Waste Reduction Model (WARM) Data Quality Assessment Report

Waste Reduction Model (WARM)

Data Quality Assessment Report

U.S. Environmental Protection Agency

Office of Land and Emergency Management

Office of Resource Conservation & Recovery

December 2023

EPA-530-R-23-016

WARM Data Quality Assessment 1

Acknowledgements

This WARM data quality assessment report was submitted to the U.S. Environmental Protection Agency,

Office of Resource Conservation and Recovery under Contract: EP‐W‐10‐056, Task Order

68HERH22F0274 by ICF Incorporated, L.L.C. This effort conducted by Deanna Lizas and her team at ICF.

Input and guidance were provided by Priscilla Halloran, Office of Land and Emergency Management,

Office of Resource Conservation and Recovery.

WARM Data Quality Assessment 2

Table of Contents

Acknowledgements ....................................................................................................................................... 1

Table of Contents .......................................................................................................................................... 2

Acronyms and Abbreviations ........................................................................................................................ 3

Executive Summary ....................................................................................................................................... 4

1. Introduction .......................................................................................................................................... 8

2. Approach ............................................................................................................................................. 10

3. Assessment of Material Datasets........................................................................................................ 15

3.1 Plastics and Bioplastics 16

3.2 Metals 19

3.3 Glass 24

3.4 Paper 26

3.5 Electronics 29

3.6 Construction Materials 32

3.7 Tires 45

3.8 Food Waste 48

3.9 Yard Trimmings 52

4. Assessment of Specific Management Pathway Datasets ................................................................... 54

4.1 Landfilling 54

4.2 Composting 56

4.3 Combustion 58

4.4 Anaerobic Digestion 61

5. Conclusion ........................................................................................................................................... 62

6. References .......................................................................................................................................... 64

Appendix: Data Quality Assessment Matrix ............................................................................................... 79

WARM Data Quality Assessment 3

Acronyms and Abbreviations

C&D Construction and demolition

DQ Data quality

DQA Data Quality Assessment

DQG Data Quality Goal

DQI Data Quality Indicator

DQS Data Quality System

EPA U.S. Environmental Protection Agency

GHG Greenhouse gas

HDPE High-density polyethylene

LCA Life Cycle Assessment

LCI Life Cycle Inventory

LDPE Low-density polyethylene

LLDPE Linear low-density polyethylene

MSW Municipal solid waste

ORD EPA Office of Research and Development

PET Polyethylene terephthalate

PP Polypropylene

PVC Polyvinyl chloride

WARM Waste Reduction Model

WARM Data Quality Assessment 4

Executive Summary

This report describes the findings from the detailed assessment of the quality of the data used to

develop the greenhouse gas (GHG) emission and energy factors in the U.S. Environmental Protection

Agency’s (EPA) Waste Reduction Model (WARM).

The purpose of this effort is to support EPA in

understanding and improving the data quality in WARM, and to provide additional transparency and

insight on the underlying data for WARM users. The report also offers recommendations for prioritizing

future updates.

This data quality assessment (DQA) involved a comprehensive review of the datasets used in the WARM

modeling for each material type and materials management pathway. The data quality for each data

source used to develop the WARM GHG emission and energy factors was evaluated for each of the flow

and process indicators described in EPA’s “Guidance on Data Quality Assessment for Life Cycle Inventory

Data” (Edelen and Ingwersen 2016). Flow indicators consider the reliability of the measurements, and

the correlation of the age of data, geographical coverage, and technological representativeness with the

study’s data quality goals. Process indicators consider the data review process used and the

completeness of the dataset.

The calculation of averages for each indicator grouping and for each material type and pathway

facilitated the assessment of the overall data quality for a material category or management pathway—

particularly across the large number of datasets and over 60 material types. Average scores were

translated into data quality levels, ranging from low to high. To give additional weight to the key data

sources driving emission and energy factor estimates for a material category, weighted average scores

also were calculated along with average scores.

Available at https://www.epa.gov/warm.

WARM Data Quality Assessment 5

Table 1 summarizes findings on data quality for the data quality indicator groupings by material category

and management pathway. The shading offers a big picture heat-map-like view of the data quality

findings with darker shading indicating higher data quality and lighter shading indicating lower data

quality. The report provides detail on these data quality results, and the Appendix: Data Quality

Assessment Matrix. This Appendix presents the scoring details in a matrix form by material,

management pathway, and dataset.

WARM Data Quality Assessment 6

Table 1: Summary of Data Quality Results by Material Type or Management Pathway

Material or Pathway

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process Review

and

Completeness

Average

Weighted

Average

Material Category

Plastics

Medium-high

Medium

Medium-high

Bioplastics

Medium-high

Medium

Medium-high

Metals

Medium

Glass

Medium

Medium-low

Paper

Medium-low

Medium

Medium-low

Medium

Electronics

Medium-high

Construction Materials

Medium-high

Asphalt Concrete

Medium-high

Medium

Medium-high

Medium

Asphalt Shingles

Medium-low

Medium

Carpet

Medium

Medium-low

Medium

Clay Bricks

Medium-high

High

Medium-high

High

Concrete

High

Medium-high

Medium

Medium-high

Dimensional Lumber

Medium-high

High

Medium-high

Drywall

Medium-high

Fiberglass Insulation

Medium

Fly Ash

Medium-high

Medium

Medium-high

Medium-density

Fiberboard

Medium-high

High

Medium-high

Structural Steel

Medium-high

Vinyl Flooring

Medium-high

Medium

Medium-high

Medium

Wood Flooring

Medium-high

Medium

Medium-high

Tires

Medium

Food Waste (non-meat)

Medium-high

Medium

Medium-high

Medium

Food Waste (meat)

Medium-high

Yard Trimmings

Medium-high

Management Pathway

Landfilling

Medium-high

Composting

Medium-high

Medium

High

Medium-high

Combustion

Medium

Medium-low

Medium

Anaerobic Digestion

Medium-high

Medium

Medium-high

Medium

Refers to data generation method and verification.

Includes temporal correlation (data year), geographical correlation (region of data), technological correlation (technology type,

scale), and data collection methods (representativeness, sample size).

Includes process review (third party or internal reviewers) and process completeness (percent of flows covered).

Average of all indicators.

Developed to give additional weight to the key data sources informing the emission factor estimates.

Separate data quality assessments for source reduction and recycling were not conducted as their data sources were already

captured under the material-specific data assessments.

WARM Data Quality Assessment 7

Key findings from this assessment include the following:

• Overall results: The average and weighted average

data quality levels for the WARM data

sources were assessed to be medium to medium-high for most material categories and

management pathways.

• Results by material category: Based on the weighted average of data quality results across the

indicators, medium-high data quality was found for plastics, bioplastics, electronics,

construction materials, food waste (meat), and yard trimmings; medium quality was found for

metals, paper, food waste (non-meat), and tires; and medium-low for glass. Within the

construction materials category, data quality results ranged from medium for asphalt concrete,

asphalt shingles, carpet, fiberglass insulation, and vinyl flooring to high data quality for bricks.

• Results by management pathway: Based on the weighted average of data quality results across

the indicators, medium-high data quality was found for landfilling and composting, and medium

for combustion and anaerobic digestion.

• Results by indicator: Process review and completeness generally had the highest data quality.

While several data sources had lower data quality for the temporal correlation indicator (a sub-

category of flow representativeness) due to age of data, this did not lead to low overall data

quality due to the other data quality considerations.

This assessment informed the following recommendations:

• Identify more recent data sources for select materials.

• Identify published data sources to update certain data inputs.

• Prioritize updates to the modeling of glass, paper, metals, food waste (non-meat), carpet,

asphalt shingles, fiberglass insulation, vinyl flooring, tires, and combustion.

• Improve the archiving and accessibility of the underlying data sources.

• Communicate the DQA findings alongside the WARM documentation.

Considers additional weighting for key data sources used in a particular category.

WARM Data Quality Assessment 8

1. Introduction

The U.S. Environmental Protection Agency’s (EPA) Waste Reduction Model (WARM) is a tool for

estimating the life-cycle greenhouse gas (GHG) emission, energy, and economic impacts of various

materials commonly found in municipal solid waste (MSW) under baseline and alternative waste

management scenarios. Currently, the model includes over 60 different materials and the materials

management pathways of source reduction, recycling, composting, combustion, landfilling, and

anaerobic digestion. EPA first developed WARM as a way to quantify the connection between waste

management practices and climate change, and to determine the potential for source reduction and

recycling of MSW to reduce GHG emissions. The first documentation report applying the WARM GHG

and energy factors, entitled Greenhouse Gas Emissions from Management of Selected Materials in

Municipal Solid Waste, was published in 1998. At the time, WARM included 17 material types commonly

found in MSW. Since then, EPA has expanded the model to include dozens of additional material types,

incorporated more sophisticated modeling of the management practice pathways, added the anaerobic

digestion pathway, quantified economic impacts, and made many other updates and improvements. In

addition, EPA organized the WARM documentation into chapters by material and pathway to provide

WARM users with detailed information about the specific materials analyzed in WARM and the

calculations behind the specific material emission, energy, and economic factors in the model. The

currently available online Excel version of the tool is version 15 available at epa.gov/warm.

WARM relies on numerous data sets for the development of material-specific GHG emission, energy,

and economic factors.

This report summarizes the detailed review of the data sources behind WARM

and an analysis of the quality of the data used to develop the emission and energy factors in the model.

The purpose of this effort is (a) to support EPA in understanding and improving upon the data quality in

WARM and prioritizing future updates, and (b) to provide additional transparency and insight on the

underlying data for WARM users. This effort is intended to shed light on areas for data quality

improvement, particularly across the numerous and diverse data sets used to develop WARM’s factors.

Importance of Data Quality to EPA

Data quality is critically important to EPA’s programs. Understanding and maintaining the quality of data

is a crucial aspect of the EPA’s scientific process, as outlined in the EPA Order CIO 2105.0 policy on

mandatory agency-wide quality systems (EPA 2000). EPA recognizes that low quality datasets or values

can in turn affect the integrity of values that rely on that data. Through a comprehensive assessment of

data quality, EPA seeks to advance the understanding of data quality, increase transparency, and

ultimately improve upon the data quality. Federal legislation is driving EPA and other federal agencies to

prioritize data accessibility. The Foundations for Evidence-Based Policymaking Act of 2018 mandates that

agencies such as the EPA improve the accessibility of data and use statistical evidence in the

development of policies and evaluation plans (H.R.4174 – 115

Congress 2017-2018). EPA implements

internal guidelines to guarantee the collection of data is done correctly and the quality of the data is

The methodology used to develop the WARM emission, energy, and economic factors is detailed in the WARM

Documentation, available at https://www.epa.gov/warm/documentation-waste-reduction-model-

warm#documentation.

WARM Data Quality Assessment 9

maintained. These regulations and guidelines exist to establish the credibility and trust of the

information produced by EPA and other federal agencies.

As part of the agency’s efforts to prioritize and carry-out data quality improvements, EPA has invested in

the development of guidance on data quality assessments and comprehensive evaluations of the quality

of data used in its analyses and programs. EPA’s Office of Research and Development (ORD) developed a

systematic methodology for data quality assessment (DQA) for life cycle inventory (LCI) data. This

approach is detailed in the “Guidance on Data Quality Assessment for Life Cycle Inventory Data” (ORD

Guidance) and discussed in further detail in the following Approach section (Edelen and Ingwersen

2016). Life cycle assessments (LCA), which evaluate environmental impacts across the life stages of a

material, product, or system, rely on many data inputs. LCA practitioners often use different

methodologies, tools, and approaches for documentation, and assessment of data quality can become

subjective. The ORD Guidance helps standardize the data quality review process to improve objectivity

in the scoring process, allowing for reproducibility of data quality scores, and improving understanding

of LCI data quality.

What is a Data Quality Assessment?

A data quality assessment (DQA) is a systematic review of a data source to determine its reliability and

level of quality as it relates to the goal and scope of the study or analysis. Rather than deeming a data

source good or bad, a DQA conducts a multi-pronged review based on several key analysis points. For

this DQA, each data source was reviewed based on several flow and process indicators. Flow indicators

consider the reliability of the measurements, and the correlation of the age of data, geographical

coverage, and technological representativeness with the study’s data quality goals (DQGs). Process

indicators consider the data review process used and the completeness of the dataset. As noted in the

ORD Guidance, the goal of a DQA with the use of a pedigree matrix scoring approach is to “see where

potential data quality issues might exist within large datasets and/or models with multiple processes”

(Edelen and Ingwersen 2016).

In understanding the results from a DQA, it is important to recognize a few key elements:

• Certain data quality results are static, while others are dynamic. Reliability of the data, based on

how the data were developed, is a static, unchanging, data quality element. Temporal

correlation is a dynamic data quality element that will change depending on a user’s timeframe

of study and the strength of a dataset’s correlation will change with the passing of time.

• A DQA may not capture all data sources or all data quality elements. Certain data may be

unavailable or inaccessible. Other data may be unknown to a data developer or may not be

possible to quantify.

• The user dimension is an important piece of how data quality results are interpreted. The data

quality can help inform how data are used by a particular user or for a specific purpose (e.g., for

certain uses or applications, a lower data quality dataset may be sufficient). In addition, while a

data developer is responsible for documenting and clearly communicating data quality

elements, a data user is responsible for assessing the appropriateness of the applications and

uses of the data.

WARM Data Quality Assessment 10

In the case of WARM, users of the tool and the emission and energy factors need to be aware that the

development of the factors includes multiple data sources and assumptions. This report describes the

data sources and assesses the quality of each of the data sets, to the extent feasible. A concerted

attempt was made to include all known data sources used for the emission and energy factor

development. The WARM documentation chapters

provide additional detail on the data sources and

discuss the boundaries of the analysis, the methodologies used to develop the factors, and limitations

related to the modeling of the emissions and energy use for the various material categories and

management practices.

The remainder of this report is organized as follows:

• Section 2: Approach

• Section 3: Assessment of Material Datasets

• Section 4: Assessment of Specific Management Pathway Datasets

• Section 5Error! Reference source not found.: Error! Reference source not found.Conclusion

• References

• Appendix: Data Quality Assessment Matrix

2. Approach

The scope of this assessment focused on the data used to develop the greenhouse gas (GHG) emission

and energy factors in EPA’s Waste Reduction Model (WARM). The factors are built with life-cycle

inventory and assessment data from various sources with a focus on prioritizing publicly available, peer-

reviewed reports, literature, and databases. The approach used for the data quality assessment (DQA)

follows that described in the “Guidance on Data Quality Assessment for Life Cycle Inventory Data”

developed by EPA’s Office of Research and Development (ORD Guidance) (Edelen and Ingwersen 2016).

The ORD Guidance specifies data review elements at the flow and process levels. The flow level

indicators cover reliability and representativeness of the data, and the process level indicators cover the

review process and completeness of the dataset.

The ORD Guidance provides data quality indicators (DQIs) to accurately assess the “functionality of data

within the boundaries of a particular study or project goal and scope” for life cycle inventory (LCI) data.

The guidance not only provides detailed information on the relevance and applicability of each

identified DQI, but also provides direction for developing a pedigree matrix data quality system (DQS)

with objective and clear scoring parameters. The DQA process involves scoring of five flow indicators

and two process indicators, as described in Table 2.

Available at https://www.epa.gov/warm.

WARM Data Quality Assessment 11

Table 2: Indicators Used for Assessing and Scoring the Quality of Data Sources

Indicator

Description

Flow Indicators

Flow Reliability

Used for reviewing if measurements and calculations in a source are verified and

reliable.

Temporal Correlation

Used for measuring the age difference between the temporal data quality goal

(DQG) and the data generation date in a source.

Geographical Correlation

Used for reviewing the relationship between the geographical DQG and the area

of study in a source.

Technological Correlation

Used for reviewing the relationship between the technological DQG and the

technological approach in a source. There are four categories of technological

representativeness reviewed by this indicator: process design, operating

conditions, material quality, and process scale. More information on these

categories can be found in the Appendix.

Data Collection Methods/

Representativeness

Used for identifying if a significant percentage of the relevant market share of an

industry is covered over an adequate time period by a source.

Process Indicators

Process Review

Used for identifying if a source has been reviewed by adequate third-party

reviewers and if proper documentation of the review accompanies the data in a

source.

Process Completeness

Used for identifying the percentage of flows determined for a process that has

been evaluated and assigned a value in a source.

The scoring for each indicator, as described in the ORD Guidance, ranges from 1-5 with the lowest score,

1, representing the highest quality data. A lower cumulative score or average score of all indicators

represents a data source with high quality data and methodology, whereas a higher score indicates

poorer data quality. The Data Quality Pedigree Matrix with the scoring range descriptions for each flow

and process DQI from the ORD Guidance is presented in Table 3.

WARM Data Quality Assessment 12

Table 3: Data Quality Pedigree Matrix for Flow and Process Indicators

Highest data quality

Lowest data quality

Indicator

Flow Indicators

Flow Reliability

Verified data based

on measurements

Verified data based

on a calculation or

non-verified data

based on

measurements

Non-verified data

based on a

calculation

Documented

estimate

Undocumented

estimate

Flow Representativeness

Temporal

Correlation

Less than 3 years of

difference

3 to 6 years of

difference

Less than 10 years

of difference

Less than 15 years

of difference

Age of data

unknown or more

than 15 years

Geographical

Correlation

Data from same

resolution and

same area of study

Within one level of

resolution and a

related area of

study

Within two levels

of resolution and a

related area of

study

Outside of two

levels of resolution

but a related area

of study

From a different or

unknown area of

study

Technological

Correlation

All technology

categories are

equivalent

Three of the

technology

categories are

equivalent

Two of the

technology

categories are

equivalent

One of the

technology

categories is

equivalent

None of the

technology

categories are

equivalent

Data Collection

Methods

Representative

data from >80% of

the relevant

market

, over an

adequate period of

time

Representative

data from 60-79%

of the relevant

market, over an

adequate period or

representative of

data from >80% of

the relevant

market, over a

shorter period of

time

Representative

data from 40-59%

of the relevant

market, over an

adequate period of

time or

representative

data from 60-79%

of the relevant

market, over a

shorter period of

time

Representative

data from <40% of

the relevant

market, over an

adequate period of

time or

representative

data from 50-59%

of the relevant

market, over a

shorter period of

time

Unknown or data

from a small

number of sites

and from shorter

periods

Process Indicators

Process Review

Documented

reviews by a

minimum of two

types

of third-

party reviewers

Documented

reviews by a

minimum of two

types of reviewers,

with one being a

third party

Documented

review by a third-

party reviewer

Documented

review by an

internal reviewer

No documented

review

Process Completeness

>80% of

determined flows

have been

evaluated and

given a value

60-79% of

determined flows

have been

evaluated and

given a value

40-59% of

determined flows

have been

evaluated and

given a value

<40% of

determined flows

have been

evaluated and

given a value

Process

completeness not

scored

Verification may take place in several ways, e.g., by on-site checking, by recalculation, through mass balances or cross checks with other

sources. For values calculated from a mass balance or another verification method, an independent verification method must be used to

qualify the value as verified.

Temporal difference refers to the difference between date of data generation and the date of representativeness as defined by the scope of

the project.

Geographical representativeness for this study set is based on EPA data quality goals of national data 1: U.S. data, national in scope; 2: U.S.

data, state or local level in scope; 3: North American data; 4: Global data; 5: Unknown location of data.

Technological categories are process design, operating conditions, material quality, and process scale.

The relevant market should be documented in the DQG. The default relevant market is measured in production units. If the relevant market

is determined using other units, this should be documented in the DQG. The relevant market established in the metadata should be

consistently applied to all flows within the unit.

Adequate time period can be evaluated as a time period long enough to even out normal fluctuations. The default time period is 1 year,

except for emerging technologies (2-6 months) or agricultural projects >3 years.

Types are defined as either industry or LCA experts.

WARM Data Quality Assessment 13

Following the ORD Guidance and using the DQIs described above, this assessment involved the following

steps:

➢ Step 1: Review of WARM DQGs: To ensure a systematic review of the various datasets, the process

began with a review of the DQGs for an ideal WARM dataset as they align with the various DQIs.

These WARM DQGs are outlined in Error! Reference source not found. below:

Table 4: WARM Data Quality Goals Aligned to the Indicators

Indicator

WARM Goal

Flow Indicators

Flow Reliability

Documented data are ideally verified by in-person authentication or

repeatable calculation measurements.

Flow Representativeness

Temporal Correlation

Temporal correlation with data collected, measured, or estimated as

recent as possible to the present without compromising in the other data

quality areas.

Geographical Correlation

Data represent U.S. conditions at the national level.

Technological Correlation

Strong technological correlation to the process or technology addressed

for each material or management pathway, with clear information on

inputs. Studies should ideally reflect the current processes employed by

the market.

Data Collection Methods

Representative of the majority of the market over a reasonable period of

time to avoid outlying data, ideally within one year.

Process Indicators

Process Review

Data are reviewed by at least one third-party reviewer, ideally multiple.

Process Completeness

Majority of the determined flows evaluated in the LCI datasets.

➢ Step 2: Creation of a DQA Review Matrix: A comprehensive data quality review matrix in Excel

covering the DQIs within the ORD Guidance was created and used to review each dataset in a

systematic way.

➢ Step 3: Identification of Data Elements and References: Multiple data sources are used to develop

the emission and energy factors for each material type in WARM. For this DQA effort, each material

type and management pathway in WARM, the key unit process data elements and corresponding

data sources and source years were identified. To do so, each source referenced in the WARM

documentation and emission and energy factor calculations spreadsheet used to develop the latest

version of WARM was included in the DQA review matrix.

➢ Step 4: Collection of the Data Sources: The ORD Guidance describes the importance of using and

assessing the original documentation. The original data sources for each data element as referenced

in the WARM documentation were identified and gathered to the extent feasible within archives. As

part of this effort, a comprehensive archive of the underlying data resources by material category

and end-of-life pathway was created. A few sources for specific material types and management

pathways could not be located, particularly documentation of prior conversations with industry

experts. These sources were given the lowest data quality scores.

WARM Data Quality Assessment 14

➢ Step 5: Review of Data Sources and Scoring Assessment of Data Quality: The gathered data sources

were reviewed against the different DQIs and the data quality findings were noted. For each data

source, each indicator was scored based on the ORD Guidance criteria in Table 3 and the totals

across the indicators were summed to obtain a total score, ranging from 7 (highest quality) to 35

(lowest quality). The average across the indicators was also taken and ranged from 1 to 5. For an

example of this process, a plastic data source received the scores shown in Table 5 for the five flow

indicators and two process indicators, which sum to the total score of 16 and average to 2.3.

Table 5: Example of DQ Scoring Assessment for a Single Data Source

Flow Indicators

Process Indicators

Total

Score

Average

Score

Flow Reliability

Flow representativeness

Process

Review

Process

Completeness

Temporal

correlation

Geographical

correlation

Technological

correlation

Data collection

methods

Data generation

method &

verification

Data year

Region of

data

Technology

type, scale

Representativeness,

sample size

Third party

or internal

reviewer(s)

% of flows

covered

2.3

To give additional weight to the key data sources driving emission and energy factor estimates for a

material category, a weighted average score was calculated along with an average score. While the

average score takes the total score for all data sources and divides by the number of sources for

each material type, the weighted average redistributes the weight of each study’s score based on

whether the data source is a driving factor in overall emissions. For example, a source was generally

considered key if it impacts several materials within a category, is a lead source in process emission

estimates, or is comprehensive enough to be used throughout the life cycle of a material’s emission

estimates. In the weighted average score, key sources were given double the weight of other, non-

key sources.

➢ Step 6: Alignment of Scores with Data Quality Levels: Across multiple data sources for a material

category or pathway, the averages for high level indicators of flow reliability, flow

representativeness, and process review and completeness were taken and used to develop average

data quality values across that material category or pathway. The averaged data quality values were

aligned with data quality levels from low to high as shown in Table 6. To present the results in a

more visual way, a shading system was applied to the scores where pale blue was assigned to the

lowest data quality level with an average data quality value of one and dark blue was assigned to the

highest data quality level corresponding with an average data quality value of five. Between those

data quality levels and values the shading scales from light blue for low data quality to dark blue for

high data quality. In the case of the example data source above, the average score of 2.3 aligned

with a data quality level of medium-high.

WARM Data Quality Assessment 15

Table 6: Alignment of Average Data Quality Values with Data Quality Levels

Average DQ Value

DQ Levels

High

Medium-high

Medium

Medium-low

Low

➢ Step 7: Assessment of Results: Based on the scoring assessment, the resulting matrix was organized

into the categories – high, medium-high, medium, medium-low, and low data quality scores. For

each material category and management pathway the following summary table is presented in the

sections that follow.

Management

Pathway

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Refers to data generation method and verification.

Includes temporal correlation (data year), geographical correlation (region of data), technological correlation (technology type,

scale), and data collection methods (representativeness, sample size).

Includes process review (third party or internal reviewers) and process completeness (percent of flows covered).

Average of all indicators.

Developed to give additional weight to the key data sources informing the emission factor estimates.

The materials or pathways with datasets receiving the lowest data quality scores and the indicators

contributing to those scores were identified and will require closer examination to inform

prioritization of future WARM updates.

➢ Step 8: Preparation of Findings and Recommendations: This report was developed to summarize

key findings, areas for improvement, and recommendations for addressing WARM’s data quality.

Weighted averages were calculated to provide additional context to the reader and give a deeper

view into how different data sources were used. These weighted averages could also be used in the

future to help prioritize updates to source materials.

3. Assessment of Material Datasets

The following sections present the results from the assessment of the quality of the datasets and data

sources by material category (e.g., metals) and type (e.g., aluminum cans). Summaries of key findings

are presented followed by recommended areas for further research and improvement of the data

quality. In the discussion of data sources, key sources used in the development of the emission and

energy factors are noted as (KEY). They are weighted more heavily for the development of the weighted

averages. Additional details on the scoring results from the assessment of the flow level and process

level indicators for each material data source are presented in the Appendix: Data Quality Assessment

Matrix.

WARM Data Quality Assessment 16

3.1 Plastics and Bioplastics

Summary of Key Findings

Data Sources. WARM includes emission and energy factors for seven plastic resins—high-density

polyethylene (HDPE), low-density polyethylene (LDPE), polyethylene terephthalate (PET), linear low-

density polyethylene (LLDPE), polypropylene (PP), general purpose polystyrene (GPPS), and polyvinyl

chloride (PVC)—a mixed plastics category, and the bioplastic, polylactide (PLA) biopolymer resin. The

development of the factors relied on the use of both key sources and additional sources, as described

below.

The primary data sources used to develop the fossil-based plastic resin factors include:

• The Life-Cycle Inventory (LCI) report, Cradle-to-Gate Life Cycle Inventory of Nine Plastic Resins

and Four Polyurethane Precursors (FAL 2011), which provides raw material acquisition and

manufacturing energy data for the production of the virgin plastic resins HDPE, LDPE, PET,

LLDPE, PP, GPPS, and PVC. This report presents a cradle-to-gate LCI quantifying the total energy

requirements, energy sources, atmospheric pollutants, waterborne pollutants, and solid waste

resulting from the production of nine plastic resins produced in North America. (KEY)

• The LCI report, Life Cycle Impacts for Postconsumer Recycled Resins: PET, HDPE, and PP (FAL

2018), which provides process energy data for the production of recycled plastics resins HDPE,

PET, and PP. This report presents a cradle-to-gate life cycle analysis quantifying total emissions

from the production of recycled HDPE, PET, and PP resins in North America. (KEY)

The bioplastics factors rely largely on two primary data sources:

• The NatureWorks U.S. LCI spreadsheet entitled “SS Polylactide Biopolymer Resin_US LCI

May_2010.xls” submitted to the U.S. LCI Database

(U.S. LCI 2010). It provides raw material

acquisition and manufacturing energy data for the production of Ingeo PLA resin. Although this

source reflects PLA resin production by NatureWorks LLC in Blair, Nebraska, it is considered

representative of U.S. PLA production due to the absence of direct competitors to NatureWorks

operating a fully industrial-scale PLA manufacturing plant in the United States. (KEY)

• Responses from NatureWorks on ICF’s preliminary review of the NatureWorks PLA LCI Data

Memo (NatureWorks 2010). The responses include updated data for net atmospheric CO

uptake during corn production, landfill carbon storage, and PLA carbon content. (KEY)

Transportation-related information was obtained from the following data sources:

• For all plastics resins and bioplastics: US Census Commodity Flow Survey Preliminary Tables,

Table 1: Shipment Characteristics by Mode of Transportation for the United States (BTS 2013).

This source is a Commodity Flow Survey (CFS) on domestic freight shipments developed jointly

U.S. Life Cycle Inventory Database | NREL, https://www.nrel.gov/lci/

WARM Data Quality Assessment 17

by the Bureau of Transportation Services (BTS), the U.S. Census Bureau, and U.S. Department of

Commerce. It provides data on retail transportation distance and fuel-type.

• For bioplastics: The Role of Recycling in Integrated Solid Waste Management to the Year 2000

(FAL 1994). This report is a study on the role of recycling in integrated solid waste management

published by Franklin Associates and provides data on transportation energy use.

• For bioplastics: Evaluation of Climate, Energy, and Soils Impacts of Selected Food Discards

Management Systems (Oregon Department of Environmental Quality [DEQ] 2014). This report

evaluates the environmental and energy impacts of specific food discards management systems

and provides data on transportation emissions. The transportation emissions data from this

report are used for bioplastics.

Scoring. The average DQA scores for plastics and bioplastics varied within the medium to medium-high

data quality levels. A summary of the results by data quality indicator grouping for plastics and

bioplastics is shown in Table 7. The key findings for each of the sources used for plastics and bioplastics

are discussed below.

Table 7: Summary of Data Quality Results for Plastics and Bioplastics Data Sources

Material

DQ Values by Indicator Grouping

Flow Reliability

Flow

Represent-

ativeness

Process Review

and

Completeness

Average

Weighted

Average

Plastics

Medium-high

Medium

Medium-high

Bioplastics

Medium-high

Medium

Medium-high

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Plastics

The medium-high data quality average and weighted average for the fossil-based plastic resins was

largely a consequence of the data from the virgin plastic resin LCI report (FAL 2011) reflecting medium-

high and high data quality for most flows and process indicators. The exception for that data source is

the process review indicator (a sub-category of the process review and completeness indicators), which

was deemed low quality due to lack of information on external reviews in that report and temporal

correlation (a sub-category of the flow representativeness indicator), which reflects medium-low data

quality due to the data used in FAL (2011) being representative of 2003 or earlier. The recycled plastic

resin LCI report (FAL 2018) was classified as medium to high data quality across the indicators as it

covers over 80 percent of processes in North America related to the emissions of recycling plastics, with

a representative data pool and documented calculation. The Role of Recycling Report (FAL 1994), which

provides general transportation equipment information, was determined to be of medium-high data

quality as it characterizes a majority of the municipal solid waste in the United States at the time, uses

calculated data, and covers a wide scope of technologies, including vehicle type, vehicle load, and

material type to inform data on transportation equipment. However, FAL (1994) is not a higher data

quality source due to low quality temporal correlation as the data were collected in 1992. This likely

impacts the validity of the data as transportation technology has changed since 1992. The Commodity

Flow Survey (BTS 2013) reflects medium data quality overall, as it was characterized as medium-low

data quality for temporal correlation and process review due to data that are over 10 years old and a

WARM Data Quality Assessment 18

lack of information on external reviews. The source also reflected low data quality for process

completeness (sub-category of process review and completeness indicators).

The plastic data sources generally reflect high data quality for flow reliability, geographical correlation,

and data collection (sub-categories of the flow representativeness indicators), and process

completeness indicators. However, the plastic data sources reflect low data quality for the temporal

correlation indicator (sub-category of the flow representativeness indicators) and process review

indicator. The mixed plastics material type in WARM is the average of the emission factors developed

for the plastic resins. Therefore, the average plastics data quality scoring was applied to assess the

“mixed plastics” material category.

Bioplastics

For bioplastics, the NatureWorks LCI dataset (U.S. LCI 2010) was characterized as having medium-high to

high data quality across the indicators, with the exception of the temporal correlation indicator, which

reflected medium-low quality. The additional NatureWorks source (Vink 2010) is of medium-high data

quality overall, as it was deemed high quality across most indicators, but low data quality for temporal

correlation and process review completeness (sub-category of process review and completeness

indicators) as it is a relatively old source and lacks documentation of external reviews. The data quality

of the additional data sources—BTS (2013), FAL (1994), Oregon DEQ (2014)—used for the PLA emission

and energy factors are described above under plastics.

Overall, the plastics datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Overall, the bioplastics datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Recommendations

Areas for improvement include:

• Identify and incorporate an additional source for raw material acquisition and manufacturing energy

data for the production of virgin plastic resins that has two or more types of documented third-party

reviews. The current source, FAL (2011), that provides process energy data for all virgin plastic resins

does not have any documented third-party reviews.

• Identify and incorporate an additional source for plastics with process data for production of

additional recycled plastic resins (i.e., LDPE, LLDPE, GPPS, and PVC). FAL (2018), the singular source

for data on recycled plastic resins, includes only HDPE, PET, and PP resins.

• Update the retail transportation distance and fuel-type information with the more recently available

US Census Commodity Flow Survey (from 2017).

WARM Data Quality Assessment 19

3.2 Metals

Summary of Key Findings

Data Sources. The types of metals modeled in WARM include aluminum cans, aluminum ingot, steel

cans, and copper wire. This WARM material category focuses on container and packaging end-uses for

aluminum and steel and electrical end-uses for copper wire. Metals can be employed in various sectors

and products; other major uses of aluminum in addition to those considered in WARM include

construction, consumer durables, electrical, machinery and equipment, transportation, and other

industrial uses. For steel, other major uses include service centers and distributors, construction (which

is modeled in WARM but discussed in the construction materials section below), transportation, and

other industrial uses. Other major uses of copper include building construction, industrial machinery and

equipment, transportation equipment, and consumer and general products. A mixed metals material

type is also modeled in WARM, reflecting the weighted average using the latest relative recovery rates

for steel and aluminum cans. See column I of Table 8 below for these recovery rates. See the “Mixed

Metals” material in Table 9 below for data quality scoring that follows this weighting scheme.

Table 8. Relative Prevalence of Metals in the Waste Stream in 2015

(a)

(I(c)

(d)

(e)

(f)

Material

Generation

(Short Tons)

% of Total

Container

Metal

Generation

Recovery

(Short Tons)

% of Total

Metals

Recovery

Recovery Rate

Aluminum Cans

1,350,000

43%

670,000

35%

50%

Aluminum Ingot

Steel Cans

1,740,000

57%

1,240,000

65%

71%

Copper Wire

Source: EPA (2018).

NA = Not available.

The WARM emission and energy factors for these metals rely on 9 primary data sources. For the

development of the aluminum can and aluminum ingot factors, the primary data sources include:

• The LCA report, Life Cycle Impact Assessment of Aluminum Beverage Cans (PE Americas 2010),

which provides process and process non-energy data for aluminum beverage cans.

It is used to

develop the process and process non-energy emission factors for aluminum cans and aluminum

ingot and to understand the current mix of inputs (recycled vs. virgin) for WARM. This source

was further disaggregated by process energy, transportation energy, and non-energy process

emissions for WARM by the Aluminum Association in a spreadsheet provided to ICF and EPA (PE

Americas 2011). (KEY)

The Aluminum Association provided a detailed spreadsheet of their calculations (titled "Data for ICF-EPA_ICF

formatted 08-04-11”) to supplement the information published in the PE Americas report. ICF had several

conversations with Senior Sustainability Specialist, Jinlong Marshall Wang to clarify the details in the calculations’

spreadsheet. Because this spreadsheet is considered an extension of the PE Americas report, the calculation

spreadsheet was not assessed for data quality separately from the PE Americas report (2010).

WARM Data Quality Assessment 20

• An unpublished database with transportation energy data developed jointly by the Research

Triangle Institute and EPA (RTI 2004). It documents energy consumption associated with virgin

and recycled production process transportation across material types and is used to develop the

transportation energy emissions factor for aluminum.

The primary data sources used to develop the steel can factors include:

• The EPA report, Background Document A: A Life Cycle of Process and Transportation Energy for

Eight Different Materials, Greenhouse Gas Emissions from Management of Selected Materials in

Municipal Solid Waste, prepared by Franklin Associates, Ltd. (EPA 1998a). This report provides

process energy, process non-energy and transportation energy data for steel cans. (KEY)

• Personal communication between ICF and Franklin Associates, Ltd. that culminated in a

documented review of recycled content values and current mix of steel can production

(identifying the percentage that is from recycled versus virgin inputs) (FAL 2003a). The

communications information was based on two key resources: Ohio Department of Natural

Resources “Full Circle: Buying Recycled-Content Products” fact sheet,

and “Municipal Solid

Waste in the United States: 2000 Facts and Figures” document

developed by the EPA.

• Loss rate data provided by Franklin Associates, Ltd. (FAL 2003b).

The primary data sources used to develop the copper wire factors include:

• The report, Energy and Greenhouse Gas Factors for Personal Computers, prepared by Franklin

Associates, Ltd. (FAL 2002). It presents life-cycle energy and GHG emissions for personal

computers, including the GHG emissions associated with relevant copper production for use in

computers. It provides the process energy, process non-energy, and transportation energy data

used for the copper wire emission factors. (KEY)

• The report, Flows of Selected Materials Associated with World Copper Smelting, prepared by the

U.S. Geological Survey (USGS 2004) that provides information on the percent of current

production from recycled vs. virgin inputs for copper wire, and the copper wire scrap mix used

to create copper ingot.

For all metals, transportation-related information was obtained from the following data sources:

• The EPA report, Greenhouse Gas Emissions from the Management of Selected Materials (1998b),

which provides retail transportation

energy data used for the aluminum and steel calculations.

This is the predecessor to the WARM documentation and bases its retail transportation energy

on data received from Franklin Associates (FAL 1998) and the Tellus Institute (Tellus 1998) in

Ohio Department of Natural Resources. Full Circle: Buying Recycled-Content Products.

www.dnr.state.oh/us/recycling/awareness/facts/buy.htm.

Municipal Solid Waste in the United States: 2000 Facts and Figures. EPA530-R-02-001. Also, Franklin Associates, A

Division of ERG, working papers for this report and previous versions.

“Retail transportation” consists of the average truck, rail, water and other-modes transportation emissions

required to get the material from the manufacturing facility to the retail/distribution point.

WARM Data Quality Assessment 21

Background Documents A and B, respectively. The Franklin Associates Background Document A

provides the aggregated process and transportation energy for eight materials, including

aluminum and steel cans. The Tellus Institute Background Document B estimates the amounts

and types of energy consumed in raw materials acquisition and manufacturing of eight

materials, including aluminum and steel cans.

• US Census Commodity Flow Survey Preliminary Tables, Table 1: Shipment Characteristics by

Mode of Transportation for the United States (BTS 2013). This source is a CFS on domestic

freight shipments developed jointly by the BTS, the U.S. Census Bureau, and U.S. Department of

Commerce. It provides data on retail transportation distance and fuel-type.

Scoring. This DQA showed that data quality is generally consistent across all metals with an average and

weighted individual value of medium. Only the flow reliability and process review and completeness

indicators and average value for steel cans are rated differently, with a medium-low values. While the

DQA values varied across individual data sources, overall, all metals materials received an average value

that corresponds with the medium data quality level. On average, the metal sources scored the best in

the geographical correlation indicator category. For that indicator, the sources had medium-high data

quality, conveying that the region of data correlates well to that of WARM (i.e., the United States). A

summary of the results by DQI is shown in Table 9. The key findings for each of the sources used for the

different metal materials are discussed below.

Table 9: Summary of Data Quality Results for Metal Data Sources

Material

DQ Values by Indicator Grouping

Flow Reliability

Flow

Represent-

ativeness

Process Review

and

Completeness

Average

Weighted

Average

Aluminum Cans &

Aluminum Ingot

Medium

Steel Cans

Medium-low

Medium

Medium-low

Medium

Copper Wire

Medium

Mixed Metals

Medium

Metals

Medium

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Aluminum Cans and Aluminum Ingot

The PE Americas (2010)

report along with the PE Americas (2011) spreadsheet had the highest data

quality among the metal data sources reviewed, showing a medium-high data quality. This result is due

to its strong geographical and technological correlation, flow reliability, process completeness, and

process review. The PE Americas (2010) report also developed its own rating using the same general

To develop the life-cycle process emission factors for aluminum, the PE Americas report uses WRI fuel emission

factors, global average grid emission factors (for bauxite mining and alumina refining); North America aluminum

industry mix and global aluminum industry mix (for smelting and casting) values; and the United States average

grid emission factors (for secondary production, can sheet rolling, and can making). These distinct calculation

inputs for the PE Americas report emission factors were sent to ICF in the “Data for ICF-EPA_ICF formatted 08-04-

11” spreadsheet. Because these are inputs to the PE Americas report, the rating for the PE Americas report is

reflective of these input sources as well.

WARM Data Quality Assessment 22

scoring metric as this report (see Appendix: Data Quality Assessment Matrix). There were additional

conversations with Marshall Wang of the Aluminum Association that also informed the development of

the process and process non-energy emission factors, but this information was included in the

supplemental spreadsheet provided to ICF and EPA (PE Americas 2011), which was assessed collectively

for the purposes of the DQA.

The RTI (2004) database that is used to develop the transportation energy emissions factor for

aluminum received a low data quality value. This is due to the age of the data, reducing the temporal

correlation, and limited to no documentation on the other data quality indicators. Details on the

development of and methodology for this database also could not be located, leading to the low data

quality value.

The scoring for EPA (1998b) was based on an average of the data quality values for both the Franklin

Associates and the Tellus Institute Background Documents (see Table 10 below). It received an average

value of medium-high due to its high quality process completeness and geographical and technological

correlation. Its data quality was lowest for temporal correlation and sample size because much of the

data were uncited, and the source is over 20 years old.

Table 10: EPA 1998b Background Document Ratings

Source

Flow Reliability

Flow

Representativeness

Process Review and

Completeness

FAL (1998) Background Document A

Medium-High

Medium

Medium-High

Tellus (1998) Background Document B

Medium-High

Medium

High

EPA 1998b (average of background

documents)

Medium-high

Medium

High

The Commodity Flow Survey (BTS 2013) has medium data quality overall. Its lowest data quality value is

for process completeness, and averages as medium for its flow representativeness. EPA (2018a) has an

average value of medium-low due to its high data quality across most categories, except for process

review.

Steel Cans

The EPA report, Background Document A: A Life Cycle of Process and Transportation Energy for Eight

Different Materials report (EPA 1998a), had high data quality for geographical correlation, technological

correlation, and process completeness because its data were representative of the United States, its

technology categories were equivalent, and it covered greater than 80 percent of the determined

process flows. EPA (1998a) received low data quality values for temporal correlation, as the source is

greater than 15 years old, and data generation and collection methods, as explanations were not found.

While there were third party reviews of the EPA (1998a) document, documented reviews of the Franklin

Associates data were not found, which impacted its process review data quality value.

The FAL (2003a) source, providing information on the current mix of steel can production received an

average value of medium-low, with low data quality for temporal correlation. This is because it is based

on data that are more than 15 years old, and low data quality for representativeness and process review

WARM Data Quality Assessment 23

and completeness as the sample size of the data is unknown, and there are no documented reviews of

the data. Because the information in FAL (2003a) is based on two key resources: Ohio Department of

Natural Resources “Full Circle: Buying Recycled-Content Products” fact sheet, and EPA’s “Municipal Solid

Waste in the United States: 2000 Facts and Figures” document, the scoring for FAL (2003a) was the

average of the scoring given to those sources. The Ohio Department of Natural Resources fact sheet was

not located, and thus was given a scoring of low data quality for each DQI category.

The FAL (2003b) source, providing the material loss rate information for steel cans, was not located, and

therefore was given low data quality scoring across all categories. The scoring for EPA (1998b), BTS

(2013), and EPA (2018a) are discussed in the aluminum can and ingot section above.

Copper Wire

FAL (2002) reflected a medium data quality value on average based on its mix of DQI values from low to

high quality. It had low data quality for temporal correlation, as much of the data are more than 15

years old, and for process completeness and data collection methods due to those aspects being

unknown and unable to assess. This source had medium-low data quality for flow reliability because it is

an old source with data based on documented estimates rather than verified measurements of

calculations. However, FAL (2002) had high data quality for geographical correlation, as the data are

U.S.-based, and medium-high quality for technological correlation and process review. Because

technology processes are slower to change, older data are not necessarily unrepresentative of the

current production and processing landscape. However, the data quality scoring matrix takes a cautious

approach by giving lower quality values to older data sources, in case the material type has a quicker

technological progress timescale.

The USGS (2004) source had an average data quality level of medium. Its lowest data quality is for

process completeness, and its highest data quality is for flow reliability, geographical correlation, and

technological correlation.

The scoring for EPA (1998b) and BTS (2013) are discussed in the aluminum

can and ingot section above.

Overall, the metals datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium

Recommendations

Areas for improvement include:

• Identify and incorporate data on energy consumption from a more recent and publicly published

study. WARM currently pulls from the RTI (2004) database, which is over 15 years old and has the

lowest data quality of all the sources. As the dataset is unpublished, there is very little information

on its methodology which contributes to its low data quality.

See the USGS methodology report for more information on the rating: https://d9-wret.s3.us-west-

2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/myb1-2004-surve-2.pdf

WARM Data Quality Assessment 24

• Identify and incorporate more recent data, particularly for steel and copper wire, to replace sources

such as EPA (1998a) and FAL (2002). Compared to other material categories, metal sources had the

lowest data quality in the temporal correlation category, with an overall medium-low level,

conveying that the data sources are on average greater than 15 years old. Because many

technologies are slower to change, older data may still be relevant for many processes. However,

there is still a clear need to update the data sources used in WARM for the metals section to studies

based on more recent datasets.

• Update the retail transportation distance and fuel-type information with the more recently available

US Census Commodity Flow Survey (from 2017).

• Assess the feasibility of updating the aluminum factors with data from a more recent assessment.

• Retain documentation for all data sources, including conversations with subject matter experts. Ask

subject matter experts that inform WARM to share published sources for any specific data elements.

3.3 Glass

Summary of Key Findings

Data Sources. The glass emission and energy factor calculations rely on two key data sources for process

energy and process non-energy emission factors and four key data sources for the transportation

emission factor calculations.

The primary data sources used for glass process energy and process non-energy data include:

• A database with process energy and process non-energy data developed jointly by the Research

Triangle Institute and EPA (RTI 2004). Process energy and process non-energy data are sourced

from this unpublished database that documents energy consumption associated with virgin and

recycled production processes across material types. (KEY)

• A U.S. Department of Energy (DOE) report, Energy and Environmental Profile of the U.S. Glass

Industry. DOE (2002), which provides assumptions on the average composition of glass and fuel

used to combust glass. This source provides an energy and environmental profile of the U.S.

glass industry.

• In-house data from Franklin Associates (FAL 2003b) provides information on the current mix of

production from virgin and recycled inputs for glass manufacturing, typical glass recycled

content values in the marketplace, and glass loss rates.

The primary data sources used for the transportation emission calculations include:

Wang 2022. "The Environmental Footprint of Semi-Fabricated Aluminum Products in North America." The

Aluminum Association. January 2022. https://www.aluminum.org/sites/default/files/2022-01/2022_Semi-

Fab_LCA_Report.pdf (Accessed: January 17, 2023).

WARM Data Quality Assessment 25

• The Role of Recycling in Integrated Solid Waste Management to the Year 2000 (FAL 1994). This

report is a study on the role of recycling in integrated solid waste management published by

Franklin Associates. This report provides GHG emissions from transportation energy usage for

transportation of waste to the combustion facility.

• US Census Commodity Flow Survey Preliminary Tables, Table 1: Shipment Characteristics by

Mode of Transportation for the United States (BTS 2013). This source is a Commodity Flow

Survey (CFS) on domestic freight shipments developed jointly by the Bureau of Transportation

Services (BTS), the U.S. Census Bureau, and U.S. Department of Commerce. It provides

additional assumptions on retail transportation energy usage (average shipping distances and

modes) for glass.

• Typical transportation fuel efficiencies are sourced from the EPA report, Greenhouse Gas

Emissions from the Management of Selected Materials, prepared by ICF for EPA, (EPA 1998b),

which is the original WARM emission factor methodology document.

Scoring. On average, the data quality for the glass data sources is highest within the geographical and

technological correlation indicators. On average, the sources reflect medium data quality, conveying

that the region of data correlates relatively well to that of WARM (the United States) and the majority of

technology categories are equivalent.

The low data quality of the key source for the glass analysis

brought down the weighted average value to medium-low. A summary of the results by data quality

indicator groupings is shown in Table 11. The key findings for each of these sources are discussed below.

Table 11: Summary of Data Quality Results for Glass Data Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Representative

-ness

Process

Review and

Completeness

Average

Weighted

Average

Glass

Medium

Medium-low

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

RTI (2004) was considered to be a low quality source as it is an unpublished database developed by the

Research Triangle Institute and EPA; and details on the development of and methodology for that

database have not been found. This is especially salient as the process energy and non-process energy

emission factors for glass is almost entirely based on RTI (2004), with additional glass composition

assumptions sourced from DOE (2002). The highest data quality was with FAL (1994), which includes a

fairly robust dataset; however, the data collection occurred in 1992.

FAL (2003a) is based on in-house data provided by Franklin Associates to ICF. This source was not found

as it is based on in-house data from Franklin Associates, and therefore was considered to reflect low

data quality across all categories.

Technology categories are process design, operating conditions, material quality, and process scale.

WARM Data Quality Assessment 26

For the development of the weighted average glass data quality value, the RTI (2004) data source is

weighted more heavily than the rest of the sources as it is the main source of process energy and

process non-energy data used for the development of WARM glass emission and energy factors.

Overall, the glass datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium-low

The difference between the medium quality average indicator value and the medium-low quality

average weighted indicator value shows the impact of a key data source being low quality. RTI (2004)

was determined to be a low quality, out of date source with a poorly documented methodology.

However, WARM relies on RTI (2004) as a key source to inform glass emissions, which brings down the

overall quality of the glass section, particularly when considering an average that weighs RTI (2004)

more heavily than other sources.

Recommendations

Areas for improvement include:

• Conduct research to identify a more recent peer-reviewed study for the glass process energy

emission factors than the current data source, RTI (2004). On average, the data quality for the glass

data sources is lowest within the temporal correlation category.

• Identify more recent and publicly available information on the current mix of production for glass.

• Update the retail transportation distance and fuel-type information with the more recently available

US Census Commodity Flow Survey (from 2017).

• Consider a more updated source for transportation fuel efficiencies (such as fuel efficiencies

published by NREL

3.4 Paper

Summary of Key Findings

Data Sources. Paper materials and products included in WARM are magazines, newspaper, office paper,

phonebooks, textbooks, and corrugated containers such as cardboard packing boxes. The paper material

emission factor and energy factor calculations rely on seven data sources. Of these, three key data

sources informed process energy, process non-energy, and transportation emission factors, and

therefore were weighted more heavily when determining overall data quality of paper sources. These

include:

• An unpublished database developed jointly by the Research Triangle Institute and the U.S.

Environmental Protection Agency Office of Research and Development (RTI 2004), which

provides information on the industrial process emissions and energy mix of paper materials

including corrugated containers, magazines, newspaper, office paper, phonebooks, and

textbooks. (KEY)

Alternative Fuels Data Center: Maps and Data - Average Fuel Economy by Major Vehicle Category (energy.gov)

WARM Data Quality Assessment 27

• The EPA Report, Greenhouse Gas Emissions from Management of Selected Materials in

Municipal Solid Waste, Background Document A: A Life Cycle of Process and Transportation

Energy for Eight Different Materials, prepared by Franklin Associates, Ltd. (EPA 1998a), which

provides information on energy requirements for production of recycled corrugated containers.

(KEY)

• Greenhouse Gas Emissions from Management of Selected Materials in Municipal Solid Waste,

Background Document A, Attachment 1: A Partial Life Cycle Inventory of Process and

Transportation Energy for Boxboard and Paper Towels, published by Franklin Associates, Ltd.

(FAL 1998), which provides information on the composition of mixed paper and energy

requirements for production of virgin and recycled boxboard. (KEY)

The paper factors also relied on other non-key data sources:

• Personal communication between ICF and Franklin Associates, Ltd. that culminated in a

documented review of recycled content values and current mix of paper material production

(identifying the percentage that is from recycled versus virgin inputs) (FAL 2003a).

• In-house data from Franklin Associates (FAL 2003b) provides information on retention rates

during recycling of paper materials.

• U.S. Census Commodity Flow Survey Preliminary Tables, published by the U.S. Bureau of

Transportation Statistics (BTS) Research and Innovative Technology Administration (BTS 2013),

which provides data on transportation energy.

• Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2013, a report published by the

U.S. EPA (EPA 2015), which provides measurements on fuel-specific carbon content and

coefficients.

Scoring. A summary of the results by data quality indicator groupings is shown in Table 12. The key

findings for each of these sources are discussed below.

Table 12: Summary of Data Quality Results for Paper Data Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Paper

Medium-low

Medium

Medium-low

Medium-Low

Medium

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Key sources for paper, RTI (2004), EPA (1998a), and FAL (1998), provide process energy, process non-

energy, and transportation energy requirements for the manufacturing of various paper materials.

Overall, the paper flow representativeness indicator reflected medium data quality, while the flow

reliability and process review and completeness indicators reflected medium-low data quality. On

average, the paper data sources were of low quality for temporal correlation; medium-low for flow

reliability, data collection methods, and process review; and medium quality for geographical

correlation, technological correlation, and process completeness. Poor temporal correlation was due to

WARM Data Quality Assessment 28

the publication years of the paper data sources, which range from 1998 to 2015. Data collection

methods saw more variable data quality, but two sources, including key source RTI (2004) and EPA

(1998a), were deemed low quality for this category as the sources did not list sites or time periods

sampled. This left representativeness unknown and indicated a poor data source. Concerning

geographical correlation, the data sources for paper were created using data from the United States,

which is the same region of data that WARM represents. On average, indicators were determined to be

of medium-low quality, and of medium quality for the weighted average giving additional weight to the

key data sources.

RTI (2004) had the lowest data quality among the key paper sources. As noted previously, RTI (2004) is

an unpublished database developed by the Research Triangle Institute and EPA. Details on the

methodology of this database are not available, resulting in an overall low-quality level. EPA (1998a) had

high data quality for geographical correlation, technological correlation, and process completeness

because its data were representative of the United States, its technology categories were equivalent,

and it covered greater than 80 percent of the determined process flows. EPA (1998a) received low data

quality values for temporal correlation, as the source is greater than 15 years old, and data generation

and collection methods, as explanations were not found. While there were third party reviews of the

EPA (1998a) document, documented reviews of the Franklin Associates data were not found, which

impacted its process review data quality value. FAL (1998) had medium-high to high data quality for five

of the seven indicators. Temporal correlation, due to age of data, was the lowest mark for this source.

One of the other sources, EPA (2015), which provides corrugated containers’ fuel-specific carbon

content, was a high data quality source, as it is a more recent report published by the EPA and verified

by multiple third parties. This report also has Annexes that detail methodology, scale, scope, and

sources that reflect a higher quality source.

Overall, the paper datasets scored as follows:

• Average indicator: Medium-low

• Average weighted indicator: Medium

Recommendations

Areas for improvement include:

• Update all paper data sources to more recent sources where applicable, based on the quality of the

temporal correlation (low data quality). Some sources may not have more recent versions, but

updated data should be used if possible. This dataset is most in need of updating.

• Update all paper data sources from those that use data generated from estimates to data generated

from verified or non-verified measurements or calculations, which would improve the data sources’

flow reliability, and therefore the reliability of WARM.

• Identify more recent and reliable source(s) to update the RTI (2004) data set providing

manufacturing and transportation energy use data for all paper types (corrugated containers,

magazines/third-class mail, newspaper, office paper, phonebooks, and textbooks).

WARM Data Quality Assessment 29

3.5 Electronics

Summary of Key Findings

Data Sources. Electronics covered in WARM include desktop CPUs, portable electronic devices (tablets,

laptops, and smartphones), flat-panel displays (TVs and monitors), CRT displays, electronic peripherals

(mice and keyboards), and hard-copy devices (i.e., printers), and a mixed electronics category, which is a

combination of the other electronic categories weighted by their prevalence in the U.S. waste stream.

The electronics material emission factors and energy factor calculations rely on 16 data sources for

information regarding process emissions of electronic components, the component mass share within

WARM’s electronic categories, recovery and recycling practices for the different electronics, and the

prevalence of electronic types in the U.S. waste stream.

Four data sources played a key role in developing the final WARM electronic emission and energy

factors:

• Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in

Transportation (GREET) 2 Model (ANL 2018), which is used to source process emissions for many

of the electronic materials’ components, including plastics and some metals. (KEY)

• Ecoinvent Centre’s life cycle dataset v3.2 (Ecoinvent Centre 2015), which was used to source

process emissions for additional components and metal types, including gold, silver, and silica

sand. (KEY)

• The report, Sustainable Materials Management for the Evolving Consumer Technology

Ecosystem (Babbitt et al. 2017), which calculates the component mass share in the electronic

types in WARM. These percentages combined with the process emissions of the component

types formed the basis for the component share of various metals, plastics, and other rare

metals found in each of WARM’s electronic categories. (KEY)

• Journal article, Comparing embodied greenhouse gas emissions of modern computing and

electronics products (Teehan and Kandlikar 2013), which includes virgin production energy data

for printed circuit boards, flat panel display modules, and batteries. (KEY)

Process emissions for the various components come largely from the ANL 2018’s GREET model,

Ecoinvent v3.2, and Teehan and Kandlikar 2013.

Multiplying component mass shares from Babbitt et

al. (2017) by these process emissions yielded a majority of the overall emissions for electronic types in

WARM.

Teehan and Kandlikar 2013 data verification methodology involved the hand disassembly of printed circuit

boards, flat panel display modules, and batteries to verify the mass share of different components. These masses

were then entered into the Ecoinvent Database to gather emission results, albeit an earlier version than the one

cited by WARM (v2.2 versus v3.2).

WARM Data Quality Assessment 30

Additional sources are used to help fill in the gaps of information regarding specific emission factors for

electronic types and component mass share in WARM, background information in the supporting

documentation, and ancillary data needed to form accurate assumptions in WARM. These include:

• End-of-life management practices and emissions from recycling are sourced mainly from Bigum

et al. (2012) and Vanegas et al. (2017). Additional information is sourced from Dewulf et al.

(2010) for information on battery recycling and Turner et al. (2015) on CRT recycling practices.

• The Electronics Recycling Landscape report (Mars et al. 2016), which provides information on

the shares of electronic types in the U.S. waste stream used in the mixed electronics category in

WARM.

• The assumption for share of electronic types in the U.S. waste stream used in the mixed

electronics material type is sourced from Mars et al. (2016).

• A report on cellphone materials and life cycle emissions of its components (Andrea and Vaija

2014).

• Four reports conducted by Franklin Associates, studying the life cycle emissions of plastic (FAL

2011a, FAL 2011b, FAL 2018) and copper (FAL 2002), including the production phase and

recycling.

• Journal article, Improving Resource Efficiency through Recycling Modelling: A Case Study for LCD

TVs report (Vanegas et al. 2015) that studies process emission information on LCD TVs.

• The report, Life Cycle Assessment of a Personal Computer (Hikwama 2005), that studies the mass

share in a personal computer.

Recycling and end-of-life information was sourced from the following sources:

• Journal article, Metal recovery from high-grade WEEE: A life cycle assessment (Bigum et al.

2012), which reviews recycling emissions for electronic types, namely rare metals in circuit

boards.

• A report on the process and associated emissions from recycling lithium cobalt oxide batteries

(Dewulf et al. 2010).

• EPA’s 2008 report Electronics Waste Management in the United States: Approach I, which

provided additional information on electronic disposal practices, used mainly in the supporting

documentation chapter.

• A report on CRT material recovery and recycling practices (Turner et al. 2015).

Scoring. Data quality for electronics data sources ranged from medium to high. The variation among

sources is due largely to differences in data collection methods (measurements versus documented

calculations), review process of the studies, and the age of the sources. A summary of the electronics

results by data quality indicator groupings is shown in Table 13. The key findings for each of these

WARM Data Quality Assessment 31

sources are discussed below. Unlike other material categories in WARM, most sources cited in the

electronics category are used by many or all material types. This is primarily because these sources

studied the emission factors from component materials (e.g., copper, plastic, aluminum) found in most

or all of the electronic materials. Final emission values for electronic materials were then calculated

using the proportion of the component materials used in each material. For this reason, the sources

were grouped together under a single “Electronics” category.

Table 13: Summary of Data Quality Results for Electronic Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Electronics

Medium-high

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Overall, flow reliability and process completeness reflect the highest data quality of the indicators across

the data sources, as the sources covered all aspects of the life-cycle flow and generated data in a precise

and repeatable manner.

Other key findings from the review include:

• The estimated mean year of publication across the electronics sources was 2013, which reflects a

temporal data quality level of medium.

• The largest amount of variance in an indicator category came from the data collection methods

indicator, which focuses on “the robustness of the sampling methods” used by the study. Those

studies that sourced data from products and surveys across numerous companies in the industry

were considered as high quality, while those that examined only a handful of products received data

quality levels of medium-low and low.

• Two of the most critical sources – ANL (2018), Babbitt et al. (2017) – had high data quality, and the

third most critical s–rce -- Ecoinvent Centre (2015) – had medium-high data quality. This was due in

large part to their high-quality data generation methods, diligent review process, and recency of

publishing.

• Rather than attempt to gather data by electronic type, the current approach used in WARM

examines studies that provide emission estimates on the components that make up these

electronics and then combines that with Babbitt et al. 2017’s findings on mass shares to calculate

overall life cycle emissions.

Overall, the electronics datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Recommendations

Areas for improvement include:

WARM Data Quality Assessment 32

• Corroborate Babbitt et al. (2017) findings with another, recent source that similarly examines the

overall material share of different components in electronic types.

• Identify alternative higher quality data sources to potentially update the sources that received a

data quality value of medium or lower on their overall assessment, including Vanegas et al. (2017),

FAL (2002), Dewulf et al. (2010), Hikwama (2005), and Mars et al. (2016).

• Update process emissions from the latest versions of the GREET model (2018 versus 2022) and

Ecoinvent (v3.2 versus v3.9).

3.6 Construction Materials

Summary of Key Findings

Data Sources. Construction materials included in WARM include asphalt concrete, asphalt shingles,

carpet, clay bricks, concrete, dimensional lumber, drywall, fiberglass insulation, fly ash, medium-density

fiberboard, structural steel, vinyl flooring, and wood flooring. The data quality analysis for construction

materials relied on a total of 52 data sources. Of these, the majority of process energy, process non-

energy, and transportation emission factors data related to construction materials relies on 21 key data

sources, which were weighted more heavily when assessing data quality due to their importance in

WARM calculations:

• Asphalt Concrete

o Life-Cycle Assessment of Warm-Mix Asphalt: An Environmental and Economic

Perspective is a life cycle assessment presentation for Louisiana State University (Hasan

2009), which provides information on the composition of hot-mix asphalt. (KEY)

o Road Rehabilitation Energy Reduction Guide for Canadian Road Builders, a guide

published by the Canadian Industry Program for Energy Conservation (Canadian Industry

Program for Energy Conservation 2005), which provides data on the energy

consumption of manufacturing asphalt. (KEY)

• Asphalt Shingles

o Life Cycle Analysis of Residential Roofing Products (Athena Sustainable Materials

Institute 2000) is a life cycle report that provides information on the manufacturing of

virgin asphalt shingles. (KEY)

o Environmental Issues Associated with Asphalt Shingle Recycling, a report prepared for

Construction Materials Recycling Association (CMRA) and the U.S. EPA (CMRA 2007),

which provides data on the composition, recycling, and combustion of shingles. (KEY)

• Carpet

o Energy and Greenhouse Gas Factors for Personal Computers (FAL 2002) is a report that

provides data on certain material components that are used to make carpet. This source

sheds light on the fuel mix and energy use in the manufacturing of components used in

the production of carpet. (KEY)

o Background Document for Life-Cycle Greenhouse Gas Emission Factors for Carpet and

Personal Computers (EPA 2003a) is a report that provides data on the process emissions

and fuel mix in carpet production. (KEY)

Vanegas et al. 2017’s score suffers due to poor scores in the data collection methods, flow reliability,

geographical correlation, and temporal correlation indicators.

WARM Data Quality Assessment 33

• Clay Bricks

o Life Cycle Analysis of Brick and Mortar Products (Athena Sustainable Materials Institute

1998) is a life cycle report that provides data on the process and transportation

emissions of clay bricks. (KEY)

o Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2016 (EPA 2018b) is a

report that provides measurements and data on the life cycle emissions factors related

to manufacturing and transportation of clay bricks. (KEY)

• Concrete

o Background Document for Life-Cycle Greenhouse Gas Emission Factors for Clay Brick

Reuse and Concrete Recycling (EPA 2003b) is a report that documents the process and

transportation emissions of the life cycle of virgin concrete. (KEY)

• Dimensional Lumber

o Life-cycle energy and GHG emissions for new and recovered softwood framing lumber

and hardwood flooring considering end-of-life scenarios (Bergman et al. 2013) is a report

that provides cradle to gate greenhouse gas emissions data for new and recycled

dimensional lumber. (KEY)

• Drywall

o Life Cycle Analysis of Gypsum Board and Associated Finishing Products, published by

Athena Sustainable Materials Institute (Venta 1997), which provides data on the

manufacturing, fuel mix, and transportation emissions associated with the life cycle of

drywall. (KEY)

o Cradle-to-Gate Life Cycle Inventory of Nine Plastic Resins and Two Polyurethane

Precursors, prepared for the Plastics Division of the American Chemistry Council (FAL

2007), which provides data on the process emissions and fuel mix associated with

producing chemicals and materials used in the manufacturing of drywall. (KEY)

• Fiberglass Insulation

o Building for Environmental and Economic Sustainability (BEES) Technical Manual and

User Guide (Lippiatt 2007) is a guide to BEES, a software that helps users select

environmentally preferred, cost-effective building products. The user guide summarizes

data found in BEES, including the manufacturing and process emissions of fiberglass

insulation. (KEY)

• Fly Ash

o Background Document for Life-Cycle Greenhouse Gas Emission Factors for Fly Ash Used

as a Cement Replacement in Concrete (EPA 2003c) is a life cycle analysis that provides

data on the recycling emissions of fly ash. (KEY)

• Medium-Density Fiberboard

o Life-cycle inventory of medium density fiberboard in terms of resources, emissions,

energy and carbon, published in Wood and Fiber Science (Wilson 2010), which provides

information on the life cycle manufacturing and transportation emissions of medium-

density fiberboard. (KEY)

• Structural Steel

o Structural Section and Hot-Dip Galvanized Steel Production in China – Life cycle

assessment report (American Iron and Steel Institute [AISI] 2017) is an industry report

that provides information on the manufacturing and transportation life cycle emissions

associated with virgin inputs of structural steel. (KEY)

WARM Data Quality Assessment 34

o Fabricated Structural Steel – Environmental product declaration supporting background

report (AISI 2016) is an industry report describing the process emissions and

manufacturing inputs of recycled structural steel. (KEY)

• Vinyl Flooring

o Life Cycle Assessment of PVC and of principal competing materials, commissioned by the

European Commission (Baitz et al. 2004), which provides data on the life cycle of

polyvinyl chloride (PVC) and its use in the composition of vinyl flooring. (KEY)

• Wood Flooring

o Environmental Impact of Producing Hardwood Lumber Using Life-Cycle Inventory,

published in Wood and Fiber Science (Bergman and Bowe 2008), is a life cycle report

that provides information on the manufacturing material consumption of wood flooring.

(KEY)

o Life-Cycle Inventory of Solid Strip Hardwood Flooring in the Eastern United States, a

graduate student report published at the University of Wisconsin — Madison (Hubbard

and Bowe 2008), provides data on the manufacturing and transportation emissions

associated with the life cycle of wood flooring. (KEY)

o Life Cycle Analysis of Residential Roofing Products (Athena Sustainable Materials

Institute 2000) is a life cycle report that provides information on the manufacturing of

virgin wood products, which are also used in wood flooring.

(KEY)

Other data sources for specific construction materials include:

• Asphalt Concrete

o A Life Cycle Inventory for Road and Roofing Asphalt is a life cycle analysis report

prepared for Athena Sustainable Materials Institute (Athena Sustainable Materials

Institute 2001), which provides information on process emission factors of asphalt

concrete.

o 1997 Economic Census, Mining (U.S. Census Bureau 1997) is a government report on the

U.S. mining industry that provides data on material and fuel mix inputs for asphalt

concrete.

o A Life-Cycle Analysis of Alternatives for the Management of Waste Hot-Mix Asphalt,

Commercial Food Waste, and Construction and Demolition Waste is a master’s in civil

engineering thesis published in the North Carolina State University library (Levis 2008),

which provides information on recycling emissions of asphalt concrete.

o U.S. Life-Cycle Inventory Database, a comprehensive report published by the National

Renewable Energy Laboratory (NREL 2009), which provides information on the energy

use of producing limestone that is used to manufacture asphalt concrete.

• Asphalt Shingles

o Construction and Demolition Debris Recycling: Methods, Markets, and Policy is a

master’s thesis for the University of Central Florida (Cochran 2006), which provides data

on recycling emissions for asphalt shingles.

o Materials Recycling and Processing in the United States, Fifth Edition is a directory of

data (Berenyi 2007), which provides recycling loss rates of asphalt shingles.

Athena Sustainable Materials Institute (2000) is a key source for two Construction Materials, Wood Flooring and

Asphalt Shingles.

WARM Data Quality Assessment 35

• Carpet

o Eco-profiles of the Plastics Industry—Polyamide (Nylon 6) (Plastics Europe 2005a) is a

material profile that informs the process and transportation emissions of chemicals

used to produce carpet.

o Eco-profiles of the Plastics Industry—Polyamide (Nylon 66) (Plastics Europe 2005b) is a

material profile that informs the process and transportation emissions of chemicals

used to produce carpet.

• Concrete

o Aggregates from Natural and Recycled Sources–Economic Assessments for Construction

Applications (Wilburn and Goonan 1998) is a report that informs the process and

transportation emissions of recycled concrete.

• Dimensional Lumber

o Environmental Product Declaration (American Wood Council 2013) is a material report

that provides cradle to gate greenhouse gas emissions data for new dimensional

lumber.

• Drywall

o Composition of Municipal Solid Waste in the United States and Implications for Carbon

Sequestration and Methane Yield (Staley and Barlaz 2009) is a report that informs the

moisture content and carbon storage factor of drywall.

o Comprehensive life-cycle analysis of RAP: Comprehensive life-cycle analysis of

plasterboard (WRAP 2008) is a life cycle analysis report that informs the composition of

recycled drywall and energy requirements for drywall recycling.

o US Census Commodity Flow Survey Preliminary Tables, published by the U.S. Bureau of

Transportation Statistics (BTS) Research and Innovative Technology Administration (BTS

2013), which provides data on transportation energy.

o 2002 Commodity Flow Survey (U.S. Census Bureau 2004) is a report that measures the

transportation energy associated with recycled drywall.

• Fiberglass Insulation

o Life Cycle Analysis of Residential Roofing Products (Athena Sustainable Materials

Institute 2000) is a life cycle report that provides information on the manufacturing of

virgin fiberglass insulation, specifically that used in roofing.

o US Census Commodity Flow Survey Preliminary Tables, published by the U.S. Bureau of

Transportation Statistics (BTS) Research and Innovative Technology Administration (BTS

2013), which provides data on transportation energy.

o Glass Recycling–Life Cycle Carbon Dioxide Emissions, prepared for the British Glass

Manufacturers Confederation – Public Affairs Committee (Enviros Consulting 2003), is a

life cycle report that provides information on glass recycling and transportation

emissions.

o U.S. Life-Cycle Inventory Database, a comprehensive report published by the National

Renewable Energy Laboratory (NREL 2009), which provides information on the sourcing

of soda ash and limestone that are inputs in the manufacturing of fiberglass insulation.

• Medium-density Fiberboard

o Environmental Product Declaration (Composite Panel Association 2018) is a cradle-to-

gate life cycle report that provides information on the manufacturing emissions, virgin

inputs, and overall medium-density fiberboard production.

WARM Data Quality Assessment 36

• Structural Steel

o US Census Commodity Flow Survey Preliminary Tables, published by the U.S. Bureau of

Transportation Statistics (BTS) Research and Innovative Technology Administration (BTS

2013), which provides data on transportation energy.

o Greenhouse Gas Emissions from the Management of Selected Materials, published by

the U.S. EPA (EPA 1998b), which provides information on retail transportation energy

use. Global Steel Trade Monitor (U.S. Department of Commerce 2020) is a government

economic report that provides information on the virgin inputs that compose structural

steel.

o 2020 World Steel Figures (World Steel Association 2020) is an annual global industry

report that provides data on the virgin inputs that compose structural steel.

• Vinyl Flooring

o Resilient Flooring: A Comparison of Vinyl, Linoleum and Cork, published by the Georgia

Tech Research Institute (Jones 1999), which provides data on the manufacturing process

and emissions of vinyl flooring.

o Cradle-to-Gate Life Cycle Inventory of Nine Plastic Resins and Two Polyurethane

Precursors, prepared for the Plastics Division of the American Chemistry Council (FAL

2007), which provides data on the manufacturing emissions associated with producing

chemicals and materials used in the production of vinyl flooring.

o Eco-profile of high volume commodity phthalate esters (DEHP/DINP/DIDP), a report

prepared for The European Council for Plasticisers and Intermediates (ECPI) (ECOBILAN

2001), which provides data on the environmental impact of chemicals used in the

production of vinyl flooring, as well as information on transportation emissions.

o US Census Commodity Flow Survey Preliminary Tables, published by the U.S. Bureau of

Transportation Statistics (BTS) Research and Innovative Technology Administration (BTS

2013), which provides data on transportation energy.

• Wood Flooring

o Life-cycle energy and GHG emissions for new and recovered softwood framing lumber

and hardwood flooring considering end-of-life scenarios, published in Wood and Fiber

Science (Bergman et al. 2013), which provides data on the material consumption

associated with manufacturing wood flooring.

o Environmental Product Declaration (American Wood Council 2013) is a material report

that provides cradle to gate greenhouse gas emissions data on virgin manufacturing

inputs for wood flooring.

o US Census Commodity Flow Survey Preliminary Tables, published by the U.S. Bureau of

Transportation Statistics (BTS) Research and Innovative Technology Administration (BTS

2013), which provides data on transportation energy.

Two data sources could not be located and thereby were deemed low quality by default:

• Carpet

o Personal communication with Matthew Realff, Associate Professor of Chemical and

Biomolecular Engineering (Realff 2011), which provides information on material

composition and recycling of carpet.

• Fiberglass Insulation

o Email communication with Scott Miller, Knauf Insulation, and Beth Moore (Miller 2010),

which provides information on recycling emission of fiberglass insulation.

WARM Data Quality Assessment 37

Scoring. Data source analysis was conducted by material, and then averaged to show that, overall,

sources for construction materials were of medium-high data quality. A summary of the results by data

quality indicator groupings is shown in Table 14. The key findings for each of these sources used for the

different construction materials are discussed below and details on the individual sources’ scores can be

found in the Appendix.

Table 14: Summary of Data Quality Results for Construction Materials Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Asphalt Concrete

Medium-high

Medium

Medium-high

Medium

Asphalt Shingles

Medium-low

Medium

Carpet

Medium

Medium-low

Medium

Clay Bricks

Medium-high

High

Medium-high

High

Concrete

High

Medium-high

Medium

Medium-high

Dimensional Lumber

Medium-high

High

Medium-high

Drywall

Medium-high

Fiberglass Insulation

Medium

Fly Ash

Medium-high

Medium

Medium-high

Medium-density

Fiberboard

Medium-high

High

Medium-high

Structural Steel

Medium-high

Vinyl Flooring

Medium-high

Medium

Medium-high

Medium

Wood Flooring

Medium-high

Medium

Medium-high

Construction Materials

Medium-high

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Asphalt Concrete

Asphalt concrete emissions factors and energy factors rely heavily on the data sourced from Hassan

(2009) and Canadian Industry Program for Energy Conservation (2005). They also use information from

the US Census Bureau (1997), Athena Sustainability Materials Institute (2001), Levis (2008), and NREL

(2009).

Hassan (2009) is a life cycle analysis of warm-mix asphalt, and provides information on emissions factors

and the composition of asphalt concrete. Despite being an older source with little information about the

sample size or reviewer process, Hassan (2009) was deemed to have, on average, medium quality data

as it was from the same area of study as WARM, and all technology categories were equivalent (e.g.,

process design, operating conditions, material quality, and process scale).

Of the asphalt concrete sources, US Census Bureau (1997), Levis (2008), and Athena Sustainability

Materials Institute (2001) were deemed to have the highest quality data, and received overall medium-

high data quality values. US Census Bureau (1997) provided data on material and fuel mix inputs, Levis

WARM Data Quality Assessment 38

(2008) informed recycling emissions, and Athena Sustainability Materials Institute (2001) included data

on process emissions factors. All other sources for asphalt concrete had a medium data quality value.

On average, asphalt concrete had medium-high quality sources for its process review and completeness

indicators. This indicates that the sources for this material tended to be reviewed by third parties to

verify data and covered a high percentage of process flows for this material.

The asphalt concrete datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium

Asphalt Shingles

Asphalt shingles emissions factors and energy factors rely on two key sources: CMRA (2007) and Athena

Sustainable Materials Institute (2000). Other non-key sources include Cochran (2006) and Berenyi

(2007).

CRMA (2007) provides information on the emissions and energy factors of the composition, recycling,

and combustion of asphalt shingles. This source has a medium-low data quality, as it has low data

quality values for temporal correlation, process indicators, and data collection methods. However, the

source did have high geographical correlation as it is focused on the United States.

Athena Sustainable Materials Institute (2000) is the source for virgin production and manufacturing for

residential roofing materials, including asphalt shingles. This source was deemed medium-high quality,

as it reflected high data quality for technological correlation, data collection methods, and process

completeness data quality indicators, but reflected low-quality data for temporal correlation and

medium quality data for geographical correlation as it is a Canadian study conducted over 15 years ago.

The asphalt shingles datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium

Carpet

Carpet emissions factors and energy factors rely on data from FAL (2002) and EPA (2003a). Other

sources also contribute: Plastics Europe (2005a) and Plastics Europe (2005b). Realff (2011) was not

located and was therefore deemed low quality.

FAL (2002) is a report on the fuel mix and energy use in the manufacturing of personal computers that

contains fuel and energy use information relevant to material components used to manufacture carpet.

The data quality of this source is medium based on its mix of DQI values from low to high quality. It had

low data quality for temporal correlation, as much of the data are more than 15 years old, and for

process completeness and data collection methods due to those aspects being unknown and unable to

assess. This source had medium-low data quality for flow reliability because it is an old source with data

based on documented estimates rather than verified measurements of calculations. However, FAL

WARM Data Quality Assessment 39

(2002) had high data quality for geographical correlation, as the data are U.S.-based, and medium-high

quality for technological correlation and process review.

EPA (2003a) is a report on the life cycle of carpet and personal computers that provided insight on fuel

mix and process emissions. The source had medium-high data quality. While this is an old source, it had

high ratings for geographical correlation, technological correlation, data collection methods, and process

completeness, as this is a U.S.-located report with verified data based on measurements that cover a

large percentage of flows, representative sample size, and a variety of technology types.

The carpet datasets scored as follows:

• Average indicator: Medium-low

• Average weighted indicator: Medium

Clay Bricks

WARM emissions factors and energy factors for clay bricks rely on two sources, both of which are key

sources: Athena Sustainable Materials Institute (1998) and EPA (2018b). Both provide key information

on the process and transportation emissions of clay bricks. Athena Sustainable Materials Institute (1998)

had medium-high data quality overall due to high and medium-high scores for flow and process

indicators as the data was based on calculations, used a representative sample size, had third party

reviewers, and a high percent of life cycle flows were covered. However, the source had low data quality

for temporal and geographical correlation for being an old life cycle analysis based in Canada. EPA

(2018b) is a high-quality source as it is more recent, based in the United States, and has other high

quality flow indicators and process indicators due to a large sample size representative of the clay bricks

industry, the use of third-party reviewers, and a large breadth of life cycle flows covered by the report.

The clay bricks datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: High

Concrete

Concrete emissions, energy, and transportation factors rely on two sources: EPA (2003b) and Wilburn

and Goonan (1998). EPA (2003b) is a medium-high quality key source that provides information into

process and transportation emissions for virgin concrete. This source had low quality for temporal

correlation, as it is over 15 years old, and medium-low quality for process review, as there was internal

review but not a documented third-party review of the study. All other process and flow indicators were

deemed high or medium-high quality as the sources covered over 80 percent of recycled and virgin

concrete life cycle flows and considered a representative sample size in the United States. Wilburn and

Goonan (1998) was also a medium-high quality source. This source provided information on process and

transportation emissions for recycled concrete, and had similar scores to EPA (2003), where its lowest

data quality indicators were temporal correlation and internal review, and all others were high to

medium quality.

WARM Data Quality Assessment 40

The concrete datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Dimensional Lumber

Dimensional lumber relies on Bergman et al. (2013) and American Wood Council (2013). Both sources

provide data related to the life cycle emissions of dimensional lumber. Bergman et al. (2013) considers

the cradle to grave emissions of new and recycled dimensional lumber, while American Wood Council

(2013) is a report on the cradle-to-grave emissions of new dimensional lumber. Both sources had

medium-high quality flow reliability as they use verified data based on calculations and medium-low

quality temporal correlation as they are over 10 years old. They also had medium-high or high quality

data quality values for all other flow and process indicators, due to geographical correlation being based

in America or North America, a highly representative sample size for data collection, documented third

party reviewers, and a high percentage of flows covered.

The dimensional lumber datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Drywall

Drywall relies on six sources to inform energy factors and emission factors: Venta (1997), FAL (2007),

WRAP (2008), BTS (2013), Staley and Barlaz (2009), and US Census Bureau (2004). Of these sources,

Venta (1997) and FAL (2007) play a greater role in informing the energy and emissions factors of drywall.

Venta (1997) provides data on manufacturing emissions and fuel input, as well as transportation

emissions for the life cycle of drywall. FAL (2007) gives insight into process emissions and energy use of

drywall from when it is produced to when it hits the market. Both were deemed medium-high quality, as

both sources had high data quality values for technological correlation, data collection methods, process

review, and process completeness. They used an equivalent variety of technology types, a

representative sample size, verified third party reviewers, and data from more than 80 percent of the

drywall market over an adequate period. While they have a similar data quality, Venta (1997) had lower

data quality than FAL (2007) as it is a much older source and is based in Canada instead of the United

States.

US Census Bureau (2004) is a commodity survey that informed transportation energy factor for recycled

drywall. For this source, which overall reflected medium-high data quality, each flow and process

indicator provided high quality data besides temporal correlation, which was low quality as the source is

over 15 years old. WRAP (2008) provides the composition of recycled drywall and energy requirements

for drywall recycling that informed process emissions. This source was a medium quality source as it was

an older summary of a study conducted in the United Kingdom. It had low quality for process review and

medium-low quality for temporal and geographic correlation, but medium to high quality for other

process and flow indicators. BTS (2013) was also medium quality, as it has a low-quality process

WARM Data Quality Assessment 41

completeness due to an unknown percentage of flows evaluated, and medium-low quality for temporal

correlation and process review. Staley and Barlaz (2009) is a report that compares 11 statewide waste

characterization studies to understand the overall composition of discarded waste in the United States

and how that impacts carbon sequestration. This source was deemed to be of medium-high data quality,

as it calculates data from a wide range of facilities in states across every region of the United States. The

report considers a variety of technology categories, includes operating conditions and material quality,

and covers the majority of process flows. Despite these high quality data indicators, the source reflected

medium-high quality due to low-quality temporal correlation, as the source is from 2009.

The drywall datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Fiberglass Insulation

One source, Lippiatt (2007), plays a key role in understanding the life cycle energy factors and emission

factors of fiberglass insulation. Other sources include: Athena Sustainable Materials Institute (2000),

Enviros Consulting (2003), NREL (2009), Miller (2010), and BTS (2013). Athena Sustainable Materials

Institute (2000) and BTS (2013) are cross cutting sources, as the former is also used to inform the life

cycle factors of asphalt shingles, and the latter is also used to inform the life cycle of drywall. The Miller

(2010) data source was unable to be located to determine data quality, so it was deemed low quality.

Lippiatt (2007) provides information on the manufacturing process and related emissions for fiberglass

insulation. This source is a report based on a tool that measures the environmental performance and life

cycle emissions of various building materials, including fiberglass insulation. This was determined to be a

medium-high quality source, as it had high data quality ratings for most flow indicators and process

indicators. However, this source is over 15 years old and based on verified calculations rather than

verified measurements resulting in low quality temporal correlation and flow reliability, respectively.

Enviros Consulting (2003) covers glass recycling and transportation emissions for fiberglass insulation.

This medium quality source is a life cycle assessment conducted in the United Kingdom over 15 years

ago, lowering its geographic and temporal correlations. The source also had a medium-low quality

process review, as the review was conducted by an internal reviewer. The remaining indicators,

technological correlation, data collection methods, process review and completeness, and flow

reliability were deemed high or medium-high quality. This is due to the source explaining process design,

operation conditions, material quality, and process scale; employing multiple third party reviewers;

using verified data; and covering the relevant fiberglass insulation market.

The fiberglass insulation datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium

WARM Data Quality Assessment 42

Fly Ash

The energy, emissions, and transportation factors of fly ash rely on one primary data source, EPA

(2003c). This document pertains to life cycle GHG emissions factors for fly ash, particularly as a cement

replacement in concrete, which is the use considered in WARM. EPA (2003c) is a medium-high quality

source. Temporally this was a low-quality source, and the process review was medium-low quality as the

document was only reviewed internally. Other indicators were high quality, including process

completeness, data collection methods, technological correlation, and geographical correlation. Flow

reliability was medium-high data quality, as data was based on verified calculations rather than verified

measurements.

The fly ash fiberboard datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Medium-density Fiberboard

The medium-density fiberboard material emissions factors and energy factors utilize two sources:

Wilson (2010) and Composite Panel Association (2018). Wilson (2010) plays a key role in determining life

cycle emissions of medium-density fiberboard, and focused on manufacturing and transportation

emissions, resources, energy, and carbon. Composite Panel Association (2018) informs the emissions

factors of manufacturing medium-density fiberboard with virgin inputs.

Wilson (2010) was a medium-high quality source that had high quality indicators besides flow reliability,

which was medium-high quality based on the use of calculations, and temporal correlation, which was

medium-low quality as the source is over 10 years old. Composite Panel Association (2018) was a high-

quality source as all process indicators and two flow indicators were found to be high quality. Three flow

indicators, geographical correlation, temporal correlation, and flow reliability, were medium-high

quality, as this source is over 5 years old, set in North America, and based on verified calculations.

The medium-density fiberboard datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Structural Steel

The assessment of material life cycle emissions for structural steel relies on six data sources: AISI (2016),

AISI (2017), BTS (2013), EPA (1998), U.S. Department of Commerce (2020), World Steel Association

(2020). Of these, AISI (2016) and AISI (2017) were key sources, and BTS (2013) and (EPA 2018) informed

the data on structural steel but were also used to inform data on several other material types.

AISI (2016) is an environmental product declaration of fabricated hot-rolled structural steel sections. The

source has an overall high quality flow representativeness. It has a high geographic correlation as it

focuses on the American steel industry, and a medium temporal correlation as it was published within

WARM Data Quality Assessment 43

the last 10 years, although the report states the data has a five-year period of validity, which has

expired. It also has high quality technological correlation and data collection methods, as the source

examines a wide scale of technology categories and uses representative data from a high percentage of

the market. The source had a medium-high quality flow reliability as it is an industry report based on

estimated calculations, and high-quality process indicator data due to third party reviews and a large

percent of process flows covered in the report, including data from all stages of the steel life cycle.

AISI (2017) is also a LCA report, focusing on structural section and hot-dip galvanized steel production in

China. It was determined to have medium-high data quality. This is a comprehensive report based on

verified measurements, giving it a high-quality flow reliability. The wide scope of flows and

manufacturing processes covered, as well as the third-party review, resulted in this source having high

quality process indicators. Finally, flow representativeness was also high quality, as technological

correlation and data collection methods were both verified based on a wide variety of data sources and

technology scopes related to the life cycle of steel. The lowest quality indicators for this source were

temporal correlation, which was medium quality as the source is more than six but less than 10 years

old, and geographic correlation, which was medium-low quality as the geographic focus is China.

The structural steel datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Vinyl Flooring

The assessment of material life cycle emissions for vinyl flooring relies on six data sources: Ecobilan

(2001), Jones (1999), Lippiatt (2007), Baitz et al. (2004), FAL (2007), and BTS (2013). Of these, Lippiatt

(2007), FAL (2007), and BTS (2013) are cross cutting sources that are also used to determine the

emissions factors and energy factors of other material types, including drywall and fiberglass insulation.

Two sources, Lippiatt (2007) and Baitz et al. (2004) are key sources to determining the life cycle

emissions factors of vinyl flooring.

Baitz et al. (2004) is an LCA of materials that informs the composition and process emissions of vinyl

flooring. Baitz et al. (2004) is considered a medium-high quality data source, as process indicators and

data collection methods indicate high quality and flow reliability shows medium-high quality. As this is a

European study conducted over 15 years ago, the temporal and geographic correlations were low or

medium-low quality. The technological correlation of this study was deemed medium quality, as it

addressed only two relevant technology categories.

Jones (1999) is an assessment of various flooring materials and provided information on the

manufacturing process emissions for vinyl flooring. This was a medium-low quality source, one of the

lowest ratings of all the construction materials data sources. This poor quality was due to its unspecified

geographic region of study, publishing date of over 15 years ago, low quality data collection methods,

and unknown percentage of flows evaluated. Most of the flow and process indicators were low quality,

with others being at most medium quality.

WARM Data Quality Assessment 44

The vinyl flooring datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium

Wood Flooring

Wood flooring materials emissions factor and energy factor calculation utilize six data sources to

understand life cycle emissions: Bergman et al. (2013), American Wood Council (2013), BTS (2013),

Bergman and Bowe (2008), Hubbard and Bowe (2008) and Athena Sustainable Materials Institute

(2000). Of these, several sources also are used to inform data on multiple materials in addition to wood

flooring: Bergman et al. (2013) and American Wood Council (2013) are sources for dimensional lumber;

BTS (2013) is a source for vinyl flooring, fiberglass insulation, and drywall; and Athena Sustainable

Materials Institute (2000) is also a source for asphalt shingles and fiberglass insulation. Three sources

were determined to provide key information into the life cycle emissions of wood flooring materials:

Bergman and Bowe (2008), Hubbard and Bowe (2008), and Athena Sustainable Materials Institute

(2000).

Bergman and Bowe (2008) was deemed a medium-high quality source that considers manufacturing

process emissions and material consumption of hardwood lumber, a primary material used for wood

flooring. The study analyzes the environmental impact of hardwood lumber production in the United

States by calculating data from industry estimates and information provided by 20 lumber mills. As there

are hundreds of mills in the United States, this small sample means data collection method was deemed

medium-low quality for this source. Also, the source is 15 years old, so it has low quality temporal

correlation. Otherwise, process and flow indicators were of high or medium-high quality, averaging out

into an overall medium-high quality source.

Hubbard and Bowe (2008) is a medium quality source with low quality temporal correlation as it is 15

years old and medium-low quality data collection methods due to a small sample size of the market.

Hubbard and Bowe (2008) is also a study into the life cycle inventory of hardwood wood flooring

materials, but it focuses specifically on the Eastern US, where the majority of lumber mills are located.

Besides the lower temporal and data collection ratings, all other flow and process indicators are of high

or medium-high quality.

The wood flooring datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Datasets for the individual construction materials ranged from medium-low to high quality, as noted at

the end of each material sub-section. Overall, the construction material datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

WARM Data Quality Assessment 45

Recommendations

Areas for improvement include:

• Identify and update construction materials data sources to more recent sources, where applicable,

based on the quality of the temporal correlation (medium-low or low data quality). Some sources

may not have more recent versions, but updated data should be used if possible.

• Replace Jones (1999) from the sources for vinyl flooring materials with a more recent study that has

higher quality process indicators, data collection methods, and geographic correlation. This is the

lowest quality construction materials data source.

• Find additional data sources for fly ash materials to the one that is currently used to provide

multiple sources that inform life cycle calculations of fly ash materials.

3.7 Tires

Summary of Key Findings

Data Sources. The tires material emission factors and energy factor calculations rely on 18 data sources

for information regarding the different end uses of scrap tires, tire and scrap tire energy content,

proportions of materials in scrap tires, and the process energy requirements of different end use

techniques.

Key data sources for the tires material pathway in WARM include the following:

• Rubber Manufacturers Association (RMA) 2009 Scrap Tire Markets in the United Sta

s: 9th

Biennial Report, which reviews the different end-of-life pathways for scrap tires and provides

the share of all tires that go to each disposal pathway (RMA 2009a). (KEY)

• Atech Group’s A National Approach to Waste Tyres, which provides the process energy

requirements for new tires (Atech Group 2001). (KEY)

Supporting information on management pathways and their associated emissions as well as additional

process emission data came from the following:

• California Integrated Waste Management Board’s (CIWMB) Tires as a fuel supplement:

Feasibility study: Report to Legislature report provides information on the energy content in

tires used for calculations regarding tire combustion (CIWMB 1992).

• EIA’s 2009 report on the fuel consumption requirements for new tires (EIA 2009).

• Venta and Nisbet’s Life Cycle Analysis of Residential Roofing Products report, which provides

offset energy values for sand used in rubber for tires (Venta and Nisbet 2000). These offsets

were applied to end-of-life management pathway calculations.

• RMA’s 2010 Facts at a Glance: How a Tire is Made report, which provides tire manufacturing

energy requirements (RMA 2010a).

• Personal communication with RMA’s Michael Blumenthal regarding the industry average scrap

tire recovery rate in the US (RMA 2010b).

WARM Data Quality Assessment 46

• RMA’s 2009 Scrap Tire Markets: Facts and Figures – Scrap Tire Characteristics report (RMA

2009b), which provides the average weight of a scrap tire used in WARM calculations.

Additional sources were used to calculate end-of-life emissions from different management pathways

and provided additional context in the supporting documentation chapter, including:

• Corti and Lombardi’s 2004 report on the retention rate and energy required for a tire recycling

process known as pulverization (Corti and Lombardi 2004).

• EPA’s Greenhouse Gas Emissions from the Management of Selected Materials report, that

included assumptions for the composition, uses, and energy of scrap tires. Information in this

report is used for context in the supporting documentation chapter (EPA 1998).

• ICF’s 2006 Life-Cycle Greenhouse Gas Emission Factors for Scrap Tires report. Information in this

report is used for context in the supporting documentation chapter (ICF 2006).

• Nevada Automotive Test Center provided information on the retreading of tires and the

associated energy required (Nevada Automotive Test Center 2006). Information in this report is

used for context in the supporting documentation chapter.

• NIST’s MEP Environmental Program, Best Practices in Scrap Tires & Rubber Recycling provided

information on the composition of different fibers in tires to help calculate scrap tire’s weight

composition by material (NIST 1997).

• Praxair’s 2009 report on cryogenic grinding of scrap tires was used for contextual information in

the supporting documentation chapter (Praxair 2009).

• Pimentel et al.’s U.S. Energy Conservation and Efficiency: Benefits and Costs report provides

information on synthetic rubber manufacturing as well as transportation requirements

(Pimentel et al. 2002).

Finally, transportation emission factors were sourced from the following reports:

• FAL’s 1994 The Role of Recycling in Integrated Solid Waste Management to the Year 2000

included transportation energy requirements in WARM calculations (FAL 1994).

• NREL’s 2015 US Life Cycle Inventory Database provides retail transport requirements (NREL

2015).

• U.S. Census Commodity Flow Survey includes additional retail transport requirements for WARM

calculations (BTS 2013).

Of the sources used in the tires section, two played key roles in determining process energy

requirements and emission factors for various end of life scenarios: Rubber Manufactures Association

(RMA — now the U.S. Tire Manufacturing Association) 2009 report on scrap tires and Atech Group’s

2001 report. The RMA (2009a) source details what share of used tires go toward different end-of-life

scenarios (e.g., combustion, reclamation, various recycling techniques) and the Aetch Group (2001)

WARM Data Quality Assessment 47

source provides process energy requirements for new tires to aid in source reduction calculations. The

remaining sources are used to help fill in gaps in data or provide additional context on the waste

management of tires in the documentation chapter. This includes detailed energy use data for different

recycling strategies (Corti and Lombardi 2004) and combustion (CIWMB 1992), background information

on tire disposal (EPA 1998), and transportation requirements (BTS 2013 and NREL 2015).

Scoring. The overall data quality levels of the 18 sources varied from medium to high. The variation is

due in large part to differences in data collection methods, review process of the studies, and the data

generation and validation methods used by the studies. Of the 18 sources, three could not be located as

they were removed from their original web location due to branding changes by the source or

untraceable written communications that were previously noted as email exchanges. These sources

were: RMA (2010a), RMA (2010b), and Praxair (2009). Due to lack of information, they were given low

data quality values for each DQI, with the exception of data year. The data quality results for tires are

shown in Table 15, and details on the individual sources’ scores can be found in the Appendix. The key

findings for each of these sources are discussed below.

Table 15: Summary of Data Quality Results for Tires Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Tires

Medium

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Overall, temporal correlation and process review indicators received the lowest data quality values

across the tire data sources, indicating the tires section would benefit from a literature review and data

collection from newer sources that have undergone more extensive review. The average year of

publication for the tires sources was 2004. The two key sources – RMA 2009 and Atech Group 2001 –

had data quality values of medium-high and medium, respectively.

Overall, the tires datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium

Recommendations

Areas for improvement include:

• Update one of the key data sources for tires with an updated report by RMA on scrap tire

management (released October 2022).

• Identify a recent source on tire process emissions to either corroborate or update the values from

the Atech Group 2001 report.

• Locate replacement studies for those sources that could not be recovered.

• Update those sources with the oldest published dates, most notably CIWMB (1992), NIST (1997) and

EPA (1998).

WARM Data Quality Assessment 48

3.8 Food Waste

Summary of Key Findings

Data Sources. Food waste materials included in WARM include beef, poultry, grains, bread, fruits and

vegetables, and dairy products. The data quality analysis for food waste involved a review of 17 data

sources. Of these, the majority of process energy, process non-energy, and transportation emission

factors data related to food waste rely on eight key data sources, which were weighted more heavily

when assessing data quality due to their importance in WARM calculations:

• Beef

o The report, More Sustainable Beef Optimization Project (Battagliese et al. 2013),

submitted by BASF Corporation, which provides data on production energy and

emissions for cradle to packing plant/case-ready plant gate. (KEY)

o Email correspondence with Thomas Battagliese, BASF (February 2014), which provides

updated data for the study, Battagliese et al. 2013 with revised boundaries. (KEY)

• Poultry

o The journal article, Environmental performance in the US broiler poultry sector: Life cycle

energy use and greenhouse gas, ozone depleting, acidifying and eutrophying emissions,

Pelletier (2008), which provides data on cradle-to-farm gate energy and emission factors

for poultry. (KEY)

o The report, What’s at Steak? Ecological Economic Sustainability and the Ethical,

Environmental, and Policy Implications for Global Livestock Production (Pelletier 2010),

which also provides data on cradle-to-farm gate energy and emission factors for poultry.

(KEY)

• Grains and Bread

o The journal article, The Carbon Footprint of Bread (Espinoza-Orias et al. 2011), which

provides process emission and energy data on bread production. (KEY)

o Estimating Wheat Supply and Food Use, prepared by the U.S. Department of Agriculture

Economic Research Service (USDA 2012a), which provides grains supply data for the

United States. (KEY)

• Fruits and Vegetables

o Comparison of Twelve Organic and Conventional Farming Systems: A Life Cycle

Greenhouse Gas Emissions Perspective (Venkat 2012), which provides data on cradle to

farm GHG emissions from fruits and vegetables production. (KEY)

o UC Davis fruits and vegetables cost production studies (Fake et al. 2009, O’Connell et al.

2009, Stoddard et al. 2007, Wunderlich et al. 2007), which provide production data for

various fruits and vegetables. (KEY)

o A life cycle assessment on bananas, providing banana production data (Luske 2010).

(KEY)

o Ecoinvent 2.0, providing potato production data. (KEY)

• Dairy Products

o Global Warming Potential of Fluid Milk Consumed in the US: A Life Cycle Assessment

(Thoma et al. 2010) of the Innovation Center for U.S. Dairy and University of Arkansas,

which provides process emissions data on milk production. (KEY)

The non-key sources for food waste, including 4 sources that were not used as key sources for a

category, are:

WARM Data Quality Assessment 49

• Grains and Bread

o Nemecek, T., and Kagi, T. (2007). Life Cycle Inventories of Agricultural Production

Systems. Ecoinvent Report No. 15., which provides data on process emissions from grain

drying.

• Fruits and Vegetables

o Apples, Bananas, and Oranges: Using GIS to Determine Distance Travelled, Energy Use,

and Emissions from Imported Fruit (Bernatz 2009), which provides data on energy and

emissions impact from transportation of fruits and vegetables.

• Dairy Products

o Food Availability (per Capita) Data System – 2010, prepared by the U.S. Department of

Agriculture Economic Research Service (USDA 2012b), which provides data on dairy

supply in the United States.

Scoring. Data source analysis was conducted by food waste category, and then averaged to show that,

overall, sources for food waste were of medium to medium-high data quality. A summary of the results

by high level data quality indicators is shown in Table 16. The key findings for each of these sources used

for the different food waste categories are discussed below and details on the individual sources’ scores

can be found in the Appendix.

Table 16: Summary of Data Quality Results for Food Waste Data Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Beef

Medium-high

Medium

Medium-high

Poultry

Medium-high

Grains

High

Medium

High

Medium-high

Bread

High

Medium

High

Medium-high

Fruits &

Vegetables

Medium-high

Medium

Dairy Products

Medium-low

Medium

Food Waste (non-

meat)

Medium-high

Medium

Medium-high

Medium

Food Waste

(meat only)

Medium-high

Food Waste

Medium-high

Medium

Medium-high

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Beef

The WARM emissions factors and energy factors developed for the beef category rely on two sources:

Battagliese et al. (2013), “More Sustainable Beef Optimization Project” and an email correspondence

with Thomas Battagliese (2014). Battagliese et al. (2013) is a key source that provides information on

the cradle-to-plant production process and production transportation emissions of beef. Email

correspondence with Thomas Battagliese (2014) was a communication between ICF and Thomas

Battagliese that provides updated data for the cradle to packing plant/case-ready plant gate process

WARM Data Quality Assessment 50

energy and emission factors in Battagliese et al. (2013) with revised boundaries that no longer consider

retail CED & direct emissions, including removal of transport from that phase.

Battagliese et al. (2013) is a moderately old analysis conducted over ten years ago and has a medium-

high data quality overall due to high and medium-high data quality scores for most flows and process

indicators except third-party reviews (sub-category of Process Review and Completeness Indicators) and

temporal correlation (sub-category of Flow-Representativeness Indicators). The email correspondence

with Thomas Battagliese (2014) also has a medium-high data quality overall due to high and medium-

high data quality scores for most flows and process indicators except process review and temporal

correlation.

Poultry

WARM emissions factors and energy factors for poultry rely on two key sources: Pelletier (2008) and

Pelletier (2010). Both sources provide energy and emission factors for a cradle-to-farm gate analysis of

poultry production. Although both sources are relatively old (2008 and 2010), they have medium-high

data quality overall due to high and medium-high data quality scores for most flows and process

indicators except process review and temporal correlation.

Grains and Bread

Grain and bread food-waste categories rely on the same sources for WARM emissions and energy

factors. Two sources, Espinoza-Orias et al. (2011), “The Carbon Footprint of Bread” and USDA (2012a),

“Estimating Wheat Supply and Food Use” played a key role in understanding life cycle emissions and

energy factors of grains and bread. Other sources included Nemecek and Kagi (2007), Life Cycle

Inventories of Agricultural Production Systems, which provides life cycle inventory data on agricultural

production processes and was used to inform the processing factors for grain drying. Espinoza-Orias et

al. (2011) provided an analysis of the carbon footprint of bread and USDA (2012a) provided wheat

supply and food usage data for the U.S.

Espinoza-Orias et al. (2011) has medium-high data quality overall due to high and medium-high data

quality scores for most flows and process indicators however, it received low data quality scores for

geographical correlation (sub-category of Flow Representativeness Indicators) as it is based in the UK.

USDA (2012a) also has medium-high data quality overall due to high and medium-high data quality

scores for most flows and process indicators, except temporal correlation, as it uses data that is older

than 15 years. Nemecek and Kagi (2007) has medium data quality overall due to low data quality scores

for temporal correlation and geographical correlation as the study is more than 15 years old and its data

is representative of Switzerland.

Fruits and Vegetables

The production energy and emissions factors for fruits and vegetables rely on the following key sources:

Venkat (2012), Comparison of Twelve Organic and Conventional Farming Systems: A Life Cycle

Greenhouse Gas Emissions Perspective, UC Davis fruits and vegetables data, a LCA of bananas (Luske et

al. 2010), and Ecoinvent data for potato production data. Venkat (2012) is a cradle-to-farm analysis

providing life cycle GHG emissions for twelve crop products grown in California through organic and

conventional farming systems. UC Davis fruits and vegetables data obtained from cost production

WARM Data Quality Assessment 51

studies (Fake et al. 2009, O’Connell et al. 2009, Stoddard et al. 2007, Wunderlich et al. 2007) informs

production emissions for fruits and vegetables through research and analysis conducted at UC Davis

using data from 2007-2009. Luske et al. (2010) and Ecoinvent were used for banana and potato

production data, respectively. Bernatz (2009) provides production transportation emissions and energy

factors for fruits and vegetables.

Venkat (2012) reflects medium-high data quality overall due to high and medium-high data quality

scores for most flows and process indicators except temporary correlation and process review, as the

study is more than 10 years old and lacks information on external reviews conducted. UC Davis fruits

and vegetables data (Fake et al. 2009, O’Connell et al. 2009, Stoddard et al. 2007, Wunderlich et al.

2007) reflected medium to medium-low data quality overall due to range of data quality across the

indicators. For each study, the data are currently 14 or more years old and there is a lack of information

on external reviews conducted. Luske (2010) had medium data quality due to a combination of both

higher and lower data quality values across the indicators. It is a comprehensive study with medium-

high to high marks on flow reliability, technological correlation, data collection methods, and process

completeness; however, lower data quality was noted due to the older age of the data and lack of

documentation of a peer review. Ecoinvent Centre (2015) had medium-high data quality due in large

part to their high-quality data generation methods, diligent review process, and recency of publishing.

Bernatz (2009) had high and medium-high data quality values for most flows and process indicators

except temporal correlation and process review, which were both low data quality.

Dairy Products

Thoma et al. (2010), “Global Warming Potential of Fluid Milk Consumed in the US: A Life Cycle

Assessment,” serves as a key source in the development of the life cycle emissions and energy factors

for dairy products. It is a life cycle assessment that estimates the GHG emissions associated with milk

consumed in the United States. Thoma et al. (2010) shows medium-high data quality overall due to high

data quality for most flows and process indicators. The study received a medium-low data value for

temporal correlation as it uses data more than ten years old. The source, USDA (2012b), “Food

Availability (per Capita) Data System – 2010” aided in developing the proportion of food types in the U.S.

waste stream. It had medium-high data quality due to high quality process completeness and data

collection methods.

Overall, the food waste datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium

Recommendations

Areas for improvement include:

• Update all food waste data sources to more recent sources, where applicable, based on the quality

of the temporal correlation (medium-low or low data quality). Some sources may not have more

recent versions, but updated data should be used if possible.

WARM Data Quality Assessment 52

• Update sources for beef and fruits and vegetables food waste categories to sources that have

information on external reviews, based on the quality of process review (medium-low or low data

quality).

• Replace Nemecek and Kagi (2007) from the sources for grains and bread with a more recent study

that has higher temporal correlation, geographic correlation, and data collection methods. This is

the lowest quality food waste data source.

• Find additional data sources to Thoma et al. (2010) to provide more sources informing life cycle

operations of dairy products.

• Review and consider potential data sources referenced in EPA’s Office of Research and

Development 2021 report “From Farm to Kitchen: Environmental Impacts of Food Waste (Part 1)” as

well as the upcoming release of Part 2.

3.9 Yard Trimmings

Summary of Key Findings

Data Sources. The yard trimmings material emission and energy factors calculations rely on seven data

sources for information regarding the different characteristics and treatments of yard trimmings,

including carbon storage calculations, data on biodegradability, and solid waste treatment techniques.

Key data sources for the yard trimmings material pathway in WARM include the following:

• Systematic evaluation of industrial, commercial, and institutional food waste management

strategies in the United States, published in Environmental Science & Technology (Hodge et al.

2016), which evaluates waste management strategies used in the United States and provides

process emission and energy data for waste management pathways of organic waste. (KEY)

• Carbon storage during biodegradation of municipal solid waste components in laboratory-scale

landfills, published in Global Biogeochemical Cycles (Barlaz 1998), which provides data on

carbon storage during biodegradation of yard trimmings.

(KEY)

• EPA report, Solid Waste Management and Greenhouse Gases: A Life-Cycle Assessment of

Emissions and Sinks (EPA 2006), which provides life-cycle emissions and energy data for yard

trimmings collection and management. (KEY)

• IPCC Guidelines for National Greenhouse Gas Inventories, Volume 5: Waste, Chapter 3: Solid

Waste Disposal (IPCC 2006), which provides data on N

O emissions from combustion of MSW.

(KEY)

https://www.epa.gov/land-research/farm-kitchen-environmental-impacts-us-food-waste

Barlaz (1998) is also a source for data for the landfill pathway in WARM.

WARM Data Quality Assessment 53

Non-key data sources for the yard trimmings material pathway in WARM include the following:

• The Role of Recycling in Integrated Solid Waste Management to the Year 2000, prepared by

Franklin Associates, Ltd. (FAL 1994), which provides data on transportation emissions.

• EPA report, Advancing Sustainable Materials Management: Facts and Figures 2015 (EPA 2018a),

which provides statistical data on U.S. yard trimmings generation and treatment.

• Landfill Gas Monte Carlo Model Documentation and Results, published by the EPA (Levis and

Barlaz 2014), which provides data on landfill gas collection efficiency.

Scoring. A summary of the results by data quality indicator groupings for yard trimmings is shown in

Table 17Error! Reference source not found.. The key findings for each of these sources are discussed

below.

Table 17: Summary of Data Quality Results for Yard Trimmings Data Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Yard Trimmings

Medium-high

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Hodge et al. (2016) has medium-high data quality overall due to high and medium-high data quality

scores for most flows and process indicators. Although Barlaz (1998) reflects medium-high data quality

overall due to high and medium-high data quality scores for most flows and process indicators, it uses

relatively old data (1998) and has a low data score for temporal correlation (sub-category of Flow

Representativeness Indicators). Both EPA (2006) and IPCC (2006) have medium-high data quality overall

but uses data more than 17 years old and EPA (2006) lacks information on external reviews. Levis &

Barlaz (2014) has medium-high data quality overall due to high and medium-high data quality scores for

most flows and process indicators except process review (sub-category of Process Review and

Completeness Indicators) due to lack of information on external reviews. Both sources, FAL (1994) and

EPA (2018a), reflect medium-high data quality overall due to high and medium-high data quality values

for most flows and process indicators.

Overall, the yard trimmings datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Additional details on the scoring results from the assessment of the flow level and process level

indicators for the yard trimmings datasets are presented in the Appendix table.

WARM Data Quality Assessment 54

Recommendations

Areas for improvement include:

• Update data sources used from Barlaz (1998) and FAL (1994) to more recent sources, based on the

low data quality of the temporal correlation. Both these sources may not have more recent versions,

but updated data should be used if possible.

4. Assessment of Specific Management Pathway Datasets

The following sections present the results from the assessment of the quality of the datasets and data

sources by management pathway of landfilling, composting, combustion, and anaerobic digestion.

Separate sections are not included for source reduction or recycling as the relevant data sources are

already included under the respective material assessment sections. Summaries of key findings are

presented followed by recommended areas for further research and improvement of the data quality. In

the discussion of data sources, key sources used in the development of the emission and energy factors

are noted as (KEY). They are weighted more heavily for the development of the weighted averages.

Additional details on the scoring results from the assessment of the flow level and process level

indicators for each management pathway data source are presented in the Appendix: Data Quality

Assessment Matrix.

4.1 Landfilling

Summary of Key Findings

Data Sources. To understand landfilling emissions factors, WARM accounts for material composition,

component-specific decay rates, anaerobic decomposition, landfill gas collection, and overall landfill

emissions, including carbon dioxide, methane, and volatile organic compounds. The modeling of the

landfilling waste management pathway in WARM underwent significant revisions in 2013-2014 that

were first incorporated into the June 2014 release of WARM version 13. The management pathway

emissions factors for landfilling rely on a total of 10 sources, including the following five key

government-published or academic peer-reviewed journal articles:

• What is the optimal way for a suburban U.S. city to sustainably manage future solid waste?

Perspectives from a Solid Waste Optimization Life-cycle Framework (SWOLF), published by North

Carolina State University’s Department of Civil, Construction, and Environmental Engineering

(Levis et al. 2013), which provides information on landfill carbon emissions. (KEY)

• Carbon storage during biodegradation of municipal solid waste components in laboratory-scale

landfills, published in Global Biogeochemical Cycles (Barlaz 1998), which provides data on

landfill methane, carbon dioxide, and material decomposition emissions.

(KEY)

• Decomposition of Forest Products Buried in Landfills, published in Waste Management (Wang et

al. 2013), which provides insight into material decomposition. (KEY)

Barlaz (1998) is also a data source for yard trimmings material emissions factors.

WARM Data Quality Assessment 55

• Estimation of Waste Component-Specific Landfill Decay Rates Using Laboratory-Scale

Decomposition Data, published in Environmental Science & Technology (De la Cruz and Barlaz

2010), which provides component-specific decay rates. (KEY)

• Landfill Gas Monte Carlo Model Documentation and Results, published by the EPA (Levis and

Barlaz 2014), which provides data on landfill gas collection. (KEY)

Five other non-key sources also informed management pathway emissions factors for landfilling:

• Carbon Storage due to Disposal of Biogenic Materials in U.S. Landfills, published in

Environmental Science (Freed et al. 2004), which provides information on anaerobic

decomposition and landfill emissions.

• The Production of Methane from Solid Wastes, published in the Journal of Geophysical Research

(Bingemer and Crutzen 1987), which provides data on landfill carbon dioxide and methane

emissions.

• Characterization of landfill gas composition at the Fresh Kills municipal solid-waste landfill,

published in Environmental Science & Technology (Eklund et al. 1998), which provides

measurement data on volatile organic compounds (VOCs) in landfill gas samples.

• Wood Biodegradation in Laboratory-Scale Landfills, published in Environmental Science

Technology (Wang et al. 2011), which provides information on material decomposition.

• Greenhouse Gas Reporting Program (GHGRP), published on EPA.gov (EPA 2018c), is an EPA

program that requires businesses and others to report data on GHG emissions from major

industrial sources in the United States. It includes estimates on the amount of methane

generated by U.S. landfills.

Scoring. A summary of the results by the high-level data quality indicators is shown in Table 18. The key

findings for each of these sources used for the different landfilling management pathways are discussed

below.

Table 18: Summary of Data Quality Results for Landfilling Data Sources

Management Pathway

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process Review

and

Completeness

Average

Weighted

Average

Landfilling

Medium-high

The data quality of these sources was medium-high. On average, the indicator with the highest data

quality was geographical correlation, as many of these sources referenced U.S.-based data. Other high-

quality indicators include process completeness, data collection methods, and technological correlation,

indicating that the landfilling sources represent a large sample of landfilling management pathways,

using a large percentage of flows and technology types.

The lowest quality indicator for landfilling was temporal correlation, which was deemed to be medium-

low quality. Many of these sources are over 10 years old, and several are over 15 years old, giving them

WARM Data Quality Assessment 56

medium-low or low quality temporal correlation. Yet despite this lower quality indicator, the other flow

and process indicators were of medium high quality.

The highest quality sources were two of the key sources, Levis and Barlaz (2014) and Wang et al. (2013),

and one other source, De la Cruz and Barlaz (2010). All of these sources had high data quality scores.

Levis and Barlaz (2014) provided information on landfill gas collection, Wang et al. (2013) focused on

material decomposition, and De la Cruz and Barlaz (2010) focused on component-specific decay rates.

Overall, the landfilling datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Recommendations

• Update landfilling data sources to more recent sources where applicable, based on the quality of the

temporal correlation (medium-low or low data quality). Some sources may not have more recent

versions, but updated data should be used if possible.

4.2 Composting

Summary of Key Findings

Data Sources. WARM considers fugitive emissions from composted material, emissions from food and

yard waste, the composition of the composting waste stream, and the capacity for carbon storage in

compost-soil for the development of composting emissions factors. The assessment of management

pathway life cycle emissions for composting relies on seven data sources. Four of these sources played a

key role in informing process emissions and weighed more heavily when determining overall data

quality of composting:

• Evaluation of Climate, Energy, and Soils Impacts of Selected Food Discards Management

Systems, published by the State of Oregon Department of Environmental Quality (Oregon

Department of Environmental Quality 2014), which provides information on composting

emissions from food waste. (KEY)

• Impact of Composting Food Waste with Green Waste on Greenhouse Gas Emissions Compost

Windrows, published in Compost Science & Utilization (Williams et al. 2019), which provides

data on fugitive emissions from composted waste. (KEY)

• The Role of Recycling in Integrated Solid Waste Management to the Year 2000, prepared for

Keep America Beautiful, Inc. by Franklin Associates, Ltd. (FAL 1994), which provides

measurements of composting emissions from yard waste. (KEY)

• U.S. Life Cycle Inventory Database, published by the National Renewable Energy Laboratory

(NREL 2015), which provides food and yard waste emissions data.

(KEY)

The data quality of NREL (2015) was assessed based on information provided in the abstract.

WARM Data Quality Assessment 57

Three non-key sources were also used to provide data on composting process emissions:

• Greenhouse gas emissions from composting and mechanical biological treatment, published in

Waste Management & Research (Amlinger at al. 2008), which provides measurements of

composting GHG emissions.

• Formation and Emission of N2O and CH4 from Compost Heaps of Organic Household Waste,

published in Waste Management & Research (Beck Friis et al. 2000), which provides data on

carbon storage in composted soil.

• EPA’s MSW Facts and Figures (EPA 2014), which informed the composition of the composting

waste stream used in the calculations of the PLA and mixed organics factors.

Two of the above sources were also used to inform other aspects of WARM: Oregon Department of

Environmental Quality (2014) was also a data source for bioplastics, and FAL (1994) was a source for

bioplastics, tires, and combustion.

Scoring. A summary of the results by high level data quality indicators is shown in Table 19Table 18. The

key findings for each of these sources used for the different composting management pathways are

discussed below.

Table 19: Summary of Data Quality Results for Composting Data Sources

Management Pathway

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process Review

and

Completeness

Average

Weighted

Average

Composting

Medium-high

Medium

High

Medium-

high

Medium-

high

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

The quality of these data sources ranges from medium to high. This is due to high quality indicators,

including flow reliability, process review, and process completeness. Data collection methods, an

indicator under flow-representativeness, was also determined to be high quality, although overall flow

representativeness was deemed to be medium quality, due to low temporal indicator quality.

The lowest quality indicator was temporal correlation, part of the flow representativeness indicators

category, as three of the composting sources are over 15 years old, and one is over 10 years old. This

resulted in composting data sources generally having a medium quality temporal correlation.

The highest quality composting data source was NREL (2015), a high-quality source due to its high-

quality process indicators, data collection methods, and geographic correlation. However, the temporal

correlation was determined to be medium quality, as the source is eight years old, which falls into the

medium quality range of five to ten years old. All other sources had an average indicator quality of high

or medium-high.

WARM Data Quality Assessment 58

Overall, the composting datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Recommendations

• Update all composting data sources to more recent sources where applicable, based on the quality

of the temporal correlation (medium-low or low data quality). Some sources may not have more

recent versions, but updated data should be used if available.

• Review and consider additional sources that collect and analyze recent data on food waste in the

United States, such as the U.S. EPA’s 2019 Wasted Food Report,

which estimates how food waste

is managed across the nation through several pathways, including composting and aerobic

processes. Also review and consider potential data sources referenced in EPA’s Office of Research

and Development 2021 report “From Farm to Kitchen: Environmental Impacts of Food Waste (Part

1)” as well as the upcoming release of Part 2.

• Identify another source for the composition of the composting waste stream in the United States as

the EPA Facts and Figures methodology changes.

4.3 Combustion

Summary of Key Findings

Data Sources. The combustion management pathway emissions and energy factors rely on 18 data

sources. Of the sources used for combustion, eight played key roles in determining the energy

consumption and emissions from combustion including:

• The BioCycle report, The State of Garbage in America (Van Haaren et al. 2008), which provides

data on the percentage of textile discards treated with combustion in the United States and the

non-biogenic carbon content of plastic, textiles, rubber and leather. (KEY)

• Climate Change 2007: The Physical Science Basis report (IPCC 2007), which provides N

emission estimates from MSW combustors. (KEY)

• Environmental impact of producing hardwood lumber using life-cycle inventory, published in

Wood and Fiber Science (Bergman and Bowe 2008), which provides energy content data for

wood flooring combustion. (KEY)

• Mandated Recycling Rates: Impacts on Energy Consumption and Municipal Solid Waste Volume

(Gaines and Stodolsky 1993), which provides data on energy content of specific materials

combusted under the MSW category. (KEY)

https://www.epa.gov/facts-and-figures-about-materials-waste-and-recycling/food-material-specific-data

https://www.epa.gov/land-research/farm-kitchen-environmental-impacts-us-food-waste

WARM Data Quality Assessment 59

• Estimation of the Effects of Various Municipal Waste Management Strategies on Greenhouse

Gas Emissions (Procter and Redfern, Ltd. & ORTECH International 1993), which provides data on

energy content and emissions of specific materials combusted under the MSW category. (KEY)

• Data Summary of Municipal Solid Waste Management Alternatives (NREL 1992), which provides

data on the energy content of refuse derived fuel (RDF) and combustion system efficiency of

RDF plants. (KEY)

• Project Fire Model. Summary Progress Report-II (Fons et al. 1962), which provides energy

content data for dimensional lumber and fiberboard combustion. (KEY)

• “The role of using carpet as a fuel in carpet recovery system development” (Realff 2010), which

provides energy content data for carpets and tires combustion. (KEY)

An additional source that was used to calculate material recovery from combustion:

• Personal communications between ICF and Covanta Energy (Bahor 2010), which provides data

on amount of steel and ferrous metal recovered per ton of mixed MSW combusted.

Finally, emission factors for transportation of waste and ash were sourced from the following reports:

• FAL’s 1994 The Role of Recycling in Integrated Solid Waste Management to the Year 2000

included transportation energy requirements in WARM calculations (FAL 1994).

• NREL’s 2015 US Life Cycle Inventory Database provided retail transport requirements (NREL

2015).

Six data sources could not be located and thereby were deemed low quality:

• Personal communication between the Fiber Economics Bureau and ICF (DeZan 2000), which

provides data on non-biogenic share of carbon in textiles.

• Incropera, F. P., & DeWitt, D. P. (1990). Introduction to Heat Transfer, Second Edition. New York:

John Wiley & Sons, pp. A3-A4, which provides specific heat data of materials that is used to

calculate the energy content of materials combusted.

• Personal communication with the Integrated Waste Services Association (Zannes 1997), which

provides data on combustion system efficiency of mass burn plants.

• Personal communication with Minnesota Office of Environmental Assistance (Harrington 1997),

which provides data on combustion system efficiency and energy content of RDF.

• The 2000 [Integrated Waste Services Association] IWSA Waste-To-Energy Directory of United

States Facilities (IWSA 2000), which provides data on combustion system efficiency of RDF

plants.

WARM Data Quality Assessment 60

• Personal communication between IWSA, American Ref-Fuel, and ICF (IWSA & American Ref-Fuel

(1997), which provides data on energy content of mixed MSW combusted and losses in

transmission and distribution of electricity specific to WTE combustion facilities.

Scoring. A summary of the results by high level data quality indicators is shown in Table 20Error!

Reference source not found.. The key findings for each of these sources used for the different

combustion management pathways are discussed below.

Table 20: Summary of Data Quality Results for Combustion Data Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Combustion

Medium

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

In general, key sources for combustion had higher data quality results relative to the full set of sources

leading to a slightly higher weighted average data quality value of medium relative to the average across

the data sources of medium-low. Five of the eight key sources (Van Haaren et al. 2008, EPA 2018a, NREL

1992, IPCC 2007, and Bergman and Bowe 2008) had medium-high data quality receiving medium to high

data results for most DQIs; however, they are older sources, which affected their temporal correlation

data quality. NREL (1992), Van Haaren et al. (2008), and EPA (2018a) also lack information on external

reviews giving them low data quality for the process review DQI sub-category. Overall, the data quality

of Gaines and Stodolsky’s (1993) study is medium due to several reasons. Because it is a relatively old

source, it has low data quality for temporal correlation. In addition, it lacks a comprehensive discussion

on data collection methods (sub-category of Flow Representativeness Indicators) and information on

external reviews, which contributes to low data quality for process review. However, it received

medium-high to high data quality results on other DQIs. Procter and Redfern, Ltd. & ORTECH

International (1993) had medium data quality overall with a range of results across the DQIs, receiving

medium high to high data quality results for process completeness, flow reliability and technological

correlation, and low data quality for temporal and geographical correlation. Fons et al. (1962) has

medium data quality overall as it is an old source and lacks a comprehensive discussion on data

collection methods; however, it had medium-high to high data quality for several other DQIs. Details on

the results for other data sources are included in the Appendix: Data Quality Assessment Matrix. The

sources that could not be located were assigned a low data quality score.

Overall, the combustion datasets scored as follows:

• Average indicator: Medium

• Average weighted indicator: Medium

Recommendations

• Update all combustion data sources to more recent sources where applicable, based on the quality

of the temporal correlation (medium, medium-low, or low data quality). Some sources may not have

more recent versions, but updated data should be used if possible.

WARM Data Quality Assessment 61

• Consider contacting authors to confirm whether external or internal reviews occurred where

documentation is lacking on this. If reviews were not conducted, sources should be updated to more

recent versions with reviews or replaced with sources with documentation of external reviews.

4.4 Anaerobic Digestion

Summary of Key Findings

Data Sources. The anaerobic digestion management pathway emissions and energy factors rely on

seven data sources. Of the sources used for combustion, five played key roles in determining the energy

consumption and emissions from anaerobic digestion including:

• Carbon storage during biodegradation of municipal solid waste components in laboratory-scale

landfills (Barlaz 1998), which provides data on carbon storage that occurs during biodegradation

of MSW components in landfills. (KEY)

• Formation and Emission of N

O and CH

from Compost Heaps of Organic Household Waste

(Beck-Friis et al. 2000), which provides data on nitrous oxide and methane emissions from

compost heaps of organic household waste. (KEY)

• Modelling of environmental impacts from biological treatment of organic municipal waste in

EASEWASTE (Boldrin et al. 2011), which provides data on the environmental impacts of

biological treatment of organic municipal waste. (KEY)

• The Landfill Methane Outreach Program (LMOP) LFGE Benefits Calculator (EPA 2013), which is a

landfill gas energy benefits calculator used to estimate direct, avoided, and total GHG reductions

as well as environmental and energy benefits from a landfill gas (LFG) energy project. (KEY)

• Anaerobic digestion and digestate use: accounting of greenhouse gases and global warming

contribution (Møller et al. 2009), which provides GHG emissions data for anerobic digestion.

(KEY)

An additional source was used to evaluate the chemical composition of material in household waste:

• Chemical composition of material fractions in Danish household waste in Waste Management

(Riber et al. 2009).

Finally, values for GHG emissions resulting from fossil fuels used in vehicles collecting and transporting

waste to the anaerobic digestion facility were sources from:

• NREL’s 2015 US Life Cycle Inventory Database provided retail transport requirements (NREL

2015).

WARM Data Quality Assessment 62

Scoring. A summary of the results by high level data quality indicators is shown in Table 21. The key

findings for each of these sources used for the different anaerobic management pathway are discussed

below.

Table 21: Summary of Data Quality Results for Anaerobic Digestion Data Sources

Material

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process Review

and

Completeness

Average

Weighted

Average

Anaerobic Digestion

Medium-high

Medium

Medium-high

Medium

Note: For details on the indicator subcategories for each indicator grouping, see Section 2: Approach.

Among the key sources, EPA (2013) and Barlaz (1998) had overall medium-high data quality. EPA (2013)

had high data quality results for geographical and technological correlation, data collection methods and

process completeness. It had lower data quality for temporal correlation and process review due to the

source being more than ten years old and lacking documentation for external or internal reviews. Barlaz

(1998) had high data quality results for five of the seven DQIs (flow reliability, geographical correlation,

technological correlation, data collection methods, and process completeness). Its low data quality

result was for temporal correlation as it is a relatively old source published more than fifteen years ago.

Boldrin et al. (2013) had an overall medium data quality due to low data scores for temporal and

geographical correlation (sub-categories of Flow Representatives Indicators). The source is over ten

years old and is based in Denmark. Møller et al. (2009) had overall medium data quality with medium-

high to high data quality results for flow reliability, technological correlation, and the process indicators,

and low data quality results for temporal and geographical correlation as it is over ten years old and is

based in Denmark. Beck-Friis et al. (2000) had an overall medium-low data quality as it is a relatively old

source affecting its temporal correlation. Its data quality was low for geographical correlation, data

collection methods (with focus on limited number of compost heaps), and process review.

Overall, the anaerobic digestion datasets scored as follows:

• Average indicator: Medium-high

• Average weighted indicator: Medium-high

Recommendations

• Update all anaerobic digestion data sources to more recent sources where applicable, based on the

quality of the temporal correlation (medium, medium-low, or low data quality) and to sources with

documentation of external reviews. Some sources may not have more recent versions, but updated

data should be used if possible.

5. Conclusion

This report provided a comprehensive assessment of data quality for the numerous data sources used to

develop the emission and energy factors for EPA’s WARM. Table 22 summarizes the results by material

category and management pathway for the DQI indicator groupings as discussed in the previous

WARM Data Quality Assessment 63

sections. In general, overall data quality was found to be medium or medium-high depending on the

material type or pathway, with the exception of glass, paper, and carpet.

Table 22: Summary of Data Quality Results by Material Type or Management Pathway

Material or Pathway

DQ Values by Indicator Grouping

Flow

Reliability

Flow

Represent-

ativeness

Process

Review and

Completeness

Average

Weighted

Average

Material Category

Plastics

Medium-high

Medium

Medium-high

Bioplastics

Medium-high

Medium

Medium-high

Metals

Medium

Glass

Medium

Medium-low

Paper

Medium-low

Medium

Medium-low

Medium

Electronics

Medium-high

Construction Materials

Medium-high

Asphalt Concrete

Medium-high

Medium

Medium-high

Medium

Asphalt Shingles

Medium-low

Medium

Carpet

Medium

Medium-low

Medium

Clay Bricks

Medium-high

High

Medium-high

High

Concrete

High

Medium-high

Medium

Medium-high

Dimensional Lumber

Medium-high

High

Medium-high

Drywall

Medium-high

Fiberglass Insulation

Medium

Fly Ash

Medium-high

Medium

Medium-high

Medium-density

Fiberboard

Medium-high

High

Medium-high

Structural Steel

Medium-high

Vinyl Flooring

Medium-high

Medium

Medium-high

Medium

Wood Flooring

Medium-high

Medium

Medium-high

Tires

Medium

Food Waste (non-meat)

Medium-high

Medium

Medium-high

Medium

Food Waste (meat)

Medium-high

Yard Trimmings

Medium-high

Management Pathway

Landfilling

Medium-high

Composting

Medium-high

Medium

High

Medium-high

Combustion

Medium

Medium-low

Medium

Anaerobic Digestion

Medium-high

Medium

Medium-high

Medium

Refers to data generation method and verification.

Includes temporal correlation (data year), geographical correlation (region of data), technological correlation (technology type,

scale), and data collection methods (representativeness, sample size).

Includes process review (third party or internal reviewers) and process completeness (percent of flows covered).

Average of all indicators.

Developed to give additional weight to the key data sources informing the emission factor estimates.

Separate data quality assessments for source reduction and recycling were not conducted as their data sources were already

captured under the material-specific data assessments.

WARM Data Quality Assessment 64

This assessment identified a number of areas for improvements to update the underlying datasets that

would improve the factors in WARM. These were described in each material and pathway subsection. A

few overarching recommendations include the following:

• Identify more recent data sources for several materials and ensure that any updated

publications are used.

• Prioritize the identification of publicly-available data sources.

• Identify published data sources to update certain data inputs.

• Prioritize updates to the modeling of glass, paper, metals, food waste (non-meat), carpet,

asphalt shingles, fiberglass insulation, vinyl flooring, tires, and combustion based on both the

average and weighted average data quality results for those categories, which fell below those

of the other material or management pathway categories.

• Improve the archiving, referencing, and accessibility of the underlying data sources.

• Communicate the DQA findings alongside the WARM documentation.

6. References

Introduction and Approach

Edelen, A. and W. Ingwersen. Guidance on Data Quality Assessment for Life Cycle Inventory Data. U.S.

Environmental Protection Agency, Washington, DC, EPA/600/R-16/096, 2016.

https://cfpub.epa.gov/si/si_publicrecord_Report.cfm?Lab=NRMRL&dirEntryId=321834.

H.R.4174 – 115th Congress (2017-2018): Foundations for Evidence-Based Policymaking Act of 2019.

Congress.gov, Library of Congress, January 14, 2019. https://www.congress.gov/bill/115th-

congress/house-bill/4174.

EPA (2000). Guidance for Data Quality Assessment: Practical Methods for Data Analysis. (EPA/600/R-

96/084). Washington, DC United States Environmental Protection Agency.

Plastics and Bioplastics

BTS (2013). Commodity Flow Survey Preliminary Tables. Table 1: Shipment Characteristics by Mode of

Transportation for the United States: 2012. Washington, DC: U.S. Bureau of Transportation

Statistics, Research and Innovative Technology Administration. Retrieved from

http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/commodity_flow_survey/2

012/united_states/table1.html.

FAL (1994). The Role of Recycling in Integrated Solid Waste Management to the Year 2000. Franklin

Associates, Ltd. (Stamford, CT: Keep America Beautiful, Inc.), September, pp. I-24.

FAL (2011). Cradle-to-Gate Life Cycle Inventory of Nine Plastic Resins and Two Polyurethane Precursors.

Revised Final Report. Prairie Village, KS: Franklin Associates, Ltd.

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WARM Data Quality Assessment 79

Appendix: Data Quality Assessment Matrix

The table below provides the detailed results on the scoring for each data source following the ORD Guidance scoring approach. Low scores equate with high

data quality.

Summary of Data Quality Assessment by Material Type and Management Pathway

Dataset

Data Source

Flow Indicators

Process Indicators

Total

Score

Average

Score

Weighted

Average

Score and

Key Sources

(Y)

Flow

Reliability

Flow representativeness

Process

Review

Process

Complete

ness

Temporal

correlation

Geographical

correlation

Technological

correlation

Data collection

methods

Unit process(es)

Reference

Data

generation

method &

verification

Data year

Region of

data

Technology

type, scale

Representativeness,

sample size

Third party

internal

reviewer(s)

% of

flows

covered

IDEAL SCORE

Plastics

Average Score

2.0

3.7

1.7

1.3

4.0

2.3

16.7

2.3

2.2

Process energy emissions to

manufacture Virgin HDPE, LDPE,

PET, LLDPE, PP

FAL 2011

2.3

Process energy emissions to

manufacture Virgin GPPS, PVC

FAL 2011

2.3

Process energy emissions to

manufacture Recycled PET, HDPE,

and PP

FAL 2018

1.7

Retail transportation distance and

fuel-type

BTS 2013

3.1

Bioplastics

Average Score

2.0

4.5

2.3

1.5

1.8

2.0

15.8

2.2

Process energy and emissions for

PLA production

NatureWorks

2010

1.9

Process energy and emissions for

PLA production

Erwin Vink's

responses 2010

2.1

Transportation energy usage

FAL 1994

1.9

Retail transportation distance and

fuel-type

BTS 2013

3.1

Transportation emissions

Oregon DEQ

(2014)

2.1

WARM Data Quality Assessment 80

Dataset

Data Source

Flow Indicators

Process Indicators

Total

Score

Average

Score

Weighted

Average

Score and

Key Sources

(Y)

Flow

Reliability

Flow representativeness

Process

Review

Process

Complete

ness

Temporal

correlation

Geographical

correlation

Technological

correlation

Data collection

methods

Unit process(es)

Reference

Data

generation

method &

verification

Data year

Region of

data

Technology

type, scale

Representativeness,

sample size

Third party

internal

reviewer(s)

% of

flows

covered

Metals

Average Score

3.0

4.6

1.8

2.0

3.6

3.2

3.3

21.5

3.1

2.9

Aluminum Cans/Ingot

Average Score

2.8

4.4

2.0

3.2

2.9

2.6

19.9

2.8

2.6

Process energy and process non-

energy data for manufacturing,

recovery, and recycling

PE Americas 2010

2 (cans); 3 (ingot)

(cans);

(ingot)

1.9

(cans);

2.0

(ingot)

Transportation energy data

RTI 2004

5.0

Retail transportation energy data

EPA 1998b

15.5

2.2

Retail transportation distance and

fuel-type

BTS 2013

3.1

Steel Cans

Average Score

3.7

4.8

2.4

4.2

3.9

3.4

24.8

3.5

3.2

Steel cans process energy and

process non-energy,

transportation energy

EPA 1998a

3.1

Current mix of steel can

production and recycled contents

of production

FAL 2003a

4.2

Steel Cans loss rates

FAL 2003b

5.0

Retail transportation energy data

EPA 1998b

15.5

2.2

Retail transportation distance and

fuel type

BTS 2013

3.1

Copper

Average Score

2.5

4.5

1.3

1.5

3.0

4.0

19.8

2.8

3.0

Process energy, process non-

energy, and transportation energy

FAL 2002

3.4

% of current production from

recycled vs. “virgin” inputs,

copper wire scrap mix used to

create copper ingot.

USGS 2004

2.6

Retail transportation energy data

EPA 1998b

15.5

2.2

Retail transportation distance and

fuel type

BTS 2013

3.1

Glass

Average Score

3.5

4.8

2.5

3.7

3.0

3.3

3.5

WARM Data Quality Assessment 81

Dataset

Data Source

Flow Indicators

Process Indicators

Total

Score

Average

Score

Weighted

Average

Score and

Key Sources

(Y)

Flow

Reliability

Flow representativeness

Process

Review

Process

Complete

ness

Temporal

correlation

Geographical

correlation

Technological

correlation

Data collection

methods

Unit process(es)

Reference

Data

generation

method &

verification

Data year

Region of

data

Technology

type, scale

Representativeness,

sample size

Third party

internal

reviewer(s)

% of

flows

covered

Process energy and process non-

energy

RTI 2004

5.0

Composition of glass and fuel

used to combust glass

DOE 2002

2.6

Current mix of production from

virgin and recycled inputs and

glass loss rates

FAL 2003b

5.0

Transportation energy usage

FAL 1994

1.9

Retail transportation distance and

fuel type

BTS 2013

3.1

Transportation fuel efficiencies

EPA 1998b

15.5

2.2

Paper

Average Score

3.50

3.48

Energy and process emissions

RTI (2004)

5.0

Process emissions

EPA (1998a)

5.0

1.0

5.0

4.0

1.0

3.1

Composition of Mixed Paper