQUISITION AND 

MANUFACTURING 

To begin our analysis, we estimated the GHG emissions from fossil fuel combustion for both (1) 

raw materials acquisition and manufacturing (referred to here as “process energy”), and (2) transportation 

(referred to as “transportation energy”). 

In this analysis, process energy GHG emissions consist primarily of CO2.

1

 The majority of CO2

emissions are from combustion of fuels used directly, e.g., to operate mining equipment or fuel a blast 

furnace. CO2 emissions from fuels used to generate electricity during the manufacturing stage also are 

included in process energy emissions. In addition, process energy GHG emissions include indirect 

emissions from “pre-combustion” activities, such as oil exploration and extraction, coal mining and 

beneficiation, and natural gas production. 

Transportation energy GHG emissions consist of CO2 emissions from combustion of fuels used to 

transport raw materials and intermediate products to the final manufacturing or fabrication facility. For 

transportation of recycled inputs, this analysis considers transportation (1) from the curbside to the 

materials recovery facility (MRF), (2) from the MRF to a broker, and (3) from a broker to the plant or 

mill where the recycled inputs are used. The transportation values for recycled inputs generally include 

the energy used to process the inputs at an MRF. Transportation of finished manufactured goods to 

consumers is not included in the analysis. We did not estimate transportation emissions of CH4 or N2O; 

these emissions are considerably less significant than CO2 emissions.2

 This omission would tend to 

understate the GHG impacts from transportation slightly. 

Emissions from raw materials acquisition and manufacturing also include CH4 associated with 

producing, processing, and transporting coal, oil, and natural gas. CH4 is emitted during the various stages 

of fossil fuel production because CH4 is trapped within coal and oil deposits, and is released when they 

are mined. Natural gas, of course, consists largely of CH4. 

 1

 Note, however, that CO2 emissions from combustion of biomass (e.g., in paper manufacturing) are not 

counted as GHG emissions (as described in Chapter 1). 

2

 The Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1999 estimates 1999 emissions from 

transportation to be 468.1 MMTCE for CO2 and 18.5 MMTCE for CH4 and N2O combined. 
16 

We developed separate estimates for GHG emissions from process and transportation energy for 

virgin inputs and recycled inputs, generating a total of four separate GHG emissions estimates for each 

material: (1) process energy with virgin inputs, (2) process energy with recycled inputs, (3) transportation 

energy with virgin inputs, and (4) transportation energy with recycled inputs. 

2.1.1 Methodology 

We developed GHG emission estimates for each material based on two sets of data: (1) the 

amount of each type of fuel used to make 1 ton of the material, and (2) the “carbon coefficient” for each 

fuel (a factor that translates the energy value of fuel combusted into the mass of GHGs emitted). 

Our methodology in using these two sets of data to estimate process and transportation energy 

GHG emissions is best illustrated by an example: To estimate process energy GHG emissions from the 

production of 1 ton of newspaper from virgin inputs, we multiplied the amount of each type of fuel 

consumed (as measured in million Btu) by the carbon coefficient for that type of fuel (as measured in 

metric tons of carbon equivalent, or MTCE, per million Btu). The result was an estimate of the GHG 

emissions (in MTCE) from the combustion of each type of fuel required to make 1 ton of newspaper. 

Total process energy GHG emissions from making 1 ton of newspaper are simply the sum of the GHG 

emissions across all of the fuel types. To estimate the GHG emissions when electricity is used, we used 

the national average mix of fuels used to generate electricity. 

We estimated GHGs from the energy used to transport raw materials for making 1 ton of a given 

product (e.g., newspaper) in the same way. The amount of each fuel used was multiplied by its carbon 

coefficient, and the resulting values for each of the fuels were summed to yield total transportation energy 

GHG emissions. 

In this way, GHG estimates for raw materials acquisition and manufacturing were developed for 

each of the manufactured materials considered. As noted in Chapter 1, much of the energy information in 

this edition of the report is drawn from an effort conducted by EPA’s Office of Research and 

Development (ORD) to construct a Decision Support Tool for solid waste managers. The remaining 

energy data was developed by Franklin Associates, Ltd. (FAL) as part of the original effort or subsequent 

updates. 

Most of the materials included in this analysis are assumed to undergo closed-loop recycling (i.e., 

materials are remanufactured into a similar product). However, mixed paper is recycled in an open loop 

into boxboard and paper towels.3

 Thus, the exhibits in this chapter show data not only for the 15 materials 

of interest, but also for boxboard and paper towels. Because recycling processes data are similar for 

HDPE, LDPE, and PET, we adopted the approach used by ORD of using a single energy profile (fuel mix 

and energy intensity) for all recycled plastics. For steel cans, we developed GHG estimates for virgin 

production using the basic oxygen furnace process,4

 and for recycled production, we used the electric arc 

furnace process.5

 3 FAL provided virgin and recycled manufacturing and transportation data for boxboard and paper towels. 

For virgin boxboard, only one type of product is manufactured, as shown in Exhibits 2-3 and 2-4. For recycled 

boxboard, there are two types of products, and we obtained two different sets of manufacturing and transportation 

data as shown in Exhibits 2-5 and 2-6. We have labeled the two types of boxboard as boxboard “A” and boxboard 

“B.” These two products differ only with respect to their recycled material inputs (i.e., the proportion of newspaper, 

corrugated cardboard, office paper, and coated paper used to manufacture either boxboard “A” or boxboard “B”); 

both products share the same manufacturing and transportation values for virgin inputs. 

4

 Note that the basic oxygen furnace process can utilize approximately 25 percent recycled inputs. 

5

 Note that when recovered steel cans are used as inputs to an electric arc furnace, the resulting steel is not 

suited for milling to the thinness of steel sheet needed for use in making new steel cans. Thus, a more precise 

approach would have been to model recovery of steel cans as an open-loop process, in which recovered steel cans 
17 

We used carbon coefficients from the U.S. Department of Energy’s Energy Information 

Administration for all fuels except electricity.6

 The carbon coefficient for electricity was based on the 

weighted average carbon coefficients for all fuels used to generate electricity in the United States.7

Because the carbon coefficients from these sources accounted for only the CO2 emissions from 

combustion of each type of fuel, we added to these carbon coefficients (1) the average amount of CH4

emitted during the production, processing, and transportation of fossil fuels, and (2) the average CO2

emissions from oil production, due to the flaring of natural gas. We calculated the average fugitive GHG 

emissions associated with U.S. production of coal, oil, and natural gas. The resulting average estimates 

for fugitive GHG emissions from fossil fuel production were 0.92 kilograms of carbon equivalent per 

million Btu (kg CE/million Btu) for coal, 0.10 kg CE/million Btu for oil, and 0.70 kg CE/million Btu for 

natural gas.8

The carbon coefficients that reflect both CO2 and CH4 emissions are supplied in Exhibit 2-1. (All 

exhibits are provided at the end of this chapter.) 

The process and transportation GHG values are shown in summary form in Exhibit 2-2. For each 

product and each type of input (virgin or recycled), we summed the estimates for process and 

transportation GHG emissions, as shown in columns “b” (for virgin inputs) and “c” (for recycled inputs) 

of Exhibit 2-2. We also estimated the energy-related GHG emissions from manufacturing each material 

from the current mix of virgin and recycled inputs. These values are shown in column “e.” (The 

remaining two columns of Exhibit 2-2 are discussed later in this chapter.) 

The energy intensity and fuel mix data are provided in Exhibits 2-3 through 2-6. For most 

materials, the data in the exhibits are for manufacturing processes that either use (1) 100 percent virgin 

inputs or (2) 100 percent recycled inputs.9

 

To estimate the types and amounts of fuels used for process and transportation energy, ORD and 

FAL relied on published data (such as engineering handbooks and published production data), contacts 

with industry experts, and review by stakeholders and trade organizations. ORD and FAL counted all 

energy, no matter where it was used. For example, much aluminum produced in the United States is made 

 

are made into some other steel product. By modeling recovery of steel cans as a closed-loop process, we implicitly 

assumed that 1 ton of steel produced from recovered steel cans in an electric arc furnace displaces 1 ton of steel 

produced from virgin inputs in a basic oxygen furnace. We believe this is a reasonable assumption. (For the 

fabrication energy required to make steel cans from steel, we used the values for fabrication of steel cans from steel 

produced in a basic oxygen furnace.) 

6

 U.S. Department of Energy, Energy Information Administration. 2000. Annual Energy Review: 1999.

7

 FAL reported the Btu value for electricity in terms of the Btu of fuel combusted to generate the electricity 

used at the factory, rather than the (much lower) Btu value of the electricity that is delivered to the manufacturer. 

Thus, FAL had already accounted for the efficiency of converting fuels to electricity, and the losses in transmission 

and distribution of electricity. We therefore did not need to account for these factors in the carbon coefficient for 

electricity. 

8

 ICF Consulting. 1995. Memorandum, “Fugitive Methane Emissions from Production of Coal, Natural 

Gas, and Oil,” August 8, updated to use global warming potential for CH4 of 21. 

9

 In the FAL data set, the one exception is the data for steel cans made from virgin inputs, for which FAL 

provided data for manufacture from 80 percent virgin inputs and 20 percent recycled inputs. We extrapolated from 

this data (and the corresponding values for production using 100 percent recycled inputs) to obtain estimates of the 

energy inputs for manufacturing these materials from 100 percent virgin inputs. Similarly, for corrugated cardboard, 

ORD assumed that a virgin corrugated box contains a minimum of 14.7 percent total recycled content. 
18 

from bauxite that is mined and processed into alumina in other countries. The energy required for 

overseas bauxite mining and processing is counted in the analysis. 

Neither the ORD nor the FAL transportation data reflect transportation of the finished 

manufactured product to the retailer and consumer. This omission is only important in estimating the 

GHG reductions associated with source reduction (i.e., source reduction reduces transportation energy). It 

is not relevant in analyzing GHG implications of recycling compared to other post-consumer management 

options, because the amount of transportation energy from the factory to the consumer is about the same 

whether the product is manufactured from virgin inputs or recycled inputs. Even for the source reduction 

analysis, we expect that the transportation energy from factory to consumer would represent a very small 

fraction of the total process and transportation energy. 

Finally, it should be noted that during our extensive review of ORD and FAL data, we examined 

the most critical assumptions and data elements that each model used to ensure that they accurately reflect 

the energy requirements of the raw materials acquisition and manufacturing for the material types 

considered. Nevertheless, we recognize that different manufacturers making the same product use 

somewhat different processes with different energy requirements and fuel mixes, and that there are 

limited data on the extent to which various processes are used. Thus, although our goal was to estimate as 

accurately as possible the national average GHG emissions for the manufacture of each material from 

virgin and recycled inputs, it is quite likely that individual companies will have GHG emissions that vary 

significantly from those estimated here. 

2.2 NON-ENERGY GHG EMISSIONS FROM MANUFACTURING AND RAW MATERIALS 

ACQUISITION 

In addition to GHG emissions from energy use, we also accounted for three additional sources of 

GHGs in manufacturing processes: 

• When limestone (calcium carbonate, or CaCO3) is converted to lime (calcium oxide, or CaO), 

CO2 is emitted. Significant quantities of lime are used in the production of steel, aluminum, 

and, to a much lesser extent, office paper. 

• CH4 emissions from natural gas pipelines and processing of natural gas are associated with 

the manufacture of plastic products. 

• Perfluorocarbons (CF4 and C2F6) are emitted during aluminum smelting. 

For plastics and office paper, process non-energy GHG emissions are associated only with 

production using virgin inputs. In the case of steel, however, these emissions result when either virgin or 

recycled inputs are used (because lime is used in the production of steel from recycled as well as virgin 

inputs). 

The process non-energy GHGs for each material are shown in the second-to-last column of 

Exhibits 2-3 and 2-5 (for manufacture from virgin inputs and recycled inputs, respectively), and are 

repeated in column “f” of Exhibit 2-2. ORD supplied the non-energy CO2 emissions for aluminum, glass, 

corrugated cardboard, and newspaper. We based our calculation for PFC emissions from aluminum on the 

Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1998.

10 

 10 To estimate aluminum PFC emissions on a per-ton basis, we divided the inventory estimates for CF4 and 

C2F6 emissions from aluminum by total primary aluminum production, yielding units in MTCE/ton. 
19 

Non-energy CO2 emissions for the other materials, as well as CH4 emissions, are based on the 

original analysis supporting the first edition of this report.11

2.3 RESULTS 

Our estimates of the total GHG emissions from raw materials acquisition and manufacturing for 

each material are shown in Exhibit 2-2, column “g.” In order to obtain these estimates, we summed the 

energy-related GHG emissions (column “e”) and the non-energy GHG emissions (column “f”). 

The process energy and transportation GHG values that were developed as described earlier in 

this chapter are shown in the third-to-last columns of Exhibits 2-3 and 2-5, and in the last columns of 

Exhibits 2-4 and 2-6 (the last columns of Exhibits 2-3 and 2-5 show the total process energy GHG 

emissions). 

Total GHG emissions associated with the raw materials acquisition and manufacturing stage of 

the product life cycle are shown in the three righthand columns of Exhibit 2-2. These three columns 

correspond to the type of inputs that occur during the recycling process: virgin inputs, recycled inputs, or 

the current mix of virgin and recycled inputs. 

2.4 LIMITATIONS 

There are several limitations to the analysis of the GHG emissions associated with raw materials 

acquisition and manufacturing, as described below. 

The approach used in this analysis provides values for the average GHG emission rates per ton of 

material produced, not the marginal emission rates per incremental ton produced. In some cases, the 

marginal emission rates may be significantly different. For example, reducing production of plastic 

products from virgin inputs may not result in a proportional decrease in CH4 emissions from natural gas 

pipelines and natural gas processing. The operating pressure in natural gas pipelines and the number and 

size of leaks in the pipeline determine CH4 emissions from natural gas pipelines. Consequently, the 

amount of natural gas consumed at one end of the pipeline (e.g., to make plastic) does not affect the level 

of pipeline CH4 emissions in a direct, linear way. As another example, long-term reductions in electricity 

demand could selectively reduce demand for specific fuels, rather than reducing demand for all fuels in 

proportion to their representation in the current average fuel mix. This analysis estimates average carbon 

conversion rates largely because the marginal rates are much more difficult to estimate. Nevertheless, we 

believe the average values provide a reasonable approximation of the GHG emissions. 

In addition, the analysis assumes that the GHG emissions from manufacturing a given product 

change in a linear fashion as the percentage of recycled inputs moves from 0 to 100 percent. In other 

words, the analysis assumes that both the energy intensity and the fuel mix change in linear paths over 

this range. However, it could be that GHG emissions from manufacturing move in a non-linear path, (e.g., 

some form of step function) when the percentage of recycled inputs changes, due to capacity limits in 

manufacturing or due to the economics of manufacturing processes. 

The transportation energy required for the final stage of transportation (to the consumer) was not 

considered. Consequently, some carbon emissions reductions for “lightweighted” products for these 

transportation stages were not considered; these savings are likely to be small. 

The information used in this analysis represents the best available data from published and 

unpublished industry sources, some of it quite dated. Therefore, the data may not necessarily reflect 

recent trends in industrial energy efficiency or changes in the fuel mix. 

 11 ICF Consulting. 1994. Memorandum, “Detailed Analysis of Greenhouse Gas Emissions Reductions from 

Increased Recycling and Source Reduction of Municipal Solid Waste,” July 29, p. 48 of the Appendix prepared by 

Franklin Associates, Ltd., dated July 14, 1994. 
20 

Finally, this static analysis does not consider potential future changes in energy usage per unit of 

output or alternative energy (e.g., non-fossil) sources. Reductions in energy inputs due to efficiency 

improvements could occur in either virgin input processes or recycled input processes. Efficiency 

improvements and switching to alternative energy sources will result directly in GHG emissions 

reductions and may change the reductions possible through increased recycling or source reduction. 
21 

Fuel Type

Metric Tons of CO2 

from Combustion Per 

Million Btu

kg Carbon 

Equivalent (CE) 

from 

Combustion Per 

Million Btu

Metric Tons of 

Fugitive CH4 

Emissions Per 

Million Btu

kg CE from 

Fugitive 

Methane 

Emissions Per 

Million Btu

Gasoline 19.25 0.00002 0.098 

0.07

LPG 16.91 0.00002 0.06 0.10 Distillate Fuel 19.87 0.00002 0.07 0.10 Residual Fuel 21.41 0.00002 0.08 0.10 Diesel 19.87 0.00002 0.07 0.10 Oil/Lubricants 20.16 0.00002 0.07 0.10 Steam (non-paper products) 18.21 0.00011 0.61 

0.07

Steam (paper products) 12.92 0.00004 0.25 

0.05

National Average Fuel Mix for Electricity 15.79 0.00010 0.59 

0.06

National Average Fossil Fuel Mix for Electricity 23.18 0.00015 0.86 

0.08

Coal Used for Electricity 24.86 0.00016 0.92 

0.09

Coal Used by Industry (Non-Coking Coal) 25.10 0.00016 0.92 

0.09

Natural Gas 13.78 0.00012 0.70 

0.05

Nuclear 0.84 

0.00

Other (Petroleum Coke) 27.78 0.00002 0.10 0.10 

Exhibit 2-1

Carbon Coefficients For Selected Fuels
22 

Explanatory notes: To estimate the GHG emissions from manufacturing, we first estimated the process and transportation GH

percent virgin inputs, or 100 percent recycled inputs, are used. For each product and each type of input (virgin or recycled), we summe

and transportation GHG emissions. Next we estimated the GHG emissions from manufacturing each material from the current mix of v

We began with estimates of the percentage of recycled inputs currently used in the manufacture of each material, as shown in column “

percentages to develop a weighted average value for the GHG emissions associated with the manufacture of each material from the cur

recycled inputs. Specifically, we used the estimate of the percentage of recycled inputs in the current mix, together with the estimates f

manufacture using virgin or recycled inputs, to develop estimates of GHG emissions from manufacture using the current mix of virgin (column “e”). 

Exhibit 2-2

GHG Emissions from the Manufacture of Selected Materials

(Metric Tons of Carbon Equivalent (MTCE) per Ton of Product)

(a) (b) (c) (d) (e) (f) 

Virgin Input Combined 

Process and 

Transportation Energy 

Emissions 

Recycled Input 

Combined Process and 

Transportation Energy 

Emissions

Current Mix Combined 

Process and Transportation 

Energy Emissions 

Type of Product

(MTCE per Ton of Product 

Made With Virgin Inputs)

(MTCE per Ton of 

Product Made With 

Virgin Inputs)

Percent Recycled Inputs 

in the Current Mix of 

Virgin and Recycled 

Inputs

(MTCE per Ton of Product 

Made With the Current Mix of 

Virgin and Recycled Inputs) Virgin Inputs

Recycled 

Inputs Cu

Aluminum Cans 3.52 0.25 49% 1.90 1.15 0.02 Steel Cans 0.77 0.27 44% 0.55 0.24 0.24 Glass Containers 0.11 0.07 23% 0.10 0.04 0.00 HDPE 0.48 0.04 9% 0.44 0.05 0.00 LDPE 0.59 0.04 4% 0.56 0.05 0.00 PET 0.55 0.04 18% 0.46 0.03 0.00 Corrugated Boxes 0.22 0.25 62% 0.24 0.00 0.00 Magazines/Third-class Mail 0.46 0.46 22% 0.46 0.00 0.00 Newspaper 0.59 0.34 52% 0.46 0.00 0.00 Office Paper 0.27 0.37 32% 0.30 0.01 0.00 Phonebooks 0.67 0.41 12% 0.64 0.00 0.00 Textbooks 0.59 0.57 13% 0.59 0.00 0.00 Dimensional Lumber 0.05 0.07 0% 0.05 0.00 0.00 Medium-density Fiberboard 0.10 0.12 0% 0.10 0.00 0.00 Mixed Paper Broad Def'n (= Boxboard "A") 0.32 0.43 51% 0.38 0.00 0.00  Residential Def'n (= Boxboard "B") 0.32 0.43 53% 0.38 0.00 0.00  Office Def'n (= Paper Towels) 0.91 0.75 38% 0.85 0.00 0.00 

Process Non-Energy Emission

per Ton of Product)
23 

Explanatory notes for Exhibit 2-2 (continued):

Column “f” shows estimates of the process non-energy GHG emissions from manufacturing. First, this column shows the pro

emissions when virgin inputs are used. Then it shows the emissions when recycled inputs are used (these values are simply copied from 

Exhibits 2-3 and 2-5). Finally, column “f” shows the process non-energy GHG emissions from manufacturing each product from the curecycled inputs. The values for the current mix are the weighted averages of the values for virgin and recycled inputs, based on the per

used in the current mix (as shown in column “d”).  The total GHG emissions from manufacturing are shown in column “g.” This column shows total GHG emissions when a provirgin or recycled inputs, or from the current mix of virgin and recycled inputs. 
24 

Process 

Energy 

Type of Product Gasoline LPG

Distillate 

Fuel

Residual 

Fuel

Biomass/H

ydro Diesel Electricity Coal

Natural 

Gas Nuclear Other Total

(MTC

Pr

Aluminum Cans 205.80 0.16 0.01 0.82 4.06 0.03 0.50 80.36 0.83 13.04 0.17 0.02 100 Steel Cans 31.58 0.21 0.00 5.06 0.35 0.00 0.00 21.02 53.90 19.45 0.00 0.00 100 Glass 6.49 0.55 0.00 1.45 0.47 0.03 0.00 10.12 7.18 79.95 0.23 0.02 100 HDPE 28.69 0.00 0.00 0.00 33.14 1.16 0.00 5.64 4.59 51.35 4.00 0.13 100 LDPE 35.26 0.00 0.00 0.00 32.59 1.56 0.00 7.66 6.15 46.52 5.36 0.17 100 PET 32.82 0.00 0.00 0.00 36.67 1.62 0.00 7.10 6.42 42.41 5.59 0.18 100 Corrugated Cardboard 25.13 0.01 0.00 0.02 0.54 61.33 1.20 14.06 15.52 7.31 0.01 0.00 100 Magazines/Third-class Mail 32.99 0.15 0.01 0.32 8.30 24.27 0.00 25.40 17.11 24.33 0.11 0.01 100 Newspaper 39.92 0.25 0.00 0.52 0.75 9.09 0.68 54.21 1.75 32.43 0.27 0.04 100 Office Paper 37.01 0.08 0.00 0.17 4.33 60.53 0.00 13.24 8.92 12.68 0.06 0.01 100 Phonebooks 39.61 0.18 0.01 0.38 9.99 8.86 0.00 30.56 20.59 29.28 0.13 0.02 100 Textbooks 35.07 0.18 0.01 0.38 9.96 9.14 0.00 30.47 20.52 29.19 0.13 0.02 100 Dimensional Lumber 2.53 1.57 0.00 0.00 0.00 32.81 15.99 43.09 0.00 6.53 0.00 0.00 100 Medium-density Fiberboard 10.18 0.14 0.00 0.38 0.05 51.90 1.26 27.61 0.00 18.68 0.00 0.00 100 Boxboard 32.26 0.00 0.00 0.00 0.94 59.34 1.36 5.32 24.01 9.02 0.00 0.00 100 Paper Towels 73.44 0.00 0.00 0.01 1.80 24.89 0.45 28.15 2.93 41.78 0.00 0.00 100 Note that totals may not add due to rounding, and more digits may be displayed than are significant.

GHG Emissions Per Ton of Product Manufactured from Virgin Inputs

Exhibit 2-3

Average Fuel Mix (in Percent) 

 (Million Btu 

Per Ton of 

Product)

Proce

GHG E

Process GHGs Only
25 

Type of Product

 (Million Btu Per 

Ton of Product) Gasoline LPG

Distillate 

Fuel

Residual 

Oil

Biomass/Hy

dro Diesel Electricity Coal

Natural 

Gas N

Aluminum Cans 7.47 0.10 0.08 0.39 79.88 0.05 11.34 0.34 0.86 6.58 Steel Cans 4.60 0.00 0.00 0.00 1.76 0.00 98.24 0.00 0.00 0.00 Glass 0.58 0.10 0.08 0.40 2.64 0.04 88.95 0.00 0.89 6.51 HDPE NA LDPE NA PET NA Corrugated Cardboard 1.31 0.05 0.00 0.05 0.27 0.01 98.51 0.00 0.00 1.07 Magazines/Third-class Mail NA Newspaper 0.50 0.10 0.08 0.39 3.63 0.05 87.97 0.00 0.86 6.53 Office Paper NA Phonebooks NA Textbooks NA Dimensional Lumber 0.88 0.00 0.00 0.00 0.00 0.00 100.00 0.00 0.00 0.00 Medium-density Fiberboard 1.01 0.00 0.00 0.00 0.06 0.00 98.10 0.00 0.00 1.84 Boxboard 1.79 0.00 0.00 0.00 0.05 0.00 99.93 0.00 0.00 0.01 Paper Towels 2.07 0.00 0.00 0.00 0.52 0.00 99.46 0.01 0.00 0.02 Note that totals may not add due to rounding, and more digits may be displayed than are significant.Note that for some materials, transportation data was included in the process energy estimates and not provided separately, denoted by "NA" in this table. 

Average Fuel Mix (in Percent)

Exhibit 2-4

GHG Emissions Per Ton of Product Manufactured from Virgin Inputs

Transportation GHGs Only

Transportation 

Energy
26 

Type of Product

 (Million Btu 

Per Ton of 

Product) Gasoline LPG

Distillate 

Fuel

Residual 

Fuel

Biomass/H

ydro Diesel Electricity Coal

Natural 

Gas Nuclear Other Total

(MTCE

Prod

Aluminum Cans 14.85 0.27 0.01 5.12 0.44 0.05 0.52 63.74 0.75 28.78 0.29 0.04 100 Steel Cans 11.78 0.01 0.17 0.07 0.03 0.00 0.00 77.28 0.65 21.80 0.00 0.00 100 Glass 4.32 0.55 0.00 0.39 0.26 0.03 0.00 5.10 0.54 92.91 0.21 0.02 100 HDPE 4.17 0.03 0.03 1.05 1.24 12.48 0.05 33.21 0.02 20.34 0.09 31.44 100 LDPE 4.17 0.03 0.03 1.05 1.24 12.48 0.05 33.21 0.02 20.34 0.09 31.44 100 PET 4.17 0.03 0.03 1.05 1.24 12.48 0.05 33.21 0.02 20.34 0.09 31.44 100 Corrugated Cardboard 11.73 0.01 0.05 0.05 0.66 0.00 0.31 51.11 38.40 9.40 0.01 0.00 100 Magazines/Third-class Mail 31.97 0.16 0.01 0.32 8.45 22.85 0.00 25.87 17.43 24.79 0.11 0.01 100 Newspaper 21.98 0.30 0.00 0.58 0.30 0.05 0.00 57.75 1.09 39.59 0.30 0.04 100 Office Paper 20.12 0.20 0.01 0.42 10.96 0.02 0.00 33.53 22.58 32.12 0.14 0.02 100 Phonebooks 22.02 0.20 0.01 0.42 10.96 0.02 0.00 33.53 22.58 32.12 0.14 0.02 100 Textbooks 33.51 0.21 0.01 0.60 10.02 8.38 0.00 30.40 20.61 29.57 0.17 0.02 100 Dimensional Lumber 3.17 0.00 0.00 0.00 0.00 0.00 23.61 76.39 0.00 0.00 0.00 0.00 100 Medium-density Fiberboard Made from Reused Dimensional Lumber 10.99 0.13 0.00 0.35 0.04 48.05 8.56 25.56 0.00 17.29 0.00 0.00 100 Boxboard Made from Broad Definition of Mixed Paper 22.53 0.00 0.03 0.02 0.36 0.00 0.49 67.46 24.36 7.25 0.00 0.00 100 Boxboard Made from Residential Definition of Mixed Paper 22.53 0.00 0.03 0.02 0.36 0.00 0.49 67.46 24.36 7.25 0.00 0.00 100 Paper Towels Made from Recoverd File Stock

51.69 0.00 0.00 0.00 0.45 6.94 0.15 36.32 0.98 55.14 0.00 0.00 100 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.* Recycled boxboard using a "broad" definition of mixed paper comprised of 24 percent newsprint, 48 percent corrugated cardboard, 20 percent office paper, and 8 percent coated paper.** Recycled boxboard using a "residential" definition of mixed paper comprised of 23 percent newsprint, 53 percent corrugated cardboard, 14 percent office paper, and 10 percent coated paper.*** Recycled boxboard using an "office paper" definition of mixed paper comprised of 21 percent newsprint, 5 percent corrugated cardboard, 38 percent office paper, and 36 percent coated paper

Process 

GHG Em

Process 

Energy 

Exhibit 2-5 

Average Fuel Mix (in Percent)

GHG Emissions Per Ton of Product Manufactured from Recycled Inputs

Process GHGs Only
27 

Type of Product

 (Million Btu Per 

Ton of Product) Gasoline LPG

Distillate 

Fuel

Residual 

Oil

Biomass/Hy

dro Diesel Electricity Coal

Natural 

Gas Nuclear O

Aluminum Cans 0.40 0.08 0.06 0.32 3.07 0.04 90.11 0.00 0.68 5.32 0.27 Steel Cans 4.03 0.00 0.00 0.00 0.01 0.00 99.99 0.00 0.00 0.00 0.00 Glass 0.34 0.10 0.08 0.41 2.59 0.04 89.01 0.00 0.89 6.51 0.35 HDPE 0.08 0.00 0.00 0.00 56.50 0.00 5.96 2.53 0.00 35.01 0.00 LDPE 0.08 0.00 0.00 0.00 56.50 0.00 5.96 2.53 0.00 35.01 0.00 PET 0.08 0.00 0.00 0.00 56.50 0.00 5.96 2.53 0.00 35.01 0.00 Corrugated Cardboard 0.80 0.05 0.00 0.05 0.22 0.01 98.55 0.00 0.00 1.07 0.04 Magazines/Third-class Mail NA Newspaper 0.03 0.10 0.08 0.39 3.75 0.05 87.87 0.00 0.86 6.53 0.32 Office Paper NA Phonebooks NA Textbooks NA Recycled Lumber from 

Di i l L b

0.97 0.00 0.00 0.00 0.00 0.00 100.06 0.00 0.00 0.00 0.00 

Medium-density Fiberboard Made from Reused Dimensional Lumber 1.27 0.00 0.00 0.00 0.05 0.00 98.46 0.00 0.00 1.47 0.00 Boxboard Using the "Broad Definition" for Mixed Paper 0.01 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Boxboard Using the "SingleFamily Residential Definition" for Mixed Paper 0.01 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Paper Towels Using "Office Paper" for Mixed Paper

0.01 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.Note that for some materials, transportation data was included in the process energy estimates and not provided separately, denoted by "NA" in this table. 

* Recycled boxboard using a "broad" definition of mixed paper comprised of 24 percent newsprint, 48 percent corrugated cardboard, 20 percent office paper,and 8 percent coated paper.** Recycled boxboard using a "residential" definition of mixed paper comprised of 23 percent newsprint, 53 percent corrugated cardboard, 14 percent office paper,and 10 percent coated paper.*** Recycled boxboard using an "office paper" definition of mixed paper comprised of 21 percent newsprint, 5 percent corrugated cardboard, 38 percent office paper, and 36 percent coated paper.

Average Fuel Mix (in Percent)

Exhibit 2-6 

GHG Emissions Per Ton of Product Manufactured from Recycled Inputs

Transportation GHGs Only

Transportation 

Energy
28 

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29 

3. FOREST CARBON SEQUESTRATION

This chapter presents estimates of the forest carbon sequestration that results from recycling or 

source reducing corrugated cardboard, magazines and third-class mail, newspaper, office paper, 

phonebooks, textbooks, dimensional lumber, and medium-density fiberboard. 

One of the large-scale processes that influences the cycling of carbon is the uptake or release of 

carbon from forests. When trees are cleared for agriculture or other activities, carbon is released 

(generally in the form of CO2). On the other hand, when forests are planted and allowed to continue 

growing, they absorb atmospheric CO2 and store it in the form of cellulose and other materials. When the 

rate of uptake exceeds the rate of release, carbon is said to be sequestered. 

In the United States, uptake by forests has exceeded release since about 1977, primarily due to 

forest management activities and the reforestation of previously cleared areas. This net sequestration of 

carbon in forests represents a large and important process. EPA estimates that the annual net CO2 flux 

(i.e., the excess of uptake minus release) in U.S. forests was about 107 million metric tons of carbon 

equivalent (MMTCE) in 1999,1

 offsetting about 7 percent of U.S. energy-related CO2 emissions. In 

addition, about 17 million metric tons of carbon was stored in wood products. 

When paper and wood products are recycled or source reduced, trees that would otherwise be 

harvested are left standing. In the short term, this reduction in harvesting results in a larger quantity of 

carbon remaining sequestered, because the standing trees continue to store carbon, whereas paper and 

wood product manufacture and use tends to release carbon.2

 In the long term, some of the short-term 

benefits disappear as market forces result in less planting of new managed forests than would otherwise 

occur, so that there is comparatively less forest acreage in trees that are growing rapidly (and thus 

sequestering carbon rapidly). 

Considering the effect of forest carbon sequestration on U.S. net GHG emissions, it was clear that 

a thorough examination was warranted for this study. The complexity and long time frame of carbon 

sequestration in forests, coupled with the importance of market dynamics that determine land use, dictated 

the use of best available models. This chapter describes our method for applying models to estimate the 

effect of forest carbon sequestration associated with paper and wood product recycling and source 

reduction. 

We worked with the U.S. Department of Agriculture Forest Service (USDA-FS) to use models of 

the U.S. forest sector to estimate the amount of forest carbon sequestration per incremental ton of paper 

and wood reduced and recycled. We used the USDA-FS system of models because (1) they are the best 

models available in modeling the species composition, inventory, and growth of forests; and (2) these 

models had been used previously to analyze climate change mitigation options for the Climate Change 

Action Plan. Because the models did not enable us to estimate the forest carbon sequestration associated 

 1 U.S. EPA. 2001. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1999. U.S. Environmental 

Protection Agency, Office of Policy, Planning and Evaluation, Washington, DC. EPA-236-R-01-001. Note that the 

estimate cited (110 MMTCE) includes only carbon storage in trees and understory, which is consistent with the 

forest components included in this report. If forest floor and soils were included as well, the total would be 171 

MMTCE. 

2

 The forest carbon inventory in any year equals the carbon inventory the year before, plus net growth, less 

harvests, less decay. Thus, when harvests are reduced, the inventory increases. However when inventories become 

high relative to the carrying capacity of the land, the rate of growth decreases because net growth (the rate at which 

growth exceeds decay) declines. 
30 

Performance of the USDA-FS Forest Models 

Researchers have never formally assessed the 

accuracy of the USDA-FS models of the forest sector. In 

analyses that compare the forest impacts of a policy 

scenario with those of a baseline scenario (such as the 

analysis described in this chapter), the USDA-FS model 

results are probably reasonable. Much of the uncertainty in 

the model results is due to assumptions that apply to both 

the baseline and policy scenarios—assumptions about 

population growth, economic growth, tree growth, and land 

use changes. Any error in these assumptions would tend to 

bias the results in the baseline and policy scenarios in the 

same direction. Thus, when the outcomes of the baseline 

and policy scenarios are compared, errors in the 

assumptions tend to cancel each other out. 

with recycling of each type of paper separately, we obtained a single estimate of the sequestration from 

recycling any type of paper. 

The methodology described in this 

chapter finds that increased recycling of 

paper or wood products results in 

incremental forest carbon sequestration of 

0.73 MTCE/ton and 0.50 MTCE/ton, 

respectively.3

 The USDA-FS models do 

not directly estimate the effect of source 

reduction. To derive these estimates we 

evaluated the mix of virgin and recycled 

inputs used to manufacture each material. 

As described later, this mix is different for 

each product. The resulting carbon 

sequestration rates range from 0.30 

MTCE/ton (for corrugated cardboard) to 

0.66 MTCE/ton (for phone books). 

The remainder of this chapter is divided into seven parts. Section 3.1 provides an overview of the 

linkages between the five models used in the paper and wood analysis. Sections 3.2 through 3.5 describe 

the models in greater detail and briefly discuss the inputs, assumptions, and outputs for each model, 

focussing on the paper analysis. Section 3.6 describes the approach used to analyze wood products. 

Section 3.7 presents the results of the analysis, and Section 3.8 discusses the limitations of the individual 

models and of the analysis as a whole. 

3.1 MODELING FRAMEWORK 

Working with the USDA-FS, we used six models to estimate the impacts of increased recovery 

and source reduction of paper and wood products on forest carbon sequestration. 

For paper and wood products, we used five linked models to arrive at forest carbon sequestration 

estimates. The first model projects the decline in U.S. pulpwood harvests when paper recovery increases. 

The second and third models use the outputs of the first model, together with other inputs and 

assumptions, to estimate the extent to which reduced pulpwood harvests due to paper recovery result in 

lower U.S. timber harvests and increased timber inventories.4

 The fourth and fifth models use the outputs 

of the second and third models, and estimate how the increased timber inventories and decreased timber 

harvests due to paper recovery translate into (1) increased forest carbon sequestration and (2) changes in 

carbon sequestration in wood-in-use carbon sinks (e.g., wood used in home construction). Exhibit 3-1 

shows how the models are linked. 

The paper analysis proceeded as follows: 

(1) We developed two recovery scenarios – an estimated baseline paper recovery rate for the year 

2000 of 50 percent and a hypothetical year 2000 paper recovery rate of 55 percent as inputs to 

 3

 Although a relationship exists, it is not directly measurable. Moreover, for the relationship to remain 

valid, there must be continued investment in tree planting and growth. We believe this continued investment will 

occur, because projections of forest product use consistently point to increases in demand. 

4

 The USDA-FS projections of forest product demand account for continued high demand for all types of 

forest products. 
31 

the North American Pulp and Paper (NAPAP) model (the model is described in Section 3.2). 

The 50 percent recovery rate used for the baseline scenario was based on previous paper 

industry projections.5

 We used a 55 percent recovery rate for the high recovery scenario 

because (1) we considered this to be a plausible recovery rate with additional government 

programs to promote recycling, and (2) this recovery rate corresponded to EPA’s goal of 

increasing recovery of MSW in the original (1993) Climate Change Action Plan. We then 

assumed that over the next 15 years, the recovery rates under both scenarios would continue to 

rise and would converge in the year 2016 at 57 percent. (We assumed convergence so that we 

 5

 Actual paper recovery in 2000 (taken from EPA’s Municipal Solid Waste in the United States: 2000 

Facts and Figures) averaged about 53%, confirming that 50 percent is a reasonable estimate for 2000. 
32 

could isolate the long-term carbon sequestration effects that might result from increasing paper 

recovery in the near term.) The paper recovery rates for both scenarios then were projected to 

rise slowly from 57 percent in 2016 to 61 percent in 2040. This adjustment to the model 

incorporated our assumption that the current trend of increasing paper recovery rates would 

continue into the future. The NAPAP model then was run to model the pulpwood harvests 

from 1985 to 2040 that would be associated with (1) the baseline paper recovery rate and (2) 

the high paper recovery rate. 

(2) The outputs from NAPAP for projected pulpwood harvests in the two scenarios were used as 

inputs to the Timber Assessment Market Model (TAMM), which projects U.S. timber 

harvests, and the Aggregate Timberland Assessment System (ATLAS) model, which projects 

timber growth and changes in the U.S. forest inventory (where inventory is a function of both 

growth and harvests). The TAMM and ATLAS models are described more fully in Section 

3.3. The TAMM and ATLAS models were run, using the NAPAP inputs, to generate estimates 

of U.S. harvest levels and forest inventories for each year through 2040, for both the baseline 

and high recovery scenarios. 

(3) The outputs from TAMM and ATLAS for forest harvest levels and forest inventories in the 

two scenarios were used as inputs to the Forest Carbon (FORCARB) model, described in 

Section 3.4, which projects forest carbon sequestration. The FORCARB model produced, as 

outputs, estimates of U.S. forest carbon sequestration for each year through 2040, for both the 

baseline and high recovery scenarios. 

(4) FORCARB outputs also were used as inputs to the WOODCARB (also known as 

HARVCARB, or Harvested Carbon) model, which tracks the flow of carbon in wood products 

(see Section 3.5). 

For wood products, we used essentially the same process, but bypassed step 1 by creating a 

scenario involving increased recycling of wood, which causes a corresponding reduction in softwood 

harvest. This harvest forecast provided the basis for inputs to ATLAS, which in turn was linked to 

FORCARB and WOODCARB to evaluate carbon flows. As with paper, the increment in carbon storage 

between the base case scenario and the higher recycling scenario is calculated. This increment is divided 

by total tons of wood recycled to estimate a carbon storage rate (MTCE per ton of wood recycled). 

3.2 THE NORTH AMERICAN PULP AND PAPER (NAPAP) MODEL 

The NAPAP model is a linear optimization model6

 that uses forecasts of the U.S. economy (e.g., 

growth in population and the economy) to estimate the quantity of hardwood and softwood trees 

harvested for pulpwood in North America each year.7 The model predicts the quantity of pulpwood 

harvested each year based on estimated demand and supply curves. The quantity harvested is the quantity 

at which these curves intersect. 

 6

 A linear optimization model begins with a set of constraints (e.g., profits = revenues - costs; costs = labor 

costs + equipment costs + administrative costs + overhead costs) and an objective function (e.g., maximize profits). 

The model uses principles of matrix algebra to find the solution (e.g., the total level of output) at which the 

objective function is optimized (e.g., profits are maximized). 

7

 A number of analyses have been conducted using results from the NAPAP models. These analyses 

include (1) USDA Forest Service. 1994. RPA Assessment of the Forest and Rangeland Situation in the United 

States - 1993 Update, USDA Forest Service Forest Resource Report No. 27 (Washington, DC: USDA Forest 

Service), 75 pp.; (2) Haynes, Richard W., Darius M. Adams, and John R. Mills. 1995. The 1993 RPA Timber 

Assessment Update, USDA Forest Service General Technical Report RM-GTR-259 (Fort Collins, CO: Rocky 

Mountain Forest and Range Experiment Station), 66 pp.; (3) Ince, Peter J. 1995. What Won’t Get Harvested Where 

and When: The Effects of Increased Paper Recycling on Timber Harvest, Yale School of Forestry and 
33 

3.2.1 Inputs to the NAPAP Model 

The NAPAP model includes four major inputs: 

• Macroeconomic forecast data (e.g., estimates of U.S. population growth and growth in percapita gross domestic product); 

• Paper manufacturing capacity as of a baseline year;8

• Manufacturing costs for each different paper manufacturing process; and 

• Assumed levels of future harvests from public forests. 

3.2.2 Equations and Assumptions Used in the NAPAP Model 

The NAPAP model incorporates equations for the following functions and assumptions: 

• Estimated pulpwood supply functions (reflecting an increasing supply of pulpwood at 

increasing market prices) for three U.S. regions (West, South, and North) and two regions in 

Canada; 

• Estimated supply functions for four principal categories of recovered paper—newspaper, 

corrugated boxes, mixed papers, and the aggregate of pulp substitutes and high-grade deinking—in each supply region (the supply functions reflect an increasing supply of recovered 

paper at increasing market prices); 

• An unlimited supply of labor and energy at the market price in each supply region; 

• A fixed-quantity supply function for residues from manufacture of solid wood products, such 

as lumber and plywood, mostly in the form of “pulp chips”; 

• Demand functions9

 for all 13 principal categories of paper and paperboard products produced 

in North America10 (the demand functions reflect increasing demand due to population 

growth and growth in the gross domestic product, and decreasing demand due to increasing 

market prices); 

• Functions for changes in paper manufacturing capacity (including capacity for both virgin 

and recycled inputs), assuming that when demand for paper increases, the investment in paper 

manufacturing capacity that is needed to meet demand will be made in those types of capacity 

where the ratio of profitability to capital cost is the highest; and 

 

Environmental Studies Program on Solid Waste Policy, Working Paper #3 (New Haven, CT: Yale University) 75 

pp.; and (4) Environmental Defense Fund. 1995. Paper Task Force Recommendations for Purchasing and Using 

Environmentally Preferable Paper: Final Report of the Paper Task Force (New York, NY: Environmental Defense 

Fund), 245 pp. 

8

 The baseline year for paper manufacturing capacity is 1986. The model predicts how capacity for each 

paper manufacturing process changes each year from 1986 onward. The model’s predictions for paper 

manufacturing capacity in 1995, based on the 1986 baseline as updated, were within 5 percent of actual 1995 paper 

manufacturing capacity. 

9

 Separate demand functions are incorporated for U.S. domestic demand, Canadian domestic demand, and 

demand from various trading regions for exported paper products from the United States and Canada. 

10 These paper grades include newspaper, coated and uncoated free sheet, coated and uncoated 

groundwood, linerboard, and corrugating medium. 
34 

• The ratio of the weight of paper recovered to the weight of the fiber actually used in 

manufacturing new paper, after accounting for discards during processing and losses during 

manufacturing. 

The major assumptions of the NAPAP model include basic assumptions of economic analysis—

i.e., that markets are perfectly competitive and that paper manufacturers seek to maximize their profits. 

Because owners of private forests may not always act to maximize their profits, NAPAP assumes that 

they will continue historical patterns of economic behavior (which USDA-FS has modeled through 

econometric methods). In addition, the model assumes (1) specific levels of harvests from public forests; 

and (2) specific future technology options.11 Finally, the NAPAP pulpwood supply functions are the same 

for both the baseline and the high recycling scenario. In other words, the supply functions do not 

incorporate market feedbacks to account for changes in the age structure of forests or the acreage of 

forested land. The age structure of forests could change as increased paper recovery reduces tree harvests, 

so that on average trees grow longer. Forested acreage could change if higher paper recovery leads to 

decreased demand for pulpwood and lower pulpwood prices, leading some landowners to convert forested 

land to farmland or ranchland.12 

For this analysis, the USDA-FS simulated different recovery rates for the two scenarios—for the 

year 2000, 50 percent in the baseline scenario and 55 percent in the high recovery scenario. The 

cumulative amounts of paper recovered under the baseline and high recovery scenarios are shown in 

Exhibit 3-2. 

 11 The model assumes that certain technologies that existed in 1995 but were not yet commercialized (e.g., 

two newspaper processes with higher yields) would enter the commercial marketplace in the period from 1995-

2000. 

12 The NAPAP pulpwood supply functions incorporate projections of timber inventories over time from a 

prior run of the linked TAMM and ATLAS models. Ideally, the NAPAP portion of this analysis would have used 

two separate projections of timber inventories over time: one projection based on the baseline paper recovery 

scenario and another based on the high paper recovery scenario. NAPAP recently has been revised so that it may 

now be run iteratively with TAMM and ATLAS; however, NAPAP did not have that capability at the time this 

analysis was conducted. 
35 

Exhibit 3-2

Cumulative Paper Recovery

Under the Baseline and High Recovery Scenarios

(Million Short Tons)

Year 2000 2010 2020 2030 2040

A. Baseline Scenario 536 1143 1893 2795 3808

B. High Recovery 

Scenario 556 1189 1975 2876 3890

C. Incremental Paper 

Recovery Under the 

High Recovery 

Scenario (B-A) 20 46 81 81 81

Cumulative Paper Recovery

Under the Baseline and High Recovery Scenarios

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

2000 2010 2020 2030 2040

Year

Baseline Scenario

High Recovery Scenario
36 

NAPAP scenarios generally are specified in terms of recovered fiber utilization rates, which 

differ somewhat from paper recovery rates. To assure that the model inputs for fiber utilization are 

consistent with paper production, recovery, and consumption projections prepared by the American 

Forest and Paper Association (AFPA), Franklin Associates, Ltd. developed a set of conversion factors. 

USDA-FS used these conversion factors to adjust the demand functions for paper products. The effect 

was to increase the projections of paper demand and increase the estimates of the equilibrium quantity of 

paper produced.13

Trade in forest products between the United States and Canada was assumed to be fixed at levels 

projected in recent USDA-FS studies. As a result, any change in North American pulpwood harvests due 

to increased U.S. paper recovery would be shown in the NAPAP outputs as a change in U.S. pulpwood 

harvests. Thus, the forest carbon effects of increased paper recovery in the United States were modeled as 

if those effects occur entirely in the United States. 

3.2.3 Outputs of the NAPAP Model 

The principal outputs of the NAPAP model, for each of the two scenarios modeled, are annual 

U.S. pulpwood harvests from the present to the year 2040. These harvests are broken down into four 

categories of pulpwood: (1) softwood roundwood; (2) softwood residues; (3) hardwood roundwood; and 

(4) hardwood residues. The NAPAP estimates of pulpwood harvests for each scenario—for selected years 

from 1995 to 2040—are shown in Exhibit 3-3. As the exhibit shows, the NAPAP model projected that 

higher paper recovery rates until the year 2016 would result in pulpwood harvests that would be 

substantially below the baseline from 1995 to 2000 (because of the recovered paper substitutes for pulp 

that would otherwise be made from trees). From 2005 to 2010, the higher recovery scenario would result 

in slightly higher pulpwood harvests than under the baseline.14 From 2020 onward, annual pulpwood 

harvests would be the same under the baseline and high recovery scenarios (because after 2016 the paper 

recovery rates would be the same in both scenarios). 

 13 Specifically, the USDA-FS adjusted the NAPAP model by increasing the elasticity of demand for paper 

products so that it reflected the historical relationship between (1) paper demand and (2) population and per capita 

gross domestic product. “Elasticity of demand” is the extent to which a change in the price of goods will affect the 

quantity of the goods demanded and is defined as the percentage change in quantity divided by the percentage 

change in price that induced the change in quantity. For example, if the quantity demanded goes down by 2 percent 

when the price goes up by 1 percent, the elasticity of demand is -2. Specifically, this is the “own-price elasticity” of 

demand because it is measured with respect to the price of the goods in question, as distinct from “cross-elasticity” 

of demand, which would be measured with respect to the price of different goods. 

14 Pulpwood harvests are projected to be higher between 2005 and 2010 under the high recycling scenario 

These harvests are expected to be higher due to the modeled consequences of reduced pulpwood harvests before 

2005. Because pulpwood harvests before 2005 are projected to be lower under the high recycling scenario, more 

pulpwood remains to be harvested in later years. The increasing supply of pulpwood ready for harvest reduces 

pulpwood prices, leading to modeled increases in industry demand for non-paper uses. The increased industry 

demand results in slightly higher pulpwood harvests after 2005. However, it is important to note that under the 

baseline scenario, pulpwood harvests are projected to decline between 2000 and 2005. This decline is because the 

increase in paper recycling during this period is projected to be greater than the increase in paper consumption. 
37 

U.S. Pulpwood Harvests as Predicted 

by the NAPAP Model

6,000

6,500

7,000

7,500

8,000

8,500

9,000

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

Year

Million Cubic Feet

Baseline Scenario

High Paper Recovery Scenario

U.S. Pulpwood Harvests as Predicted by the NAPAP

Model for Selected Years (Million Cubic Feet)

Year 1995 2000 2010 2020 2030 2040

Baseline 

Scenario 7,152 7,230 7,328 7,808 7,989 8,173

High Paper 

Recovery 

Scenario 6,982 6,858 7,362 7,808 7,989 8,173

Exhibit 3-3
38 

3.3 THE TIMBER ASSESSMENT MARKET MODEL (TAMM) AND THE AGGREGATE 

TIMBERLAND ASSESSMENT SYSTEM (ATLAS) 

TAMM and ATLAS are spatial equilibrium models.15 TAMM models U.S. timber harvests 

through 2040, and ATLAS models changes in U.S. forest growth, and inventory of growing stock 

volume, through 2040.16 The two models are interrelated, because timber harvests depend in part on 

timber inventory, and timber inventory depends in part on prior harvest levels. (This interrelationship is 

shown graphically in Exhibit 3-1 with arrows going in both directions between the two models.) To 

obtain consistency in the projections of the two models, an iterative process is used. TAMM outputs for 

timber removals are used as inputs to ATLAS, and the resulting ATLAS outputs for forest growth and 

inventory are used as inputs to TAMM. This cycle is continued until the difference in projections between 

one cycle and the next has been reduced to an acceptably small amount. To reduce the costs of modeling 

in this analysis, no hand linkages were made to transfer price estimates from TAMM back to the Area 

Models (see Exhibit 3-1), nor to transfer timberland area estimates from the Area Models back to 

ATLAS. Implicitly, the forested area was modeled as being unaffected by increased paper recovery rates. 

TAMM’s estimates of timber harvests are based on four factors: (1) estimated demand for solid 

forest products (such as softwood and hardwood lumber and panel products such as plywood) based on 

projected macroeconomic data (e.g., growth in population and in the economy); (2) estimates of 

pulpwood harvests from the NAPAP model; (3) estimates of fuelwood harvests (held constant at recent 

levels); and (4) estimates of annual forest growth from ATLAS. 

The ATLAS estimates of forest growth and inventory are based on (1) the previous year’s 

inventory, (2) timber harvests from TAMM, and (3) estimated forest growth parameters. 

3.3.1 Inputs to the TAMM Model 

The TAMM model is based on eight major inputs:17

• U.S. pulpwood harvests, from the NAPAP model; 

• U.S. fuelwood harvests, from a fuelwood model; 

 15 A spatial equilibrium model is an optimization model (see footnote 6 in this chapter) that accounts for 

costs of transportation of products from producing regions to consuming regions. 

16 The descriptions of the TAMM and ATLAS models are drawn from Richard W. Haynes et al. 1993. 

Alternative Simulations of Forestry Scenarios Involving Carbon Sequestration Options: Investigation of Impacts on 

Regional and National Timber Markets, U.S. Department of Agriculture Forest Service, Pacific Northwest Station, 

August 5. Two articles which give a more detailed description of the TAMM model are (1) Adams, D.M. and R.W.

Haynes. 1980. The 1980 Softwood Timber Assessment Market Model: Structure, Projections, and Policy 

Simulations, Forest Science Monograph No. 22 (Washington, DC: USDA Forest Service), 62 pp., and (2) Adams, 

D.M. and R.W. Haynes, A Spatial Equilibrium Model of U.S. Forest Products Markets for Long-Range Projection 

and Policy Analysis. In Andersson et al., eds., “Systems Analysis in Forestry and Forest Industries,” TIMS Studies 

in the Management Sciences 21(1986)73-87. Two journal articles which describe analyses based on the TAMM 

model are (1) Adams, D.M. and R.W. Haynes, Softwood Timber Supply and the Future of the Southern Forest 

Economy, Southern Journal of Applied Forestry 15(1991):31-37, and (2) Adams, D.M and R.W. Haynes. 1991. 

“Estimating the Economic Impacts of Preserving Old-Growth on Public Lands in the Pacific Northwest,” The 

Northwest Environmental Journal 6(2):439-441. 

17 Inputs to the TAMM model are documented in: Haynes, R.W. 1990. An Analysis of the Timber Situation 

in the United States: 1989-2040, Gen. Tech. Rep. RM-199 (Ft. Collins, Colorado: USDA Forest Service, Rocky 

Mountain Forest and Range Experiment Station), 286 pp. 
39 

• Assumed levels of future timber harvests from public forests, from USDA-FS harvest plans; 

• U.S. net imports of forest products, from a trade model; 

• Changes in U.S. forested acreage over time, from a prior run of forest Area Models;18

• Growth in forest inventory, from the ATLAS model; 

• Macroeconomic forecast data, e.g., on U.S. housing starts, housing repairs, and remodeling; 

and 

• Installed capacity as of 1990 for producing timber products, such as lumber or plywood, from 

harvested trees. 

3.3.2 Equations and Assumptions Used in the TAMM Model 

The TAMM model incorporates equations for the following: 

• Estimated timber product supply functions (reflecting an increasing supply of timber products 

at increasing market prices) for eight U.S. regions; and 

• Estimated demand functions for U.S. demand for all major uses of lumber and plywood 

(reflecting decreasing demand for such products at increasing market prices). 

Also, changes in supply capacity for timber products are predicted by the model, based on 

anticipated changes in relative regional profitability or rate of return from capital investment.19

The TAMM model includes several major assumptions:20

• General assumptions of competitive markets, increasing demand for wood products with 

increasing economic activity, profit maximization by owners of lumber and plywood mills, 

and continued historical patterns of economic behavior by owners of forest land (these 

behavior patterns may not be strictly profit maximizing); and 

• Specific assumptions regarding particular levels of public harvests and projected changes in 

technology. 

In addition, TAMM and ATLAS assume (1) specified levels for net imports of softwood 

products, and (2) no net imports of hardwood lumber. 

 18 In the NAPAP portion of this analysis, timber inventories over time were not affected by the different 

paper recovery rates in the two different scenarios analyzed, but in the TAMM and ATLAS models, timber 

inventories were estimated independently for the two different scenarios. 

19 Specifically, TAMM uses an assumption that changes in capital investment are a function of past 

changes in output (i.e., that manufacturers’ expectations about the profitability of capital investment are based on 

past changes in output). 

20 Assumptions of the TAMM model are documented in the following two reports: (1) Haynes, R.W. 1990. 

An Analysis of the Timber Situation in the United States: 1989-2040, Gen. Tech. Rep. RM-199. (Fort Collins, 

Colorado: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station), 286 pp.; and (2) Haynes, 

R.W., D.M. Adams, and J.R. Mills. 1995. The 1993 RPA Timber Assessment Update, Gen. Tech. Rep. RM-GTR259 (Fort Collins, Colorado: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station), 66 pp. 
40 

3.3.3 Inputs to the ATLAS Model 

The ATLAS model, for each simulation year, relies on four major inputs: 

• Forest inventory at the beginning of the previous period, from a prior ATLAS model run; 

• Forest removals during the previous period, from the TAMM model; 

• Changes in forest acreage, from a prior run of a modified version of the Southern Area 

Model; and 

• State-by-state data on the number of forested acres and the volume of timber per forested acre 

(shown as “Forest Inventory Data” in Exhibit 3-1). 

3.3.4 Equations and Assumptions of the ATLAS Model 

The ATLAS model incorporates equations that allow the model to simulate shifts in forest 

management intensities and consequent changes in yields. Projected shifts in forest management 

intensities are based on (1) the modeled prices of forest products, (2) the costs of various management 

practices, and (3) the timber yields associated with each management practice. 

The only major assumption in the ATLAS model is that owners of private forests manage their 

forests at the level of intensity indicated by recent average forest planting rates. Otherwise, the model is 

very simple, relying on a basic mathematical proposition that forest inventory in any period equals forest 

inventory in the previous period, plus net growth, minus harvests. Net growth is gross growth less 

mortality from fire, storm, insects, and disease. 

3.3.5 Outputs of the TAMM/ATLAS Models 

The outputs of the linked TAMM and ATLAS models are projections, through 2040, of U.S. 

inventories of forest growing stock volumes (i.e., the volume of trees growing in forests), annual U.S. 

sawtimber harvests, and forest growth. 

We used the TAMM/ATLAS data on forest growing stock inventories as inputs to FORCARB. 

Exhibit 3-4 shows the growing stock inventories of privately owned forest lands in the United States as 

projected by the TAMM/ATLAS models. As the exhibit shows, forest growing stock inventories range 

from 1 to 2 billion cubic feet higher under the high recovery scenario than under the baseline scenario for 

the entire simulation period. 
41 

Exhibit 3-4

Growing Stock Inventories of

Privately Owned Forest Lands in the US

As Projected by the TAMM/ATLAS Models

(Billion Cubic Feet)

Year 1995 2000 2010 2020 2030 2040

Baseline 

Scenario 478 488 515 532 541 545

High Paper 

Recovery 

Scenario 478 489 517 534 544 548

Growing Stock Inventories of Privately 

Owned Forest Lands in the US as 

Projected by the TAMM/ATLAS Models

470

480

490

500

510

520

530

540

550

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Year

Baseline Scenario

High Paper

Recovery Scenario
42 

3.4 THE FOREST CARBON MODEL (FORCARB) 

The Forest Carbon Model (FORCARB) projects U.S. forest carbon sequestration (including soil, 

forest floor, and understory carbon) each year through 2040, based on outputs from the TAMM/ATLAS 

linked models.21

3.4.1 Inputs to the FORCARB Model 

The major inputs to the FORCARB model are the following: 

• Forest growing stock inventories—by tree species, age, and region—from the linked 

TAMM/ATLAS models; and 

• The percentage carbon composition for different species of trees, as grown in different forest 

regions. 

3.4.2 Assumptions of the FORCARB Model 

The USDA-FS tracks information in TAMM/ATLAS in terms of growing stock volume, i.e., the 

merchantable portion of trees. Tree volume is larger than growing stock volume, due to additional volume 

in non-merchantable portions of the tree such as roots and branches. The FORCARB model uses the 

simplifying assumption that tree volume is a constant multiple of growing stock volume. Carbon in the 

tree volume in the U.S. forest industry then is estimated based on the percentage carbon content of 

different species of trees. 

When a tree is harvested, FORCARB no longer counts the carbon remaining in the nonmerchantable portion of the tree (e.g., tree roots) following harvest. In other words, FORCARB uses the 

simplifying modeling assumption that the carbon in the non-merchantable portion of the tree is no longer 

sequestered and is converted to CO2 emissions. 

3.4.3 Outputs of the FORCARB Model 

As outputs, the FORCARB model produces estimates of total U.S. forest carbon inventories and 

estimates of sawtimber and pulpwood harvests for each year through 2040. The amount of forest carbon 

sequestration in a given year equals the increase in forest carbon inventories during that year. If forest 

carbon inventories decrease, net emissions, i.e., negative forest carbon sequestration, would occur. 

Exhibit 3-5 shows the projected carbon inventories of U.S. forests, as projected by the 

FORCARB model, for the baseline and high paper recovery scenarios. The forest carbon inventories that 

served as the basis for these annual changes counted carbon in trees and understory (e.g., small trees), but 

not carbon in the soil and forest floor. These carbon stocks were not included because of the high level of 

uncertainty in estimating and modeling their carbon content. 

 21 The description of the FORCARB model here is drawn from Birdsey, Richard A., and Linda S. Heath. 

1993. Carbon Sequestration Impacts of Alternative Forestry Scenarios - Draft (Radnor, PA: U.S. Department of 

Agriculture Forest Service, Global Change Research Program), pp. 47-51. A number of studies analyzing forest 

issues using the FORCARB and HARVCARB models have been published in journal articles. Among these are 

three that also explain the FORCARB and HARVCARB models. These three articles are (1) Plantinga, A.J. and 

R.A. Birdsey. 1993. “Carbon Fluxes Resulting from U.S. Private Timberland Management,” Climatic Change

23:37-53; (2) Heath, L.S. and R.A. Birdsey. 1993. “Carbon Trends of Productive Temperate Forests of the 

Coterminous United States,” Water, Air, and Soil Pollution 70:279-293; and (3) Heath, L.S. and R.A. Birdsey. 

1993. “Impacts of Alternative Forest Management Policies on Carbon Sequestration on U.S. Timberlands,” World 

Resource Review 5:171-179. 
43 

 

Exhibit 3-5

U.S. Forest Carbon Inventory, Trees, Understory, Soil, and Forest Floor

As Predicted by the FORCARB Model

(Million Metric Tons of Carbon)

Year 2000 2010 2020 2030 2040

A. Baseline Scenario 8 9 ,641 9 ,076 9 ,322 9 ,442 ,497

B. High Paper Recovery Scenario 8 9 ,665 9 ,118 9 ,364 9 ,480 ,537

C. Incremental Carbon Stored 

Under the High Paper Recovery 

Scenario (B-A) 42 24 42 38 40 

U.S. Forest Carbon Inventory

As Predicted by the FORCARB Model

(Trees, Understory, Soil, and Forest Floor)

8,000

8,200

8,400

8,600

8,800

9,000

9,200

9,400

9,600

9,800

2000 2010 2020 2030 2040

Year

Baseline Scenario

High Paper Recovery

Scenario
44 

Exhibit 3-6 shows the change in U.S. forest carbon inventories, expressed as an annual average 

for decades from 2000 to 2040. Inventories increase more quickly under the high recycling scenario than 

under the baseline recycling scenario, through the decade ending 2010. After 2010, the rate of increase in 

forest carbon inventories is essentially the same for both scenarios. This consistency is because the paper 

recovery rate is modeled as converging in 2016 to the same rate in both scenarios. 

3.5 THE HARVESTED CARBON MODEL (WOODCARB) 

WOODCARB (also known as the Harvested Carbon Model, or HARVCARB) can be thought of 

as a spreadsheet model that projects the disposition of harvested wood across four different potential 

scenarios, for 50 years into the future.22 The spreadsheet would include estimates of the percentage of four 

categories of wood that will be found in four potential fates at 10-year intervals: (1) products (a “wood-inuse” sink); (2) landfills; (3) combustion for energy; and (4) aerobic decomposition. Some change in the 

fate of a wood product occurs over time: wood products that are in use in the early years are likely to be 

landfilled or combusted in later years. The four different categories of wood considered in the model are 

softwood and hardwood pulpwood, and softwood and hardwood sawtimber. The model has separate fate 

estimates for three regions of the United States: west, south, and north. 

We combined the average annual sawtimber and pulpwood harvest estimates from FORCARB 

with the fate estimates in the WOODCARB spreadsheet, to obtain estimates of the amount of carbon from 

harvested wood that would be found in each of the four potential fates for 50 years into the future. 

3.5.1 Inputs to the WOODCARB Model 

As the only input to the WOODCARB model, the USDA-FS used the annual sawtimber and 

pulpwood harvests from the FORCARB model. 

3.5.2 Assumptions of the WOODCARB Model 

The WOODCARB model assumes that the material management patterns for the four categories 

of wood over a 50-year period do not change (e.g., the model does not assume any change in the 

proportion of waste or disposed wood burned for energy).

 22 This USDA-FS model is an adaptation of the HARVCARB model developed earlier (C. Row, and R.B. 

Phelps, 1990, “Tracing the flow of carbon through the U.S. forest products sector,” Presentation at the 19th World 

Congress, International Union of Forestry Organizations, 5-11 August 1190, Montreal, Quebec), and described 

more fully in Row and Phelps, 1996, “Wood Carbon Flows and Storage after Timber Harvest,” in Forests and 

Global Change. Vol 2, R. Neil Sampson and Dwight Hair, eds. American Forests, Washington, DC, p 27-58. This 

description of the USDA-FS implementation of the model is based on R.A. Birdsey and L.S. Heath, op cit, pp. 50-

51. 
45 

Exhibit 3-6

Average Annual Change

In U.S. Forest Carbon Inventories

As Predicted by the FORCARB Model

(Million Metric Tons of Carbon)

Decade Decade Decade Decade Decade

Time Period Ending Ending Ending Ending Ending

2000 2010 2020 2030 2040

A. Baseline 

Scenario 45.48 43.47 24.56 11.96 5.52

B. High Paper 

Recovery 

Scenario 47.89 45.25 24.59 11.70 5.74

C. Incremental 

Annual Forest 

Carbon 

Sequestration in 

the High Paper 

Recovery 

Scenario [B-A] 2.40 1.79 0.03 -0.26 0.22

Average Annual Change in U.S. 

Forest Carbon Inventories

0

10

20

30

40

50

60

2000 2010 2020 2030 2040

Decade Ending

Baseline Scenario

High Paper Recovery

Scenario
46 

3.5.3 Outputs of the WOODCARB Model 

In this analysis, WOODCARB provided outputs for the amount of carbon (1) retained in woodin-use sinks; (2) landfilled; (3) combusted for energy; and (4) aerobically decomposed, for each year from 

1995 to 2040. Because other parts of our analysis address landfills and combustion, and aerobic 

decomposition has no GHG effects, we used only the estimates of the amount of carbon retained in woodin-use sinks (a form of carbon sequestration). We included this amount in our estimate of total “forest 

carbon,” even though this carbon is stored in locations outside of forests. 

Exhibit 3-7 shows the wood-in-use sinks for the baseline and high recovery scenarios from 1990 

to 2040, as predicted by the WOODCARB model. As shown in the exhibit, the wood-in-use sinks are 

slightly less under the high recovery scenario than under the baseline scenario. The WOODCARB model 

predicts this result because under the high recovery scenario, tree harvests are reduced. Under the fixed 

proportions of the fates of wood assumed in WOODCARB, less wood is available for each of the fates for 

wood products, including wood-in-use sinks. As noted above, WOODCARB uses fixed proportions for 

the disposition of harvested wood (e.g., paper, housing, and furniture). With increased paper recovery, 

wood prices would be expected to decline (due to reduced demand), and more wood probably would be 

used for housing and furniture. Because WOODCARB does not account for any change in the price of 

wood and its impacts on wood-in-use sinks, the values in Exhibit 3-7 are probably a slight underestimate 

of the amount of carbon in wood-in-use sinks under the high recovery scenario. 

3.6 APPLYING THE MODELS FOR WOOD PRODUCTS 

As the preceding discussion indicates, the USDA-FS modeling system is quite complex and 

requires extensive coordination between model components. The modeling of the effects of paper 

recycling and source reduction was conducted over a 2-year period and involved efforts of several 

experts. After publication of the first edition of this report, EPA received several requests to evaluate the 

effect of recycling and source reduction of solid wood products, especially dimensional lumber and 

medium-density fiberboard. For these products, the USDA-FS, EPA, and ICF Consulting conducted a 

more streamlined analysis to characterize forest carbon flows. 

The streamlined analysis bypassed the use of NAPAP and TAMM. Rather than creating a marketbased harvest scenario by using these models, a harvest scenario was developed based on the expert 

judgment of two USDA-FS experts in forest products and carbon flows: Dr. Ken Skog of the Forest 

Products Laboratory and Dr. Linda Heath of the Northeast Research Station. Dr. Skog indicated that the 

majority of solid wood products are derived from softwood, and a large-scale wood recycling program 

might result in a corresponding reduction in softwood harvest of about 1.7 percent. This harvest forecast 

provided the basis for inputs to ATLAS, which in turn was linked to FORCARB and WOODCARB to 

evaluate carbon flows. 

The reductions were distributed throughout the USDA-FS regions in proportion to baseline 

harvest for the period 1998-2007. The cumulative reduction in softwood harvest was 26.4 million short 

tons. 

The effect of this reduction in harvest is to increase carbon sequestration in forests. To be 

consistent with the approach for paper recycling, effects were analyzed only for the tree and understory 

components (and excluded forest floor and soils). The total carbon sequestration was converted to a rate 

per increment of (a) recycling or (b) source reduction. Dr. Skog provided the following rough estimates of 

the system efficiencies, on a mass basis, for producing wood products from virgin inputs or recycled 

inputs: 
47 

Exhibit 3-7

U.S. Cumulative (Since 1990) Wood-in-Use Sinks

as Predicted by the HARVCARB Model

(Million Metric Tons of Carbon)

Year 2000 2010 2020 2030 2040

A. Baseline Scenario 733 1,216 1,634 2,028 2,381

B. High Paper Recovery 

Scenario 726 1,208 1,630 2,026 2,379

C. Change in Carbon 

Storage in Wood-in-Use 

Sinks [B-A] -7 -8 -4 -2 -2

2000 2010 2020 2030 2040

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

Change in Carbon Storage in Wood-in-Use Sinks 

Between the Baseline and High Paper Recovery Scenarios,

as Predicted by the HARVCARB Model

(Million Metric Tons of Carbon)
48 

• 1.1 tons roundwood input per ton finished product; and 

• 1.25 tons recycled wood input per ton of finished product. 

Based on these factors: 

• For every ton of solid wood product that is source reduced, the reduction in timber harvest is 

1.1 tons; and 

• Assuming that overall demand for wood products is constant, increases in recycling reduce 

timber harvest so that for every ton of solid wood product recycled, the reduction in timber 

harvest is 0.88 tons (=1.1/1.25). 

To develop estimates of the incremental forest carbon sequestration rates, we divided the change 

in forest carbon sequestration by the rates of recycling or source reduction that correspond to the reduced 

tonnages of softwood harvest. 

The final step was to estimate effects on the product pool. For wood products, we assumed a 

carbon density of 0.531 tons C/ton wood (or 0.48 MTCE per short ton wood), corresponding to softwoods 

in a Southeast and South Central pine forest (one of the principal sources of softwood harvests), based on 

Birdsey 1992.23 Other key assumptions were the following: 

• For source reduction, every ton of wood product removed from the product pool results in a 

corresponding decline in carbon mass in that pool; and 

• For increased recycling (i.e., at levels above the current rate), every 1 ton of wood recycled 

yields 0.8 ton of product (=1/1.25). According to Dr. Skog, it is reasonable to assume that the 

mass lost in the recycling process is burned. Thus the carbon loss from the product pool is (1 

ton wood recycled - 0.8 ton wood retained) * 0.48 MTCE/ton wood = 0.10 MTCE/ton. 

Note that the effect on the product pool from both source reduction and recycling is to decrease carbon 

sequestration. This decrease offsets some of the benefit of increasing sequestration in the forest pool. 

3.7 RESULTS 

As noted at the beginning of this chapter, we first obtained estimates of the forest carbon 

sequestration24 from paper recycling, and then used those estimates to develop estimates of the forest 

carbon sequestration from source reduction of paper. 

We estimated the forest carbon sequestration per ton of paper recycled at various points in the 

future by dividing the cumulative difference in forest carbon between the high recovery and baseline 

scenarios by the cumulative difference in the amount of paper recovered between the two scenarios. To 

estimate the forest carbon sequestration in each scenario, we summed the forest carbon sequestration 

estimates generated by the FORCARB model and the wood-in-use sink estimates generated by the 

WOODCARB model. 

The USDA-FS projected forest carbon inventories under the baseline and high recovery scenarios 

at several points in time (i.e., 2000, 2010, 2020, 2030, and 2040). The estimates of incremental forest 

carbon sequestration per ton of paper recovered vary across time, as shown in Exhibit 3-8. Note that the 

estimates of incremental forest carbon sequestration decline from 2000 to 2020, and then stabilize. 

 23 Birdsey, Richard A. 1992. Carbon Storage and Accumulation in the United States Forest Ecosystems. 

USDA Forest Service. General Technical Report WO-59. Table 1.2 

24 As noted earlier, the term forest carbon sequestration is intended to include both the carbon stored in 

forests and the carbon stored in wood-in-use sinks. 
49 

We chose the forest carbon sequestration factor for the period ending in 2010 as the best 

approximation of the forest carbon benefits from increasing source reduction and recycling over the near 

term. This value—0.73 MTCE per short ton of paper recovered—falls between the higher value for 2000 

and the lower values for later years in the simulation period. We selected this value to approximate the 

short-term carbon sequestration benefits of source reduction and recycling because it balances the 

following: (1) relatively high carbon sequestration benefits will be achievable in the near term; (2) forest 

carbon sequestration benefits drop somewhat over time; and (3) more uncertainty is associated with the 

long-term carbon sequestration effects and market response (because model predictions far into the future 

are more uncertain than near-term predictions). In sum, we believe that the value for the year 2010 strikes 

the best balance in capturing the relatively higher short-term benefits of forest carbon sequestration and 

recognizing that these benefits decline over time.25 

Using the forest carbon sequestration estimate for paper recovery, we developed estimates for 

forest carbon sequestration associated with source reduction of paper, as shown in Exhibit 3-9. We 

estimated source reduction values under two assumptions: that source reduction displaces only virgin 

inputs, and that it displaces the current mix of virgin and recycled inputs.26 We estimated that forest 

carbon sequestration for source reduction, assuming displacement of virgin inputs, is the same as for 

paper recovery. Although this approach for estimating the effects of source reduction does not consider 

the loss rates associated with paper recovery, we believe it is a reasonable first approximation. To 

estimate the forest carbon sequestration for source reduction assuming displacement of the current mix of 

inputs, we used an additional factor, i.e., the percentage of virgin inputs in the current mix of inputs. For 

this calculation (column “d” in Exhibit 3-9), we account for the fact that displacement of recycled inputs 

does not have any impact on forest carbon sequestration. 

 25 The impact of increased paper recycling and source reduction on forest growing stock inventories (3 

billion cubic feet in addition to the baseline scenario of 541 cubic feet in 2030, as shown in Exhibit 3-4) is only 0.5 

percent. This amount is less than the likely statistical error in measuring the inventories. Although the estimated 

effect is a small proportion of the total inventory, the relationship between recycling and stocks is clear, and the 

magnitude of the effect is plausible and is significant on a per-ton basis. 

26 Source reduction may conceivably displace 100 percent virgin inputs if the quantity of paper recovered 

does not change with source reduction, and all recovered paper is used to make new paper. In that case, if the 

quantity of paper manufactured is reduced through source reduction, all of the reduction in inputs would come from 

virgin inputs. It is more likely, however, that source reduction reduces both virgin and recycled inputs. 
50 

Exhibit 3-8

Increased Forest Carbon Storage Per Ton of Paper Recovered

Cumulative Change Between the Baseline and High 

Paper Recovery Scenarios for: 2000 2010 2020 2030 2040

A. Forest Carbon Stocks* (million MTCE) 24.0 41.9 42.2 39.7 41.9

B. Wood-in-Use Stocks (million MTCE) -7.0 -8.0 -4.0 -2.0 -2.0

C. Incremental Carbon Stored (million MTCE) [A+B] 17.0 33.9 38.2 37.7 39.9

D. Incremental Paper Recovery (million short tons) 19.7 46.2 81.4 81.4 81.4

E. Incremental Carbon Sequestration (MTCE/ton) [C/D] 0.9 0.7 0.5 0.5 0.5

*Includes trees and understory.

Increased Forest Carbon Sequestration Per Ton of Paper 

Recovered

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2010 2020 2030 2040

Year
51 

 

Exhibit 3-9 

Forest Carbon Sequestration Per Ton of Paper Product Recycled or Source Reduced

(a)

Material

(b)

Recycling, 

Recovering 1 

Incremental 

Ton of Paper 

(MTCE)

(c)

Source 

Reduction, 

Assuming 

Displacement of 1 

Ton of Paper 

Made from the 

Virgin Inputs 

(MTCE)

(d)

Percent 

Virgin 

Inputs in 

the Current 

Mix of 

Inputs

(e)

(e = b * d) 

Source Reduction, 

Assuming Displacement of 

One Ton of Paper Made 

From the Current Mix of 

Virgin and Recycled Inputs 

(MTCE)

Corrugated 

Cardboard 0.73 0.73 41% -0.30 

Magazines/ 

Third-class 

Mail 

0.73 0.73 84% -0.61 

Newspaper 0.73 0.73 49% -0.36 

Office Paper 0.73 0.73 67% -0.49 

Phonebooks 0.73 0.73 89% -0.66 

Textbooks 0.73 0.73 87% -0.64 

Exhibit 3-10 displays the results of the analysis for dimensional lumber and medium-density 

fiberboard (the results are the same for both of these wood products). As shown in the top of the exhibit, 

the ratio of carbon stored per ton of reduced harvest is 0.99 MTCE/metric ton wood for 2010. Using the 

system efficiencies for wood products conversion rates and expressing emission factors in MTCE per 

short ton, the effects on the forest pool as of 2010 are the following: 

• Recycling: 0.79 MTCE/ton 

• Source reduction: 0.98 MTCE/ton 

As noted earlier, recycling and source reduction would reduce the amount of carbon in the wood products 

pool; this effect is shown in the middle section of Exhibit 3-10. The bottom section shows the effect of 

summing the increase in forest carbon and the decrease in product pool carbon. Using 2010 as the most 

relevant period, the results are the following: 

• Recycling: 0.69 MTCE/ton 

• Source reduction: 0.50 MTCE/ton 

Recycling has higher net carbon storage. Although it has a lower rate of forest carbon sequestration than 

source reduction, it also has a much smaller decrement in carbon storage in the product pool. 
52 

3.8 LIMITATIONS 

Any analysis based on a complex system of models is subject to the limitations introduced by 

each model in the system. The limitations of each component model derive from (1) the assumptions 

made in developing the model; (2) the input equations used in the model; and (3) the potential impact of 

factors not included in the model. Because of these limitations, the actual behavior of markets for paper, 

wood, and other forest products (and the actual choices made by owners of private forestland) could differ 

from those predicted by the system of forest models. We believe that most of these limitations would tend 

to bias estimates under the baseline and high recycling scenarios in the same direction, so that the 

estimated differences between the two scenarios should be relatively accurate. Some limitations could 

result in unequal bias in the estimates, however, leading to biased estimates of the differences. 

This section first discusses limitations associated with the geographic scope of the analysis. 

Secondly, we discuss limitations that could bias the estimates. Limitations that could bias both scenarios 

in the same direction are listed next. This section concludes with a brief discussion of the uncertainties 

introduced by the choice of a time period over which incremental forest carbon sequestration is estimated. 

3.8.1 Limitations of Geographic Scope of Analysis and Results 

Although the goal of this analysis is to estimate the impact of paper recycling and source 

reduction on GHG emissions in the United States, the actual effects would occur in Canada and other 

countries as well.

2000 2010 2020 2030 2040

Per ton reduced wood harvest (MT/MT) 0.96 0.99 0.99 0.99 0.97 

Per ton reduced carbon harvest (MT/MT) 1.81 1.86 1.87 1.86 1.83 

Per ton increased recycling (MT/short ton) 0.77 0.79 0.79 0.79 0.78 

Per ton reduced prodn of solid wood product 

(source reduction) (MT/short ton) 0.98 0.96 0.99 0.98 0.97 

2000 2010 2020 2030 2040

Per ton increased recycling (MT/short ton) -0.10 -0.10 -0.10 -0.10 -0.10

Per ton reduced prodn of solid wood product 

(source reduction) (MT/short ton) -0.48 -0.48 -0.48 -0.48 -0.48

2000 2010 2020 2030 2040

Per ton increased recycling (MT/short ton) 0.67 0.69 0.70 0.69 0.68 

Per ton reduced prodn of solid wood product 

(source reduction) (MT/short ton) 0.50 0.48 0.51 0.50 0.49 

Emission factor -- increase in forest + product pool carbon per unit change in recycling or 

Based on carbon content of softwoods in Southeast & South Central Region, Pine Forest Type

Emission factor -- increase in forest carbon (trees + understory) per unit change in harvest 

Based on density and carbon content of softwoods in Southeast & South Central Region, Pine 

Forest Type

Emission factor -- change in product pool carbon per unit change in recycling or source 

Based on carbon content of softwoods in Southeast & South Central Region, Pine Forest Type

Exhibit 3-10

Increase in Forest Carbon per Unit Change in Harvest
53 

• The USDA-FS models treat forest product markets in the United States and Canada as a 

single integrated economic and biological system. But they do not treat Canadian forest 

inventories in the same way and degree of detail as U.S. forest inventories. The estimated 

impacts of increases in recycling and source reduction were treated as impacts on U.S. 

forests. Because much of the economically marginal paper production is from Canadian pulp 

sources, source reduction, in particular, would lower demand for Canadian timber. In any 

case, U.S. and Canadian forests actually would share the effects. 

• More than 20 percent of the paper currently recovered in the two countries is exported. Some 

proportion of the increased amounts of recycled paper—probably more than 20 percent—

would undoubtedly be exported. Current exports comprise 43 percent of the world trade in 

recovered paper. The major buyers of this paper are developing countries in Asia and Latin 

America, with Korea, Taiwan, and Mexico being major destinations. The alternative sources 

of fiber for the paper industry in these countries are pulp and fiber from non-forest sources 

(agricultural refuse, hemp, bamboo, and rubber and palm oil trees). Very little comes from 

forest harvests. Forests in these countries, however, are not necessarily managed on a 

sustainable basis. It is difficult to determine which of these effects would dominate—

displacement of non-forest fiber (with no forest carbon impact) or displacement of 

unsustainably managed forest fiber (with a benefit larger than that in U.S. forests). 27 

• NAPAP does not account for any effects of lower pulpwood prices (due to higher paper 

recycling rates) on net exports of U.S. pulpwood to non-Canadian markets. Lower pulpwood 

prices would be expected to result in increased exports and possibly changes in foreign 

timber inventories. Though U.S. pulpwood exports are currently less than 1 percent of U.S. 

pulpwood production, some virgin pulp fiber is now being exported from southern and 

western ports in the form of pulp chips. The future potential for pulp chip exports is difficult 

to estimate. 

• The competition to U.S. and Canadian exports of both recovered and newly manufactured 

paper is likely to come from two sources. First, all other developed countries are also likely to 

intensify recycling and source reduction programs, with additional recovery of paper fiber. 

Second, a major developing source of fiber for paper is the establishment of intensive forest 

plantations in tropical and southern hemisphere countries, particularly Australia, Brazil, 

Chile, Indonesia, New Zealand, and South Africa. The effect of additional world sources of 

paper fiber from developed countries on these forest plantation programs is difficult to 

estimate. 

3.8.2 Limitations Expected to Bias the Results 

Two limitations in the system of forest sector models could result in biased estimates of the 

incremental forest carbon sequestration from increased paper recycling. The limitations are as follows: 

• The modeling system does not account for any conversion of U.S. forestland to farmland or 

rangeland that might occur in response to lower prices for pulpwood due to higher paper 

recycling rates. The NAPAP model does not account for potential changes in timber 

inventory in the near term due to lower harvests associated with higher paper recovery. Nor 

does it account for potential changes in forest acreage in the longer term if higher paper 

recovery depresses pulpwood prices enough to induce landowners to convert forested acreage 

 27 A comprehensive description of the world paper industry, its fiber sources, and environmental concerns 

can be found in International Institute for Environment and Development (IIED), 1996, Towards a Sustainable 

Paper Cycle, IIED: London, 258 pp. This study, prepared for the World Business Council for Sustainable 

Development, treats many of the issues covered in this chapter, but on a global basis. 
54 

to other uses. The TAMM and ATLAS models likewise do not allow for long-term changes 

in forested acreage due to increased paper recovery. These effects, however, may be small. 

Converting forestland to agriculture or to industrial, commercial, or residential uses is far 

more likely to result from much higher land values for crops or development, if the land is 

suitable or in a favorable location. 

• This analysis did not consider carbon storage in forest soils and forest floors, because of the 

high level of uncertainty in projecting changes in carbon storage. Nonetheless, projections of 

carbon storage in forest soils and floors under the baseline and high recycling scenarios, as 

generated by the FORCARB model, suggest that incremental carbon storage under the high 

recycling scenario could be slightly higher than shown here, if storage in soils and the forest 

floor were included. 

3.8.3 Limitations Not Expected to Bias the Results 

We expect that several limitations in the system of forest models would bias—to about the same 

extent—the estimates of forest carbon sequestration in the baseline and high recycling scenarios. The 

limitations thus would not result in significant bias in the estimate of the difference in forest carbon 

sequestration between the two scenarios. These limitations are as follows: 

• The macroeconomic forecasts used in the models (e.g., for population growth and growth in 

per-capita gross domestic product) are simply forecasts, and may turn out to be inaccurate; 

• The historical supply and demand functions used in the models may change in the future. For 

example, (1) demand for newspaper may drop sharply due to competition from electronic 

news media, or (2) improved technologies or tree diseases not anticipated in the models may 

significantly change the cost of producing forest products; and 

• Future harvests from public forestland may be different from those projected. 

3.8.4 The Use of a Point Estimate for Forest Carbon Sequestration 

As shown in Exhibit 3-8, estimates of forest carbon sequestration due to increased paper 

recycling vary over time. As noted above, in choosing a single point estimate, we selected the time period 

that best balances the competing criteria of (1) capturing the long-term forest carbon sequestration effects, 

and (2) limiting the uncertainty inherent in projections made well into the future. The range of forest 

carbon sequestration estimates over time, and the limitations of the analysis discussed above, indicate that 

there is considerable uncertainty in the point estimate selected. In comparison to the estimates of other 

types of GHG emissions and sinks developed in other parts of this analysis, the magnitude of forest 

carbon sequestration is relatively high. Based on these forest carbon sequestration estimates, source 

reduction and recycling of paper are found to have substantial net GHG reductions. Because paper 

products comprise the largest share of municipal waste generation (and the largest volumes of waste 

managed through recycling, landfilling, and combustion), it is important to bear in mind the uncertainty in 

the forest carbon sequestration values when evaluating the results of this report. 
55 

4. SOURCE REDUCTION AND RECYCLING 

This chapter presents estimates of GHG emissions and carbon sequestration resulting from source 

reduction and recycling of 15 manufactured materials: aluminum cans, steel cans, glass containers, plastic 

containers (LDPE, HDPE, and PET), corrugated boxes, magazines/third-class mail, newspaper, office 

paper, phonebooks, textbooks, dimensional lumber, medium-density fiberboard, and mixed paper. 

To estimate GHG emissions associated with source reduction and recycling (and other MSW 

management options), we used a baseline scenario in which the material is manufactured from the current 

mix of virgin and recycled inputs, but has not yet been disposed of or recycled. Thus, the baseline for 

each material already incorporates some emissions from raw materials acquisition and manufacturing 

using the current mix of virgin and recycled inputs. Using this measurement convention, it follows that 

source reduction1

 reduces GHG emissions from the raw material acquisition and manufacturing phase of 

the life cycle for all materials. Moreover, source reduction of paper results in forest carbon sequestration 

(as discussed in Chapter 3). 

Manufacturing from recycled inputs generally requires less energy, and thus lower GHG 

emissions, than manufacturing from virgin inputs. Our recycling analysis indicates that recycling reduces 

GHG emissions for each of the materials studied. 

4.1 GHG IMPLICATIONS OF SOURCE REDUCTION 

When a material is source reduced (i.e., less of the material is made), the GHG emissions 

associated with making the material and managing the post-consumer waste are avoided. As discussed 

above, under the measurement convention used in this analysis, source reduction has (1) negative raw 

material and manufacturing GHG emissions (i.e., it avoids baseline emissions attributable to current 

production); (2) forest carbon sequestration benefits for paper products (also treated as negative 

emissions, as estimated in Chapter 3); and (3) zero waste management GHG emissions. Exhibit 4-1 

presents the GHG implications of source reduction. The values for forest carbon sequestration were 

copied from Exhibit 3-8. 

In order to compare source reduction to other solid waste management alternatives, we compared 

the GHG reductions from source reduction to the life-cycle GHG emissions of another solid waste 

management option (e.g., landfilling). This approach enables policy makers to evaluate, on a per-ton 

basis, the overall difference in GHG emissions between (1) source reducing 1 ton of material and (2) 

manufacturing and then managing (post-consumer) 1 ton of the same material. Such comparisons are 

made in the Executive Summary and in Chapter 8 of this report. For most materials, source reduction has 

lower GHG emissions than the other waste management options. 

 1

 In this analysis, the values reported for source reduction apply to material lightweighting or extension of a 

product’s useful life. We assume no substitution by another material or product, and thus we assume no offsetting 

GHG emissions from another material or product. Thus, the data do not directly indicate GHG effects of source 

reduction that involves material substitution. Considerations for estimating the GHG effects of material substitution 

are presented in Section 4.3 below. 
56 

Net E

For Current For 100% For Current For 100% For 

Material Mix of Inputs Virgin Inputs Post-consumer Mix of Inputs Virgin Inputs Mix 

Aluminum Cans -2.49 -4.67 0.00 0.00 0.00 Steel Cans -0.79 -1.01 0.00 0.00 0.00 Glass -0.14 -0.16 0.00 0.00 0.00 HDPE -0.49 -0.53 0.00 0.00 0.00 LDPE -0.61 -0.64 0.00 0.00 0.00 PET -0.49 -0.58 0.00 0.00 0.00 Corrugated Cardboard -0.24 -0.22 0.00 -0.28 -0.73 Magazines/Third-class Mail -0.46 -0.46 0.00 -0.58 -0.73 Newspaper -0.46 -0.59 0.00 -0.35 -0.73 Office Paper -0.31 -0.28 0.00 -0.50 -0.73 Phonebooks -0.64 -0.67 0.00 -0.65 -0.73 Textbooks -0.59 -0.59 0.00 -0.64 -0.73 Dimensional Lumber -0.05 -0.05 0.00 -0.50 -0.50 Medium-density Fiberboard -0.10 -0.10 0.00 -0.50 -0.50 Mixed Paper  Broad Definition NA NA NA NA NA  Residential Definition NA NA NA NA NA  Office Paper Definition NA NA NA NA NA Mixed MSW NA NA NA NA NA 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

Exhibit 4-1

GHG Emissions for Source Reduction

(MTCE/Ton of Material Source Reduced)

Avoided GHG Emissions from Raw 

Materials Acquistion and Manufacturing Changes in Forest Carbon Storage 
57 

4.2 GHG IMPLICATIONS OF 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.2 As with source reduction of paper products, 

recycling of paper also results in forest carbon sequestration. 

Most of the materials considered in this analysis are modeled as being recycled in a closed loop 

(e.g., newspaper is recycled into new newspaper). However, several paper types are recycled in an open 

loop (i.e., they are recycled into more than one product) under the general heading of mixed paper.3

Mixed paper is included because it is recycled in large quantities, and is an important class of scrap 

material in many recycling programs. However, presenting a single definition of mixed paper is difficult 

because each mill using recovered paper defines its own supply, which varies with the availability and 

price of different grades of paper. 

For the purpose of this report, we identified three definitions for mixed paper: broad, office, and 

residential. To assist recyclers in determining which definition corresponds most closely to mixed paper 

streams they manage, the composition of each is presented in Exhibit 4-2. The broad definition of mixed 

paper includes almost all printing-writing paper, folding boxes, and most paper packaging. Mixed paper 

from offices includes copier and printer paper, stationary and envelopes, and commercial printing. The 

typical mix of papers from residential curbside pick-up includes high-grade office paper, magazines, 

catalogs, commercial printing, folding cartons, and a small amount of old corrugated containers. Mixed 

paper as characterized by the broad and residential definitions can be remanufactured via an open loop 

into recycled boxboard. Mixed paper from offices is typically used to manufacture commercial paper 

towels. 

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 or in the 

remanufacturing stage. Consequently, less than 1 ton of new material generally is made from 1 ton of 

recovered materials. Material losses are quantified and translated into loss rates. In this analysis, we used 

estimates of loss rates provided by Franklin Associates, Ltd. (FAL) for steel, dimensional lumber, and 

medium-density fiberboard (the same materials for which we used FAL’s energy data, as described in 

Chapter 2). EPA’s Office of Research and Development (ORD) provided loss rates for the other 

materials. These values are shown in Exhibit 4-3. 

GHG emission reductions associated with remanufacture using recycled inputs are calculated by 

taking the difference between (1) the GHG emissions from manufacturing a material from 100 percent 

recycled inputs, and (2) the GHG emissions from manufacturing an equivalent amount of the material 

(accounting for loss rates) from 100 percent virgin inputs. 

 2

 Note that when paper is manufactured from recycled inputs, the amount of paper sludge produced is 

greater than when paper is made from virgin inputs. This difference is because recycled paper has more short fibers, 

which must be screened out. We made a preliminary estimate of the GHG emissions from paper sludge managed in 

landfills; our results indicated that net GHG emissions (i.e., CH4 emissions minus carbon sequestration) were close 

to zero. Because the emissions are small and highly uncertain, no quantitative estimate is included in this report. 

3

 This report also includes estimates for mixed MSW, mixed plastics, mixed organics, and mixed 

recyclables, i.e., a mixture of the principal paper, metal, and plastic materials that are recycled. These other mixed 

materials are discussed in Chapter 8. 
58 

Exhibit 4-2 

Summary of Mixed Paper Scenarios 

(Composition as a percentage of total) 

Paper Grade 

All Paper and 

Paperboard in 

MSW (1) 

Mixed Paper: 

Broad Definition (2) Mixed Paper: 

Offices (3) 

Mixed Paper: 

Single-Family 

Residential (4) 

Uncoated groundwood paper 4.9% 4.9% 7.9% 2.2% 

Coated free sheet paper 5.0% 12.0% 13.9% 11.5% 

Coated groundwood paper 4.3% 11.5% 30.7% 17.7% 

Uncoated free sheet paper 14.3% 37.6% 41.6% 18.4% 

Cotton fiber paper 0.1% 0.4% 1.8% 0.2% 

Bleached bristols 1.5% 3.9% 4.1% 2.8% 

Newspaper 13.3% 2.9% 2.9% 

Virgin corrugated boxes 29.6% 12.2% 

Recycled corrugated boxes 6.8% 2.8% 

Unbleached kraft folding boxes 1.5% 5.7% 4.1% 

Bleached kraft folding boxes 2.8% 5.7% 5.8% 

Recycled folding boxes 3.0% 7.9% 8.0% 

Bleached bags and sacks 0.4% 1.0% 1.6% 

Unbleached bags and sacks 2.1% 5.6% 9.0% 

Unbleached wrapping paper 0.1% 0.2% 

Converting paper 0.3% 

Special industrial paper 1.3% 

Other paperboard 2.5% 

Paper plates and cups 1.2% 

Tissue, towels 3.9% 

Set-up boxes 0.3% 0.7% 0.6% 

Other paper packaging 0.8% 

Totals 100.0% 100.0% 100.0% 100.0% 

(1) All grades of paper and paperboard in MSW. 

(2) Excludes newspaper, old corrugated containers, tissue produce, paper plates and cups, converting and special 

industrial papers, non-packaging paperboard such as album covers and posterboard, and paper labels. 

(3) Includes the high-grade papers (ledger and computer printout) as well as stationery, mail, magazines, and 

manila folders. Could be recovered as “File Stock.” 

(4) Represents a typical collection of mixed paper from a single-family curbside program. Includes printingwriting papers, corrugated boxes, folding cartons, and bags and sacks. 

Source: Working papers prepared by Franklin Associates, Ltd., October 1997. 

 
59 

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.” 

The results of our analysis are shown in Exhibit 4-4. In this exhibit, for each material we present 

the differences between manufacture from virgin and recycled inputs for (1) energy-related GHG 

emissions (both in manufacturing processes and transportation), (2) process non-energy-related GHG 

emissions, and (3) forest carbon sequestration. Our method of accounting for loss rates yields estimates of 

GHG emissions on the basis of metric tons of carbon equivalent (MTCE) per short ton of material 

collected for recycling (rather than emissions per ton of material made with recycled inputs). 

We recognize that some readers may find it more useful to evaluate recycling in terms of tons of 

recyclables as marketed rather than tons of materials collected. To adjust the emission factors reported in 

Exhibit 4-4 for that purpose, one would scale up the recycled input credits shown in columns “b” and “d” 

of that exhibit by the ratio of manufacturing loss rate to total loss rate (i.e., Exhibit 4-3 column “c” 

divided by column “d”). 

Another way that recycling projects can be measured is in terms of changes in recycled content of 

products. To evaluate the effects of such projects, one could use the following algorithm:4

 4

 This approach would apply only where the products with recycled content involve the same “recycling 

loop” as the ones on which the values in this report are based (e.g., aluminum cans are recycled in a closed loop into 

more aluminum cans). 

(a) (b) (c) (d) (e)

(e = c * d)

Material Data Source

Percent of 

Recovered 

Materials 

Retained in the 

Recovery Stage

Product Made 

per Ton of 

Recycled 

Inputs In the 

Manufacturing 

Stage

Tons of Product 

Made Per Ton 

Recovered 

Materials

Aluminum Cans FAL & ORD * 100 0.93 0.93

Steel Cans FAL 100 0.98 0.98

Glass FAL & ORD * 90 0.98 0.88

HDPE FAL & ORD * 90 0.86 0.78

LDPE FAL & ORD * 90 0.86 0.78

PET FAL & ORD * 90 0.86 0.78

Corrugated Cardboard FAL & ORD * 100 0.93 0.93

Magazines/Third-class Mail FAL & ORD * 95 0.71 0.67

Newspaper FAL & ORD * 95 0.94 0.90

Office Paper FAL & ORD * 91 0.66 0.60

Phonebooks FAL & ORD * 95 0.71 0.68

Textbooks FAL & ORD * 95 0.69 0.66

Dimensional Lumber FAL 88 0.91 0.80

Medium-density Fiberboard FAL 88 0.91 0.80

Exhibit 4-3

Loss Rates For Recovered Materials

* FAL provided data for column (c), while ORD provided data for column (d). 
60 

Trecyc = Tprod*(RCp-RCi)/L, where 

Trecyc = tons of material recycled, as collected 

Tprod = tons of the product with recycled content 

RCp = recycled content (in percent) after implementation of the project 

RCI = recycled content (in percent) initially 

L = loss rate (from Exhibit 4-3, column “d”) 

Then, one could use the emission factors in this report directly with the tons of material recycled 

(as collected) to estimate GHG emissions. 

In order to compare GHG emissions from recycling to those attributable to another solid waste 

management option such as landfilling, we compared the total GHG emissions from recycling the 

material to the GHG emissions from managing the disposal of the same material under another waste 

management option. The baseline for a given material (which includes GHG emissions from raw 

materials acquisition and manufacturing for the current mix of virgin and recycled inputs) for both 

options is the same. Overall, because recycling reduces the amount of energy required to manufacture 

materials (as compared to manufacture with virgin inputs) and leads to avoided process non-energy GHG 

emissions, recycling has lower GHG emissions than all other waste management options except for 

source reduction. 

4.3 SOURCE REDUCTION WITH MATERIAL SUBSTITUTION

As noted above, our 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. Analyzing the GHG 

impacts of this type of source reduction becomes more complicated. Essentially, one would need to 

estimate the net GHG impacts of (1) source reduction of the original material, and (2) manufacture of the 

substitute material and its disposal fate. A quantitative analysis of source reduction with material 

substitution was beyond the scope of this report because of the large number of materials that could be 

substituted for the materials analyzed in this report (including composite materials, e.g., a composite of 

paper and plastic used in juice boxes), and the need for application-specific data. Where both the original 

material and the substitute material are addressed in this report, however, the GHG impacts of source 

reduction with material substitution may be estimated. 

The estimate would be based on (1) the data provided in this report for the material that is source 

reduced; (2) the mass substitution rate for the material that is substituted; and (3) data in this report for the 

material substituted. The mass substitution rate is the number of tons of substitute material used per ton of 

original material source reduced. Note, however, that in calculating the mass substitution rate, one should 

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. 

To estimate the GHG impacts of source reduction with material substitution (per ton of material 

source reduced), one should consider the following: a specific baseline scenario, including waste 

management; an alternative scenario, involving the substitute material and a waste management method; 

the number of tons of material used in each scenario, using the mass substitution rate; the net GHG 

emissions for the baseline; the GHG impacts of source reduction of the original material; the GHG 

impacts of manufacturing the substitute material; and the GHG impacts of waste management for the 

substitute material. Among other factors, these considerations will allow for a comparison of net GHG 

emissions from source reduction with material substitution to the baseline. 
61 

4.4 LIMITATIONS 

Because the data presented in this chapter were developed earlier in Chapters 2 and 3, the 

limitations discussed in those chapters also apply to the values presented here. Five other limitations are 

as follows: 

• There may be 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, we did not include them in our estimates. We did perform a screening analysis for 

paper sludge, however, based on (1) data on sludge generation rates and sludge composition 

(i.e., percentage of cellulose, hemicellulose, lignin, etc. in sludge),5

 and (2) professional 

judgment on the CH4 generation rates for cellulose, etc. The screening analysis indicated that 

net GHG emissions (CH4 emissions minus carbon storage) from paper sludge are probably on 

the order of 0.00 MTCE per ton of paper made from virgin inputs to 0.01 MTCE per ton for 

recycled inputs. Our worst case bounding assumptions indicated maximum possible net GHG 

emissions ranging from 0.03 to 0.11 MTCE per ton of paper (depending on the type of paper 

and whether virgin or recycled inputs are used). 

• The recycling results are reported in terms of GHG emissions per 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 estimated here. 

• The models used to evaluate forest carbon sequestration and those used to evaluate energy 

and non-energy emissions differ in their methods for accounting for loss rates. Although one 

can directly adjust the emission factors reported here for process emissions so that they apply 

to tons of materials as marketed (rather than tons as collected), there is no straightforward 

way to adjust the forest carbon estimate. 

• Because our modeling approach assumes closed-loop recycling for all materials except mixed 

paper, it does not fully reflect the prevalence and diversity of open-loop recycling. Most of 

the materials in our analysis are recycled into a variety of manufactured products, not just 

into the original material. Resource limitations prevent an exhaustive analysis of all the 

recycling possibilities for each of the materials analyzed. 

• For the purpose of simplicity, we assumed that increased recycling does not change overall 

demand for products. In other words, we assumed that each incremental ton of recycled 

inputs would displace virgin inputs in the manufacturing sector. In reality, there may be a 

relationship between recycling and demand for products with recycled content, since these 

products become cheaper as the supply of recycled materials increases. 

 5

 ICF Consulting. 1996. Memorandum to EPA Office of Solid Waste, “Methane Generation from Paper 

Sludge,” December. 
62 

(a) (b) (c) (d) (e) 

(f 

G

Recycled Input Recycled Input Recycled Input 

Credit*: Credit*: Credit*: R

Process Transportation Process Non- Forest Carbon 

Material Energy Energy Energy Sequestration 

Aluminum Cans -2.92 -0.14 -1.05 0.00 Steel Cans -0.48 -0.01 0.00 0.00 Glass -0.03 0.00 -0.04 0.00 HDPE -0.34 0.00 -0.04 0.00 LDPE -0.43 0.00 -0.04 0.00 PET -0.40 0.00 -0.02 0.00 Corrugated Cardboard 0.04 -0.01 0.00 -0.73 Magazines/Third-class Mail 0.00 0.00 0.00 -0.73 Newspaper -0.21 -0.01 0.00 -0.73 Office Paper 0.06 0.00 0.00 -0.73 Phonebooks -0.18 0.00 0.00 -0.73 Textbooks -0.01 0.00 0.00 -0.73 Dimensional Lumber 0.02 0.00 0.00 -0.69 Medium-density Fiberboard 0.01 0.00 0.00 -0.69 Mixed Paper  Broad Definition 0.08 -0.02 0.00 -0.73  Residential Definition 0.08 -0.02 0.00 -0.73  Office Paper Definition -0.08 -0.02 0.00 -0.73 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.*Material that is recycled after use is then substituted for virgin inputs in the production of new products. This credit represents the difference iresults from using recycled inputs rather than virgin inputs. The credit accounts for loss rates in collection, processing, and remanufacturing. R

based on a weighted average of closed- and open-loop recycling for mixed paper. All other estimates are for closed-loop recycling.

Exhibit 4-4

GHG Emissions for Recycling

(MTCE/Ton of Material Recovered)
63 

Explanatory notes for Exhibit 4-4: Columns “b” and “c” show the reduction in process energy GHGs and transportation ene

each material from recycled inputs, rather than virgin inputs. The values in columns “b” and “c” are based on (1) the difference in ener

between making 1 ton of the material from 100% virgin inputs and from 100% recycled inputs, multiplied by (2) the estimated tons of from 1 ton of material recovered, after accounting for loss rates in the recovery and remanufacturing stages. We first estimated the valu

based on data provided by FAL and ORD, as shown in Exhibits 2-2 through 2-5. Note that for two of the mixed paper definitions, the p

emissions are higher when using recycled inputs than when using virgin inputs (as shown by positive values in column “b”). This diffemanufacture of boxboard (the product of open-loop recycling of these types of mixed paper) from virgin inputs uses a high proportion 

biogenic CO2 emissions from biomass fuels are not counted as GHG emissions (see the discussion of biogenic CO2 emissions in Chapt

forest carbon sequestration, the net GHG emissions from recycling these two mixed paper definitions are negative. 

For column “d, ” which presents the process non-energy GHG emissions from recycling, we used (1) data showing the differe

GHG emissions between making 1 ton of the material from 100% virgin inputs and from 100% recycled inputs (as shown in the secondExhibits 2-2 and 2-4) multiplied by (2) the estimated amount of material manufactured (in tons) from 1 ton of material recovered, after 

in the recovery and remanufacturing steps. 

Next, column “e ” shows the estimated forest carbon sequestration from recycling of paper products, as estimated in Chapter 3

(column “f”) sums columns “b” through “e” to show the GHG implications of recycling each material. 
64 

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65 

5. COMPOSTING

This chapter has been extensively revised since the first edition of this report. The revised chapter 

presents the results of an in-depth analysis to determine the net GHG impacts of composting yard 

trimmings and food discards. As research in the areas of erosion control, soil fertility, and bio-based 

products continues, we are likely to uncover additional GHG and other benefits of composting. 

This chapter presents estimates of GHG emissions and sinks from composting yard trimmings 

and food discards.1 The chapter is organized as follows: 

Section 5.1 presents an estimate of potential CO2 and CH4 emissions from composting; 

Section 5.2 quantifies the potential carbon storage benefits of applying compost to soils; 

Section 5.3 presents net GHG emissions from composting; and 

Section 5.4 discusses the limitations of this analysis. 

Composting may result in (1) CH4 emissions from anaerobic decomposition; (2) long-term carbon 

storage in the form of undecomposed carbon compounds; and (3) non-biogenic CO2 emissions from 

collection and transportation of the organic materials to the central composting site, and from mechanical 

turning of the compost pile.2 Composting also results in biogenic CO2 emissions associated with 

decomposition, both during the composting process and after the compost is added to the soil. Because 

this CO2 is biogenic in origin, however, it is not counted as a GHG in the Inventory of U.S. Greenhouse 

Gas Emissions and Sinks3 (as explained in Section 1.4.2) and is not included in our accounting of 

emissions and sinks. 

Our analysis suggests that composting, when managed properly, does not generate CH4

emissions, but it does result in some carbon storage (associated with application of compost to soils), as 

well as minimal CO2 emissions from transportation and mechanical turning of the compost piles. In order 

to maintain consistency with other chapters in this report, we selected point estimates from the range of 

emission factors—covering various compost application rates and time periods—developed in our 

analysis. The point estimates were chosen based on a “typical” compost application rate of 20 tons of 

compost per acre, averaged over three soil-crop scenarios. In terms of timing, the carbon storage values 

for the year 2010 were selected to be consistent with forest carbon storage estimates presented in Chapter 

4 of this report. Overall, we estimate that centralized composting of organics results in net GHG storage 

of 0.05 MTCE/wet ton of organic inputs composted and applied to agricultural soil. 

 1

 Although paper and mixed MSW can be composted, we did not analyze the GHG implications of 

composting them because of time and resource constraints. 

2

 CO2 emissions from delivery of compost to its final destination were not counted because compost is a 

marketable product, and CO2 emissions from transportation of other marketable, finished goods to consumers have 

not been counted in other parts of this analysis. 

3

 U.S. EPA. 2001. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1999. U.S. 

Environmental Protection Agency, Office of Policy, Planning and Evaluation, Washington, DC. EPA-236-R-01-

001. 
66 

5.1 POTENTIAL GREENHOUSE GAS EMISSIONS 

Two potential types of GHG emissions are associated with composting: (1) CH4 from anaerobic 

decomposition; and (2) non-biogenic CO2 from transportation of compostable materials, and turning of 

the compost piles. 

5.1.1 CH4 

To research the issue of CH4 emissions, we first conducted a literature search for articles on CH4

generation from composting. We found very few articles specifically addressing CH4 emissions from 

composting published between 1991 and 1999,4

 and thus decided not to continue searching for earlier 

articles. Because CH4 emissions from composting are addressed only occasionally in the literature, we 

contacted several composting experts from universities and the U.S. Department of Agriculture to discuss 

the potential for CH4 generation, based on the nature of carbon flows during composting. Our CH4

analysis is based on their expert opinions. 

The researchers we contacted stated that well-managed compost operations usually do not 

generate CH4 because they typically maintain an aerobic environment with proper moisture content to 

encourage aerobic decomposition of the materials. The researchers also noted that even if CH4 is 

generated in anaerobic pockets in the center of the compost pile, the CH4 is most likely oxidized when it 

reaches the oxygen-rich surface of the pile, where it is converted to CO2. Several of the researchers 

commented that anaerobic pockets are most apt to develop when too much water is added to the compost 

pile. They noted that this problem rarely occurs because compost piles are much more likely to be 

watered too little rather than too much. 

We concluded from the available information that CH4 generation from centralized compost piles 

is essentially zero. 

5.1.2 CO2 from Transportation of Materials and Turning of Compost 

This study estimated the indirect CO2 emissions associated with collecting and transporting 

organics to centralized compost facilities, and turning the compost piles. We began with estimates 

developed by Franklin Associates, Ltd. (FAL) for the amount of diesel fuel required to (1) collect and 

transport 1 ton of organics5

 to a central composting facility (363,000 Btu) and (2) turn the compost pile 

(221,000 Btu).6

 We converted these estimates to units of metric tons of carbon equivalent (MTCE) per ton 

of organics, based on a carbon coefficient of 0.02 MTCE per million Btu of diesel fuel. This resulted in 

an estimate of 0.01 MTCE of indirect CO2 emissions per ton of material composted in a centralized 

facility. 

5.2 POTENTIAL CARBON STORAGE 

We also evaluated the effect of compost application on soil carbon storage. We did not find 

information on carbon storage associated with compost derived specifically from yard trimmings or food 

discards. Nevertheless, it is reasonable to expect that these materials are basically homogeneous with 

respect to the fate of their stored carbon, even though their initial moisture and carbon content differs. 

 4

 Among the papers with pertinent information is that of H.J. Hellebrand, 1998, Emission of Nitrous Oxide 

and other Trace Gases during Composting of Grass and Green Waste, J. Agric. Engineering Research 69:365-375. 

5

 Measured on a wet weight basis, as MSW is typically measured. 

6

 Franklin Associates, Ltd. 1994. The Role of Recycling in Integrated Solid Waste Management to the Year 

2000 (Stamford, CT: Keep America Beautiful), pp. I-27, 30, and 31. 
67 

To develop carbon storage estimates for composted organics, we researched the processes that 

affect soil carbon storage, reviewed the results of experiments on the soil carbon impacts of applying 

organic amendments (e.g., compost, manure, biosolids, and crop residues), and interviewed experts on the 

potential carbon storage benefits of composting organics as compared to other methods of disposal. 

During this process, four hypotheses were proposed regarding the benefits of applying organics compost 

to soil: 

(1) Many soils have been depleted in organic matter through cultivation and other practices. 

Adding compost can raise soil carbon levels by increasing organic matter inputs. Soils 

degraded by intensive crop production, construction, mining, and other activities lose organic 

matter when decomposition rates and removals of carbon in harvests exceed the rate of new 

inputs of organic materials. Adding compost shifts the balance so that soil organic carbon 

levels are restored to higher levels. Some of the compost carbon is retained by the system. 

(2) Nitrogen in compost can stimulate higher productivity, thus generating more crop residues. 

This “fertilization effect” would increase soil carbon due to the larger volume of crop residues, 

which serve as organic matter inputs. 

(3) The composting process leads to increased formation of stable carbon compounds (e.g., humic 

substances, aggregates) that then can be stored in the soil for long (>50 years) periods of time. 

Humic substances comprise 60-80 percent of soil organic matter and are made up of complex 

compounds that render them resistant to microbial attack.7

 In addition to humic substances, 

soil organic carbon may be held in aggregates (i.e., stable organo-mineral complexes in which 

carbon is bonded with clay colloids and metallic elements) and protected against microbial 

attack.8

(4) 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 noncompost 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. 

Our research efforts did not yield any primary data that could be used to develop quantitative 

estimates of the soil carbon storage benefits of compost. Therefore, we developed modeling approaches to 

investigate the possible effects of compost application on soil carbon storage. Section 5.2.2 describes 

application of the CENTURY model to quantify soil carbon restoration and nitrogen fertilization

associated with compost application to carbon-depleted soils. We conducted a bounding analysis, 

described in Section 5.2.6, to address the third hypothesis, incremental humus formation. Although 

several of the experts we spoke with cited persuasive qualitative evidence of the existence of a multiplier 

effect, we were unable to develop an approach to quantify this process. In that sense, our carbon storage 

estimates are likely to be conservative (i.e., understate carbon storage rates), at least for soils with high 

silt and/or clay content where this process is most likely to apply. 

 7

 N. Brady and R. Weil. 1999. The Nature and Properties of Soils (Upper Saddle River, NJ: Prentice Hall). 

8

 R. Lal et al. 1998. The Potential of U.S. Cropland to Sequester Carbon and Mitigate the Greenhouse 

Effect (Ann Arbor, MI: Sleeping Bear Press, Inc). 
68 

Our analyses of soil carbon restoration, nitrogen fertilization, and incremental humus formation 

apply relatively simple models of very complex processes. These processes probably are controlled by a 

number of biological, physicochemical, and compost management factors, such as application (i.e., 

silviculture, horticulture, agriculture, and landscaping); application rate; regional and local climatic 

factors; soil type; and, to a lesser extent, compost feedstock (e.g., grass, leaves, branches, yard trimmings, 

food discards). In addition, the results are time-dependent, so the year in which benefits are assessed has 

an effect on the magnitude of carbon storage. 

Note that the framework used here describes the soil carbon benefits of composting relative to 

landfilling and combustion. In all three management methods, yard trimmings are collected and removed 

from soils in residential or commercial settings. This removal may result in some loss of organic carbon 

from the “home soil.” An estimate of the “absolute” soil carbon storage value would net out whatever loss 

occurs due to the removal of the yard trimmings. This effect is probably a negligible one, however, and 

we were unable to find empirical data on it. Because the decrement in carbon in “home soil” applies 

equally to all three management practices, and emission factors are intended to be viewed relative to other 

management practices (see Chapter 8), neglecting the carbon loss from the home soil does not 

compromise the validity of the results. 

5.2.1 Modeling Soil Carbon Restoration and Nitrogen Fertilization 

As mentioned above, this 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, we evaluated the feasibility of a simulation modeling 

approach. We initially identified two simulation models with the potential to be applied to the issue of 

soil carbon storage from compost application: CENTURY9

 and the Rothamsted C (ROTHC-26.3)10

model. Both are peer-reviewed models whose structure and application have been described in scores of 

publications. They share several features: 

• Ability to run multi-year simulations; 

• Capability to construct multiple scenarios covering various climate and soil conditions and 

loading rates; and 

• 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 United States, its availability on the Internet, and its 

ability to address many of the processes important to compost application, we decided to use CENTURY 

rather than ROTHC-26.3. 

5.2.2 CENTURY Model Framework 

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 due to the addition of compost. 

 9

 Metherell, A., L. Harding, C. Cole, W. Parton. 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). 

10 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. 
69 

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 CO2, 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,11 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)12 of a 

few months to 10 years.13 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.14 The passive pool of soil organic matter includes very stable materials remaining 

in the soil for hundreds to thousands of years.15

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, we 

developed a separate analysis, described in Section 5.2.6.) 

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. 

5.2.3 Inputs 

The CENTURY model simulates the long-term dynamics of various plant-soil ecosystems (e.g., 

grassland, agricultural land, forest, and savanna). The model uses a series of input files to specify 

modeling conditions: Crop, Harvest, Fertilization, Cultivation, Organic Matter Addition, Irrigation, 

Grazing, Fire, Tree Type, Tree Removal, Site, and Weather Statistics. A schedule file is used to specify 

the timing of events. 

For this analysis, we developed a basic agricultural scenario where land was converted from 

prairie to farmland (growing corn) in 1921 and remains growing corn through 2030. We then evaluated 

more than 30 scenarios to examine the effect of several variables on soil carbon storage: 

• Compost application rate and frequency; 

 11 Detritus refers to debris from dead plants and animals. 

12 The term “mean residence time (MRT)” is used interchangeably with “turnover time” and is the average 

time in which a unit (e.g., a carbon atom) resides within a “state” where there is both an input and an output. MRT 

is only strictly defined at steady-state (i.e., inputs = outputs), but as most soils systems have a continuing input of 

carbon and an approximately equal output through decomposition and transfer to other pools, MRT is often used to 

describe carbon dynamics in soils. Mathematically, it is the ratio of (a) mass in the pool to (b) throughput of carbon. 

For example, if a given carbon pool has a mass of 1,000 kg and the inflow is 1 kg/yr, the MRT is 1,000 kg / (1 

kg/yr) = 1,000 yr. 

13 Metherell et al. 1993, Brady and Weil 1999. 

14 Ibid.

15 Ibid.
70 

• Site characteristics (rainfall, soil type, irrigation regime); 

• Fertilization rate; and 

• Crop residue management. 

Compost application rates were adjusted using the organic matter (compost) files for each 

compost application rate included in our analysis. We 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 tons 

compost/acre (corresponding to 60-1,850 grams of carbon per square meter).16 We also investigated the 

effect of compost application frequency on the soil carbon storage rate and total carbon levels. We ran the 

model to simulate compost applications of 1.3 wet tons compost/acre and 3.2 wet tons compost/acre every 

year for 10 years (1996-2005) and applications of 1.3 wet tons compost/acre and 3.2 wet tons 

compost/acre applied every five years (in 1996, 2001, and 2006). The simulated compost was specified as 

having 33 percent lignin,17 17:1 carbon-to-nitrogen (C:N) ratio,18 60:1 carbon-to-phosphorus ratio, and 

75:1 carbon-to-sulfur ratio.19 We also ran a scenario with no compost application for each combination of 

site-fertilization-crop residue management. This scenario allowed us to control for compost application, 

i.e., to calculate the change in carbon storage attributable only to the addition of compost. 

The majority of inputs needed to specify a scenario reside in the site file. The input variables in 

this file include the following: 

• Monthly average maximum and minimum air temperature; 

• Monthly precipitation; 

• Lignin content of plant material; 

• Plant nitrogen, phosphorus, and sulfur content; 

• Soil texture; 

• Atmospheric and soil nitrogen inputs; and 

• Initial soil carbon, nitrogen, phosphorus, and sulfur levels. 

 16 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). Our 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. 

17 Percent lignin was estimated 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, “Biodegradative Analysis of Municipal Solid Waste in Laboratory-Scale 

landfills,” EPA 600/R-97-071, 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. 

18 The C:N ratio was taken from Brady and Weil, 1999, The Nature and Property of Soils: Twelfth Edition, 

Prentice Hall. 

19 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. 
71 

Several sets of detailed site characteristics from past modeling applications are available to users. 

We 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. The Colorado scenario is available as a site 

file on the CENTURY Web site;

20 Dr. Keith Paustian, an expert in the development and application of 

CENTURY, provided the specifications for the Iowa site (as well as other input specifications and results 

for several of the runs described here).

We also varied fertilization rate. As discussed earlier, one of our hypotheses was that the 

mineralization of nitrogen in compost could stimulate crop growth, leading to production of more organic 

residues, which in turn would increase soil organic carbon levels. The strength of this effect would vary

depending on the availability of other sources of nitrogen. To investigate this hypothesis, we analyzed 

different rates of synthetic fertilizer addition ranging from zero up to a typical rate to attain average crop 

yield (90 lbs. N/acre for the Colorado site, 124 lbs. N/per acre for the Iowa site). We also analyzed 

fertilizer application at half of these typical rates. 

Finally, we 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 us 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).

5.2.4 Outputs 

CENTURY is capable of providing a variety of output data, including carbon storage in soils, 

CO2 emissions due to microbial respiration, and monthly potential evapotranspiration. The outputs we 

chose were carbon levels for each of the eight soil pools: structural carbon in surface litter, metabolic 

carbon in surface litter, structural carbon in soil litter, metabolic carbon in soil litter, surface pool, active 

pool, slow pool, and passive pool. Our output data cover the period from 1900 through 2030. In general, 

we focussed on the difference in carbon storage between a baseline scenario, where no compost was 

applied, and a with-compost scenario. We calculated the delta between the two scenarios to isolate the 

effect of compost application. Output data in grams of carbon per square meter were converted to MTCE 

by multiplying by area (in square meters). 

To express results in units comparable to those for other sources and sinks, we divided the 

increase in carbon storage by the short tons of organics required to produce the compost.

21 That is, we 

express the factors as a carbon storage rate in units of MTCE per wet short ton of organic inputs (not 

MTCE per short ton of compost). 

5.2.5 Results 

The carbon storage rate declines with time after initial application. The rate is similar across application 

rates and frequencies, and across the site conditions we simulated. Exhibit 5-1 displays 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 per wet ton organics immediately after compost 

application (in 1997) to about 0.02 MTCE per ton in 2030 (24 years after the last application in 2006). 

 20 http://www.nrel.colostate.edu/PROGRAMS/MODELING/CENTURY/CENTURY.html

21 We assume 2.1 tons of yard trimmings are required to generate 1 ton of composted yard trimmings. 

Thus, to convert the results in this report (in MTCE per wet ton yard trimmings) to MTCE per wet ton of compost, 

multiply by 2.1. To convert to MTCE per dry ton compost, multiply values in this report by 4.2 (assuming 50 

percent moisture content). 
72 

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. 

Several secondary effects, however, result in some variation in the carbon storage rate.22 We had 

hypothesized that where a crop’s demand for nitrogen exceeds its availability from other sources, 

mineralization of compost nitrogen can stimulate increased productivity. Simulation of this effect showed 

that where there is a shortage of nitrogen, compost application can result in higher productivity, which 

translates into higher inputs of crop residues to the soil. These higher inputs in turn increase the carbon 

storage rate per unit of compost inputs. This effect is a relatively modest one, however. 

 22 In addition to the nitrogen fertilization effect, compost also affects moisture retention in soils, which in 

turn modifies the water balance relations simulated by CENTURY. 

Exhibit 5-1 Soil C Storage-CO and IA sites; 10, 20, and 40 tpa application rates

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

Year

MT C / ton Yard Trimmings

CO 10 tpa

CO 20 tpa

CO 40 tpa

IA 10 tpa

IA 20 tpa

IA 40 tpa
73 

Exhibit 5-2 shows the carbon storage rate for the Iowa site and the effect of nitrogen fertilization. 

The two curves in the exhibit both represent the difference in carbon storage between (a) a with-compost 

scenario (20 tons per acre) and (b) 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 scenario. Because the nitrogen added via 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. 

• 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. 

Exhibit 5-2 

Incremental Carbon Storage as a function of Nitrogen Application Rate 

Iowa site, corn harvested for grain

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

19971999200120032005200720092011201320152017201920212023202520272029

Year

MTCE/t YT

Typical N

application

Half N application
74 

The difference in incremental carbon storage rates between the two fertilization scenarios is less than 0.01 

MTCE per ton, indicating that the nitrogen fertilization effect is 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. 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, the effect would be larger. 

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 5-3). Soil organic carbon concentrations increase 

throughout the period of compost application, peak in 2006 (the last year of application), and decline 

thereafter due to decomposition of the imported carbon. Exhibit 5-3 displays 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 applications with a longer term). 

Exhibit 5-3 Total Soil C 

Iowa site, corn harvested for grain

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

1997

2000

2003

2006

2009

2012

2015

2018

2021

2024

2027

2030

Year

gm C / m2

0

10

20

30

40

50

60

70

metric tonnes C / acre

Baseline

1.3 tons/acre

20 tons/acre
75 

5.2.6 Incremental Humus Formation 

The third of the four hypotheses describing the benefits of composting, as compared to alternative 

management methods, is predicated on incremental formation of stable carbon compounds that can be 

stored in the soil for long periods of time. CENTURY does not simulate this process, i.e., it does not 

allow for organic matter additions with high humus content to directly increase the magnitude of the 

passive pool. Therefore, we used a bounding analysis to estimate the upper and lower limits of the 

magnitude of this effect. In this analysis, we evaluated the amount of long-term soil carbon storage when 

organics are composted and applied to soil. 

During the process of decomposition, organic materials typically go through a series of steps 

before finally being converted to CO2, water, and other reaction products. The intermediate compounds 

that are formed, and the lifetime of these compounds, can vary widely depending on a number of factors, 

including the chemical composition of the parent compound. Parent compounds range from readily 

degradable molecules such as cellulose and hemicellulose to molecules more resistant to degradation, 

such as lignin, waxes, and tannins. 

Composting is designed to promote rapid decomposition of organics, thus reducing their volume. 

Some evidence suggests that composting produces a greater proportion of humus than that typically 

formed when organics are left directly on the ground. The conditions in the two phases are different. The 

heat generated within compost piles favors ‘thermophilic’ (heat-loving) bacteria, which tend to produce a 

greater proportion of stable, long-chain carbon compounds (e.g., humic substances) than do bacteria and 

fungi that predominate at ambient soil temperatures. 

Increased humus formation associated with compost application is a function of two principal 

factors: 

(1) The fraction of carbon in compost that is considered “passive” (i.e., very stable); and 

(2) The rate at which passive carbon is degraded to CO2. 

Estimates for the first factor are based on experimental data compiled by Dr. Michael Cole of the 

University of Illinois. Dr. Cole found literature values indicating that between 4 and 20 percent of the 

carbon in finished compost degrades quickly.23 Dr. Cole averaged the values he found in the literature and 

estimated that 10 percent of the carbon in compost can be considered “fast” (i.e., readily degradable). The 

remaining 90 percent of carbon in compost can be classified as either slow or passive. We were not able 

to locate experimental data that delineates the fractions of slow and passive carbon in compost; therefore, 

we developed upper and lower bound estimates based on Dr. Cole’s professional judgement. He 

suggested values of 30 percent slow and 60 percent passive, and 45 percent slow and 45 percent passive 

for the upper and lower bounds on passive content, respectively.24 

For the second factor, we chose a mean residence time for passive carbon of 400 years based on 

the range of values specified in the literature.25

 23 Very little information is available on the characteristics of compost derived from yard trimmings or 

food discards. However, Dr. Cole found that the composition of composts derived from other materials is broadly 

consistent, suggesting that his estimates may be reasonably applied to yard trimmings or food scrap compost. 

24 We focussed only on the passive pool because (1) the CENTURY model does not allow for direct input 

of organic carbon into the passive pool, and (2) the model runs resulted in very little indirect (i.e., via other pools) 

formation of passive carbon. Although the first factor is also true for the slow pool, the second is not. Had we 

analyzed slow carbon in the same way as passive carbon, there would be potential for double-counting (see 

discussion in Section 5.3). 

25 Metherell et al. 1993, Brady and Weil 1999. 
76 

Combining the two bounds for incremental humus formation (60 percent passive and 45 percent 

passive), we estimated the incremental carbon storage implied by each scenario (see Exhibit 5-4). 

The upper bound on the incremental carbon storage from composting is more than 0.05 MTCE 

per ton of organics (shown in the top left of the graph); the lower bound is approximately 0.03 MTCE per 

ton (shown in the bottom right of the graph) after about 100 years. Incremental storage is sensitive to the 

fraction of carbon in compost that is passive but is not very sensitive to the degradation rate (within a 

100-year time horizon, over the range of rate constants appropriate for passive carbon). 

To select a point estimate for the effect of incremental humus formation, we took the average 

storage value across the two bounding scenarios, when time equals 10 years (i.e., approximately 2010). 

The resulting value is 0.05 MTCE/ton. The 2010 time frame was chosen for this analysis because the 

forest carbon estimates presented in Chapter 3 of this report are for the period ending in 2010. 

5.3 NET GHG EMISSIONS FROM COMPOSTING 

The approaches described in Section 5.2 were adopted to capture the range of carbon storage 

benefits associated with compost application. However, this dual approach creates the possibility of 

double counting. In an effort to eliminate double counting, we evaluated the way that CENTURY 

partitions compost carbon once it is applied to the soil. 

To do so, we ran a CENTURY model simulation of compost addition during a single year and 

compared the results to a corresponding reference case (without compost). We 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, we 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 5-5 shows, CENTURY does not introduce 

significant increments (over the base case) of recalcitrant carbon into the passive pool at any time. 

Exhibit 5-4 Incremental Carbon Storage: 

MTCE/wet ton vs time 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0 10 20 30 40 50 60 70 80 90 100

Time since compost application (years)

Incremental C Storage

(MTCE/ton)

Upper bound

Low er bound
77 

Based on our 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), 

we 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). 

Exhibit 5-6 shows the soil carbon storage and transportation-related emissions and sinks, and 

sums these to derive estimates of a net GHG emission factor, using the same sign convention as our 

broader analysis. A negative value denotes carbon storage; a positive value denotes emissions. 

Summing the values corresponding to typical application rate and the 2010 time frame for soil 

carbon restoration (-0.02 MTCE/ton), increased humus formation (-0.05 MTCE/ton), and transportation 

emissions (0.01 MTCE/ton), the result is –0.05 MTCE/ton.26

 26 The addends do not sum to the total, due to rounding. 

Exhibit 5-5 Difference in Carbon Storage Between Compost Addition and Base Case

yearly application with 20 tons compost 

0

100

200

300

400

500

600

700

800

900

1000

199019921994199619982000200220042006200820102012201420162018202020222024202620282030

Year

Carbon (g/m2)

Passive

Total Carbon
78 

5.4 LIMITATIONS 

Due to 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 associated with 

centralized composting of yard trimmings and food discards (henceforth, organics) under a limited set of 

scenarios. Our analysis was limited by the lack of primary research on carbon storage and CH4 generation 

associated with composting. The limited availability of data forced us to rely on two modeling 

approaches, each with its own set of limitations. In addition, our analysis was limited by the scope of the 

report, which is intended to present life-cycle GHG emissions of waste management practices for selected 

material types, including food discards and yard trimmings. 

5.4.1 Limitations of Modeling Approaches 

Due to data and resource constraints, we were 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 importantly, 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., presettlement grazing intensity, nitrogen 

input during soil development).27 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.28 In addition, the model is 

not designed to capture the hypothesis that, due to compost application, soil ecosystem dynamics change 

so that more carbon is stored than is actually being added to the soil (i.e., the multiplier effect). 

 27 Parton, W., D.Schimel, C. Cole, and D. Ojima. 1987. “Analysis of Factors Controlling Soil Organic 

Matter Levels in Great Plains Grasslands.” Soil Sci. Soc. Am. J. Vol. 51 (1173-1179). 

28 Paustian, K., W. Parton, and Jan Persson. 1992. “Modeling Soil Organic Matter in Organic-Amended 

and Nitrogen-Fertilized Long-Term Plots.” Soil Sci. Soc. Am. J. Vol. 56 (476-488). 

Increased 

Humus 

Formation

Transportation 

Emissions Net Carbon Flux

Unweighted

Proportion of 

C that is not 

passive

Weighted 

estimate

-0.04 48% -0.02 -0.05 0.01 -0.05

Soil Carbon Restoration

Exhibit 5-6

Net GHG Emissions from Composting

(In MTCE Per Short Ton of Yard Trimmings Composted)

Emission/ Storage Factor (for 2010)
79 

CENTURY simulates carbon movement through organic matter pools. Although the model is 

designed to evaluate additions of organic matter in general, to our knowledge it has not 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. We 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. We believe 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 appears to be the best available option. 

5.4.2 Limitations Related to the Scope of the Report 

As indicated above, this chapter presents our estimates of the GHG-related impacts of composting 

food discards and yard trimmings. These estimates were developed within the framework of the larger 

report; therefore, our presentation of results, estimation of emissions and sinks, and description of 

ancillary benefits was not comprehensive. The remainder of this section describes specific limitations of 

our compost analysis. 

As in the other chapters of this report, the GHG impacts of composting reported in this chapter 

are relative to other possible disposal options for yard trimmings (i.e., landfilling and combustion). In 

order 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, we would 

need to analyze the home soil. In particular, the carbon storage benefits of composting would need to be 

compared to the impact that removal of yard trimmings has on the home soil. 

As mentioned in Section 5.4.1, due to data and resource constraints, our analysis considers a 

small sampling of feedstocks and a single compost application (i.e., agricultural soil). We 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, we 

assumed that compost was applied to degraded agricultural soils, despite widespread use of compost in 

land reclamation, silviculture, horticulture, and landscaping. 

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 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.29

 29 R. Lal et al. 1998. The Potential of U.S. Cropland to Sequester Carbon and Mitigate the Greenhouse 

Effect (Ann Arbor, MI: Sleeping Bear Press, Inc). 
80 

As is the case in other chapters, the methodology used to estimate GHG emissions from 

composting did not allow for variations in transportation distances. We recognize 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 less and would 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, potentially would require more fuel because 

of the large distance separating the sites. 

Emission factors presented in this chapter do not capture the full range of possible GHG 

emissions from compost. Some of the nitrogen in compost is volatilized and released into the atmosphere 

as N2O shortly after application of the compost. Based on a screening analysis, we estimated N2O 

emissions to be less than 0.01 MTCE per wet ton of compost inputs and thus considered this effect to be 

negligible. 

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 report. Manufacturing these 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 we understand that compost is generally 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.30

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. As discussed earlier, nutrients in 

compost tend to foster soil fertility.31 In fact, composts have been used to establish plant growth on land 

previously unable to support vegetation. In addition to these biological improvements, compost also may 

lead to cost savings associated with avoided waste disposal, particularly for feedstocks such as sewage 

sludge and animal manure. 

 30 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. 

31 N. Brady and R. Weil. 1999. The Nature and Properties of Soils (Upper Saddle River, NJ: Prentice 

Hall). 
81 

6. COMBUSTION

This chapter presents estimates of the net GHG emissions from combustion of each of the 

materials considered in this analysis and several categories of mixed waste streams (e.g., mixed paper, 

mixed recyclables, and mixed MSW). Combustion of MSW results in emissions of CO2 (because nearly

all of the carbon in MSW is converted to CO2) and N2O. Note that CO2 from burning biomass sources 

(such as paper products and yard trimmings) is not counted as a GHG because it is biogenic (as explained 

in Section 1.4). 

Combustion of MSW with energy recovery in a waste-to-energy (WTE) plant also results in 

avoided CO2 emissions at utility and metals production facilities. First, the electricity produced by a WTE 

plant displaces electricity that would otherwise be provided by an electric utility power plant. Because 

most utility power plants burn fossil fuels and thus emit CO2, the electricity produced by a WTE plant 

reduces utility CO2 emissions. These avoided GHG emissions must be subtracted from the GHG 

emissions associated with combustion of MSW. Second, most MSW combusted with energy recovery in 

the United States is combusted in WTE plants that recover ferrous metals (e.g., steel) and non-ferrous 

materials (e.g., non-ferrous metals and glass).

1

 The recovered ferrous metals and non-ferrous materials 

then are recycled.2

 As discussed in Chapter 4, processes using recycled inputs require less energy than 

processes using virgin inputs. In measuring GHG implications of combustion, one also must account for 

the change in energy use due to recycling associated with metals recovery. 

WTE facilities can be divided into three categories: (1) mass burn, (2) modular, or (3) refusederived fuel (RDF). A mass burn facility generates electricity and/or steam from the combustion of mixed 

MSW. In the United States, about 70 mass burn facilities process approximately 21 million tons of MSW 

annually.

3

 Modular WTE plants generally are 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 (shredding and material 

recovery), which result in a finely divided fuel. Processing MSW into RDF yields a more uniform fuel 

that has a higher heating value than is produced by mass burn or modular WTE.

4

 In the United States, 

approximately 12 facilities process and combust RDF, 7 facilities combust RDF using off-site processing, 

 1 We did not consider any recovery of materials from the MSW stream that may occur before MSW is 

delivered to the combustor. We considered such prior recovery to be unrelated to the combustion operation—unlike 

recovery of steel from combustor ash, an activity that is an integral part of the operation of many combustors. 

2

 Note that material recovery at WTE facilities has increased in recent years, and this trend may continue as 

more facilities install material recovery systems. According to the Integrated Waste Services Association’s 2000 

Waste-to-Energy Directory of United States Facilities (www.wte.org), ferrous metal recovery at WTE facilities 

increased from more than 773,000 tons in 1999 to more than 788,000 tons in 2000. During the same period, on-site 

recycling more than doubled, from approximately 462,000 tons to 939,000 tons. 

3

 Integrated Waste Services Association, The 2000 IWSA Waste-To-Energy Directory of United States 

Facilities, Table 1. This estimate assumes that 92 percent of combustion system capacity gets utilized, per e-mail 

correspondence with Maria Zannes of IWSA (June 12, 2001). 

4 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. 
82 

and 7 facilities process RDF for combustion off-site. These 26 facilities process approximately 8 million 

tons of MSW annually.5

This study analyzed the net GHG emissions from combustion of mixed waste streams, and the 

following individual materials at mass burn and RDF facilities: 

• Aluminum Cans; 

• Steel Cans; 

• Glass Containers; 

• HDPE Plastic; 

• LDPE Plastic; 

• PET Plastic; 

• Corrugated Cardboard; 

• Magazines and Third-class Mail; 

• Newspaper; 

• Office Paper; 

• Phonebooks;6

• Textbooks;7

• Dimensional Lumber; 

• Medium-density Fiberboard; 

• Food Discards; and 

• Yard Trimmings.

Net emissions consist of (1) emissions of non-biogenic CO2 and N2O minus (2) avoided GHG 

emissions from the electric utility sector and from processing with recycled inputs (e.g., steel produced 

from recycled inputs requires less energy than steel from virgin inputs). There is some evidence that as 

combustor ash ages, it absorbs CO2 from the atmosphere. We did not count absorbed CO2, however, 

because we estimated the quantity to be less than 0.01 MTCE per ton of MSW combusted.8

 Similarly, the 

residual waste from processing MSW into RDF is typically landfilled. Some potential exists for the 

organic fraction of this residual waste to yield GHG emissions when landfilled. We did not count these 

emissions, however, because the quantity emitted is estimated to be less than 0.01 MTCE per ton of MSW 

processed into RDF.9

 5

 Integrated Waste Services Association, The 2000 IWSA Waste-To-Energy Directory of United States 

Facilities, Table 1. 

6

 Newspaper used as proxy, as material-specific data were unavailable. 

7

 Office paper used as proxy, as material-specific data were unavailable. 

8

 Based on data provided by Dr. Jurgen Vehlow, of the Institut fur Technische Chemie in Karlsruhe, 

Germany, we estimated that the ash from 1 ton of MSW would absorb roughly 0.004 MTCE of CO2. 

9

 Based on data provided by Karen Harrington, principal planner for the Minnesota Office of 

Environmental Assistance, we estimated that landfilling the residual waste would emit roughly 0.003 MTCE of CO2 
83 

The results showed that combustion of mixed MSW has small negative net GHG emissions (in 

absolute terms). Combustion of paper products, dimensional lumber, medium-density fiberboard, food 

discards, and yard trimmings results in negative net GHG emissions. Processing steel cans at a combustor, 

followed by recycling the ferrous metal, likewise results in negative net GHG emissions. Combustion of 

plastic produces positive net GHG emissions, and combustion of aluminum cans and glass results in small 

positive net GHG emissions. The reasons for each of these results are discussed in the remainder of this 

chapter.10

6.1 METHODOLOGY 

The study’s general approach was to estimate the (1) gross emissions of CO2 and N2O from MSW 

and RDF combustion (including emissions from transportation of waste to the combustor and ash from 

the combustor to a landfill) and (2) CO2 emissions avoided due to displaced electric utility generation and 

decreased energy requirements for production processes using recycled inputs.11 To obtain an estimate of 

the net GHG emissions from MSW and RDF combustion, we subtracted the GHG emissions avoided 

from the direct GHG emissions. We 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 

we developed these estimates. 

6.1.1 Estimating Direct CO2 Emissions from MSW Combustion 

The carbon in MSW has two distinct origins. Some of it is derived from sustainably harvested 

biomass (i.e., carbon in plant and animal matter that was converted from CO2 in the atmosphere through 

photosynthesis). The remaining carbon in MSW is from non-biomass sources, e.g., plastic and synthetic 

rubber derived from petroleum. 

For reasons described in Section 1.4, this study did not count the biogenic CO2 emissions from 

combustion of biomass. On the other hand, we did count CO2 emissions from combustion of non-biomass 

components of MSW—plastic, textiles, and rubber. Overall, only a small portion of the total CO2

emissions from combustion are counted as GHG emissions. 

For mixed MSW, we used the simplifying assumptions that (1) all carbon in textiles is nonbiomass carbon, i.e., petrochemical-based plastic fibers such as polyester (this is a worst-case 

assumption); and (2) the category of “rubber and leather” in EPA’s MSW characterization report12 is 

composed almost entirely of rubber. Based on these assumptions, this study estimated that there are 0.11 

pounds of non-biogenic carbon in the plastic, textiles, rubber, and leather contained in 1 pound of mixed 

MSW.13 We assumed that 98 percent of this carbon would be converted to CO2 when the waste is 

 

per ton of MSW processed into RDF. Facsimile from Karen Harrington, Minnesota Office of Environmental 

Assistance to ICF Consulting, October 1997. 

10 Note that Exhibits 6-1, 6-2, and 6-5 do not show mixed paper. Mixed paper is shown in the summary 

exhibit (Exhibit 6-6). The summary values for mixed paper are based on the proportions of the four paper types 

(newspaper, office paper, corrugated cardboard, and magazines/third-class mail) that comprise the different “mixed 

paper” definitions. 

11 A comprehensive evaluation also would consider the fate of carbon remaining in combustor ash. 

Depending on its chemical form, carbon may be aerobically degraded to CO2, anaerobically degraded to CH4, or 

remain in a relatively inert form and be stored. Unless the ash carbon is converted to CH4 (which we considered to 

be unlikely), the effect on the net GHG emissions would be very small. 

12 U.S. EPA Office of Solid Waste. 2002. Municipal Solid Waste in the United States: 2000 Facts and 

Figures. EPA 530-R-02-001. 

13 ICF Consulting. 1995. Memorandum. “Work Assignment 239, Task 2: Carbon Sequestration in 

Landfills,” April 28, Exhibit 2-A, column “o.” 
84 

combusted, with the balance going to the ash. Then we converted the 0.11 pounds of non-biomass carbon 

per pound of mixed MSW to units of MTCE per ton of mixed MSW combusted. The resulting value for 

mixed MSW is 0.10 MTCE per ton of mixed MSW combusted,14 as shown in Exhibit 6-1. 

The study estimated that HDPE and LDPE are 84 percent carbon, while PET is 57 percent carbon 

(based on a moisture content of 2 percent). We assumed that 98 percent of the carbon in the plastic is 

converted to CO2 during combustion. The values for CO2 emissions, converted to units of MTCE per ton 

of plastic combusted, are shown in column “b” of Exhibit 6-1. 

6.1.2 Estimating N2O Emissions from Combustion of Waste 

Studies compiled by the Intergovernmental Panel on Climate Change (IPCC) show that MSW 

combustion results in measurable emissions of N2O, a GHG with a high global warming potential 

(GWP).15 The IPCC compiled reported ranges of N2O 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 MTCE of N2O per short ton of MSW. The resulting estimate is 0.01 MTCE of N2O emissions 

per ton of mixed MSW combusted. Because the IPCC did not report N2O values for combustion of 

individual components of MSW, we used the 0.01 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.16

6.1.3 Estimating Indirect CO2 Emissions from Transportation of Waste to the WTE Plant 

Next, this study estimated the indirect CO2 emissions from the transportation of waste. For the 

indirect CO2 emissions from transporting waste to the WTE plant, and ash from the WTE plant to a 

landfill, we used an estimate for mixed MSW developed by Franklin Associates, Ltd. (FAL).17 We then 

converted the FAL estimate from pounds of CO2 per ton of mixed MSW to MTCE per ton of mixed 

MSW. This resulted in an estimate of 0.01 MTCE of CO2 emissions from transporting 1 ton of mixed 

MSW and the resulting ash. We assumed that transportation of any individual material in MSW would 

use the same amount of energy as transportation of mixed MSW. 

6.1.4 Estimating Gross GHG Emissions from Combustion 

To estimate the gross GHG emissions per ton of waste combusted, we summed the values for 

emissions from combustion CO2, combustion N2O, and transportation CO2. The gross GHG emission 

estimates, for mixed MSW and for each individual material, are shown in column “e” of Exhibit 6-1. 

6.1.5 Estimating Utility CO2 Emissions Avoided 

Most WTE plants in the United States produce electricity. Only a few cogenerate electricity and steam. In 

this analysis, we assumed that the energy recovered with MSW combustion would be in the form of 

electricity. This analysis is shown in Exhibit 6-2. We used three data elements to estimate the avoided 

electric utility CO2 emissions associated with combustion of waste in a WTE plant: (1) the energy

 14 Note that if we had used a best-case assumption for textiles, i.e., assuming they have no petrochemicalbased fibers, the resulting value for mixed MSW would have been 0.09 MTCE per ton of mixed MSW combusted. 

15 Intergovernmental Panel on Climate Change, Greenhouse Gas Inventory Reference Manual, Volume 3,

(undated) p. 6-33. The GWP of N2O is 310 times that of CO2. 

16 This exception was made because at the relatively low combustion temperatures found in MSW 

combustors, most of the nitrogen in N2O emissions is derived from the waste, not from the combustion air. Because 

aluminum and steel cans do not contain nitrogen, we concluded that running these metals through an MSW 

combustor would not result in N2O emissions. 

17 Franklin Associates, Ltd. 1994. The Role of Recycling in Integrated Solid Waste Management to the 

Year 2000 (Stamford, CT: Keep America Beautiful, Inc.), p. I-24. 
85 

(a) (b) (c) (d) (e)

 Material Combusted

Combustion CO2 

Emissions From 

Non-Biomass 

Per Ton 

Combusted

 Combustion 

N2O Emissions 

Per Ton 

Combusted

 Transportation 

CO2 Emissions 

Per Ton 

Combusted

 (e = b + c + d) 

Gross GHG 

Emissions Per 

Ton Combusted

Aluminum Cans 0.00 0.00 0.01 0.0

Steel Cans 0.00 0.00 0.01 0.0Glass 0.00 0.00 0.01 0.0HDPE 0.76 0.00 0.01 0.7LDPE 0.76 0.00 0.01 0.7PET 0.56 0.00 0.01 0.5Corrugated Cardboard 0.00 0.01 0.01 0.0

Magazines/Third-class Mail 0.00 0.01 0.01 0.0

Newspaper 0.00 0.01 0.01 0.0Office Paper 0.00 0.01 0.01 0.0Phonebooks 0.00 0.01 0.01 0.0Textbooks 0.00 0.01 0.01 0.0Dimensional Lumber 0.00 0.01 0.01 0.0Medium-density Fiberboard 0.00 0.01 0.01 0.0

Food Discards 0.00 0.01 0.01 0.0Yard Trimmings 0.00 0.01 0.01 0.0

Mixed MSW 0.10 0.01 0.01 0.1Carpet 0.47 0.00 0.01 0.4Personal Computers 0.75 0.00 0.01 0.7

Note that totals may not add due to rounding, and more digits may be displayed than are significant.Note that Exhibits 6-1, 6-2, and 6-5 show coated paper but not mixed paper; mixed paper is shown in the summary exhibit (Exhibit 6-6).The summary values for mixed paper are based on the proportions of the four paper types (newspaper, office paper, corrugated cardboard, and coated paper) that comprise the different "mixed paper" definitions. The values for phone books and textbooks are proxies, based on newspaper and office paper, respectively. 

Exhibit 6-1

Gross Emissions of GHGs from MSW Combustion (MTCE/Ton)
86 

(a) (c) (d) (e) (f)

Material Combusted

Energy Content (Btu 

Per Pound)

Energy 

Content 

(Million Btu Per 

Ton)

Mass Burn 

Combustion 

System 

Efficiency 

(Percent)

RDF 

Combustion 

System 

Efficiency 

(Percent)

Emission Factor for 

Utility-Generated 

Electricity (MTCE/

Million Btu of 

Electricity 

Delivered)

(g = c * d * f

Avoided Utility 

Per Ton Combu

at Mass Bur

Facilities (MTC

Aluminum Cans -335 a -0.7 17.8% 16.3% 0.081 -0.01 * Steel Cans -210 a -0.4 17.8% 16.3% 0.081 -0.01 * Glass -235 a -0.5 17.8% 16.3% 0.081 -0.01 * HDPE 18,687 b 37.4 17.8% 16.3% 0.081 0.54 LDPE 18,687 b 37.4 17.8% 16.3% 0.081 0.54 PET 9,702 c,d 19.4 17.8% 16.3% 0.081 0.28 Corrugated Cardboard 7,043 b 14.1 17.8% 16.3% 0.081 0.20 

Magazines/Third-class Mail 5,258 d 10.5 17.8% 16.3% 0.081 0.15 

Newspaper 7,950 b 15.9 17.8% 16.3% 0.081 0.23 

Office Paper b,f 13.6 17.8% 16.3% 0.081 6,800 0.20 

Phonebooks 7,950 g 15.9 17.8% 16.3% 0.081 0.23 

Textbooks 6,800 h 13.6 17.8% 16.3% 0.081 0.20 

Dimensional Lumber 8,300 i 16.6 17.8% 16.3% 0.081 0.24 

Medium-density Fiberboard 8,300 i 16.6 17.8% 16.3% 0.081 0.24 

Food Discards 2,370 b 4.7 17.8% 16.3% 0.081 0.07 

Yard Trimmings 2,800 j 5.6 17.8% 16.3% 0.081 0.08 

Mixed MSW** 5,000 k 10.0 17.8% 16.3% 0.081 0.14 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.* The amount of energy absorbed by 1 ton of steel, aluminum cans, or glass in an MSW combustor would, if not absorbed, result in less than 0.01 MTCEof avoided utility CO2.** Mixed MSW represents the entire waste stream as disposed of.a We developed these estimates based on data on the specific heat of aluminum, steel, and glass and calculated the energy required to raise the  of aluminum, steel, and glass from ambient temperature to the temperature found in a combustor (about 750° Celsius). We obtained the 

 specific heat data from Incropera, Frank P.and David P. DeWitt, Introduction to Heat Transfer, Second Edition (New York: John Wiley & Sons) 1

b MSW Fact Book.c Gaines and Stodolsky.d For PET plastic, we converted the value of 9,900 Btu/pound dry weight, to 9,702 Btu/pound wet weight, to account for a moisture content of 2 pee We used Franklin Associates, Ltd.'s value for magazines as a proxy for the value for coated paper.f We used the MSW Fact Book's value for mixed paper as a proxy for the value for office paper.g. We used newspapers as a proxy for phonebooks. h We used office paper as a proxy for textbooks.i We used the higher end of the Btu factor for Basswood from the USFS. Basswood is a relatively soft wood so its high end Btu content should be  similar to an average factor for all wood types. Fons, W. L.; Clements, H. B.; Elliott, E. R.; George, P. M. 1962. Project Fire Model. Summary Pr Report-II. Period May 1, 1960, to April 30, 1962. Macon, GA: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Sta

 Forest Fire Laboratory. 58 p. [16824]j Procter and Redfern, Ltd. and ORTECH International.k Telephone conversation among IWSA, American Ref-Fuel, and ICF Consulting, October 28, 1997.

(b) (g) 

Exhibit 6-2

Avoided Utility GHG Emissions from Combustion at Mass Burn and RDF Facilities
87 

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 CO2 emissions 

avoided per kilowatt-hour of electricity delivered by WTE plants. 

Energy content: For the energy content of mixed MSW, we used a value of 5,000 Btu per pound 

of mixed MSW combusted, which is a value commonly used in the WTE industry.18 This estimate is 

within the range of values (4,500 to 6,500 Btu per pound) reported by FAL19 and is slightly higher than 

the 4,800 Btu per pound value reported in EPA’s MSW Fact Book.

20 For the energy content of RDF, we 

used a value of 5,700 Btu per pound of RDF combusted.21 This estimate is within the range of values 

(4,800 to 6,400 Btu per pound) reported by the DOE’s National Renewable Energy Laboratory (NREL).22

For the energy content of specific materials in MSW, we consulted three sources: (1) EPA’s MSW Fact 

Book (a compilation of data from primary sources), (2) a report by Environment Canada,23 and (3) a 

report by Argonne National Laboratories.24 We assume 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 implied this but did not explicitly state it). The Argonne study reported energy content on a dry 

weight basis. 

Combustion system efficiency: To estimate the combustion system efficiency of mass burn 

plants, we used a net value of 550 kilowatt-hours (kwh) generated by mass burn plants per ton of mixed 

MSW combusted.25 To estimate the combustion system efficiency of RDF plants, we evaluated three 

sources: (1) data supplied by an RDF processing facility located in Newport, Minnesota; (2) the 

Integrated Waste Services Association (IWSA) report Waste-to-Energy Directory: Year 2000; and (3) the 

U.S. Department of Energy’s (DOE) National Renewable Energy Laboratory. We used the Newport 

Processing Facility’s reported net value of 572 kwh generated per ton of RDF for two reasons.26 First, 

 18 Telephone conversation among representatives of Integrated Waste Services Association, American RefFuel, and ICF Consulting, October 28, 1997. 

19 Franklin Associates, Ltd. 1994. The Role of Recycling in Integrated Solid Waste Management to the 

Year 2000 (Stamford, CT: Keep America Beautiful, Inc.), pp. 1-16. 

20 U.S. Environmental Protection Agency, Office of Solid Waste. 1995. MSW Fact Book, Version 2.0

(Washington, D.C.: U.S. Environmental Protection Agency). 

21 Note that this is a value reported by an RDF facility located in Newport, Minnesota; the data were 

provided by the Minnesota Office of Environmental Assistance. Facsimile from Karen Harrington, Minnesota 

Office of Environmental Assistance to ICF Consulting, October 1997. 

22 U.S. Department of Energy, National Renewable Energy Laboratory. 1992. Data Summary of Municipal 

Solid Waste Management Alternatives Volume IV: Appendix B - RDF Technologies (Springfield, VA: National 

Technical Information Service, NREL/TP-431-4988D), p. B-5. 

23 Procter and Redfern, Ltd. and 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). 

24 Gaines, Linda, and Frank Stodolsky. 1993. Mandated Recycling Rates: Impacts on Energy Consumption 

and Municipal Solid Waste Volume (Argonne, IL: Argonne National Laboratory), pp. 11 and 85. 

25 Note that this is the value reported by Integrated Waste Services Association in its comments to the draft 

version of the first edition of this report. This value is within the range of values reported by others in response to 

this draft. Letter received from Maria Zannes, Integrated Waste Services Association, Washington, DC, August 25, 

1997. 

26 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. 
88 

this value is within the range of values reported by the other sources. Second, the Newport Processing 

Facility provided a complete set of data for evaluating the overall system efficiency of RDF plants.27

Next, we considered losses in transmission and distribution of electricity. Using a transmission 

and distribution loss rate of 5 percent,28 we 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 

We then used the value for the delivered kwhs 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 first estimated the Btu of MSW needed to deliver 

1 kwh of electricity. We divided the Btu per ton of waste by the delivered kwh per ton of waste to obtain 

the Btu of waste per delivered kwh. The result is 19,200 Btu per kwh for mass burn and 21,000 Btu per 

kwh for RDF. Next we divided the physical constant for the energy in 1 kwh (3,412 Btu) by the Btu of 

MSW and RDF needed to deliver 1 kwh, to estimate the total system efficiency at 17.8 percent for mass 

burn and 16.3 percent for RDF (Exhibit 6-2, columns “d” and “e”).29 

Electric utility carbon emissions avoided: To estimate the avoided utility CO2 from waste 

combustion, we used the results in columns “c” and “d,” together with a “carbon coefficient” of 0.081 

MTCE emitted per million Btu of utility-generated electricity (delivered), based on the national average 

fossil fuel mix used by utilities30 as shown in Exhibits 6-3 and 6-4. This approach uses the average fossil 

fuel mix as a proxy for the fuels displaced at the margin when utility-generated electricity is displaced by 

electricity from WTE plants. In other words, we assume that nuclear, hydropower, and other non-fossil 

sources generate electricity at essentially fixed rates; marginal demand is met by fossil sources.31 The 

actual carbon reductions could vary depending on which type of fuel used to generate electricity is 

displaced at the margin. The resulting estimates for utility carbon emissions avoided for each material are 

shown in columns “g” and “h” of Exhibit 6-2. 

6.1.6 Approach to Estimating CO2 Emissions Avoided Due to Increased Steel Recycling 

Next, the study estimated the avoided CO2 emissions from increased steel recycling made 

possible by steel recovery from WTE plants for (1) mixed MSW and (2) steel cans. Note that we did not 

credit increased recycling of non-ferrous materials, because of lack of data on the proportions of those 

materials being recovered. The result tends to overestimate net GHG emissions from combustion. 

For mixed MSW, we 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 83 WTE facilities in the United States 

 27 The data set included 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. 

28 Personal communication among representatives of Integrated Waste Services Association, American 

Ref-Fuel, and ICF Consulting, October 28, 1997. 

29 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. The losses in delivering electricity are the transmission and distribution losses, estimated at 5 percent. 

30 Value estimated using data from the Energy Information Administration, Annual Energy Review 2000

(Washington, DC: U.S. Government Printing Office, DOE/EIA-0384(2000)) August 2001. 

31 Non-fossil sources are expected to meet baseload energy requirements because of the financial incentive 

for these energy sources to generate at capacity. In general, the marginal cost of producing more power from these 

sources is minimal compared to the capital costs associated with establishing the facility. 
89 

and at seven RDF processing facilities that do not generate power on-site. These facilities recovered a 

total of nearly 789,000 tons per year of ferrous metals in 2000.32 By dividing 789,000 tons (total U.S. 

steel recovery at combustors) by total U.S. combustion of MSW, which is approximately 30 million tons, 

we estimated that 0.03 tons of steel are recovered per ton of mixed MSW combusted (as a national 

average). 

For steel cans, we first estimated the national average proportion of steel cans entering WTE 

plants that would be recovered. As noted above, approximately 90 percent of MSW destined for 

combustion goes to facilities with a ferrous recovery system. At these plants, approximately 98 percent of 

the steel cans would be recovered. We multiplied these percentages to estimate the weight of steel cans 

recovered per ton of steel cans combusted—about 0.88 tons per ton. 

Finally, to estimate the avoided CO2 emissions due to increased recycling of steel, we multiplied 

(1) the weight of steel recovered by (2) the avoided CO2 emissions per ton of steel recovered. The result 

was an estimated avoided CO2 emissions of approximately 0.43 MTCE per ton for steel cans and 0.01 

MTCE per ton for mixed MSW, as shown in column “d” of Exhibit 6-5. 

 32 Integrated Waste Services Association, The 2000 IWSA Waste-To-Energy Directory of United States 

Facilities.
90 

Estimating the Emission Factor for Utility-Generated Electricity

Value Value

17.5

3.10.8

21.4

2.9E+11

6,2681,692

290

72

2,0542,0546,268

33%

9%

91%30%

All Fuels Fossil Fuels Only

16.38 24.04

0.01638 0.02404

30% 30%

0.05493 0.08060

Net Generation: Before Transmission and Distribution Losses (Fossil Fuels Only)

Sour

Total (Quadrillion Btu)Energy Value of one Quadrillion Btu (measured in Kilowatt-hours)Total (Billion kwh)Electric Utility Consumption of Fossil Fuels to Generate ElectricityCoal (Quadrillion Btu)Natural Gas (Quadrillion Btu)Petroleum (Quadrillion Btu)Coal (Billion kwh)Natural Gas (Billion kwh)Petroleum (Billion kwh)Total (Billion kwh) Generation Efficiency (Fossil Fuels Only)Generation Output (Billion kwh)Consumption (Billion kwh)Efficiency (Percent)Efficiency of Energy Conversion From Fossil Fuels to Delivered ElectricityTransmission and Distribution Losses (TDL) (Percent)Delivered Electricity Efficiency (Percent)Efficiency of Energy Conversion and Delivery for Fossil Fuels (Percent)Estimated Emission Factor for Delivered Electricity (MTCE/MBtu of Electricity Delivered)Weighted Average Emission Factor of the U.S. Mix of Fuels Used to Generate Electricity  (Kilograms of Carbon in CO2 per Million Btu Consumed)

Weighted Average Emission Factor (MTCE/million Btu)Efficiency of Energy Conversion and Delivery (Percent)Emission Factor for Delivered Electricity (MTCE/million Btu)

DOE, EIA, "Annual Energy Review: 2000DOE, EIA, "Annual Energy Review: 2000DOE, EIA, "Annual Energy Review: 2000The sum of coal, natural gas, and petroleDOE, EIA, "Form EIA 1605 (1997)," Appe(21.44 Quad Btu) x (2.92875x1011 kWh/Q

DOE, EIA, "Annual Energy Review: 2000DOE, EIA, "Annual Energy Review: 2000DOE, EIA, "Annual Energy Review: 2000The sum of coal, natural gas, and petroleCalculated above.Weighted Average Emission Factor (MTCEfficiency.

Exhibit 6-3

Generation Efficiency x Delivered ElectricExhibit 6-4 of this report.Converting kilograms of carbon (kg C) to Calculated above.Calculated above.Generation Output / Consumption, i.e. 2,DOE, EIA, "Annual Energy Review: 2000Calculated as 100 percent (Deliverable E
91 

Estimating the Weighted Average Carbon Coefficient of the 

U.S. Average Mix of Fuels Used to Generate Electricity

(MTCE/Million Btu)

Fuel

Net Generation* 

(Billion kwh)

Percentage of Generation: 

All Fuels (%)

Percentage of 

Generation: Fossil 

Fuels (%)

Co

(Kg 

Pe

C

Coal 1,692 56.1% 82% Natural Gas 290 9.6% 14% Petroleum*** 72 2.4% 4% Nuclear 705 23.4% Hydroelectric 253 8.4% Other 2 0.1% Total 3,015 100% 100% Weighted Average - All Fuels Weighted Average - Fossil Fuels 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.* Source: EIA's Annual Energy Review: 2000, "Table 8.3 Electricity Net Generation at Electric Utilities, 1949-2000," for 2000.

** Values include fugitive methane emissions (weighted by the GWP of methane).*** The carbon coefficient for residual fuel is used as a proxy for petroleum.

Exhibit 6-4
92 

(a) (b) (c) (d) 

Material Combusted

Tons of Steel 

Recovered Per 

Ton of Waste 

Combusted 

(Tons)

Avoided CO2 

Emissions Per 

Ton of Steel 

Recovered 

(MTCE/Ton)

Avoided CO2 

Emissions Per 

Ton of Waste 

Combusted 

(MTCE/Ton)

Aluminum Cans 0.00 0.00 0.00

Steel Cans 0.88 0.49 0.43Glass 0.00 0.00 0.00HDPE 0.00 0.00 0.00LDPE 0.00 0.00 0.00PET 0.00 0.00 0.00Corrugated Cardboard 0.00 0.00 0.00

Magazines/Third-class Mail 0.00 0.00 0.00

Newspaper 0.00 0.00 0.00

Office Paper 0.00 0.00 0.00Phonebooks 0.00 0.00 0.00Textbooks 0.00 0.00 0.00Dimensional Lumber 0.00 0.00 0.00Medium-density Fiberboard 0.00 0.00 0.00

Food Discards 0.00 0.00 0.00Yard Trimmings 0.00 0.00 0.00

Mixed MSW 0.02 0.49 0.01

Note that totals may not add due to rounding, and more digits may be displayed than are significant.*The value in column "d" is a national average and is weighted to reflect 98 percent recovery at the90 percent of facilities that recover ferrous metals.

Avoided GHG Emissions Due to Increased Steel Recovery from 

MSW at WTE Facilities

Exhibit 6-5
93 

6.2 RESULTS 

The results of this analysis are shown in Exhibit 6-6. The results from the last columns of 

Exhibits 6-1, the last two columns of Exhibit 6-2, and the last column of Exhibit 6-3 are shown in 

columns “b” through “e” in Exhibit 6-6. 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. 

They are not values relative to some other waste management option. They are expressed in terms of short 

tons of waste input (i.e., tons of waste prior to processing). 

We estimate that combustion of mixed MSW at mass burn and RDF facilities reduces net postconsumer GHG emissions to -0.04 and -0.03 MTCE per ton, respectively. Combustion of paper products 

has negative net post-consumer GHG emissions ranging from -0.14 to -0.22 MTCE per ton at mass burn 

facilities and from -0.13 to -0.20 MTCE per ton at RDF facilities. Net GHG emissions are negative 

because CO2 emissions from burning paper are not counted (because they are biogenic) and fossil fuel 

burning by utilities to generate electricity is avoided. Likewise, combustion of medium-density fiberboard 

and dimensional lumber also results in negative net GHG emissions, with both equaling -0.23 MTCE at 

mass burn facilities and -0.21 at RDF facilities. Finally, net GHG emissions for food discards and yard 

trimmings (two other forms of biomass) are also negative, but of a smaller magnitude (-0.05 and -0.07 

MTCE per ton of material, respectively, for mass burn and -0.05 and -0.06 MTCE per ton of material, 

respectively, for RDF). 

Combustion of plastics results in substantial net GHG emissions, estimated from 0.21 to 0.27 

MTCE per ton of material combusted for mass burn facilities, and from 0.25 to 0.30 MTCE per ton of 

material input to RDF facilities. This result is primarily because of the high content of non-biomass 

carbon in plastics. Also, when combustion of plastic 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 plastic. 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, estimated at -0.42 MTCE per ton of steel cans, due to the increased steel 

recycling made possible by ferrous metal recovery at WTE plants. 

6.3 LIMITATIONS OF THE ANALYSIS 

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 CO2 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 a daily, seasonal, and annual 

basis. Thus, the values used for the RDF analysis should be interpreted as approximate 

values.
94 

(a) (b) (c) (d) (e) (f) 

(f = b - c - 

Material Combusted

Gross GHG 

Emissions Per 

Ton Combusted 

(MTCE/Ton)

Avoided Utility 

CO2 Per Ton 

Combusted at 

Mass Burn 

Facilities 

(MTCE/Ton)

Avoided Utility 

CO2 Per Ton 

Combusted at 

RDF Facilities 

(MTCE/Ton)

Avoided CO2 

Emissions Per 

Ton Combusted 

Due to Steel 

Recovery 

(MTCE/Ton)

Net GHG 

Emissions f

Combustio

Mass Bur

Facilities 

(MTCE/To

Aluminum Cans 0.01 -0.01 -0.01 0.00 Steel Cans 0.01 -0.01 -0.01 0.43 Glass 0.01 -0.01 -0.01 0.00 HDPE 0.77 0.54 0.49 0.00 LDPE 0.77 0.54 0.49 0.00 PET 0.56 0.28 0.25 0.00 Corrugated Cardboard 0.02 0.20 0.18 0.00 

Magazines/Third-class Mail 0.02 0.15 0.14 0.00 

Newspaper 0.02 0.23 0.21 0.00 Office Paper 0.02 0.20 0.18 0.00 

Phonebooks 0.02 0.23 0.21 0.00 Textbooks 0.02 0.20 0.18 0.00 Dimensional Lumber 0.02 0.24 0.22 0.00 Medium-density Fiberboard 0.02 0.24 0.22 0.00 

Yard Trimmings 0.02 0.08 0.07 0.00 

Food Discards 0.02 0.07 0.06 0.00 Mixed Paper

 Broad Definition 0.02 0.20 0.19 NA  Residential Definition 0.02 0.20 0.18 NA  Office Paper Definition 0.02 0.19 0.17 NA 

Mixed MSW 0.12 0.14 0.13 0.01 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.

Exhibit 6-6

Net GHG Emissions from Combustion at WTE Facilities
95 

The reported ranges for N2O 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 N2O 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 an approximate value. 

• 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 we assumed that all carbon in textiles is non-biogenic, we counted all 

of the CO2 emissions from combustion of textiles as GHG emissions. 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. 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, we 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). We 

did not credit increased recycling of non-ferrous 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 used the national average fossil fuel mix for electricity as the proxy for fuel 

displaced at the margin when WTE plants displace utility electricity. If some other fuel or 

mix of fuels is displaced at the margin (e.g., coal), the avoided utility CO2 would be different 

(e.g., for coal, the avoided utility CO2 would be about 0.01 MTCE per ton higher for mixed 

MSW, and the net GHG emissions would be -0.05 MTCE instead of -0.04 MTCE per ton). 
96 

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97 

7. LANDFILLING

This chapter presents estimates of GHG emissions and carbon storage from landfilling the 

materials considered in this analysis. For this study, we estimated the CH4 emissions, transportationrelated CO2 emissions, and carbon storage that will result from landfilling each type of organic waste and 

mixed MSW. The GHG accounting principles used in the analysis follow.1

• When food discards, yard trimmings, paper, and wood are landfilled, anaerobic bacteria 

degrade the materials, producing CH4 and CO2. CH4 is counted as an anthropogenic GHG, 

because even though it is derived from sustainably harvested biogenic sources, degradation 

would not result in CH4 emissions if not for deposition in landfills. The CO2 is not counted as 

a GHG in this context because if it were not emitted from landfills, it would be produced 

through natural decomposition. Because metals do not contain carbon, they do not generate 

CH4 when landfilled. Plastics do not biodegrade, and therefore do not generate any CH4. 

• Transportation of waste materials to a landfill results in anthropogenic CO2 emissions, due to 

the combustion of fossil fuels in the vehicles used to haul the wastes. 

Because food discards, yard trimmings, and paper are not completely decomposed by 

anaerobic bacteria, some of the carbon in these materials is stored in the landfill. Because this 

carbon storage would not normally occur under natural conditions (virtually all of the organic 

material would degrade to CO2, completing the photosynthesis/respiration cycle), this is 

counted as an anthropogenic sink.2

 

We developed separate estimates of emissions from landfills without gas recovery systems, those 

that flare CH4, those that combust CH4 for energy recovery, and from the national average mix of these 

three categories. Our national average emission estimate accounts for the extent to which CH4 will be 

flared at some landfills and combusted for energy recovery at others.3

 

From the standpoint of post-consumer GHG emissions, landfilling some materials—including 

magazines/third-class mail, newspaper, phonebooks, dimensional lumber, medium-density fiberboard, 

leaves, and branches—results in net storage (i.e., carbon storage exceeds CH4 plus transportation energy 

emissions) at all landfills, regardless of whether gas recovery is present. At the other extreme, office 

paper, textbooks, and food discards result in net emissions regardless of landfill gas collection and 

recovery practices. The remaining materials have net post-consumer emissions that are either very low 

(aluminum, steel cans, and plastics have transportation-related emissions of 0.01 MTCE per ton, 

regardless of whether gas collection is present) or borderline, depending on whether the landfill has gas 

recovery (e.g., mixed MSW has net emissions at landfills without gas recovery, but net carbon storage at 

landfills with gas recovery). 

 1

 These principles are described in broad terms in Section 1.5 of this report. 

2

 However, carbon in plastic that remains in the landfill is not counted as stored carbon, because it is of 

fossil origin. 

3

 Currently, most landfill CH4 recovery in the United States—both for flaring and electricity—is occurring 

in response to a 1996 EPA rule that requires a well-designed and well-operated landfill gas collection system at 

landfills that (1) have a design capacity of at least 2.5 million metric tons or 2.5 million cubic meters; (2) are 

calculated to emit more than 50 metric tons of non-CH4 organic compounds per year; and (3) received waste on or 

after November 11, 1987 (Federal Register, Vol. 61, No. 49, p. 9905, March 12, 1996). For the year 2000, an 

estimated 43 percent of landfill CH4 was generated at landfills with landfill gas recovery systems subject to these 

requirements or installed on a voluntary basis. 
98 

7.1 EXPERIMENTAL VALUES FOR CH4 GENERATION AND CARBON STORAGE 

To estimate CH4 emissions and carbon storage from landfilling of specific materials, we used data 

from laboratory experiments conducted by Dr. Morton Barlaz.4

 The experiments provided data on (1) the 

amount of CH4 generated by each type of organic material, when digested by bacteria in anaerobic 

conditions simulating those in a landfill; and (2) the amount of carbon remaining, undecomposed (i.e., 

stored) at the end of the experiment. 

7.1.1 Experimental Design 

Dr. Barlaz placed each type of organic waste and mixed MSW in separate reactor vessels, in 

which he maintained anaerobic conditions similar to those in a landfill, but controlled to favor maximum 

CH4 generation. Dr. Barlaz measured the amount of CH4 generated in each reactor and the amount of 

undecomposed carbon remaining in each reactor at the end of the experiment. Each material was tested in 

four reactors, and the results from each reactor were averaged.5

At the start of the experiment, Dr. Barlaz dried a sample of each material and analyzed the 

amount of cellulose, hemicellulose, and lignin (and, for food discards, protein) in each material. 

Cellulose, hemicellulose, and protein partly decompose in a landfill, resulting in CH4 generation. Lignin 

is relatively stable and non-decomposable under anaerobic conditions. 

Portions of each material were weighed, placed in two-liter plastic containers (i.e., reactors), and 

allowed to decompose anaerobically under warm, moist conditions designed to accelerate decomposition. 

The reactors were seeded with a small amount of well-decomposed refuse containing an active population 

of CH4-producing microorganisms (the “seed”), to ensure that CH4 generation was not limited due to an 

insufficient population of microorganisms. To promote degradation, water was cycled through each 

reactor. Nitrogen and phosphorus were added so that CH4 generation would not be limited by a lack of 

these nutrients. 

The reactors were allowed to run for periods varying from three months to two years. The 

experiment ended for each reactor when one of two conditions was met: (1) no measurable CH4 was being 

emitted (i.e., any CH4 that was being emitted was below the detection limits of the analytical equipment); 

or (2) a curve generated mathematically from an analysis of the reactor’s prior CH4 generation indicated 

that the reactor had produced at least 95 percent of the CH4 that it would produce if allowed to run 

indefinitely. 

Dr. Barlaz measured the amount of CH4 generated during the experimental period and subtracted 

the amount of CH4 attributable to the seed. At the end of the experiment, he opened the reactors, drained 

the leachate, dried and weighed the contents, and analyzed the percentage composition of cellulose, 

hemicellulose, and lignin (and, for food discards, protein) in the remaining contents. He then measured 

the percentage of total volatile solids in the remaining contents. This amount included the cellulose, 

hemicellulose, lignin, and protein, and any other carbon-containing components such as waxes and 

tannins. 

The experimental results were used to estimate the amount of carbon remaining in the reactor that 

was attributable to the seed6

 and the amount attributable to the material. The experiment was assumed to 

reflect actual landfill conditions, and the organic carbon remaining undegraded in the reactors was 

assumed to remain undegraded over the long term in landfills, i.e., it would be stored. 

 4

 Barlaz, M.A. 1997. “Biodegradative Analysis of Municipal Solid Waste in Laboratory-Scale Landfills,” 

EPA 600/R-97-071. Dr. Barlaz’s work was funded by EPA’s Air and Energy Engineering Research Laboratory. 

5

 Barlaz, op. cit. 

6

 Dr. Barlaz tested seed alone to be able to control for the amount of CH4 generation and carbon storage 

that was attributable to the seed. 
99 

Dr. Barlaz’s experiment did not specifically test all of the paper grades described in this report. 

He did evaluate four specific grades: newspaper, corrugated boxes, office paper, and coated paper. We 

identified proxies for five additional material types for which we had no experimental data. We assumed 

that magazines placed in a landfill will have characteristics similar to those observed for coated paper. 

Similarly, we assumed that phonebooks and textbooks would behave in the same way as newspaper and 

office paper, respectively. Experimental results for branches were used as a proxy for dimensional lumber 

and medium-density fiberboard. 

As discussed in Section 4.2, we included the following three definitions of mixed paper among 

the materials analyzed in this report: 

• Broadly defined mixed paper, which includes almost all printing-writing paper, folding 

boxes, and most paper packaging; 

• Residential mixed paper, which includes the typical mix of papers from residential curbside 

pick-up (e.g., high-grade office paper, magazines, catalogs, commercial printing, folding 

cartons, and a small amount of old corrugated containers); and 

• Mixed paper from offices, which includes copy and printer paper, stationary and envelopes, 

and commercial printing. 

To develop estimates of CH4 emissions and carbon storage for these three categories of mixed paper, we 

used the detailed characterization of mixed paper (shown in Exhibit 4-2) developed by Franklin 

Associates, Ltd., and we assigned analogs among the four paper grades tested by Dr. Barlaz. Exhibit 7-1 

characterizes the composition of the two products made from mixed paper: boxboard (made using either a 

broad or a residential mix of recycled paper) and paper towels (made from recycled office paper). 

Emissions were calculated using these characterizations of the mixed paper grades and the values 

obtained from Dr. Barlaz’s experiment for newspaper, corrugated boxes, office paper, and coated paper.7

 

 7 Note that Exhibits 7-2 through 7-4 do not show mixed paper; however, mixed paper is shown in Exhibits 

7-6 through 7-8. Exhibits 7-2 through 7-8 appear at the end of the chapter. 
100 

Exhibit 7-1 

Proxies for Composition Mixed Paper (Percent)

Paper Grade 

Broad Definition for 

Mixed Paper 

Mixed Paper from 

Residential Sources 

Mixed Paper from 

Offices 

Newspaper1 24 23 21 

Corrugated Boxes 2 48 53 5 

Office Paper 3 20 14 38 

Coated Paper 4 8 10 36 

Total 100 100 100 

Explanatory Notes: 

1 Includes newspaper, uncoated groundwood paper, recycled folding boxes, and set-up boxes. 2 Includes virgin and recycled corrugated boxes. 

3 Includes uncoated free sheet paper, cotton fiber paper, bleached bristols, unbleached kraft folding boxes, bleached 

kraft folding boxes, bleached bags and sacks, unbleached bags and sacks, and unbleached wrapping paper. 4 Includes coated free sheet paper and coated groundwood paper. 

7.1.2 CH4 Generation: Experimental Data and Adjusted Values 

The amount of CH4 generated by each type of organic material (after deducting the CH4

attributable to the seed), is shown in column “b” of Exhibit 7-2. 

As a check on his experimental results, Dr. Barlaz estimated the amount of CH4 that would have 

been produced if all of the cellulose, hemicellulose, and protein from the waste material that was 

decomposed during the experiment had been converted to equal parts of CH4 and CO2 (CH4-producing 

microorganisms generate equal amounts, by volume, of CH4 and CO2 gas).8

 Dr. Barlaz referred to this 

amount as the material’s “CH4 potential.” He then calculated the percentage of the CH4 potential for each 

material accounted for by the sum of (1) the measured CH4 generation, and (2) the amount of CH4 that 

could be formed from the carbon in the leachate that was removed from the reactor and from the carbon in 

the refuse that remained in the reactor at the end of the experiment.9

 The resulting percentages of the CH4

potential are shown in column “c” of Exhibit 7-2. CH4 potential not accounted for could be due to either 

(1) leaks of CH4; (2) measurement error; or (3) carbon in the cell mass of microorganisms (which was not 

measured). 

CH4 recovery was below 85 percent of the CH4 potential for five materials: coated paper, office 

paper, food discards, leaves, and branches. In using Dr. Barlaz’s data, we needed to make a choice 

regarding how to allocate this missing carbon. We chose to assume that some of it had been converted to 

microorganism cell mass, and the remainder had been degraded. Dr. Barlaz postulated a higher CH4 yield 

based on assumptions that (1) 5 percent of the carbon in cellulose and hemicellulose (and protein in the 

case of food discards) that was degraded was converted into the cell mass of the microbial population; 

and (2) 90 percent of the carbon-containing compounds that were degraded but not converted to cell mass 

were converted to equal parts of CH4 and CO2. The “corrected yields,” based on these assumptions, are 

shown in column “d” of Exhibit 7-2. 

 8 Ibid. Lignin was not considered in this check because cellulose, hemicellulose, and protein account for 

nearly all of the CH4 generated. 

9

 Note that any carbon that was converted to cell mass in microorganisms was not considered in this 

calculation. 
101 

We decided, in consultation with Dr. Barlaz, to use the “corrected yields” for leaves, branches, 

and office paper because we believed that these values were more realistic than the measured yields.10, 11

The CH4 values that we used for each material (either the measured yield, or the “corrected” 

yield) are shown again in column “f” of Exhibit 7-2. In order to maintain consistent units with the other 

parts of our analysis, we converted the units for CH4 generation from milliliters per dry gram of waste, to 

MTCE per wet ton of waste.12 The resulting values are shown in column “g” of Exhibit 7-2. The value for 

yard trimmings is a weighted average of the values for grass, leaves, and branches, based on an 

assumption that yard trimmings are composed of 50 percent grass, 25 percent leaves, and 25 percent 

branches (on a wet weight basis). 

7.1.3 Carbon Storage: Experimental Data and Calculations 

Carbon storage was estimated by calculating the amount of carbon remaining in each reactor at 

the end of the experiment and then subtracting the amount of carbon remaining that was attributable to the 

seed. The difference between the two values is the amount of carbon from the waste material that 

remained in the reactor, undecomposed, at the end of the experiment. Because the conditions in the 

reactor simulated landfill conditions (favorable to optimized decomposition), approximately this amount 

of carbon would be stored if the material were landfilled. Carbon storage for each material is presented in 

Exhibit 7-3.13

7.2 FATES OF LANDFILL CH4: CONVERSION TO CO2, EMISSIONS, AND FLARING OR 

COMBUSTION WITH ENERGY RECOVERY 

In this analysis, we accounted for (1) the conversion in the landfill of some portion of landfill 

CH4 to CO2 and (2) the capture of CH4, either for flaring or for combustion with energy recovery (in 

either case, the captured CH4 is converted to CO2).14 Exhibit 7-4 presents this analysis. 

The exhibit begins with the CH4 generation per wet ton of each material, which is shown in 

column “b” (the values were simply copied from the last column of Exhibit 7-2). Columns “c” through 

“k” calculate net GHG emissions from CH4 generation for each of three categories of landfills: (1) 

landfills without landfill gas (LFG) recovery; (2) landfills with LFG recovery that flare LFG; and (3) 

landfills with LFG recovery, which generate electricity from the LFG. Columns “l” through “n” show the 

 10 The corrected yield was not available for coated paper/magazines. For food discards, even though the 

CH4 potential recovery percentage was lower than 85 percent, we used the measured yield, as shown in column “b.” 

We made this choice for food discards because the “corrected yield” for food discards was greater than the 

maximum possible yield (shown in column “e” of the exhibit). Dr. Barlaz had calculated the maximum possible 

yield for each material based on the CH4 yield if all of the cellulose, hemicellulose, and protein in the material (1) 

decomposed and (2) was converted to equal parts of CH4 and CO2. 

11 Note that EPA’s Office of Research and Development (ORD) uses the same data as the basis for its 

estimation of CH4 yields. In that analysis, ORD does not use “corrected” values for materials with low CH4

recovery, but rather uses observed experimental values for all materials. 

12 To make the conversion, we used the ratio of dry weight to wet weight for each material and a global 

warming potential of 21 for CH4. 

13 The approach for estimating carbon storage is more fully described in, Barlaz, Morton, “Carbon Storage 

During Biodegradation of Municipal Solid Waste Components in Laboratory-Scale Landfills,” paper submitted for 

publication, Department of Civil Engineering, North Carolina State University, Raleigh, NC, 1997. 

14 The CO2 that is emitted is not counted as a GHG because it is biogenic in origin (as described in “CO2

Emissions from Biogenic Sources: in Chapter 1). 
102 

estimated percentage of landfills in each category in 2000.15,16 The final column shows the weighted 

average GHG emissions from CH4 generation across all types of landfills. 

To estimate MSW CH4 emissions from each category of landfill, we first estimated the 

percentage of landfill CH4 that is oxidized near the surface of the landfill. We estimated that 10 percent of 

the landfill CH4 generated is either chemically oxidized or converted by bacteria to CO2,

 17 and the 

remaining 90 percent is available for atmospheric CH4 emissions.

To estimate MSW CH4 emissions from landfills with LFG recovery, we used the assumption that 

these landfills have an average LFG recovery efficiency of 75 percent.18 We then calculated avoided 

utility GHG emissions from landfills with electricity generation. Because energy recovery systems 

experience down time, during which CH4 is flared rather than used to generate electricity, we 

incorporated a 15 percent system efficiency loss into our estimates for avoided utility emissions.19 

We also estimated the percentage of MSW disposed in each category of landfill in 2000. 

According to our estimates, 49 percent of all landfill CH4 was generated at landfills with recovery 

systems, and the remaining 51 percent was generated at landfills without LFG recovery.20 Of the 49 

percent of all CH4 generated at landfills with LFG recovery, 49 percent (or 24 percent of all CH4) was 

generated at landfills that use LFG to generate electricity, and 51 percent (or 25 percent of all CH4) at 

landfills that flare LFG.21, 22 

Our results are shown in the final column of Exhibit 7-4. The materials with the highest rates of 

net GHG emissions from CH4 generation, as shown in column “o”—corrugated boxes, office paper, and 

textbooks—also have the highest gross CH4 generation, as shown in column “b.” The recovery of CH4 at 

landfills reduces the CH4 emissions for each material in proportionate amounts but does not change the 

 15 Draft U.S. Climate Action Report – 2001 (CAR). At the time of publication of this report, the CAR was 

still being reviewed; however, EPA expected that these estimates will not change in the final version. 

16 Note that estimates of percent CH4 generation at landfills with recovery have decreased since the first 

edition of this report was published (in the first edition, we estimated that 54 percent of CH4 would be generated at 

landfills with recovery). This difference is because the first edition relied on 1995 projections of year 2000 

generation and recovery, whereas this version uses the most recent estimates of conditions in 2000. 

17 An oxidation rate of 10 percent is cited by Liptay, K., J. Chanton, P. Czepiel, and B. Mosher, “Use of 

Stable Isotopes to Determine Methane Oxidation in Landfill Cover Soils,” Journal of Geophysical Research, April 

1998, 103(D7), pp. 8243-8250; and Czepiel, P.M., B. Mosher, P.M. Crill, and R.C. Harriss. 1996. “Quantifying the 

Effects of Oxidation on Landfill Methane Emissions,” Journal of Geophysical Research, 101, pp. 16721-16729. 

18 Several commenters on the draft version of the first edition of this report suggested a range of values; 75 

percent was most often cited as a best estimate. Moreover, EPA has used this figure in its most recent publications 

[see, for example, U.S. Methane Emissions 1990-2020: Inventories, Projections, and Opportunities for Reductions 

(Washington, D.C.: U.S. EPA) September 1999]. 

19 EPA. 1999. Landfill Gas-to-Energy Project Opportunities: Background Information on Landfill Profiles, 

Office of Air and Radiation, EPA 430-K-99-002, pp. 3-13. 

20 Based on data on (1) year 2000 MSW landfill CH4 generation of 72.7 million MTCE (from draft U.S.

Climate Action Report – 2001), (2) year 2000 landfill CH4 recovery of 26.7 million MTCE (also from draft U.S.

Climate Action Report – 2001), and (3) estimated landfill CH4 recovery efficiency of 75 percent (from U.S. Methane 

Emissions 1990-2020: Inventories, Projections, and Opportunities for Reductions). 

21 Draft U.S. Climate Action Report – 2001. 

22 The assumption that 49 percent of landfills recovering CH4 will use it to generate electricity is subject to 

change over time based upon changes in the cost of recovery and the potential payback. Additionally, new 

technologies may arise that use recovered CH4 for purposes other than generating electricity. 
103 

ranking of materials by CH4 emissions. Leaves, branches, and the two wood products have the lowest 

rates of net GHG emissions from CH4 generation. 

7.3 UTILITY CO2 EMISSIONS AVOIDED 

Exhibit 7-5 presents a list of conversion factors and physical constants used to convert CH4

combusted for electricity production to avoided CO2 emissions. Using data on Btu per cubic feet of CH4, 

kwh of electricity generated and delivered per Btu, and kilograms of utility carbon avoided per Btu 

delivered, we estimated that 0.18 MTCE is avoided per MTCE of CH4 combusted. This figure then was 

incorporated into exhibit 7-4 to estimate net GHG emissions from landfills with electricity generation. As 

mentioned earlier in this chapter, our analysis assumes that 24 percent of landfills in the United States 

combust landfill CH4 for electricity generation. We also assume a 15 percent system efficiency loss, 

reflecting the fact that landfill gas-to-energy facilities incur some system “down time,” as shown in 

column 1. 

7.4 NET GHG EMISSIONS FROM LANDFILLING 

To determine the net GHG emissions from landfilling each material, we summed the net GHG 

emissions from CH4 generation, carbon storage (treated as negative emissions), and transportation CO2

emissions. The results are shown in Exhibit 7-6. The four columns under section “e” of the exhibit may 

be used by local MSW planners to estimate GHG emissions from MSW in a given community. 

As the exhibit shows, the post-consumer results for organic materials vary widely. For some 

materials—in particular, magazines/third-class mail, newspaper, phonebooks, dimensional lumber, 

medium-density fiberboard, and yard trimmings (in particular, leaves and branches)—landfilling results 

in substantial net GHG reductions. For others—including corrugated cardboard, office paper, textbooks, 

and food discards—net emissions are significant. For the rest, net emissions and reductions are relatively 

small. 

7.5 LIMITATIONS 

Perhaps the most important caveat to the analysis of GHG emissions and storage associated with 

landfilling is that the results are based on a single set of laboratory experiments, those conducted by Dr. 

Morton Barlaz. Although researchers other than Dr. Barlaz have conducted laboratory studies that track 

the degradation of mixed MSW, his experiments were the only ones we identified that rigorously tested 

materials on an individual basis. Dr. Barlaz is recognized as an expert on the degradation of different 

fractions of MSW under anaerobic conditions, and his findings with respect to the CH4 potential of mixed 

MSW are within the range used by landfill gas developers. Nevertheless, given the sensitivity of the 

landfill results to estimated CH4 generation and carbon storage, we recognize that more research is needed 

in this area. 

Another important caveat relates to our estimate that 49 percent of MSW landfill CH4 is 

generated at landfills with LFG recovery systems. 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 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 effects of different rates of LFG recovery are shown in Exhibit 7-7. Column 

“b” of the exhibit shows net GHG emissions if 20 percent of waste was disposed of at landfills with 

recovery. The remaining columns show net GHG emissions at increasing LFG recovery rates, up to a 60 

percent rate. As the exhibit shows, the net post-consumer GHG emissions for landfilling mixed MSW 

decline significantly as recovery increases. At the local level, the GHG emissions from landfilling MSW 

depend on whether the local landfill has LFG recovery, as shown in Exhibit 7-6. 
104 

Because the national average estimate of emissions is based on estimated year 2000 LFG 

recovery levels, there are several limitations associated with the use of this emission factor. First, because 

landfill CH4 generation occurs over time and has significant timing delays (i.e., CH4 generation may not 

begin until a few years after the waste is deposited in the landfill and can continue for many years after 

the landfill is closed), the values listed in this chapter represent total CH4 generated, over time, per ton of 

waste landfilled. To the extent that LFG recovery rates shift dramatically over time, these shifts are not 

reflected in the analysis. Second, landfills with LFG recovery may be permitted, under EPA regulations, 

to remove the LFG recovery equipment when three conditions are met: (1) the landfill is permanently 

closed, (2) LFG has been collected continuously for at least 15 years, and (3) the landfill emits less than 

50 metric tons of non-CH4 organic compounds per year.23 Although the removal of LFG recovery 

equipment will permit CH4 from closed landfills to escape into the atmosphere, the amounts of CH4

emitted should be relatively small, because of the relatively long time period required for LFG collection 

before LFG recovery equipment is removed. Third, several methodological issues are associated with 

applying the CH4 generation estimates from the Climate Action Report to develop the national average 

emission factors: 

(1) The generation estimates in the CAR include closed landfills (generation is modeled as a 

function of waste in place), whereas the estimates used in this report apply to ongoing 

generation (which is routed to open landfills); 

(2) Likewise, both the flaring and landfill gas-to-energy estimates also include closed landfills; 

and 

(3) The distribution of waste in place is not a perfect proxy for the destination of ongoing waste 

generation. 

CH4 oxidation rate and landfill gas collection system efficiency are also important factors driving 

results. We used values of 10 percent and 75 percent, respectively, as best estimates for these factors. 

Reviewers who commented on the draft of the first edition of this report and sources in the literature have 

reported estimates ranging from about 5 percent to 40 percent for oxidation, and from about 60 to 95 

percent for collection system efficiency. We investigated the sensitivity of our results to these 

assumptions, and our results are shown in Exhibit 7-8. We portray the sensitivity as a bounding analysis; 

i.e., we use the combinations of variables yielding the upper bound emission factor (5 percent oxidation, 

60 percent collection efficiency) and the lower bound (40 percent oxidation, 95 percent efficiency).24 As 

the exhibit shows, the materials most sensitive to these variables are those with the highest CH4

generation potential, i.e., corrugated cardboard, office paper, textbooks, food discards, and mixed paper. 

Sensitivity varies: the difference between upper and lower bounds ranges from 0.06 MTCE/ton for leaves 

and branches to 0.43 MTCE/ton for office paper and textbooks. The post-consumer emission factors of 

several materials and mixed material combinations—corrugated cardboard, grass, mixed paper, and mixed 

MSW—change from having net storage under the lower bound to having net emissions under the upper 

bound. 

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 organic 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 

 23 Federal Register, Vol. 61, No. 49, p. 9907. 

24 The table also reports two intermediate combinations, including the best estimate values. 
105 

amount of CH4 possible. If the CH4 yields used in this analysis were higher than yields in a landfill, the 

net GHG emissions from landfilling organic materials would be lower than estimated here. 

We assumed that once wastes are disposed in a landfill, they are never removed. In other words, 

we 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 CO2 in the combustor. 

Our estimate of carbon avoided utility GHG emissions per unit of CH4 combusted assumes that 

all landfill gas-to-energy projects are electricity producing. 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 essentially 

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 our analysis. 

For landfilling of yard trimmings (and other organic materials), we assumed that all carbon 

storage in a landfill environment is incremental to the storage that occurs in a non-landfill environment. In 

other words, we assumed that in a baseline where yard trimmings are returned to the soil (i.e., in a nonlandfill environment), all of the carbon is decomposed relatively rapidly (i.e., within several years) to 

CO2, 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 post-consumer GHG 

emissions are understated. 

Finally, our spreadsheet analysis is limited by the assumptions that were made at various steps in 

the analysis, as described throughout this chapter. The key assumptions that have not already been 

discussed as limitations are the assumptions used in developing “corrected” CH4 yields for organic 

materials in MSW. Because of the high global warming potential 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. 
106 

(a) (b) (c) (d) (e) (f) 

Material

Average 

Measured 

Methane Yield 

(ml per dry 

gm)

Percentage of 

"Methane 

Potential" 

Accounted for

"Corrected" 

Methane 

Yield (ml per 

dry gram)

Maximum 

Possible 

Methane 

Yield (ml per 

dry gram)

Selected 

Methane 

Yield (ml 

per dry 

gm)

Corrugated Cardboard 152.3 87.7 NA 279.7 152.3 Magazines/Third-class Mail 84.4 83.7 NA NA 84.4 Newspaper 74.2 98.0 NA 239.4 74.2 Office Paper 217.3 55.5 346.0 398.2 346.0 Food Discards 300.7 77.4 386.2 357.6 300.7 Yard Trimmings Grass 144.3 89.3 NA 153.2 144.3 Leaves 30.5 75.2 56.0 108.0 56.0 Branches 62.6 82.8 76.3 224.9 76.3 Mixed MSW 92.0 97.6 NA 157.6 92.0 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.Note that Exhibits 7-1 to 7-3 show coated paper but not mixed paper; mixed paper is shown in Exhibits 7-5 and 7-6. The values for 

types of mixed paper are based on the proportion of the four paper types (newspaper, office paper, corrugated cardboard, and coa

that comprise the different "mixed paper" definitions.

Methane Yield for Solid Waste Components

Exhibit 7-2
107 

Explanatory Notes for Exhibit 7-3: (1) Because MSW is typically measured in terms of its wet weight, we needed to convert the ratios for carbon stored as a fraction of dr

as a fraction of wet weight. To do this, we used the estimated ratio of dry weight to wet weight for each material. These ratios are showexhibit. For most of the materials, we used data from an engineering handbook.25 For grass, leaves, and branches, we used data provide

(2) For consistency with the overall analysis, we converted the carbon storage values for each material to units of MTCE stored per sho

landfilled. The resulting values are shown in column “e” of the exhibit. 

25 Tchobanoglous, George, Hilary Theisen, and Rolf Eliassen. 1977. Solid Wastes: Engineering Principles and Management I

McGraw-Hill Book Co.), pp. 58 and 60. 

(a) (b) (c) (d) (e)

Material

Ratio of 

Carbon 

Storage to 

Dry Weight 

(gm C/dry 

gm)

Ratio of 

Dry 

Weight to 

Wet 

Weight

(d = b * c) 

Ratio of 

Carbon 

Storage to Wet 

Weight (gm 

C/wet gm)

Amount of 

Carbon Stored 

(MTCE per Wet 

Ton)

Corrugated Cardboard 0.26 0.95 0.25 0.22Magazines/Third-class Mail 0.34 0.94 0.32 0.29Newspaper 0.42 0.94 0.39 0.36Office Paper 0.05 0.94 0.05 0.04Food Discards 0.08 0.30 0.02 0.02Yard Trimmings 0.23 0.21Grass 0.32 0.40 0.13 0.12Leaves 0.54 0.80 0.43 0.39Branches 0.38 0.60 0.23 0.21Mixed MSW 0.13 0.84 0.11 0.10

Note that more digits may be displayed than are significant.

Exhibit 7-3

Carbon Storage for Solid Waste Components
108 

Flaring

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (l) (m) (n) 

Material

CH4 

Generation 

(MTCE/Wet 

Ton)

Percentage 

of CH4 Not 

Oxidized to 

CO2

Net GHG 

Emissions 

From CH4 

Generation 

(MTCE/Wet 

Ton)

Percentage of 

CH4 Not 

Recovered 

(100% Minus 

LFG 

Collection 

System 

Efficiency)

Percentage 

of CH4 Not 

Recovered 

That Is Not 

Oxidized to 

CO2

Net GHG 

Emissions 

From CH4 

Generation 

(MTCE/Wet 

Ton)

Utility CO2 

Emissions 

Avoided per 

MTCE CH4 

Combusted 

(MTCE)

Percentage of 

CH4 Recovered 

for Electricity 

Generation Not 

Utilized Due to 

System "Down 

Time"

Utility CO2 

Emissions 

Avoided 

(MTCE/Wet 

Ton)

Percentage 

of CH4 From 

Landfills 

Without LFG 

Recovery in 

2000

Percentage 

of CH4 From 

Landfills With 

LFG 

Recovery 

And Flaring 

in 2000

CH4 From 

Landfills W

LFG 

Recovery a

Electricity 

Generation 

2000

Corrugated Cardboard 0.537 90% 0.48 25% 90% 0.12 -0.18 0.15 -0.06 51% 25% 24Magazines/Third-class Mail 0.294 90% 0.26 25% 90% 0.07 -0.18 0.15 -0.03 51% 25% 24Newspaper 0.259 90% 0.23 25% 90% 0.06 -0.18 0.15 -0.03 51% 25% 24Office Paper 1.207 90% 1.09 25% 90% 0.27 -0.18 0.15 -0.14 51% 25% 24Phonebooks 0.259 90% 0.23 25% 90% 0.06 -0.18 0.15 -0.03 51% 25% 24Textbooks 1.207 90% 1.09 25% 90% 0.27 -0.18 0.15 -0.14 51% 25% 24Dimensional Lumber 0.170 90% 0.15 25% 90% 0.04 -0.18 0.15 -0.02 51% 25% 24Medium-density Fiberboard 0.170 90% 0.15 25% 90% 0.04 -0.18 0.15 -0.02 51% 25% 24Food Discards 0.335 90% 0.30 25% 90% 0.08 -0.18 0.15 -0.04 51% 25% 24Yard Trimmings 0.191 90% 0.17 25% 90% 0.04 -0.18 0.15 -0.02 51% 25% 24Grass 0.214 90% 0.19 25% 90% 0.05 -0.18 0.15 -0.02 51% 25% 24Leaves 0.166 90% 0.15 25% 90% 0.04 -0.18 0.15 -0.02 51% 25% 24Branches 0.170 90% 0.15 25% 90% 0.04 -0.18 0.15 -0.02 51% 25% 24Mixed MSW 0.28601 90% 0.26 25% 90% 0.06 -0.18 0.15 -0.03 51% 25% 24

Note that totals may not add due to rounding, and more digits may be displayed than are significant.

Methane from Landfills 

Without Methane 

Recovery

Methane from Landfills With LFG Recovery and:

Percentage of Methane from Each Ty

Electricity Generation of Landfill in 2000

Net GHG Emissions from CH4 Generation

Exhibit 7-4
109 

Step Value Source

Metric tons CH4/MTCE CH4 0.17 1/((12/44)*Global warming potential of CH4)

Grams CH4/metric ton CH4 1.00E+06 Physical constant

Cubic ft. CH4/gram CH4 0.05 1/20: 20 grams per cubic foot of methane at standard temperature and press

Btu/cubic ft. CH4 1,000 "Opportunity for LF Gas Energy Recovery in Kentucky," OAR September 97

kwh Electricity generated/Btu 0.00008 1/13,000: from "Opportunity" report p. 2-11, assumes use of internal combus

kwh electricity delivered/kwh electricity generated 0.95

Telephone conversation among IWSA, American Ref-Fuel, and ICF Consult28, 1997.

Btu/kwh electricity delivered 3,412 Physical constant

Kg utility C avoided/Btu delivered electricity 8.060E-05

0.08349 MTCE/mmBtu delivered electricity, from Exhibit 6-3. This assumes energy recovery displaces fossil fuel generation.

Metric Tons avoided utility C/kg utility C 0.001 1000 kg per metric ton

Ratio of MTCE avoided utility C per MTCE CH4 0.18 Product from multiplying all factors

Exhibit 7-5

Calculation to Estimate Utility GHGs Avoided through Combustion of Landfill CH4
110 

(a) (b) (c) (d) (e) (e = b + c + d

Net GHG Emissions from CH4 Generation Net GHG Emissions from 

(MTCE/Wet Ton) (MTCE/Wet Ton

Material

Landfills 

Without LFG 

Recovery

Landfills 

With LFG 

Recovery 

and Flaring

Landfills With 

LFG Recovery 

and Electric 

Generation

Year 2000 

National 

Average

Net Carbon 

Storage 

(MTCE/Wet 

Ton)

GHG 

Emissions 

From 

Transportati

on 

(MTCE/Wet 

Ton)

Landfills 

Without LFG 

Recovery

Landfills With 

LFG Recovery 

and Flaring

Landf

LFG R

and 

Gen

Aluminum Cans 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Steel Cans 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Glass 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 HDPE 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 LDPE 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 PET 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Corrugated Cardboard 0.48 0.12 0.06 0.29 -0.22 0.01 0.27 -0.09 Magazines/Third-class Mail 0.26 0.07 0.03 0.16 -0.29 0.01 -0.02 -0.21 Newspaper 0.23 0.06 0.03 0.14 -0.36 0.01 -0.12 -0.29 Office Paper 1.09 0.27 0.14 0.66 -0.04 0.01 1.05 0.24 Phonebooks 0.23 0.06 0.03 0.14 -0.36 0.01 -0.12 -0.29 Textbooks 1.09 0.27 0.14 0.66 -0.04 0.01 1.05 0.24 Dimensional Lumber 0.15 0.04 0.02 0.09 -0.21 0.01 -0.04 -0.16 Medium-density Fiberboard 0.15 0.04 0.02 0.09 -0.21 0.01 -0.04 -0.16 Food Discards 0.30 0.08 0.04 0.18 -0.02 0.01 0.29 0.06 Yard Trimmings 0.17 0.04 0.02 0.10 -0.21 0.01 -0.03 -0.15 Grass 0.19 0.05 0.02 0.12 -0.12 0.01 0.09 -0.06 Leaves 0.15 0.04 0.02 0.09 -0.39 0.01 -0.23 -0.34 Branches 0.15 0.04 0.02 0.09 -0.21 0.01 -0.04 -0.16 Mixed Paper Broad Definition 0.53 0.13 0.07 0.32 -0.23 0.01 0.31 -0.08  Residential Definition 0.49 0.12 0.06 0.29 -0.24 0.01 0.26 -0.10  Office Paper Definition 0.58 0.15 0.07 0.35 -0.21 0.01 0.38 -0.05 Mixed MSW 0.26 0.06 0.03 0.16 -0.10 0.01 0.17 -0.02 Note that totals may not add due to rounding, and more digits may be displayed than are significant.

Net GHG Emissions from Landfilling

Exhibit 7-6
111 

(a) (b) (c) (d) (e) (f)

17% 20% 49% 55% 60%

Material

of Waste 

Disposed At 

Landfills 

With LFG 

Recovery

of Waste 

Disposed At 

Landfills 

With LFG 

Recovery

of Waste 

Disposed At 

Landfills 

With LFG 

Recovery

of Waste 

Disposed At 

Landfills 

With LFG 

Recovery

of Waste 

Disposed 

Landfills 

With LFG 

Recovery

Corrugated Cardboard 0.20 0.19 0.06 0.04 0.0Magazines/Third-class Mail -0.05 -0.06 -0.13 -0.14 -0.1Newspaper -0.15 -0.16 -0.21 -0.23 -0.2Office paper 0.89 0.87 0.59 0.54 0.4

Phonebooks -0.15 -0.16 -0.21 -0.23 -0.2Textbooks 0.89 0.87 0.59 0.54 0.4Dimensional Lumber -0.07 -0.07 -0.11 -0.12 -0.1

Medium-density Fiberboard -0.07 -0.07 -0.11 -0.12 -0.1Food Discards 0.25 0.24 0.16 0.15 0.1Yard Trimmings -0.05 -0.06 -0.10 -0.11 -0.1Grass 0.06 0.05 0.01 0.00 -0.0Leaves -0.25 -0.26 -0.30 -0.30 -0.3Branches -0.07 -0.07 -0.11 -0.12 -0.1Mixed Paper 

 Broad Definition 0.23 0.22 0.09 0.06 0.0 Residential Definition 0.19 0.18 0.06 0.03 0.0 Office Paper Definition 0.30 0.28 0.14 0.11 0.0

Mixed MSW 0.13 0.12 0.06 0.05 0.0

Exhibit 7-7

Net GHG Emissions from CH4 Generation at Landfills

Sensitivity Analysis: Varying the Percentage of Waste Disposed at Landfills with Methane Recovery
112 

Oxidation Rate: 40% 25% 10% 5%

Collection Efficiency: 95% 85% 75% 60%

Material

Lower-bound 

Emissions

Conservative 

(High) 

Emissions

Best 

Estimate

Upperbound 

Emissions

Corrugated Cardboard 0.15 0.22 0.29 0.35Magazines/Third-class Mail 0.08 0.12 0.16 0.19Newspaper 0.07 0.11 0.14 0.17Office Paper 0.35 0.49 0.66 0.78

Phonebooks 0.07 0.11 0.14 0.17Textbooks 0.35 0.49 0.66 0.78Dimensional Lumber 0.05 0.07 0.09 0.11Medium-density Fiberboard 0.05 0.07 0.09 0.11Food Discards 0.10 0.14 0.18 0.22Yard Trimmings 0.05 0.08 0.10 0.12Grass 0.06 0.09 0.12 0.14Leaves 0.05 0.07 0.09 0.11Branches 0.05 0.07 0.09 0.11Mixed Paper 

 Broad Definition 0.17 0.24 0.32 0.38 Residential Definition 0.16 0.22 0.29 0.35 Office Paper Definition 0.19 0.26 0.35 0.42

Mixed MSW 0.08 0.12 0.16 0.19

Sensitivity Analysis: Varying Oxidation and Gas Collection Efficiency Rates. Based on 

Estimated National Mix of Landfill Gas Recovery Systems in 2000.

Exhibit 7-8

Net GHG Emissions from CH4 Generation at Landfills
113 

8. ACCOUNTING FOR EMISSION REDUCTIONS 

Earlier chapters of this report examined GHG emissions from each of five waste management 

options. This chapter recapitulates the emission factors for each option, explains the analytic framework 

for applying emission factors, and reviews opportunities for GHG emission reductions. 

In the discussion that follows, we focus on national average conditions. For example, we 

represent landfills as having the national average landfill gas recovery systems, and we represent 

combustors based on mass burn units with the national average system efficiency for collection of ferrous 

metal. As shown in the previous chapters, GHG emissions are sensitive to many variables, including 

several that are site-specific. At specific locations, the GHG emission factors can differ from those 

described below. To allow for customizing of emission factors to better reflect local conditions, EPA has 

developed a spreadsheet accounting tool, the Waste Reduction Model (WARM), which enables users to 

input several key variables (e.g., information on landfill gas collection systems, electric utility fuel mix, 

transportation distances).

1

 We encourage readers to take advantage of this model when assessing their 

waste management options. 

8.1 NET GHG EMISSIONS FOR EACH WASTE MANAGEMENT OPTION 

This section presents the net life-cycle GHG emissions for each waste management option for 

each material considered. These emissions are shown in 12 exhibits that summarize the GHG emissions 

and sinks in MTCE/ton and MTCO2E/ton, which are described in detail in earlier chapters. In these 

exhibits, emission factors are shown for mixed plastics, mixed recyclables, and mixed organics. We 

developed the emission factor for mixed recyclables by calculating the average (weighted by tons 

recycled in 2000) of emission factors for aluminum cans, steel cans, HDPE, LDPE, PET, corrugated 

cardboard, magazines/third-class mail, newspaper, office paper, phonebooks textbooks, and wood 

products. The emission factor for mixed plastics is the average (weighted by tons recycled in 2000) of 

emission factors for HDPE, LDPE, and PET. The mixed organics emission factor is the average 

(weighted by tons composted in 2000) of emission factors for yard trimmings and food discards.

2

As mentioned in Chapter 1, we used a waste generation reference point for measuring GHG 

emissions; i.e., we begin accounting for GHG emissions at the point of waste generation. All subsequent 

emissions and sinks from waste management practices then are counted. Changes in emissions and sinks 

from raw material acquisition and manufacturing processes are captured to the extent that certain waste 

management practices (i.e., source reduction and recycling) affect these processes (for reference, GHG 

emissions from raw materials acquisition and manufacturing are shown in the first column of several 

exhibits in this chapter). Negative emission factors indicate that from the point of waste generation, some

MSW management options can reduce GHG emissions. 

 1

 Microsoft Excel and Web-based versions of this tool are available online at the following Web site:

http://www.epa.gov/globalwarming/actions/waste/tools.html. 

2

 All data on recycling and compost rates are from U.S. EPA Office of Solid Waste. 2002. Municipal Solid 

Waste in the United States: 2000 Facts and Figures, EPA 530-R-02-001. 
114 

Exhibits 8-1 and 8-2 show the life-cycle GHG reductions associated with source reduction, 

presented in MTCE/ton and MTCO2E/ton, respectively. In brief, the exhibits show that, for all of the 

manufactured materials evaluated, source reduction results in GHG emission reductions. On a per-ton 

basis, aluminum cans and several paper grades have the greatest potential for emission reduction, due 

primarily to reductions in energy use in the raw material acquisition and manufacturing step and (for 

paper) forest carbon sequestration. 

Exhibits 8-3 and 8-4 show the life-cycle GHG emissions associated with recycling in MTCE/ton 

and MTCO2E/ton, respectively. The third through fifth columns in the exhibits show the GHG reductions 

associated with using recycled inputs in place of virgin inputs when the material is remanufactured. As 

the final column indicates, recycling results in negative emissions (measured from the point of waste 

generation) for all the materials considered in this analysis. Emission reductions associated with recycling 

are due to several factors, including avoided waste management emissions and reduced process energy 

emissions.3

 In addition, emission reductions from recycling paper products (when measured at the point 

of waste generation) are due in part to the forest carbon sequestration benefits of recycling paper. 

Exhibits 8-5 and 8-6 present the life-cycle GHG emissions from composting food discards, yard 

trimmings, and mixed organics in MTCE/ton and MTCO2E/ton, respectively. The exhibits show that 

composting these materials results in net emissions of -0.05 MTCE/ton, or -0.20 MTCO2E/ton, based on 

the difference between the emissions associated with transporting the materials to the composting facility 

and the soil carbon sequestration benefits. 

Exhibits 8-7 and 8-8 present the life-cycle GHG emissions from combusting each of the materials 

considered in MTCE/ton and MTCO2E/ton, respectively. These exhibits show emissions for mass burn 

facilities with the national average rate of ferrous recovery. Results for RDF facilities are similar. As the 

exhibits show, mixed MSW combustion has net emissions of -0.04 MTCE/ton, or -0.16 MTCO2E/ton. 

Net GHG emissions are positive for plastics, aluminum, and glass, and negative for the other materials. 

GHG emissions from landfilling each of the materials in MTCE/ton are shown in Exhibit 8-9. 

Exhibit 8-10 presents these values in MTCO2E/ton. The values in the final columns indicate that net GHG 

emissions from landfilling mixed MSW, under national average conditions in 2000, are positive. Among 

individual materials, emissions are lowest for newspaper, phonebooks, magazines/third-class mail, wood 

products, and yard trimmings, and highest for office paper, textbooks, and food discards. 

As discussed in Chapter 7 and shown in Exhibit 7-6, the results for landfills are very sensitive to 

site-specific factors. Landfill gas collection practices significantly influence the net GHG emissions from 

landfilling the organic materials. For mixed MSW, net emissions are 0.17 MTCE/ton in landfills without 

landfill gas collection, and -0.06 MTCE/ton in landfills with landfill gas collection and energy recovery. 

The largest differences attributable to landfill gas recovery are for office paper and textbooks (both have a 

range of approximately 1 MTCE/ton), corrugated cardboard, and mixed paper. The CH4 oxidation rate 

and gas collection system efficiency also have a strong influence on the estimated net emissions for mixed 

waste and the organic materials. 

 3

 Process energy emissions for recycled corrugated cardboard, office paper, wood products (i.e., 

dimensional lumber and medium-density fiberboard), and mixed paper (broad and residential definitions) are 

actually higher than those for virgin production because production with recycled inputs tends to use fossil fuelderived energy, while production with virgin inputs uses higher proportions of biomass fuel (CO2 from such fuel is 

not counted in GHG inventories). In the case of dimensional lumber, production with recycled inputs requires more 

energy than virgin production. 
115 

Exhibits 8-11 and 8-12 display the national average emissions for each management option and 

each material in MTCE/ton and MTCO2E/ton, respectively. When reviewing the emission factors, it is 

important to recall caveats that appear throughout this report. In particular, these estimates do not reflect 

site-specific variability, and they are not intended to compare one material to another. Rather, these 

estimates are designed to support accounting for GHG emissions and sinks from waste management 

practices. A brief recap of how to apply the emission factors appears in the following section. 

8.2 APPLYING EMISSION FACTORS 

The net GHG emission estimates presented in Exhibits 8-1 through 8-10 (and the more detailed 

estimates in the preceding chapters) provide emission factors that may be used by organizations interested 

in quantifying and voluntarily reporting emissions reductions associated with waste management 

practices. In conjunction with the U.S. Department of Energy (DOE), EPA has used these estimates as the 

basis for developing guidance for voluntary reporting of GHG reductions, as authorized by Congress in 

Section 1605(b) of the Energy Policy Act of 1992. Other applications have included evaluating the 

progress of voluntary programs aimed at source reduction and recycling, such as EPA’s WasteWise and 

Pay-as-You-Throw programs. 

EPA has also assisted the Climate Neutral Network by using the methods and data described in 

this report to develop company-specific GHG “footprints.” As part of the program, companies develop 

GHG footprints, which include “downstream” waste management activities, for their specific product 

lines or facilities. These footprints then are used to determine the reductions or offsets that are necessary 

to become GHG-neutral. Companies may use changes in waste management practices as part of their 

offset portfolio. 

Additionally, EPA worked with the International Council for Local Environmental Initiatives 

(ICLEI) to incorporate GHG emission factors into its municipal GHG accounting software. Currently, 

350 communities participate in ICLEI’s Cities for Climate Protection Campaign, which helps cities and 

towns establish a GHG emissions reduction target and implement a comprehensive local action plan 

designed to achieve that target. The program has resulted in 7.5 million metric tons of annual GHG 

emissions reductions. 

In order to apply the emission factors presented in this report, one must first establish two 

scenarios: (1) a baseline scenario that represents current management practices (e.g., disposing 10 tons per 

year of office paper in a landfill with national average characteristics in terms of LFG collection); and (2) 

an alternative scenario that represents the alternative management practice (e.g., recycling the same 10 

tons of office paper).4 The emission factors developed in this report then can be used to calculate 

emissions under both the baseline and the alternative management practices. Once emissions for the two 

scenarios have been determined, the next step is to calculate the difference between the alternative 

scenario and the baseline scenario. The result represents the GHG emission reductions or increases 

attributable to the alternative waste management practice. 

 4

 The emission factors are expressed in terms of GHG emissions per ton of material managed. In the case 

of recycling, we define 1 ton of material managed as 1 ton collected for recycling. As discussed in Chapter 4, the 

emission factors can be adjusted to calculate GHG emissions in terms of tons of recycled materials as marketed

(reflecting losses in collection and sorting processes), or changes in the recycled content of products. 
116 

Exhibits 8-13 and 8-14 illustrate the results of this procedure in a scenario where the baseline 

management scenario is disposal in a landfill with national average conditions (i.e., the weighted average 

in terms of landfill gas recovery practice). Alternative scenarios involve source reduction, recycling, 

composting, or combustion. The values in the cells of the matrix are expressed in MTCE/ton in Exhibit 8-

13 and in MTCO2E/ton in Exhibit 8-14, and represent the incremental change in GHG emissions. For 

example, recycling 1 ton of office paper, rather than landfilling it, reduces GHG emissions by 1.30 

MTCE, or 4.76 MTCO2E (see the “Recycling” columns of the exhibits). Continuing the example from the 

previous paragraph, if a business implements an office paper recycling program and annually diverts 10 

tons of office paper (that would otherwise be landfilled) to recycling, the GHG emission reductions are: 

10 tons/yr * -1.30 MTCE/ton = -13.0 MTCE/yr 

Under the sign convention used in this report, the negative value indicates that emissions are 

reduced. 

Due to resource and data limitations, emission factors have not been developed for all material 

types reported by WasteWise partners, the Voluntary Reporting of Greenhouse Gas Program—or 1605(b) 

as it is commonly called—and other parties interested in reporting voluntary emission reductions. 

However, existing emission factors will continue to be updated and improved and new emission factors 

will be developed as more data becomes available. The latest emission factors, reflecting these ongoing 

revisions, can be found on the EPA Global Warming Web site 

<http://www.epa.gov/globalwarming/actions/waste/w-online.htm>. 

 In cases where parties have been using source reduction or recycling techniques for materials 

not specifically analyzed in this report, it is possible to estimate the GHG emission reductions by 

assigning surrogate materials. A list of materials not specifically analyzed, and their corresponding 

surrogates, is presented in Exhibit 8-15. Surrogates are assigned based on consideration of similarities in 

characteristics likely to drive life-cycle GHG emissions, such as similarities in energy consumption 

Applying Emission Factors: Non-linear Relationship between Recycling and Emission Reductions and 

Forest Carbon Leakage 

 Two caveats should be considered when applying the emission factors to analyze large-scale shifts in 

waste management. First, increased recycling and GHG emission reductions may have a non-linear relationship, 

such that emission reductions increase at a declining rate as recycling increases. This decline may be due to three 

factors: (1) energy use in manufacturing processes may be non-linear with respect to recycled content; (2) 

manufacturing capacity for recycled materials may be limited in the short term, so that large-scale increases in 

recycling would require additional capital investment in capacity; and (3) market penetration of recyclables may 

have limits (e.g., due to performance characteristics), such that recyclables cannot completely replace virgin 

inputs in the short term. 

In terms of the second caveat, the forest carbon sequestration benefits of paper and wood source 

reduction and recycling are based on the assumption that reduced demand for a given paper or wood product 

translates directly into reduced tree harvesting. Given that pulpwood and roundwood can be used for many 

products, some of the forest carbon sequestration benefits may be lost by an increase in harvests for these other 

products. This phenomenon is a form of what is sometimes termed “leakage” in the context of GHG mitigation 

projects. 

 Although both of these issues are important considerations in applying the emission factors in this 

report, we note that the emission factors are primarily designed for use by local waste managers. The factors are 

intended to assess the GHG impacts of waste management decisions at a small-to-moderate scale. Readers should 

be cautious when applying the emission factors at a larger scale, however, since the non-linear nature of the 

factors and the issue of leakage become most relevant in the larger context. 
117 

during the raw material acquisition and manufacturing life-cycle stages. Note that the use of these 

surrogates involves considerable uncertainty. 

Exhibit 8-15 Recommended Surrogates for Voluntary Reporting 

Material Source Reduced Surrogate Material 

Metal (type unknown) Average of Aluminum and Steel 

Mixed Metals Average of Aluminum and Steel 

Copper Steel Cans 

Iron Steel Cans 

Other Ferrous Metals Steel Cans 

Other Non-Ferrous Metals Steel Cans 

Steel Steel Cans 

Plastic (resin unknown) (PET+HDPE+LDPE)/3 

PVC/Vinyl (PET+HDPE+LDPE)/3 

Polypropylene (PET+HDPE+LDPE)/3 

Polystyrene (PET+HDPE+LDPE)/3 

Other plastic (resin known, but not 41-46) (PET+HDPE+LDPE)/3 

Rubber (PET+HDPE+LDPE)/3 

Textiles (PET+HDPE+LDPE)/3 

Boxboard Corrugated Cardboard 

Kraft Paper Corrugated Cardboard 

Coated Paper Magazines/Third-class Mail 

High Grade Paper Office Paper 

Paper (type unknown) Mixed Paper – Broad Definition 

Wood Dimensional Lumber 

Food Food Discards 

Organics (type unknown) Yard Trimmings 

Other Yard Waste Yard Trimmings 

In our effort to continually expand and update life-cycle GHG emission factors for MSW 

materials, we are in the process of developing emission factors for carpet and personal computers. The 

emission factors will be based on data compiled by Franklin Associates, Ltd. These emission factors will 

differ from the other emission factors presented in this report because they are for products, each of which 

contain a variety of individual materials. In turn, the life-cycle emission factors will need to account for 

GHG emissions associated with the life cycle of each component material. Given the complexity of this 

task and the relatively limited life-cycle data on components of these products, EPA welcomes input from 

industry stakeholders to augment or verify the activity data that will be the basis for new emission factors 

for these products. 
118 

8.3 OTHER LIFE-CYCLE GHG ANALYSES AND TOOLS 

Life-cycle analysis is increasingly being used to quantify the GHG impacts of private and public 

sector decisions. In addition to the life-cycle analyses that underpin the emission factors in this report, 

Environmental Defense,

5

 ICLEI, Ecobilan, and others have analyzed the life-cycle environmental impacts 

of various industry processes (e.g., manufacturing) and private and public sector practices (e.g., waste 

management). In many cases, the results of life-cycle analyses are packaged into life-cycle software tools 

that distill the information according to a specific user’s needs. 

As mentioned earlier, the WARM model was designed as a tool for waste managers to weigh the 

GHG impacts of their waste management practices. As a result, the model focuses exclusively on waste 

sector GHG emissions, and the methodology used to estimate emissions is consistent with international 

and domestic GHG accounting guidelines. Life-cycle tools designed for broader audiences necessarily

include other sectors and/or other environmental impacts, and are not necessarily tied to the 

Intergovernmental Panel on Climate Change (IPCC) guidelines for GHG accounting or the methods used 

in the Inventory of U.S. Greenhouse Gas Emissions and Sinks. 

• WARM covers 21 types of materials and 5 waste management options: source reduction, 

recycling, combustion, composting, and landfilling. WARM accounts for upstream energy

and non-energy emissions, transportation distances to disposal and recycling facilities, carbon 

sequestration, and utility offsets that result from landfill gas collection and combustion. The 

tool provides participants in DOE’s 1605(b) program with the option to report results by year, 

by gas, and by year and by gas. WARM software is available free of charge in both a Webbased calculator format and a Microsoft Excel

 spreadsheet. The tool is ideal for waste 

planners interested in tracking and reporting voluntary GHG emission reductions from waste 

management practices and comparing the climate change impacts of different approaches. To 

access the tool, visit: <http://www.epa.gov/globalwarming/actions/waste/warm.htm>. The 

latest version of WARM has the additional capacity to calculate energy savings resulting 

from waste management decisions. 

• The Cities for Climate Protection (CCP) Campaign’s Greenhouse Gas Emission Software 

was developed by Torrie Smith Associates for ICLEI. This Windows-based tool, targeted for 

use by local governments, can analyze emissions and emission reductions on a communitywide basis and for municipal operations alone. The community-wide module looks at 

residential, commercial, and industrial buildings, transportation activity, and communitygenerated waste. The municipal operations module considers municipal buildings, municipal 

fleets, and waste from municipal in-house operations. In addition to computing GHG 

emissions, the CCP software estimates reductions in criteria air pollutants, changes in energy

consumption, and financial costs and savings associated with energy use and other emission 

reduction initiatives. A version of the software program was made available for use by private 

businesses and institutions during the summer of 2001. CCP software subscriptions, 

including technical support, are available to governments participating in ICLEI for a 

subsidized price of $240. The full retail price of the software in the United States is $2,000. 

For more information, visit: <http://www.iclei.org/us/ccpsoftware.html> or contact the U.S. 

ICLEI office at (510)-540-8843, iclei_usa@iclei.org. 

 5

 Blum, L., Denison, R.A., and Ruston, V.F. 1997. “A Life-Cycle Approach to Purchasing and Using 

Environmentally Preferable Paper: A Summary of the Paper Task Force Report,” Journal of Industrial Ecology; 

Volume 1; No. 3; pp, 15-46. Denison, R.A. 1996. “Environmental Life-Cycle Comparison of Recycling, 

Landfilling, and Incineration: A Review of Recent Studies;” Annual Review of Energy and the Environment; 

Volume 21, Chapter 6, pp.191-237. 
119 

• The MSW Decision Support Tool (DST) and life-cycle inventory database for North America 

have been developed through funding by EPA’s Office of Research and Development (ORD) 

through a cooperative agreement with the Research Triangle Institute (CR823052). The 

methodology is based on a multi-media, multi-pollutant approach and includes analysis of 

GHG emissions as well as a broader set of emissions (air, water, and waste) associated with 

MSW operations. The MSW-DST is available for site-specific applications and has been used 

to conduct analyses in several states and 15 communities, including use by the U.S. Navy in 

the Pacific Northwest. The tool is intended for use by solid waste planners at state and local 

levels to analyze and compare alternative MSW management strategies with respect to cost, 

energy consumption, and environmental releases to the air, land, and water. The costs are 

based on full-cost accounting principles and account for capital and operating costs using an 

engineering economics analysis. The MSW-DST calculates not only projected emissions of 

GHGs and criteria air pollutants, but also emissions of more than 30 air- and water-borne 

pollutants. The DST models emissions associated with all MSW management activities, 

including waste collection and transportation, transfer stations, materials recovery facilities, 

compost facilities, landfills, combustion and refuse-derived fuel facilities, utility offsets, 

material offsets, and source reduction. The differences in residential, multi-family, and 

commercial sectors can be evaluated individually. The software has optimization capabilities 

that enable one to identify options that evaluate minimum costs as well as solutions that can 

maximize environmental benefits, including energy conservation and GHG reductions. 

At the time of the publication of this report, the LCI database for North America was to be 

released in the winter of 2002. All supporting documentation for the MSW-DST and LCI 

database is to be released by spring 2002. Plans to develop a Web-based version are being 

considered. The MSW-DST provides extensive default data for the full range of MSW 

process models and requires minimum input data. The defaults can be tailored to the specific 

communities using site-specific information. For further information, refer to the project Web 

site at http://www.rti.org/units/ese/p2/lca.cfm#life. The MSW-DST also includes a calculator 

for source reduction and carbon sequestration using a methodology that is consistent with the 

IPCC in terms of the treatment of biogenic CO2 emissions. For more information, refer to the 

project Web site: <http://www.rti.org/units/ese/p2/lca.cfm#life> or contact Susan Thornloe, 

U.S. EPA, (919)-541-2709, thornloe.susan@epamail.epa.gov, or Keith Weitz, Research 

Triangle Institute, (919)-541-6973, kaw@rti.org. 

• The Tool for Environmental Analysis and Management (TEAM), developed by Ecobilan, 

simulates operations associated with product design, processes and, activities associated with 

several industrial sectors. The model considers energy consumption, material consumption, 

transportation, waste management, and other factors in its evaluation of environmental 

impacts. Many firms and some government agencies have used the model. Users pay a 

licensing fee of $3,000 and an annual maintenance contract of $3,000. This model is intended 

for use in Europe and was not developed for use in North America. For more information, 

visit: <http://www.ecobalance.com/software/gb_software.html>. 

8.4 OPPORTUNITIES FOR GHG REDUCTIONS 

Although this report has focused on the five most common waste management practices—source 

reduction, recycling, composting, combustion, and landfilling—for select materials, future quantification 

efforts may include a number of emerging practices: 

• Co-firing waste biomass. For utilities and power generating companies with coal-fired 

capacity, co-firing with waste biomass may represent one of the least-cost renewable energy 

options. Co-firing involves replacing a portion of the coal with biomass at an existing power 
120 

plant boiler. This replacement can be achieved by either mixing biomass with coal before fuel 

is introduced into the boiler or by using separate fuel feeds for coal and biomass. Specific 

biomass feedstocks include agricultural and wood waste, MSW, and industrial wastes. Given 

the increasing use of co-firing technology as an energy source, understanding its GHG 

benefits will likely be an important future EPA effort. 

• Compost as landfill cover. Using compost as landfill cover on closed landfills provides an 

excellent environment for the bacteria that oxidize CH4. Under optimal conditions, compost 

covers can practically eliminate CH4 emissions. Furthermore, the covers offer the possibility 

of controlling these emissions in a cost-effective manner. This technology is particularly 

promising for small landfills, where landfill gas collection is not required and the economics 

of landfill gas-to-energy projects are not attractive. Ancillary benefits also might arise in the 

compost market from this technique if using compost as a landfill cover becomes a 

widespread practice. An increase in composting could reduce the quantity of organic waste 

disposed of at MSW landfills, thereby reducing CH4 emissions. Given the recent 

development of this practice, quantifying its GHG impacts will likely prove useful as landfill 

owners consider adopting the technology. 

• Bioreactors. Bioreactors are a form of controlled landfilling with the potential to provide 

reliable energy generation from solid waste, as well as significant environmental and solid 

waste management benefits. The concept is to accelerate the decomposition process of 

landfill waste through controlled additions of liquid and leachate recirculation, which 

enhances the growth of the microbes responsible for solid waste decomposition. The result is 

to shorten the time frame for landfill gas generation, thereby rendering projections of landfill 

gas generation rates and yields that are much more reliable for landfill gas recovery. 

• Anaerobic digestion. Several facilities are using this technique to produce CH4 from mixed 

waste, which is then used to fuel energy recovery. The approach generates CH4 more quickly 

and captures it more completely than in a landfill environment, and thus, from a GHG 

perspective, offers a potentially attractive waste management option.6

 

• The paperless office. The rise of computer technology for research, communications, and 

other everyday workplace functions has presented a major opportunity for source reduction in 

the modern office. Today’s offices are commonly equipped with all the necessary 

technologies to bypass paper entirely and rely instead on electronic communication. This 

form of “comprehensive” source reduction comes with significant GHG benefits, as described 

in Chapter 4. Therefore, attempting to quantify and communicate these benefits to the 

business community will be an important task in the coming years. 

• Product stewardship. Increasingly, companies are taking responsibility for the environmental 

impacts associated with the full life cycle of their products. Two industries in particular—

carpet and electronics—have been on the forefront of product stewardship efforts. 

Carpet: Currently, more than 6 billion pounds of carpet are shipped each year, of which 

approximately 200 million pounds are recycled. Although carpet is difficult to recycle due to 

its varied make-up, any incremental increase in recycling could have significant climate 

benefits. As a result, EPA is working with a group of carpet industry representatives, state 

environmental agencies, and non-profit recycling organizations to reach voluntary agreement 

on a phase-out of carpet disposal. This product stewardship activity has focused on setting 

rates and dates for carpet recovery over the next 10 years and encouraging the carpet industry 

 6

 Environment Canada. 2001. Determination of the Impact of Waste Management Activities on 

Greenhouse Gas Emissions. Submitted by ICF Consulting, Torrie-Smith Associates, and Enviros-RIS. 
121 

to develop a new third-party organization to help coordinate industry efforts. The carpet 

industry and states have signed a memorandum of understanding that outlines the process and 

guiding framework for developing a 10-year plan on carpet disposal phase-out. EPA will 

continue to facilitate this effort in the coming years.

7

 

Electronics: Understanding GHG emissions associated with waste management options for 

electronics products is important for a number of reasons. First, electronics are among the 

most rapidly growing categories of the U.S. waste stream. Sales of electronics have been 

increasing dramatically, and, due to the fairly short period between purchase and discard, the 

quantity of electronics discarded is expected to grow significantly in the future. Second, 

electronics contain valuable materials that can be reused and/or recycled. Third, many

electronics products contain toxic materials that are covered by hazardous waste regulations. 

These three factors have motivated interest on the part of electronics manufacturers, waste 

managers, and others in recycling. Electronics will therefore become an increasingly essential 

addition to the list of materials analyzed in this report. 

EPA will continue to evaluate new opportunities to reduce emissions from waste management as 

they become known. We also encourage readers to consider creative approaches to waste management, 

particularly those with associated life-cycle energy benefits or carbon storage implications. 

All of the exhibits presented so far in this report have expressed GHG emissions in units of 

MTCE or MTCO2E, calculated as the sum of the individual gases (CO2, CH4, N2O, and PFCs) weighted 

by their global warming potential. In the Voluntary Reporting of Greenhouse Gas Program—also known 

as the 1605(b) program—established by DOE’s Energy Information Administration, reporting companies 

are asked to provide emission reductions for each of the individual gases. In addition, the 1605(b) 

program requires emission reductions to be reported in the year they are achieved and does not allow 

participants to take credit for future emission reductions. Because the GHG emission factors presented in 

this report reflect the “present value” of future emissions and sinks as well as emissions and sinks 

occurring in the reporting year, our emission factors are not directly transferrable to the 1605(b) program. 

For purposes of supporting the program, we developed a revised set of 1605(b) program emission factors 

that reflect emissions by gas and by year. These emission factors provide incremental emissions for a 

baseline of landfilling and alternative scenarios of source reduction and recycling. Detailed reporting 

instructions and forms are available on DOE’s Web site at http://www.eia.doe.gov/oiaf/1605/forms.html. 

* * * * * 

We close with a final note about the limitations of the GHG emission estimates in this report. We 

based our analysis on what we believed to be the best available data; where necessary, we made 

assumptions that we believe are reasonable. The accuracy of the estimates is limited, however, by the use 

of these assumptions and limitations in the data sources, as discussed throughout this report. Where 

possible, the emission factors reported here can be improved by substituting process- or site-specific data 

to increase the accuracy of the estimates. For example, a commercial firm with a large aluminum

recycling program may have better data on the specific fuel mix of its source of aluminum and could thus 

calculate a more exact value for the emission factor. Despite the uncertainty in the emission factors, they

provide a reasonable first approximation of the GHG impacts of solid waste management, and we believe 

that they provide a sound basis for evaluating voluntary actions to reduce GHG emissions in the waste 

management arena. 

 7

 Additional information on this activity is available on the Minnesota Office of Environmental Assistance 

Web site at http://www.moea.state.mn.us/carpet/care.cfm. 

123 

(b) Forest Carbon Sequestration (c)

Aluminum Cans -2.49 -4.67 0.00 0.00 0.00 Steel Cans -0.79 -1.01 0.00 0.00 0.00 Glass -0.14 -0.16 0.00 0.00 0.00 HDPE -0.49 -0.53 0.00 0.00 0.00 LDPE -0.61 -0.64 0.00 0.00 0.00 PET -0.49 -0.58 0.00 0.00 0.00 Corrugated Cardboard -0.24 -0.22 -0.28 -0.73 0.00 Magazines/Third-class Mail -0.46 -0.46 -0.58 -0.73 0.00 Newspaper -0.46 -0.59 -0.35 -0.73 0.00 Office Paper -0.31 -0.28 -0.50 -0.73 0.00 Phonebooks -0.64 -0.67 -0.65 -0.73 0.00 Textbooks -0.59 -0.59 -0.64 -0.73 0.00 Dimensional Lumber -0.05 -0.05 -0.50 -0.50 0.00 Medium-density Fiberboard -0.10 -0.10 -0.50 -0.50 0.00 Food Discards NA NA NA NA NA Yard Trimmings NA NA NA NA NA Mixed Paper Broad Definition NA NA NA NA NA  Residential Definition NA NA NA NA NA  Office Paper Definition NA NA NA NA NA Mixed Plastics NA NA NA NA NA Mixed Recyclables NA NA NA NA NA Mixed Organics NA NA NA NA NA Mixed MSW (as disposed) NA NA NA NA NA 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point (once the material has already undergon

materials acquisition and manufacturing phase).

Exhibit 8-1

GHG Emissions for Source Reduction

Source Redu

Displaces Cu

Mix of Virgin 

Recycled In

Material 

Source 

Reduction 

Displaces 

Current Mix of 

Virgin and 

Recycled 

Inputs

Source 

Reduction 

Displaces 

Virgin Inputs

Source 

Reduction 

Displaces 

Current Mix of 

Virgin and 

Recycled 

Inputs

Source 

Reduction 

Displaces 

Virgin Inputs

Waste 

Management 

Emissions

(MTCE/Ton of Material Source Reduced)

(a) Raw Materials Acquisition 

and Manufacturing

Emissions Measured from a Waste Generation Reference Point1

 (d 

(d) N
124 

(b) Forest Carbon Sequestration (c)

Aluminum Cans -9.15 -17.11 0.00 0.00 0.00 Steel Cans -2.89 -3.69 0.00 0.00 0.00 Glass -0.50 -0.57 0.00 0.00 0.00 HDPE -1.79 -1.95 0.00 0.00 0.00 LDPE -2.25 -2.34 0.00 0.00 0.00 PET -1.78 -2.14 0.00 0.00 0.00 Corrugated Cardboard -0.88 -0.82 -1.01 -2.69 0.00 Magazines/Third-class Mail -1.69 -1.69 -2.11 -2.69 0.00 Newspaper -1.69 -2.15 -1.29 -2.69 0.00 Office Paper -1.13 -1.02 -1.82 -2.69 0.00 Phonebooks -2.33 -2.44 -2.37 -2.69 0.00 Textbooks -2.15 -2.16 -2.35 -2.69 0.00 Dimensional Lumber -0.17 -0.17 -1.84 -1.84 0.00 Medium-density Fiberboard -0.36 -0.36 -1.84 -1.84 0.00 Food Discards NA NA NA NA NA Yard Trimmings NA NA NA NA NA Mixed Paper Broad Definition NA NA NA NA NA  Residential Definition NA NA NA NA NA  Office Paper Definition NA NA NA NA NA Mixed Plastics NA NA NA NA NA Mixed Recyclables NA NA NA NA NA Mixed Organics NA NA NA NA NA Mixed MSW (as disposed) NA NA NA NA NA 

Note that totals may not add due to rounding and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

GHG Emissions for Source Reduction

(MTCO2E/Ton of Material Source Reduced)

Material

(d

Waste 

Management 

Emissions

Source Red

Displaces C

Mix of Virg

Recycled 

(a) Raw Materials Acquisition 

and Manufacturing

Source 

Reduction 

Displaces 

Current Mix of 

Virgin and 

Recycled 

Inputs

Source 

Reduction 

Displaces 

Virgin Inputs

Source 

Reduction 

Displaces 

Current Mix of 

Virgin and 

Recycled 

Inputs

Source 

Reduction 

Displaces 

Virgin Inputs

Emissions Measured from a Waste Generation Reference Point1

 

Exhibit 8-2
125 

(a) (b) (c) (d) (e) (f) (

 

Material

RMAM Emissions 

Not Included in 

Baseline3 (current 

mix of inputs)

Waste 

Generation 

Baseline

Process 

Energy

Transportation 

Energy

Process 

Non-Energy

Forest Carbon 

Sequestration

Wa

Manag

Emis

Aluminum Cans 2.49 0.00 -2.92 -0.14 -1.05 0.00 Steel Cans 0.79 0.00 -0.48 -0.01 0.00 0.00 Glass 0.14 0.00 -0.03 0.00 -0.04 0.00 HDPE 0.49 0.00 -0.34 0.00 -0.04 0.00 LDPE 0.61 0.00 -0.43 0.00 -0.04 0.00 PET 0.49 0.00 -0.40 0.00 -0.02 0.00 Corrugated Cardboard 0.24 0.00 0.04 -0.01 0.00 -0.73 Magazines/Third-class Mail 0.46 0.00 0.00 0.00 0.00 -0.73 Newspaper 0.46 0.00 -0.21 -0.01 0.00 -0.73 Office Paper 0.31 0.00 0.06 0.00 0.00 -0.73 Phonebooks 0.64 0.00 -0.18 0.00 0.00 -0.73 Textbooks 0.59 0.00 -0.01 0.00 0.00 -0.73 Dimensional Lumber 0.05 0.00 0.02 0.00 0.00 -0.69 Medium-density Fiberboard 0.10 0.00 0.01 0.00 0.00 -0.69 Food Discards NA 0.00 NA NA NA NA Yard Trimmings NA 0.00 NA NA NA NA Mixed Paper Broad Definition 0.38 0.00 0.08 -0.02 0.00 -0.73  Residential Definition 0.38 0.00 0.08 -0.02 0.00 -0.73  Office Paper Definition 0.85 0.00 -0.08 -0.02 0.00 -0.73 Mixed Plastics 0.51 0.00 -0.38 0.00 -0.03 0.00 Mixed Recyclables 0.36 0.00 -0.10 -0.01 -0.02 -0.63 Mixed Organics NA NA NA NA NA NA Mixed MSW (as disposed) NA NA NA NA NA NA 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point (once the material has already undergone the 

 and manufacturing phase).

2Material 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 fr

Exhibit 8-3

Raw Materials Acquisition and 

Manufacturing (RMAM) Recycled Input Credit2

Recycling 

(GHG Emissions in MTCE/Ton)

Emissions Measured from a Waste Generation Reference Point1
126 

(a) (b) (c) (d) (e) (f) (

 

Material

RMAM Emissions 

Not Included in 

Baseline3 (Current 

Mix of Inputs)

Waste 

Generation 

Baseline

Process 

Energy

Transportation 

Energy

Process 

NonEnergy Forest Carbon Sequestration

Wa

Manag

Emis

Aluminum Cans 9.15 0.00 -10.70 -0.51 -3.86 0.00 Steel Cans 2.89 0.00 -1.75 -0.04 0.00 0.00 Glass 0.50 0.00 -0.12 -0.02 -0.14 0.00 HDPE 1.79 0.00 -1.26 0.00 -0.15 0.00 LDPE 2.25 0.00 -1.57 0.00 -0.15 0.00 PET 1.78 0.00 -1.48 0.00 -0.08 0.00 Corrugated Cardboard 0.88 0.00 0.13 -0.04 -0.01 -2.69 Magazines/Third Class Mail 1.69 0.00 -0.01 0.00 0.00 -2.69 Newspaper 1.69 0.00 -0.76 -0.03 0.00 -2.69 Office Paper 1.13 0.00 0.22 0.00 -0.02 -2.69 Phonebooks 2.33 0.00 -0.65 0.00 0.00 -2.69 Textbooks 2.15 0.00 -0.05 0.00 0.00 -2.69 Dimensional Lumber 0.17 0.00 0.07 0.01 0.00 -2.53 Medium-density Fiberboard 0.36 0.00 0.05 0.02 0.00 -2.53 Food Discards NA 0.00 NA NA NA NA Yard Trimmings NA 0.00 NA NA NA NA Mixed Paper 0.00 Broad Definition 1.38 0.00 0.29 -0.06 0.00 -2.69  Residential Definition 1.39 0.00 0.29 -0.06 0.00 -2.69  Office Paper Definition 3.12 0.00 -0.29 -0.07 0.00 -2.69 Mixed Plastics 1.85 0.00 -1.40 0.00 -0.12 0.00 Mixed Recyclables 1.32 0.00 -0.38 -0.04 -0.09 -2.30 Mixed Organics NA NA NA NA NA NA Mixed MSW (as disposed) NA NA NA NA NA NA 

Note that totals may not add due to rounding and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

 (once the material has already undergone the raw materials acquisitionand manufacturing phase).

2Material 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 fro

Emissions Measured from a Waste Generation Reference Point1

Raw Materials Acquisition and 

Manufacturing (RMAM) Recycled Input Credit2

Exhibit 8-4

Recycling 

(GHG Emissions in MTCO2E/Ton)
127 

(a) (b) (c) (d) 

(e 

Material

RMAM Emissions 

Not Included in 

Baseline2

Waste Generation 

Baseline

Transportation to 

Composting

Soil Carbon 

Sequestration

Net 

(Pos

Aluminum Cans -2.49 0.00 NA NA Steel Cans -0.79 0.00 NA NA Glass -0.14 0.00 NA NA HDPE -0.49 0.00 NA NA LDPE -0.61 0.00 NA NA PET -0.49 0.00 NA NA Corrugated Cardboard -0.24 0.00 NA NA Magazines/Third-class Mail -0.46 0.00 NA NA Newspaper -0.46 0.00 NA NA Office Paper -0.31 0.00 NA NA Phonebooks -0.64 0.00 NA NA Textbooks -0.59 0.00 NA NA Dimensional Lumber -0.05 0.00 NA NA Medium-density Fiberboard -0.10 0.00 NA NA Food Discards NA 0.00 0.01 -0.07 Yard Trimmings NA 0.00 0.01 -0.07 Mixed Paper Broad Definition 0.38 0.00 NA NA  Residential Definition 0.38 0.00 NA NA  Office Paper Definition 0.85 0.00 NA NA Mixed Plastics 0.51 0.00 NA NA Mixed Recyclables 0.36 0.00 NA NA Mixed Organics NA 0.00 0.01 -0.07 Mixed MSW (as disposed) NA NA NA NA 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2 The value for mixed MSW is the weighted average of the RMAM emissions for those materials we studied. 

Exhibit 8-5

Composting

(GHG Emissions in MTCE/Ton)

Values are for Mass Burn Facilities with National Average Rate of Ferrous Recovery. Emissions Measured from a Waste 

Reference Point1 

Raw Materials Acquisition and 

Manufacturing (RMAM)
128 

(a) (b) (c) (d) 

(e 

Material

RMAM Emissions 

Not Included in 

Baseline2

Waste Generation 

Baseline

Transportation to 

Composting

Soil Carbon 

Sequestration

Net E

(Post-

Aluminum Cans -9.15 0.00 NA NA Steel Cans -2.89 0.00 NA NA Glass -0.50 0.00 NA NA HDPE -1.79 0.00 NA NA LDPE -2.25 0.00 NA NA PET -1.78 0.00 NA NA Corrugated Cardboard -0.88 0.00 NA NA Magazines/Third-class Mail -1.69 0.00 NA NA Newspaper -1.69 0.00 NA NA Office Paper -1.13 0.00 NA NA Phonebooks -2.33 0.00 NA NA Textbooks -2.15 0.00 NA NA Dimensional Lumber -0.17 0.00 NA NA Medium-density Fiberboard -0.36 0.00 NA NA Food Discards NA 0.00 0.04 -0.24 Yard Trimmings NA 0.00 0.04 -0.24 Mixed Paper 0.00 Broad Definition 1.38 0.00 NA NA  Residential Definition 1.39 0.00 NA NA  Office Paper Definition 3.12 0.00 NA NA Mixed Plastics 1.85 0.00 NA NA Mixed Recyclables 1.32 0.00 NA NA Mixed Organics NA 0.00 0.04 -0.24 Mixed MSW (as disposed) NA NA NA NA 

Note that totals may not add due to rounding and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2 The value for mixed MSW is the weighted average of the RMAM emissions for those materials we studied. 

Raw Materials Acquisition and 

Manufacturing (RMAM)

Exhibit 8-6

Composting

(GHG Emissions in MTCO2E/Ton)

Values are for Mass Burn Facilities with National Average Rate of Ferrous Recovery. Emissions Measured from a Waste G

Reference Point1 
129 

(a) (b) (c) (d) (e) (f) (g) 

Material

RMAM 

Emissions 

Not Included 

in Baseline2

Waste 

Generation 

Baseline

Transportation 

to Combustion

CO2 from 

Combustion

N2O from 

Combustion

Avoided 

Utility 

Emissions

Ferrou

Recove

Aluminum Cans -2.49 0.00 0.01 0.00 0.00 0.01 0Steel Cans -0.79 0.00 0.01 0.00 0.00 0.01 -0Glass -0.14 0.00 0.01 0.00 0.00 0.01 0HDPE -0.49 0.00 0.01 0.76 0.00 -0.54 0LDPE -0.61 0.00 0.01 0.76 0.00 -0.54 0PET -0.49 0.00 0.01 0.56 0.00 -0.28 0Corrugated Cardboard -0.24 0.00 0.01 0.00 0.01 -0.20 0Magazines/Third-class Mail -0.46 0.00 0.01 0.00 0.01 -0.15 0Newspaper -0.46 0.00 0.01 0.00 0.01 -0.23 0Office Paper -0.31 0.00 0.01 0.00 0.01 -0.20 0Phonebooks -0.64 0.00 0.01 0.00 0.01 -0.23 0Textbooks -0.59 0.00 0.01 0.00 0.01 -0.20 0Dimensional Lumber -0.05 0.00 0.01 0.00 0.01 -0.24 0Medium-density Fiberboard -0.10 0.00 0.01 0.00 0.01 -0.24 0Food Discards NA 0.00 0.01 0.00 0.01 -0.07 0Yard Trimmings NA 0.00 0.01 0.00 0.01 -0.08 0Mixed Paper Broad Definition 0.38 0.00 0.01 0.00 0.01 -0.20 0 Residential Definition 0.38 0.00 0.01 0.00 0.01 -0.20 0 Office Paper Definition 0.85 0.00 0.01 0.00 0.01 -0.19 0Mixed Plastics 0.51 0.00 0.01 0.67 0.00 -0.43 0Mixed Recyclables 0.36 0.00 0.01 0.02 0.01 -0.18 -0Mixed Organics NA 0.00 0.01 0.00 0.01 -0.07 0Mixed MSW (as disposed) NA 0.00 0.01 0.10 0.01 -0.14 -0

Note that totals may not add due to rounding, and more digits may be displayed than are significant.

NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2 The value for mixed MSW is the weighted average of the RMAM emissions for those materials we studied. 

(GHG Emissions in MTCE/Ton)

Exhibit 8-7

Raw Materials Acquisition 

and Manufacturing (RMAM)

Combustion

Values are for Mass Burn Facilities with National Average Rate of Ferrous Recovery. Emissions Measured from a Waste Generatio
130 

(a) (b) (c) (d) (e) (f) (g) 

Material

RMAM 

Emissions 

Not Included 

in Baseline2

Waste 

Generation 

Baseline

Transportation 

to Combustion

CO2 from 

Combustion

N2O from 

Combustion

Avoided 

Utility 

Emissions

Ferrou

Recove

Aluminum Cans -9.15 0.00 0.03 0.00 0.00 0.04 0Steel Cans -2.89 0.00 0.03 0.00 0.00 0.02 -1Glass -0.50 0.00 0.03 0.00 0.00 0.02 0HDPE -1.79 0.00 0.03 2.79 0.00 -1.97 0LDPE -2.25 0.00 0.03 2.79 0.00 -1.97 0PET -1.78 0.00 0.03 2.04 0.00 -1.02 0Corrugated Cardboard -0.88 0.00 0.03 0.00 0.04 -0.74 0Magazines/Third-class Mail -1.69 0.00 0.03 0.00 0.04 -0.55 0Newspaper -1.69 0.00 0.03 0.00 0.04 -0.84 0Office Paper -1.13 0.00 0.03 0.00 0.04 -0.72 0Phonebooks -2.33 0.00 0.03 0.00 0.04 -0.84 0Textbooks -2.15 0.00 0.03 0.00 0.04 -0.72 0Dimensional Lumber -0.17 0.00 0.03 0.00 0.04 -0.87 0Medium-density Fiberboard -0.36 0.00 0.03 0.00 0.04 -0.87 0Food Discards NA 0.00 0.03 0.00 0.04 -0.25 0Yard Trimmings NA 0.00 0.03 0.00 0.04 -0.30 0Mixed Paper Broad Definition 1.38 0.00 0.03 0.00 0.04 -0.74 0 Residential Definition 1.39 0.00 0.03 0.00 0.04 -0.74 0 Office Paper Definition 3.12 0.00 0.03 0.00 0.04 -0.68 0Mixed Plastics 1.85 0.00 0.03 2.47 0.00 -1.56 0Mixed Recyclables 1.32 0.00 0.03 0.06 0.03 -0.67 -0Mixed Organics NA 0.00 0.03 0.00 0.04 -0.27 0Mixed MSW (as disposed) NA 0.00 0.03 0.37 0.04 -0.53 -0

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2 The value for mixed MSW is the weighted average of the RMAM emissions for those materials we studied. 

Combustion

(GHG Emissions in MTCO2E/Ton)

Exhibit 8-8

Values are for Mass Burn Facilities with National Average Rate of Ferrous Recovery. Emissions Measured from a Waste Generat

Raw Materials Acquisition 

and Manufacturing (RMAM)
131 

(a) (b) (c) (d) (e) (f

(f=b+c

Material

RMAM 

Emissions 

Not Included 

in Baseline2

Waste 

Generation 

Baseline

Transportation 

to Landfill

Net 

Landfill 

CH4

Landfill Carbon 

Sequestration Net Emi

Aluminum Cans 2.49 0.00 0.01 0.00 0.00 Steel Cans 0.79 0.00 0.01 0.00 0.00 Glass 0.14 0.00 0.01 0.00 0.00 HDPE 0.49 0.00 0.01 0.00 0.00 LDPE 0.61 0.00 0.01 0.00 0.00 PET 0.49 0.00 0.01 0.00 0.00 Corrugated Cardboard 0.24 0.00 0.01 0.31 -0.22 Magazines/Third-class Mail 0.46 0.00 0.01 0.17 -0.29 Newspaper 0.46 0.00 0.01 0.15 -0.36 Office Paper 0.31 0.00 0.01 0.69 -0.04 Phonebooks 0.64 0.00 0.01 0.15 -0.36 Textbooks 0.59 0.00 0.01 0.69 -0.04 Dimensional Lumber 0.05 0.00 0.01 0.10 -0.21 Medium-density Fiberboard 0.10 0.00 0.01 0.10 -0.21 Food Discards NA 0.00 0.01 0.19 -0.02 Yard Trimmings NA 0.00 0.01 0.11 -0.21 Mixed Paper Broad Definition 0.38 0.00 0.01 0.33 -0.23  Residential Definition 0.38 0.00 0.01 0.31 -0.24  Office Paper Definition 0.85 0.00 0.01 0.37 -0.21 Mixed Plastics 0.51 0.00 0.01 0.00 0.00 Mixed Recyclables 0.36 0.00 0.01 0.26 -0.21 Mixed Organics NA 0.00 0.01 0.15 -0.12 Mixed MSW (as disposed) NA 0.00 0.01 0.16 -0.10 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.

NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2The value for mixed MSW is the weighted average of the RMAM emissions for those materials we studied. 

Landfilling 

(GHG Emissions in MTCE/Ton)

Values for Landfill Methane and Net Emissions Reflect Projected National Average Methane Recovery in year 2000. Em

Measured from a Waste Generation Reference Point1 

Raw Materials Acquisition 

and Manufacturing (RMAM)

Exhibit 8-9
132 

(a) (b) (c) (d) (e) (f

(f=b+c

Material

RMAM 

Emissions 

Not Included 

in Baseline2

Waste 

Generation 

Baseline

Transportation 

to Landfill

Net 

Landfill 

CH4

Landfill Carbon 

Sequestration Net Em

Aluminum Cans 9.15 0.00 0.04 0.00 0.00 Steel Cans 2.89 0.00 0.04 0.00 0.00 Glass 0.50 0.00 0.04 0.00 0.00 HDPE 1.79 0.00 0.04 0.00 0.00 LDPE 2.25 0.00 0.04 0.00 0.00 PET 1.78 0.00 0.04 0.00 0.00 Corrugated Cardboard 0.88 0.00 0.04 1.12 -0.82 Magazines/Third-class Mail 1.69 0.00 0.04 0.61 -1.07 Newspaper 1.69 0.00 0.04 0.54 -1.32 Office Paper 1.13 0.00 0.04 2.52 -0.16 Phonebooks 2.33 0.00 0.04 0.54 -1.32 Textbooks 2.15 0.00 0.04 2.52 -0.16 Dimensional Lumber 0.17 0.00 0.04 0.35 -0.76 Medium-density Fiberboard 0.36 0.00 0.04 0.35 -0.76 Food Discards NA 0.00 0.04 0.70 -0.08 Yard Trimmings NA 0.00 0.04 0.40 -0.76 Mixed Paper Broad Definition 1.38 0.00 0.04 1.22 -0.83  Residential Definition 1.39 0.00 0.04 1.13 -0.87  Office Paper Definition 3.12 0.00 0.04 1.35 -0.76 Mixed Plastics 1.85 0.00 0.04 0.00 0.00 Mixed Recyclables 1.32 0.00 0.04 0.95 -0.75 Mixed Organics NA 0.00 0.04 0.54 -0.43 Mixed MSW (as disposed) NA 0.00 0.04 0.60 -0.37 

Note that totals may not add due to rounding and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2The value for mixed MSW is the weighted average of the RMAM emissions for those materials we studied. 

Values for Landfill Methane and Net Emissions Reflect Projected National Average Methane Recovery in year 2000. Em

Measured from a Waste Generation Reference Point1 

Exhibit 8-10

Landfilling 

(GHG Emissions in MTCO2E/Ton)

Raw Materials Acquisition 

and Manufacturing (RMAM)
133 

Source Recycling Composting Combustion3 Landfilling4

Material Reduction2

Aluminum Cans -2.49 -4.11 NA 0.02 0.0Steel Cans -0.79 -0.49 NA -0.42 0.0Glass -0.14 -0.08 NA 0.01 0.0HDPE -0.49 -0.38 NA 0.23 0.0LDPE -0.61 -0.47 NA 0.23 0.0PET -0.49 -0.42 NA 0.28 0.0Corrugated Cardboard -0.51 -0.71 NA -0.19 0.0Magazines/Third-class Mail -1.04 -0.74 NA -0.13 -0.1Newspaper -0.81 -0.95 NA -0.21 -0.2Office Paper -0.80 -0.68 NA -0.18 0.6Phonebooks -1.28 -0.91 NA -0.21 -0.2Textbooks -1.23 -0.75 NA -0.18 0.6Dimensional Lumber -0.55 -0.67 NA -0.22 -0.1Medium-density Fiberboard -0.60 -0.67 NA -0.22 -0.1Food Discards NA NA -0.05 -0.05 0.1Yard Trimmings NA NA -0.05 -0.06 -0.0Mixed Paper Broad Definition NA -0.67 NA -0.19 0.1 Residential Definition NA -0.67 NA -0.18 0.0 Office Paper Definition NA -0.83 NA -0.17 0.1Mixed Plastics NA -0.41 NA 0.25 0.0Mixed Recyclables NA -0.76 NA -0.17 0.0Mixed Organics NA NA -0.05 -0.06 0.0Mixed MSW (as disposed) NA NA NA -0.04 0.0

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2 Source reduction assumes displacement of current mix of virgin and recycled inputs.3 Values are for mass burn facilities with national average rate of ferrous recovery.4 Values reflect projected national average methane recovery in year 2000.

Emissions Measured from a Waste Generation Reference Point1 

Net GHG Emissions from Source Reduction and MSW Management Options

(MTCE/Ton)Exhibit 8-11
134 

Source Recycling Composting Combustion3 Landfilli

Material Reduction2

Aluminum Cans -9.15 -15.07 NA 0.06 Steel Cans -2.89 -1.79 NA -1.53 Glass -0.50 -0.28 NA 0.05 HDPE -1.79 -1.40 NA 0.85 LDPE -2.25 -1.71 NA 0.85 PET -1.78 -1.55 NA 1.04 Corrugated Cardboard -1.89 -2.60 NA -0.68 Magazines/Third-class Mail -3.80 -2.70 NA -0.49 Newspaper -2.97 -3.48 NA -0.77 Office Paper -2.95 -2.48 NA -0.65 Phonebooks -4.70 -3.34 NA -0.77 Textbooks -4.49 -2.74 NA -0.65 Dimensional Lumber -2.01 -2.45 NA -0.81 Medium-density Fiberboard -2.20 -2.47 NA -0.81 Food Discards NA NA -0.20 -0.19 Yard Trimmings NA NA -0.20 -0.23 Mixed Paper Broad Definition NA -2.47 NA -0.68  Residential Definition NA -2.47 NA -0.68  Office Paper Definition NA -3.05 NA -0.62 Mixed Plastics NA -1.51 NA 0.93 Mixed Recyclables NA -2.80 NA -0.61 Mixed Organics NA NA -0.20 -0.21 Mixed MSW (as disposed) NA NA NA -0.13 

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Under the accounting convention used in this analysis, emissions are quantified from a waste generation reference point 

(once the material has already undergone the raw materials acquisition and manufacturing phase).

2 Source reduction assumes displacement of current mix of virgin and recycled inputs.3 Values are for mass burn facilities with national average rate of ferrous recovery.4 Values reflect projected national average methane recovery in year 2000.

Exhibit 8-12

Net GHG Emissions from Source Reduction and MSW Management Options

(MTCO2E/Ton)

Emissions Measured from a Waste Generation Reference Point1 
135 

Source Reduction Recycling Composting 

Net Emissions Net Emissions Net Emissions 

Minus Landfilling Net Emissions Minus Landfilling Minus Landfillin

Material Current Mix of Inputs 100% Virgin Inputs Net Emissions Net Emissions 

Aluminum Cans -2.50 -4.68 -4.12 NSteel Cans -0.80 -1.02 -0.50 NGlass -0.15 -0.17 -0.09 NHDPE -0.50 -0.54 -0.39 NLDPE -0.63 -0.65 -0.48 NPET -0.50 -0.59 -0.43 NCorrugated Cardboard -0.59 -1.03 -0.79 NMagazines/Third-class Mail -0.92 -1.07 -0.62 NNewspaper -0.60 -1.11 -0.74 NOffice Paper -1.43 -1.63 -1.30 NPhonebooks -1.07 -1.19 -0.70 NTextbooks -1.85 -1.94 -1.37 NDimensional Lumber -0.44 -0.44 -0.56 NMedium-density Fiberboard -0.50 -0.50 -0.57 NFood Discards NA NA NA -0.2Yard Trimmings NA NA NA 0.0Mixed Paper Broad Definition NA NA -0.78 N Residential Definition NA NA -0.74 N Office Paper Definition NA NA -0.99 NMixed Plastics NA NA -0.42 NMixed Recyclables NA NA -0.82 NMixed Organics NA NA NA -0.0Mixed MSW (as disposed) NA NA NA N

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Values for landfilling reflect projected national average methane recovery in year 2000.2 Values are for mass burn facilities with national average rate of ferrous recovery.

Net GHG Emissions of MSW Management Options Compared to Landfilling1

Exhibit 8-13(MTCE/Ton)

Negative values indicate emission reductions.
136 

Source Reduction Recycling Composting 

Net Emissions Net Emissions Net Emissions 

Minus Landfilling Net Emissions Minus Landfilling Minus Landfillin

Material Current Mix of Inputs 100% Virgin Inputs Net Emissions Net Emissions 

Aluminum Cans -9.18 -17.15 -15.11 NSteel Cans -2.92 -3.72 -1.83 NGlass -0.54 -0.61 -0.32 NHDPE -1.82 -1.99 -1.44 NLDPE -2.29 -2.38 -1.75 NPET -1.82 -2.18 -1.59 NCorrugated Cardboard -2.17 -3.79 -2.88 NMagazines/Third-class Mail -3.36 -3.94 -2.26 NNewspaper -2.21 -4.07 -2.72 NOffice Paper -5.23 -5.99 -4.77 NPhonebooks -3.94 -4.37 -2.57 NTextbooks -6.78 -7.13 -5.03 NDimensional Lumber -1.63 -1.63 -2.07 NMedium-density Fiberboard -1.82 -1.82 -2.09 NFood Discards NA NA NA -0.8Yard Trimmings NA NA NA 0.1Mixed Paper Broad Definition NA NA -2.84 N Residential Definition NA NA -2.72 N Office Paper Definition NA NA -3.62 NMixed Plastics NA NA NMixed Recyclables NA NA -2.99 NMixed Organics NA NA NA -0.3Mixed MSW (as disposed) NA NA NA N

Note that totals may not add due to rounding, and more digits may be displayed than are significant.NA: Not applicable, or in the case of composting of paper, not analyzed.

1 Values for landfilling reflect projected national average methane recovery in year 2000.2 Values are for mass burn facilities with national average rate of ferrous recovery.

Exhibit 8-14

Net GHG Emissions of MSW Management Options Compared to Landfilling1

Negative values indicate emission reductions.

(MTCO2E/Ton)