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Contract No. DE-AC36-08GO28308

Estimating U.S. Methane 

Emissions from the Natural Gas 

Supply Chain: Approaches, 

Uncertainties, Current 

Estimates, and Future Studies

Garvin Heath1, Ethan Warner1, Daniel 

Steinberg1, and Adam Brandt2

1 Joint Institute for Strategic Energy Analysis

2 Stanford University

Technical Report

NREL/TP-6A50-62820

August 2015
The Joint Institute for Strategic Energy Analysis is operated by the Alliance 

for Sustainable Energy, LLC, on behalf of the U.S. Department of Energy’s 

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15013 Denver West Parkway

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303-275-3000 • www.jisea.org Contract No. DE-AC36-08GO28308

Estimating U.S. Methane 

Emissions from the Natural 

Gas Supply Chain: 

Approaches, Uncertainties, 

Current Estimates, and Future 

Studies

Garvin Heath1, Ethan Warner1, Daniel 

Steinberg1

, and Adam Brandt2

1 Joint Institute for Strategic Energy Analysis

2 Stanford University

Prepared under Task No. DOCC.LT19

Technical Report

NREL/TP-6A50-62820

August 2015
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Foreword

JISEA advances its mission of guiding the transformation of the global energy economy through 

conducting seminal analysis and innovative, collaborative research. JISEA papers and 

publications deliver groundbreaking strategic insights and explain their real-world implications. 

We look forward to your feedback and thank you for your interest in the work of JISEA.

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Acknowledgments

This work was funded by the U.S. Department of Energy’s (DOE’s) Office of Energy Policy and 

Systems Analysis (EPSA). The authors wish to thank the following individuals for their 

thoughtful comments, input, and review of the document in its various stages: Elke Hodson, 

Adrian Down, and Judi Greenwald (DOE); Jeffrey Logan, Emily Newes, and Dave Mooney of 

the National Renewable Energy Laboratory (NREL); Doug Arent of the Joint Institute for 

Strategic Energy Analysis; David McCabe of the Clean Air Task Force (CATF); Fiji George of 

the Southwestern Energy Company; and Timothy Skone of the National Energy Technology 

Laboratory. These reviews serve to make this report as technically sound as possible, however, 

any remaining errors or omissions are those of the authors. Furthermore, the views and opinions 

of authors expressed herein do not necessarily state or reflect those of the reviewers, their 

associated institutions, or the United States government or of any institution thereof. We also 

wish to thank Scott Gossett and Kendra Palmer of NREL for their editorial support.
iii

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Acronyms and Abbreviations

CBM coal bed methane

CH4 methane

CO2 carbon dioxide

CO2e CO2 equivalent

DOE U.S. Department of Energy

EIA Energy Information Administration

EPA Environmental Protection Agency

FY funding year

GHG greenhouse gas

GHGI greenhouse gas inventory

GHGRP greenhouse gas reporting program

GWP global warming potential

HAP hazardous air pollutants

IPCC Intergovernmental Panel on Climate Change

LNG liquefied natural gas

Mcf thousand cubic feet

MMt million metric tonnes

NG natural gas

PM particulate matter

t metric tonnes

UNFCCC United Nations Framework Convention on Climate 

Change

VOC volatile organic compounds

yr year
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Glossary

Activity Factor: Count of activities for an emission source category.

Anthropogenic: Originating in human activity.

Associated Gas: Natural gas extracted from deposits of petroleum. The natural gas is either 

dissolved in the oil or as a free “gas cap” above the oil in the deposit.

“Behind the Customer Meter”: Natural gas infrastructure (i.e., pipes, fittings, valves and other 

components) after distribution service lines and customer meters, but before use (e.g., 

combustion) of the natural gas. 

Bottom-up Study: these studies take a disaggregated approach to developing estimates of 

emissions at the level of individual sources and source categories. 

• Inventory: Estimates emissions from sources and source categories based on the product 

of activity and emission factors then summed across all sources and source categories 

within the defined temporal, spatial and sectoral scope. 

• Measurement: Measurements made directly from individual components, activities, or 

facilities. Multiple of such measurements often are used to develop emission factors used 

in inventories to estimate emissions from a given source or source category. 

Coal Bed Methane (CBM) Gas: Natural gas produced from coal formations.

Conventional Gas: Natural gas produced from porous rock formations such as carbonates, 

sandstones, and siltstones that do not require stimulation to release the gas.

Emission Factor: An average mass of pollutant emitted per unit of activity from a population of 

emissions activities that represents an emission source category.

Emission Source(s):

• Source: Emissions from a single component, activity, or facility.

• Source Category: Emissions from a population of sources. The population is based on 

grouping similar sources. For example, natural gas production wells or gathering system 

pneumatic devices. Source categories used in this report match those used in the U.S. 

GHGI (EPA 2014a).

• Source Sector: A population of emission sources from an industrial sector (e.g., natural 

gas industry), which is the collection of all source categories in the selected source sector. 

Source sectors used in this report match those in the U.S. Greenhouse Gas Inventory 

(GHGI) (EPA 2014a).

Fugitives: Unintentional emissions to the atmosphere.
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GHGRP: Greenhouse Gas Reporting Program. In 2009, the U.S. EPA published a rule requiring 

reporting of GHG emissions from sources emitting >25,000 metric tonnes (t) carbon dioxide 

equivalent (CO2e) per year, including many types of facilities in the natural gas industrial sector

(EPA 2014d).

Service Lines: Pipelines in the distribution segment that transport gas from a common source of 

supply to an end user’s meter or piping.

Shale Gas: Natural gas produced from shale formations, which requires hydraulic fracturing.

Tight Gas: Natural gas produced from low permeability reservoir rocks, which requires 

hydraulic fracturing.

Top-down Measurement Study: Top-down studies start with a measurement (“in-situ”, or 

within the air mass) or estimate (e.g., from satellite remote sensing) of the concentration of 

methane in the atmosphere, and then use different modeling approaches to estimate mass flux 

(mass of methane emitted per unit time and area) from observed or estimated concentrations.

Unconventional gas: Any sources of natural gas that do not meet the definition of 

“conventional” gas. This includes CBM, shale gas, and tight gas.

Venting: Emissions emitted to the atmosphere by the design of routine operations, engine 

combustion, or other non-fugitive sources.
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Executive Summary

The annual Inventory of United States (U.S.) Greenhouse Gas Emissions and Sinks (GHGI) 

identifies and quantifies emission sources and sinks of greenhouse gases (GHG) from human 

activities in the United States as required by the United Nations Framework Convention on 

Climate Change (UNFCCC). The U.S. GHGI is a critical resource for understanding the U.S. 

contribution to global climate change, tracking trends in GHG emission sources and sinks, 

identifying and prioritizing abatement opportunities within the United States, and for informing 

policy and industrial decision making. Because they provide a high level of granularity and 

repeatability, inventories like the U.S. GHGI are relied upon by many governmental agencies 

(e.g., Federal Register 2014; White House 2015; DOE 2015), stakeholder organizations (e.g., 

Bradbury et al. 2013; Harvey, Gowrishankar, and Singer 2012; ICF 2014), researchers (e.g., 

Allen et al. 2015a; Heath et al. 2014a; Mitchell et al. 2015) and companies (e.g., Laurenzi and 

Jersey 2013) for analyses and decision making. 

The energy sector was responsible for approximately 84% of carbon dioxide equivalent (CO2e) 

GHG emissions in the U.S. in 2012 (EPA 2014a).1 Methane is the second most important GHG, 

contributing 9% of total U.S. CO2e GHG emissions. A large portion of those methane emissions 

result from fossil energy production and use; the natural gas, coal, and oil industries produce 

approximately 39% of anthropogenic methane emissions in the U.S. Over the last decade, the 

U.S. has experienced dramatic growth in the domestic natural gas and oil industries. As a result, 

estimates of GHG emissions from these industries in the U.S. GHGI as well as in other 

inventories have garnered heightened interest.

The U.S. GHGI is a unique resource to inform industrial and policy decision-making. It is based 

on the best available knowledge and methods, within constraints of UNFCCC requirements, and 

is continuously improved. Many recent studies (e.g., Brandt et al. 2014; Pieschl et al. 2015; 

Lamb et al. 2015; Zimmerle et al. 2015) have reported conflicting conclusions as to the 

magnitude of methane emissions from the natural gas and oil industries and how measured 

emissions compare to those estimated in the U.S. GHGI. Based on a review of recent research 

and analysis of the U.S. GHGI emission estimates, this report briefly summarizes the methods 

and results of the 2014 U.S. GHGI (EPA 2014a), discusses challenges to constructing the U.S. 

GHGI, and suggests some opportunities that could improve the accuracy of the methane 

emissions estimates.

This report builds on the solid foundation of past and current U.S. GHGIs to suggest 

opportunities to strengthen its accuracy and reduce uncertainty. Although this report focuses on 

the U.S. GHGI, to the extent other inventories share common methods, data sources, and 

challenges, the observations and conclusions discussed in this report could apply.

Conclusions from Analysis of U.S. GHGI Results

The U.S. GHGI classifies four main segments of the natural gas industry: (1) production, 

gathering and boosting (~33% of total natural gas [NG] sector methane emissions in 2012); (2) 

processing (~14%); (3) transmission and storage (~33%); and (4) distribution (~20%) (EPA 

 1 The U.S. GHGI reports emissions with a two year lag (e.g., the 2014 U.S. GHGI reports emissions through 2012). 

Until recently, the latest available U.S. GHGI was published in 2014 (EPA, 2014a). The 2014 U.S. GHGI was 

analyzed for this report; use of the 2015 U.S. GHGI would not change the conclusions of this report.
vii

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2014a). Our analysis of the U.S. GHGI reveals that approximately 43% of total methane 

emissions across all four segments of the natural gas industry are from compressors and 

compressor stations, mostly from the processing and transmission and storage segments. We also 

find that leaks from cast iron and unprotected steel pipeline main and service lines contribute 

33% of total distribution segment emissions despite representing the fewest miles of any piping

material. 

Conclusions from Assessment of Challenges to Inventory Development

A synthesis of more than 20 years of previous comparisons of methane emission estimates

revealed a consistent gap between estimates originating from inventories as compared to those 

originating from direct measurements. Brandt et al. (2014) observed that across years, locations 

and scales, measurements of emissions were typically higher than those estimated by inventories 

including the U.S. GHGI, and highlighted the need for improvements to both approaches to 

reconcile the differences and better support decision making. This report builds on the discussion 

of the Brandt et al. (2014) study regarding challenges to inventory accuracy. In addition, we 

identify potential improvements to inventory methods, focusing on the U.S. GHGI. Finally, we 

discuss how integration of the results of recent and forthcoming studies since the Brandt et al. 

(2014) publication, including measurements from both “top-down”2 atmospheric and “bottomup”3 component-level perspectives4 could be used to improve the accuracy of inventory 

estimates, particularly the U.S. GHGI, and reduce uncertainties. 

In their current states, neither measurement- (top-down or bottom-up) nor inventory-based 

estimates of methane emissions are considered “gold standard” benchmarks, yet both can be 

useful. For instance, top-down measurement studies can be compared to inventory emission 

estimates for validation purposes. Such comparisons are most useful when the system boundaries 

of both studies are well-aligned (spatially, temporally, and sectorally). Differences found in such 

comparisons can help form hypotheses potentially explaining any gaps between the estimates 

and suggest pathways for improvements for both approaches. Likewise, bottom-up measurement 

studies can update and augment emission factors used in inventories. Such studies would be most 

directly applicable and easily incorporated into inventories like the U.S. GHGI if they were 

designed for strong alignment with current inventory structure. Strong sampling design and 

 2 Top-down studies start with a measurement (“in-situ”, or within the air mass) or estimate (e.g., from satellite 

remote sensing) of the concentration of methane in the atmosphere, and then use different modeling approaches to 

estimate mass flux (mass of methane emitted per unit time and area) from the observed or estimated concentrations. 3 Bottom-up studies can be based on modeling or measurement. Bottom-up modeling studies use engineering-based 

calculations (typically, as in the U.S. GHGI, estimated as an emission factor multiplied by an activity factor, or 

count of emission sources) to estimate emissions from each category of source (i.e., type of component or activity) 

emitting the pollutant of concern within a given area and time. The sum of emissions from each tracked source 

category is the inventory estimate, for instance, annual methane emissions from the domestic natural gas industry. 

Sometimes this process is performed at the facility instead of component or activity level. Bottom-up measurement 

studies are those that measure emissions directly at a given component, activity, or facility, and can be used to 

develop the emission factors that are used in inventories. Life cycle assessments are another form of bottom-up 

modeling study but are not discussed in this report.

4

As this report is focused on potential improvements to the U.S. GHGI, and to some extent emissions inventories 

broadly, readers should seek more extensive description and discussion of bottom-up and top-down measurement 

study methods and their associated strengths and weaknesses elsewhere (e.g., Brandt et al. 2014). We only include

discussion of these issues to the extent relevant to explaining how top-down and bottom-up measurement studies can 

contribute to bottom-up inventory improvements.
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robust sample size are also critical to maximize inventory improvement from new studies. 

Activity factor5 data sources and methods should advance in a complimentary fashion with 

emission factors. 

Challenges exist for both top-down measurements and bottom-up inventories of methane 

emissions the natural gas industry. Those related to the accuracy of inventories include: 

1. Completeness and accuracy of inventoried sources

2. Variable and heavily skewed distributions of emissions within a source category

3. Representativeness of emission samples

4. Quantifying uncertainty.

Each of these issues is described further in Section 4 of the report. 

Based on examination of available evidence and of current challenges to both bottom-up and topdown studies as well as considering assumptions and limitations of inventories first described in 

Brandt et al. (2014), we identify a set of opportunities for improvement to estimates of methane 

emissions from inventories with particular attention to opportunities relevant to the U.S. GHGI 

given its prominent role. Section 5 describes these recommendations. 

In conclusion, to maintain the critical functions of the U.S. GHGI and to address challenges to its 

accuracy in a rapidly-changing industrial sector such as natural gas, it is important to continually 

improve data sources and methods. This report offers analysis and guidance for agencies, 

researchers, and research-sponsoring organizations to consider when developing and designing 

studies that can help to improve accuracy of the U.S. GHGI and other inventories.

 5 Activity factor is a count of activities for an emission source category.
ix

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Table of Contents

1 Background........................................................................................................................................... 1

2 How the U.S. GHGI is Constructed ..................................................................................................... 4

3 U.S. Methane Emissions from the Natural Gas Supply Chain ......................................................... 8

4 Challenges to Constructing the U.S. GHG Inventory...................................................................... 13

4.1 Inventory Completeness.............................................................................................................. 16

4.2 Inventory Representativeness...................................................................................................... 17

4.3 Activity Factor Data.................................................................................................................... 19

4.4 Uncertainty Quantification.......................................................................................................... 19

5 Recommendations for Improvements to the U.S. GHGI Leveraging Bottom-up and Top-down 

Studies................................................................................................................................................. 20

6 References .......................................................................................................................................... 24

7 Measurement and Inventory Study References (as Displayed in Figures 6 and C-1)................. 29

Appendix A. U.S. GHGI Data .................................................................................................................... 39

Appendix B. Inventory Studies................................................................................................................ 48

List of Tables

Table 1. U.S. Natural Gas Methane (CH4) Activity Factor and Emission Factors in 2012 for Distribution 

Pipelines (Main Lines and Service Lines) as Reported in the U.S. GHGI............................. 12

Text Box 4-1. Defining Top-down and Bottom-up Studies........................................................................ 14

Text Box 5-1. Top-down and Bottom-up Studies....................................................................................... 21

Table A-1. U.S. GHGI Estimates of Natural Gas Methane (CH4) Emissions in 2012 from the Production 

and Gathering and Boosting Segment of the Natural Gas Supply Chain............................... 40

Table A-2. U.S. GHGI Estimates of Natural Gas Methane (CH4) Emissions in 2012 from the Processing 

Segment of the Natural Gas Supply Chain ............................................................................ 42

Table A-3. U.S. GHGI Estimates of Natural Gas Methane (CH4) Emissions in 2012 from the 

Transmission and Storage Segment of the Natural Gas Supply Chain .................................. 44

Table A-4. U.S. GHGI estimates of natural gas methane (CH4) emissions in 2012 from the distribution 

segment of the natural gas supply chain ................................................................................ 46

List of Figures

Figure 1. U.S. GHGI estimates of anthropogenic methane emissions in the U.S. in 2012........................... 3

Figure 2. Organization of the natural gas supply chain into four segments.................................................. 9

Figure 3. U.S. GHGI estimates of methane emissions from natural gas compressors within each supply 

chain segment in 2012............................................................................................................ 11

Figure 4. U.S. GHGI estimates of methane emissions from the U.S. natural gas distribution system (in 

MMt CO2e) in 2012............................................................................................................... 12

Figure 5. A recent synthesis of methane emission measurements that were compared to inventory 

estimates of methane emissions............................................................................................. 15

Figure 6. Mapping of exemplary methane emission measurement studies published through February 

2015 by type and location ...................................................................................................... 21

Figure A-1. U.S. GHGI estimates of natural gas methane emissions in 2012 from the production and 

gathering and boosting segment of the natural gas supply chain........................................... 41

Figure A-2. U.S. GHGI estimates of natural gas methane emissions in 2012 from the processing segment 

of the natural gas supply chain............................................................................................... 43

Figure A-3. U.S. GHGI estimates of natural gas methane emissions in 2012 from the transmission and 

storage segment of the natural gas supply chain.................................................................... 46

Figure B-1. Emission inventory studies through May 2014, aggregated by the type of emissions included 

and by location....................................................................................................................... 48
1

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1 Background

Under the United Nations Framework Convention on Climate Change (UNFCCC), the United 

States (U.S.) is obligated to annually develop nationally representative estimates of emission 

sources and sinks of specific greenhouse gases (GHGs)6 from human activities7 for a time series 

starting with 1990. The U.S. fulfills the UNFCCC national GHG tracking and reporting 

responsibilities with its annual Inventory of the Greenhouse Gas Emissions and Sinks (GHGI) 

(EPA 2014a). The U.S. GHGI is a critical resource for understanding the U.S. contribution to 

global climate change, tracking trends in GHG emission sources and sinks, identifying and 

prioritizing emission sources, and informing policy and industrial decision making, including 

potential abatement opportunities. 

An inventory represents an accounting of the sources of gases emitted or removed from the 

atmosphere over a period of time from a specific set of components, activities, or facilities and 

within a defined geographic area. The U.S. GHGI uses an engineering-based approach, described 

further in Section 2, to generate national GHG emission estimates for each source category 

tracked by the inventory based on the best available information.8 Each source category 

represents a grouping of similar components or activities. Tracking of emissions is done at 

relatively high resolution, e.g., there are almost 110 source categories tracked within the natural 

gas sector in the U.S. GHGI. The level of disaggregation of the U.S. GHGI enables analysis of 

the relative importance of GHG source categories over time. The U.S. GHGI can also be used to 

suggest priorities for GHG emission reductions (e.g., Bradbury et al. 2013; Harvey, 

Gowrishankar, and Singer 2012; ICF 2014). 

The energy sector represented 84% of total U.S. GHG emissions in 2012 in the U.S. GHGI, with 

a similar percentage contribution in prior years (EPA 2014a). The dramatic growth of the 

domestic natural gas and oil industries over the last decade has heightened interest in the 

estimates of GHG emissions from these industries in the U.S. GHGI. Methane is the main 

component of natural gas, which is also a constituent of associated gas from oil production, and 

can be released from natural gas and oil production activities.

 6 CO2, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride emission estimates 

have been historically required under the UNFCCC. Starting in 2015, countries are also required to report nitrogen 

trifluoride to the UNFCCC and may elect to report fluorinated ethers (UNFCCC 2014). 7 UNFCCC requires use of IPCC guidance for calculation of national GHG inventories like the U.S. GHGI, which 

defines specific anthropogenic emissions and removals to be reported that are a result of human activities. There are 

other sources of methane emissions to the atmosphere that are natural (e.g., termites and natural wetlands) and 

therefore not included in the U.S. GHGI. As such, the U.S. GHGI does not by itself allow prioritization between all 

sources of GHG emissions for climate change mitigation, if natural sources are also considered. 8 There are also cases of known emission sources having no available estimate of emissions, but these are likely 

relatively small emission contributors. 
2

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Methane is an important climate forcing gas. Methane has a large global warming potential 

(GWP) relative to CO2, but shorter atmospheric lifetime (IPCC 2013).9 Thus, reducing methane 

emissions has been included in the interagency Methane Strategy of the President’s Climate 

Action Plan White House 2015). It is therefore important to understand the sources and trends of 

methane emissions.

At the time of this analysis, the most current U.S. GHGI was published in 2014 (EPA 

2014a). That U.S. GHGI used a methane GWP of 21, in conformance with UNFCCC reporting 

requirements for national GHG inventories (UNFCCC 1998), based on scientific consensus at 

the time (IPCC 1995). Starting in 2015, the UNFCCC reporting requires the use of a methane

GWP of 25 (UNFCCC 2014; IPCC 2007). This report uses a GWP of 25 following its use in 

EPA’s GHG Reporting Program as well as its recently published 2015 U.S. GHGI (EPA 2015a;

EPA 2014b; Federal Register 2014). It should be noted, however, that recent scientific evidence 

suggests that the 100-year GWP of methane is greater than 25 (IPCC 2013).

10

Nine percent of 2012 U.S. anthropogenic CO2-equivalent GHG emissions are from methane 

(EPA 2014a), making it the second most important GHG after CO2. The natural gas industry is 

consistently one of the largest contributors to U.S. methane emissions (e.g., ~23%, or 675 

million metric tonnes [MMt] of CO2e in 2012) (EPA 2014a). In addition, another 6% of methane 

is emitted by the petroleum industry, in part through processes designed to separate and transport

natural gas produced in association with oil. 

 9 Global warming potential (GWP) is a metric defined as the time-integrated radiative forcing of a gas due to a pulse 

emission and relative to that of carbon dioxide, which by definition has a GWP of 1. The GWP has become the 

default metric for transforming emissions of different gases to a common scale; often called “carbon dioxide 

equivalent (CO2e) emissions”. GWP’s are assessed over fixed time periods (e.g., 20, 100, and 500-years) since gases 

have different atmosphere lifetimes, with the 100-year time horizon being the most commonly used. However these 

specific time horizons should not be considered as having any special scientific significance (IPCC 2013). The 100-

year GWP was later adopted as a metric to implement the multi-gas accounting approach embedded in the United 

Nations Framework Convention on Climate Change (IPCC 2013). 10 The latest international assessment of scientific evidence (IPCC 2013) reports that the 100-year GWP for methane 

of fossil origin is 36 g CO2e per g methane when including climate-carbon feedbacks (IPCC 2013). If this GWP 

were used, all instances of GHG emissions in CO2e in this report would increase by about 45%.
3

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Figure 1. U.S. GHGI estimates of anthropogenic methane emissions in the U.S. in 2012. Total 

emissions are 675 MMt CO2e/yr. (NG=natural gas)

Source: EPA 2014a

Recent studies (e.g., Brandt et al. 2014; Pieschl et al. 2015; Lamb et al. 2015; Zimmerle et al. 

2015) have reported conflicting conclusions as to the magnitude of methane emissions from the 

natural gas and oil industries and how measured emissions compare to those reported in the U.S. 

GHGI and other inventories. Conflicting conclusions from measurement studies and inventories 

is an issue previously encountered with emission inventories for criteria air pollutants (Miller et 

al. 2006). Based on a review of recent research and analysis of the U.S. GHGI emission 

estimates, this report briefly summarizes the methods and results of the 2014 U.S. GHGI (EPA 

2014a), discusses challenges to constructing a GHG inventory with particular focus on the U.S. 

GHGI, and suggests some opportunities that could improve the accuracy of the methane 

emissions estimates. Many of our recommendations are specific to the natural gas industrial 

sector, while others are more broadly applicable to improving inventory accuracy generally. 

Although this report focuses on the U.S. GHGI, to the extent other inventories share common 

methods, data sources, and challenges, the observations and conclusions discussed in this report 

could apply.
4

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2 How the U.S. GHGI is Constructed

The U.S. GHGI is constructed annually by the U.S. EPA and estimates emissions of the six 

GHGs required to be reported to UNFCCC—CO2, methane, nitrous oxide, hydro-fluorocarbons,

perfluorocarbons, and sulfur hexafluoride11—from anthropogenic sources and sinks associated 

with energy, industrial processes, waste, agriculture, and land use (EPA 2014a). Natural gasrelated GHG emissions are primarily tracked in two different sections of the U.S. GHGI. 

Emissions from the natural gas industrial sector, the focus of this report, are tracked under the 

heading “Natural Gas Systems.” Emissions from combustion of natural gas in residential, 

commercial, and industrial energy use are tracked under the heading “Fossil Fuel Combustion.” 

Production of natural gas can be classified as derived from onshore or offshore sources, with 

onshore consisting of conventional gas, tight gas, coal bed methane (CBM) gas, shale gas, and 

associated gas. Associated gas refers to natural gas produced from wells classified as oil wells. 

Currently, methane emissions from oil wells are for the most part tracked in the U.S. GHGI in 

the under the heading “Petroleum Systems” (including emissions from wells that produce coproducts like natural gas);12 once any oil-well co-produced natural gas enters natural gas 

infrastructure (i.e., as it is collected by gathering lines), the U.S. GHGI categorizes those GHG 

emissions in the natural gas sector. The focus of this report is on emissions from the natural gas 

sector, but where relevant, the oil sector will be mentioned with regards to the current U.S. 

GHGI and EPA’s (2015a) proposed updates to allocation of associated well GHG emissions 

between the natural gas and petroleum sectors.

Within the natural gas energy sector calculations, the U.S. GHGI tracks national GHG emissions 

from segments of the natural gas supply chain (as shown in the schematic of Figure 2), grouped 

as follows: 

1. Production, gathering, and boosting13

2. Processing

3. Transmission and storage

4. Distribution.

The U.S. GHGI collects activity factor data from multiple natural gas-producing U.S. regions, 

but only emissions from the production, gathering, and boosting segments are calculated and 

reported in the U.S. GHGI for each of six natural gas-producing regions14 (EPA 2014a). National 

estimates of emissions from those segments are calculated as the sum of the emissions from each 

source category in each region. Emissions from the processing, transmission and storage, and 

distribution segments are tracked solely at the national level. 

 11 Starting in 2015, countries are also required to report nitrogen trifluoride to the UNFCCC and may elect to report 

fluorinated ethers.

12 There are subtleties to the split of emissions of methane from oil wells whereby certain emission sources are 

categorized in the natural gas and others in the petroleum sector. The interested reader can refer to the U.S. GHGI 

and its supporting Appendices for more information (EPA 2014a). 13 Emissions from gathering and boosting are not always clearly differentiated from production in this grouped U.S. 

GHGI category. 14 North East, Midcontinent, Rocky Mountain, South West, West Coast, and Gulf Coast.
5

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Methane emissions from the natural gas supply chain are the result of fugitive and vented 

sources across the supply chain. Fugitive emissions are unintended emissions from leaks; vented 

emissions are emitted by design such as from maintenance tasks or incomplete combustion of 

natural gas in engines. GHG emissions from so-called “beneficial uses” of natural gas, such as 

powering transmission equipment, are considered within the U.S. GHGI but not explicitly 

categorized for reporting. Combusting natural gas for these “beneficial uses” produces CO2 and 

small amounts of un-combusted methane that are tracked and reported separately in the U.S 

GHGI. 

The U.S. GHGI reports net emissions for each of about 110 source categories within the natural 

gas industrial sector. EPA defines net emissions as potential emissions minus any emission 

reductions, as shown in Equation 1. Potential emissions represent emissions in the absence of 

controls. To account for controls, associated reductions are subtracted, as in Eq. 1 below. 

Emission reductions included in the calculation of net emissions result from existing voluntary or 

mandatory control measures.

NNNNNN EEEEEEEEEEEEEEEEEE = PP PPPP EEEEEEEEEEEEEEEEEE − EEEEEEEEEEEEEEEE RR RRRRRR Eq. 1

For a few source categories, EPA estimates net emissions directly (e.g., gas well completions, 

liquids unloading) (EPA 2014a). These cases are rare largely due to a lack of emission factors for 

net emissions at the necessary levels of aggregation (both technologically and regionally). EPA 

has therefore relied upon available potential emission estimates for most source categories. 

Moreover, data on control practices (discussed below) that reduce potential emissions can be

challenging to obtain at the source category and regional resolution of the U.S. GHGI, thus 

requiring the U.S. EPA to make simplifying assumptions or aggregate categories to calculate net 

emissions.

Potential emissions represent uncontrolled emissions from a given source category (a component 

type or activity), and are calculated by multiplying an emission factor (representing the mass of 

pollutant emitted per unit of activity) for potential emissions by the number of emission sources

(called an activity factor) in that source category. Potential emissions are calculated as: 

PPPPPPPPPPPPPPPPPP EEEE EEEE EE = ∑ EEEEEEEEEEEEEEEE FF FF xx AA AA AA FF FF Eq. 2

An emission factor is a point estimate, typically an arithmetic average that is developed based on 

measurements of sources sampled within a source category. When estimates of certain source 

categories do not have emission measurements available, engineering approaches are utilized to 

develop an estimate based on proxies and assumptions. An emission factor has certain 

characteristics associated with it, including sample size (number of measurements), measurement 

method (including its associated measurement uncertainty), sampling design (e.g., 

stratification/classification, method used to select the sample), and treatment of measurements 

that are below the measurement detection limit. Methane emission factors used in the U.S. GHGI 

are mostly drawn from an EPA/GRI bottom-up measurement study15 conducted in the early 

1990s and published in 1996 (Harrison et al. 1996). Harrison et al. (1996) measured emissions 

from a sample of sources from each segment of the U.S. natural gas supply chain. Recent studies 

 15 Please see Section 4 text box for definition of this term. 
6

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are starting to provide results that could be used to update the EPA/GRI emission factors (e.g., 

Lamb et al. 2015; Subramanian et al. 2015; Allen et al. 2015a; Allen et al. 2015b), as discussed 

further in Section 4.

Activity factors in the U.S. GHGI are estimated from a variety of surveys or other data sources, 

including the U.S. Energy Information Administration (EIA), the Oil and Gas Journal, DI 

Desktop powered by HPDI, and EPA’s Greenhouse Gas Reporting Program (GHGRP) (EIA 

2015; Oil and Gas Journal 2014; DI Desktop 2014; EPA 2014b). Some activity factors used in 

the GHGI are directly available; others are derived through calculations involving surrogates. 

For example, the activity factor used in the U.S. GHGI for pneumatic devices in the production 

segment is estimated by multiplying the number of wells by an estimate of the average number 

of pneumatic devices per well (i.e., not a direct count of pneumatic valves nationally). Because 

many source categories are inherently challenging to directly count, surrogate approaches are 

common within the GHGI and can involve complex, approximate or indirect methods.

The definition of emission reductions (Eq. 1) is shown in Equation 3, and is the sum of emissions 

reductions resulting from federal or state policies or regulation, plus those resulting from 

voluntary actions. Data on voluntary actions that reduce natural gas emissions are based on

EPA’s voluntary Natural Gas STAR program (EPA 2014c). Only those voluntary emission 

reductions reported to EPA are included in the U.S. GHGI. Estimates of emissions reductions 

from policies that reduce natural gas emissions are primarily based on analysis of the effects of 

two federal regulations (National Emission Standards for Hazardous Air Pollutants and New 

Source Performance Standards [EPA 2013a]). 

EEEE EEEE RR RRRRRR = VV AA + PP PP Eq. 3

The U.S. GHGI is continually assessed and improved. For instance, EPA annually reviews new 

scientific information and updates emission estimates in the inventory. The U.S. GHGI also 

annually undergoes a review by expert stakeholders, a 30-day public review, and then a review 

by an UNFCCC expert review team, which generates comments that result in further changes to 

the U.S. GHGI before it is published.

In recent years, increased attention has been placed on the methods used for estimating methane 

emissions from the natural gas supply chain in the U.S. GHGI. Through 2008, emissions factors 

had changed little since the early 1990s. However, beginning in 2009, the set of methods used to 

estimate emissions across the natural gas industry began to be revised and improved to account 

for industry development and new data sources. These changes have impacted emission 

estimates, and these updated emissions estimates are retroactively incorporated into estimates for 

prior years.16 For instance, EPA has switched to a direct emission calculation (i.e., rather than 

using equation 1) for completions with hydraulic fracturing and work-overs with hydraulic 

fracturing (i.e., refracturing), liquids unloading, condensate storage tanks, and centrifugal 

compressors (EPA 2014a).

 16 As methods for calculating emissions from a particular source category change, EPA recalculates emissions from 

all prior years using the revised methodology in order to make the time series consistent and comparable across 

years.
7

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For the 2015 U.S. GHGI, EPA implemented several changes as compared to previous U.S. 

GHGIs that were intended to improve inventory accuracy and representativeness (EPA 2015a), 

including:

• The data source and methods for estimating well counts

• Emission factors for hydraulic fracturing gas well completions and work-overs as well as 

offshore oil and natural gas

• The allocation of NG STAR program methane emission reductions to pneumatic 

controllers in the oil and gas sectors.

In addition, the GHGRP is a core part of EPA’s plan for updating the U.S. GHGI (EPA 2015a).

In 2009, the U.S. EPA published a rule requiring reporting of GHG emissions and supporting 

information from sources in certain industrial sectors emitting greater than 25,000 metric tons of

CO2 equivalent emissions per year, including many types of facilities in the natural gas industrial 

sector. GHGRP includes data from over 2,000 facilities in the natural gas and petroleum energy

sectors, beginning with data for the year 2011 (EPA 2015b). The GHGRP is used to provide a 

better understanding of facility-level GHG emissions sources and to guide emission reduction 

policies and programs. For example, GHGRP data was used in the 2013 U.S. GHGI to validate 

updates to emission estimates for liquids unloading. In addition, GHGRP data was used in the 

2014 U.S. GHGI to update the methodology for estimating emissions from gas well completion 

with hydraulic fracturing with practice-specific net emission factors instead of potential emission 

factors.

Additional improvements using GHGRP data are being implemented for future U.S. GHGIs 

(EPA 2015a). In March 2015, GHGRP petroleum and natural gas systems facilities began 

reporting additional data to EPA. In many cases, these additional data were reported for years

2011, 2012, 2013, and 2014, therefore providing four years of new data. The additional data 

includes, in some cases, equipment counts and other information that could allow for further 

improvements to the U.S. GHGI. EPA plans to review this new data reported to GHGRP for 

potential updates to methods across all segments of petroleum and natural gas systems.
8

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3 U.S. Methane Emissions from the Natural Gas 

Supply Chain

The natural gas infrastructure system in the United States is complex. For the purposes of 

inventory emissions estimates, this complex system is often divided into broad segments that 

serve specific functions (even if some functions are not present in certain regions). Figure 2 

displays these segments schematically, along with U.S. GHGI (year 2012) estimates of net 

methane emissions (in MMt CO2e/yr). Below is a list that describes the six segments of the

supply chain, grouped as in the U.S. GHGI (i.e., the production segment is grouped with that of 

gathering and boosting; transmission is grouped with storage) (EIA 2014 and Skone 2014 except 

as noted):

1. Production; gathering and boosting

o Production of natural gas from wellheads or fields, comprised of ~500,000 

existing gas wells, ~40,000 new gas wells drilled annually, and ~6,300 production 

companies (EPA 2014f).

o Gathering and boosting (compression) of natural gas from wellheads to a 

processing plant or to an interconnection with a larger pipeline. The U.S. natural 

gas gathering and boosting network includes ~10,000 gathering stations, which 

house ~100,000 gathering compressors and other equipment, and ~200,000 miles 

of gathering pipe. 

2. Processing of natural gas in ~580 processing plants to extract natural gas liquids and

remove impurities from the natural gas stream, as well as compress the natural gas to 

transmission line pressure.

3. Transmission and storage

o Transmission of natural gas through long-distance pipelines from the producing 

area to market areas (so-called “city gates”) through ~320,000 miles of largediameter and high-pressure pipes and ~9,000 compressors. Transmission can be 

inter- or intra-state. 

o (temporary) Storage of natural gas for future use in ~400 underground facilities,

including depleted oil and gas reservoirs, aquifers, and salt caverns. Storage also 

includes liquefied natural gas (LNG) storage infrastructure. 

4. Distribution of natural gas from city gates to customers through ~1,200 distribution 

companies, ~2.1 million miles of small-diameter distribution pipes, and ~62 million 

homes with natural gas service lines.
9 

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Figure 2. Organization of the natural gas supply chain into four segments. The figure also shows 

U.S. GHGI estimates of 2012 methane emissions from each segment and as a percentage of total natural 

gas sector methane emissions.

Source: Figure modified from EPA 2013b; data from EPA 2014a

The following analysis of methane emissions from the U.S. GHGI focuses mostly on the largest 

emission sources downstream of natural gas production. Production, gathering, and boosting 

remains one of the largest sources of methane emissions in the natural gas supply chain (Figure 

2). Yet, for the last eight years, the U.S. GHGI reports that emissions from production, gathering, 

and boosting have been decreasing due to a combination of voluntary industry actions and 

government policies (EPA 2014a). Other segments within the natural gas supply chain are now 

estimated to contribute a larger portion of overall methane emissions from the natural gas sector. 

The U.S. GHGI estimates that methane emissions downstream of production contributed more 

than 68% of the total emissions from the natural gas supply chain in 2012 (EPA 2014a).17

Emissions downstream of natural gas processing constitute approximately 53% of total 

emissions from the natural gas supply chain in 2012, with the majority of this amount from 

transmission and storage (EPA 2014a). 

Across the entire natural gas supply chain, the U.S. GHGI estimates that 60% and 40% of 

emissions are fugitive and venting, respectively (EPA 2014a). Downstream of production, 78% 

and 22% of emissions are estimated as fugitive and venting, respectively (EPA 2014a). Thus, the 

majority of methane emissions from the natural gas industry are from fugitive emissions, in 

particular from emissions downstream of production. 

Figures A-1 through A-3 in Appendix A provide detail on the specific sources of emissions 

within each supply chain segment from production, gathering and boosting; processing; and 

17 We have phrased as “more than 68%” because gathering and boosting emissions are not quantified separately 

from production, yet are nevertheless downstream of production.
10

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transmission and storage, respectively. Tables A-1 through A-4 in Appendix A report how we 

used U.S. GHGI data to create Figures A-1 through A-3 and Figure 4. 

Two observations from the 2014 U.S. GHGI are worth noting. First, our analysis of the U.S. 

GHGI reveals that the majority of the methane emissions downstream of production come from 

compressor stations. Emissions from compressor stations include venting and fugitives from 

compressor engines and those from other equipment and processes at compressor stations.18

Figure 3 illustrates the portion of total 2012 emissions released from compressor stations (or 

compressor engines alone when stand-alone compressors are utilized) in each of the four natural 

gas supply chain segments used by the U.S. GHGI. Although compressors account for only 

about 8% of emissions in the production, gathering, and boosting segment and none from 

distribution (because compressors are not used in distribution), EPA estimates that compressors 

account for 94% of emissions from the processing segment and in the transmission and storage

segment, 79% of emissions are from compressor stations. As a result, approximately 43% of 

total emissions across the full natural gas supply chain are estimated to be emitted from 

compressor stations (inclusive of stand-alone compressors). Given the magnitude of the set of 

similar sources, efforts to reduce emissions from compressor stations could result in significant 

reductions across the entire supply chain, as has been suggested by EPA (2014f) and ICF (2014).

 18 The source categories are: miscellaneous station fugitive and vented emissions, centrifugal compressor fugitives 

(from wet seals and dry seals), reciprocating compressor fugitives (from rod packing), compressor engine exhaust 

(unburned methane), and compressor turbine exhaust (unburned methane).
11

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Figure 3. U.S. GHGI estimates of methane emissions from natural gas compressors within each 

supply chain segment in 2012. Our analysis of the U.S. GHGI reveals that compressors19 are the 

largest source of methane emissions in 2012 at processing plants, transmission stations, and storage 

facilities.

Source: EPA 2014a

Second, 2014 U.S. GHGI estimates that within the distribution segment, fugitive emissions

comprise over 97% of total emissions, about half of which come from meters and regulators, and 

the other half from pipeline mains and service lines.

20 Figure 4 illustrates the contribution of 

source categories to 2012 distribution segment emissions. In particular, leaks from cast iron and 

unprotected steel pipeline main and service lines are estimated to contribute 33% of total 

distribution segment emissions despite representing the fewest miles of any piping material 

(Table 1) (EPA 2014a). Cast iron and unprotected steel pipe and service lines have the largest 

emission factors per mile of pipe. Other types of main and service lines, such as plastic and 

protected steel, represent more miles of pipe and number of service lines, but are estimated to 

contribute only 17% of total emissions from the distribution segment given substantially lower 

emission factors. Compared to cast iron and unprotected steel pipe and service lines, reducing 

fugitive emissions from other types of main and service lines would require relatively larger 

changes to infrastructure (more miles) in order to achieve the same reductions in fugitive 

emissions.

 19 Compressor emissions in Figure 3 include U.S. GHGI categories of centrifugal and reciprocating compressor 

fugitives, compressor station fugitives and exhaust, compressor station pipeline fugitives, compressor engine 

exhaust, and compressor turbine exhaust. 20 Service lines transport gas from a common source of supply to an end user’s meter or piping.
12

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Figure 4. U.S. GHGI estimates of methane emissions from the U.S. natural gas distribution system

in 2012

Source: EPA 2014a21

Table 1. U.S. Natural Gas Methane (CH4) Activity Factor and Emission Factors in 2012 for 

Distribution Pipelines (Main Lines and Service Lines) as Reported in the U.S. GHGI

Pipeline Category Activity Factor Emission Factor Total Emissions

Cast Iron Mains ~ 32,000 miles 240 Mcf CH4/mile-yr 7.7 million Mcf CH4/yr

Unprotected Steel Mains ~ 64,000 miles 110 Mcf CH4/mile-yr 7.0 million Mcf CH4/yr

Plastic Mains ~ 660,000 miles 9.9 Mcf CH4/mile-yr 6.5 million Mcf CH4/yr

Protected Steel Mains ~ 490,000 miles 3.1 Mcf CH4/mile-yr 1.5 million Mcf CH4/yr

Unprotected Steel Service ~ 3.9 million services 1.7 Mcf CH4/service 6.6 million Mcf CH4/yr

Protected Steel Services ~ 15 million services 0.18 Mcf CH4/service 2.7 million Mcf CH4/yr

Copper Services ~ 1 million services 0.25 Mcf CH4/service 0.25 million Mcf CH4/yr

Plastic Services ~ 45 million services 0.01 Mcf CH4/service 0.45 million Mcf CH4/yr

Mcf = thousand cubic feet

Source: EPA 2014a

 21 Figure 3 is created from aggregated U.S. GHGI data shown in Table A-4 in Appendix A.
13

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4 Challenges to Constructing the U.S. GHG Inventory

All inventories, including the U.S. GHGI, rely on measurements in combination with estimates 

of the quantity of emissions from myriad sources and aggregate these emissions values to various 

regional or sectoral levels. Direct measurement of the quantity of emissions from every source 

would be infeasible given the vast number of sources, so inventories must rely on statistical 

approaches to calculating the emissions quantities. As with any statistically-based calculation, 

there is an inherent level of uncertainty associated with the results. Given that the U.S. GHGI is 

leveraged for a variety of regulatory, institutional, and academic purposes, it is crucial to

understand the challenges in constructing the U.S. GHGI so it can be properly interpreted and to 

explore opportunities for improvement to the accuracy and reliability of its estimates. Although 

this section focuses on the U.S. GHGI, many of the challenges are applicable to other inventories 

to the extent they use similar methods.

Given the importance of methane as a climate-forcing agent as well as the desire to understand 

emissions implications from the rapidly changing natural gas and oil industry in the United 

States, researchers have conducted analyses comparing various measurement-based estimates of 

methane emissions rates (See Text Box 4-1 for definitions of top-down and bottom-up studies, 

including measurement studies and inventories) and those that use inventory-based estimates of 

emissions rates. Brandt et al. (2014) synthesized more than 20 years of such emissions 

measurements from North America, wherein results of numerous prior studies were compared to 

corresponding results from GHG inventories, which included but were not limited to the U.S. 

GHGI. This synthesis included measurements taken at numerous scales, from individual 

components to entire continents, using a variety of methods. 

Brandt et al. (2014) found that estimates of methane emissions from inventories were 

consistently (though not exclusively)22 lower than those estimated through measurement 

techniques (see Figure 5). For studies conducted at the scale of regions or larger, Brandt et al. 

(2014) reported that measured emissions were generally 50% higher than those based on the U.S. 

GHGI, with a range of 25 to 75%.23 The authors were not able to specifically quantify the 

contribution of methane emissions from natural gas systems to this total underestimation. 

However, they noted that available evidence suggests that the natural gas supply chain 

contributes to the underestimation, but likely does not account for all of the underestimation 

observed. 

 22 It should be noted that at smaller spatial scales (e.g., at facilities and components) some ratios of measurement to 

inventory estimate (which were emission factors at the component level) were found less than one (i.e., 

measurement < inventory estimate), although a substantial majority were greater than one. 

23 Not all studies originally compared their measurement-based estimate to the U.S. GHGI; Brandt et al. adjusted 

each study’s comparison to consistently use the U.S. GHGI, appropriately scaled to the same year, spatial domain 

and included sectors. 
14

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Text Box 4-1. Defining Top-down and Bottom-up Studies 

The measurement studies and inventories discussed in this report are divided into those that employ 

“top-down” and “bottom-up” approaches. The following definitions are used in this paper, with a 

warning to readers that the delineation between study types can vary between authors.

Top-down studies are those that measure (“in-situ”, or within the air mass) or estimate (e.g., from 

satellite remote sensing) the concentration of methane in the atmosphere, and then use different 

modeling approaches to estimate mass flux (mass of methane emitted per unit time and area) from 

observed or estimated concentrations. Typically, top-down studies measure gross methane flux rate 

within the experimental area of study. Measurements taken from airborne platforms, such as planes 

or satellites, have more limited spatial resolution than those taken from ground-based platforms, 

such as vehicles or stationary towers. Attributing flux estimated through measurements to specific 

sources or classes of equipment can be challenging. 

Bottom-up studies take a disaggregated approach to developing estimates of emissions at the level 

of individual sources and source categories. Inventories and life cycle assessments are two common 

types of bottom-up modeling studies, each employing different system boundaries and scaling 

approaches in engineering-based calculations to estimate emissions from a set of components or 

activities to represent emissions of a defined population (e.g., pneumatic valves or shale gas well 

completions). Inventories are the focus of this report; life cycle assessments are discussed 

thoroughly in Heath et al. (2014a) and contrasted with inventories in Heath et al. (2014b). Bottomup inventories sum GHGs emitted or removed from the atmosphere over a period of time from a 

specific set of components, activities, or facilities within a geographic region of interest. 

Bottom-up measurement studies measure flux directly from individual components, activities, or 

facilities that are then used to estimate inventories. Although attributing emissions to a specific 

source is often easier in bottom-up measurement studies compared to top-down measurement 

studies, the populations of sources from which samples are measured by bottom-up studies can be 

large. When sampling from large source populations, it can be challenging to establish a 

representative sample with enough statistical power to reliably characterize the full distribution of 

the emissions source category. Measurement techniques that only measure concentration can be 

useful for assessing safety risk or identifying leaking components, but only provide a qualitative 

assessment of mass flux (e.g., Phillips et al. 2013). Some studies measure concentration only while 

others measure both concentration and mass flux. Studies that measure mass flux are generally more 

useful for updating inventories.

When considering both top-down and bottom-up types of studies, and especially when comparing 

them, it is important to consider the spatial and temporal scales of each. Studies can span a range of 

scales from specific components to continents, and measurements can occur over timescales of 

minutes to years. It is also important to consider the scope of emission sources assessed and whether 

there could be multiple source sectors contributing to the measured emissions. Note that while more 

commonly an issue for top-down studies, this challenge can also exist for bottom-up studies. For 

example, which components’ emissions are routed to a common vent stack being measured or does 

the inventory estimate exclude certain emission sources that would be emitting in the zone of 

measurement? There are a host of other important factors critical to the proper interpretation and 

use of measurement study results, such as the year of study, the vintage of the equipment measured 

in the study, and the accuracy of the measurement method employed.
15 

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Figure 5. A recent synthesis of methane emission measurements that were compared to inventory 

estimates of methane emissions, which found that inventory-based estimates have consistently 

underestimated measured emissions across a wide range of measurement scales, locations, 

source categories and years. Ratios greater than one in the plot indicate measured emissions are 

larger than expected from emission factors or inventory estimates. Given that some studies compared 

their results to inventories other than the U.S. GHGI, the figure inset adjusts published comparisons to 

consistently compare measurements to the U.S. GHGI, scaled to the year of study and, in some cases, 

the sector under examination. Multiple points for each study (designated by different marker types) 

correspond to different device classes or different cases measured in that study.

Source: Brandt et al. 2014

Inaccuracy and uncertainty in both measurement and inventory studies could be contributing to 

the discrepancy observed between measured methane emissions and inventory-based estimates. 

However, given the focus of this report on inventories, we explore the challenges (and potential 

sources of uncertainty) associated with inventories, with particular attention to those relevant to 
16

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the U.S. GHGI, that could at least partly explain this discrepancy. The level of accuracy of 

emission estimates used in inventories depends upon three key criteria (Brandt et al. 2014):

1. All sources of emissions relevant to the inventory are assessed.

2. The sample of sources used to construct emission factors are representative of the 

emitting source categories assessed in the inventory.

a. Samples are from, or can represent, the time period covered by the inventory.

b. Sample sizes are sufficiently large and diverse to capture the variability in 

emissions that exists within source categories.

c. The point estimates of a distribution of emission measurements for a given source 

category are unbiased and consider the shape of the distribution of available 

emission estimates. 

3. Activity factors (counts) are complete and accurate.

The purpose of this analysis is to offer perspectives for understanding the challenges of 

successfully meeting these criteria, as well as to suggest pathways to advance knowledge that 

can improve inventory accuracy and representativeness (Section 5); some of these suggestions 

may already be under consideration by EPA in developing the U.S. GHGI. 

4.1 Inventory Completeness

Assessing all sources of emissions is crucial to ensuring that the inventory provides an accurate 

representation of total U.S. methane emissions. In some cases, there are no estimates of 

emissions because knowledge to quantify emissions from such sources is not available. Brandt et 

al. (2014), ICF (2014), and EPA (2014a) identify a small number of sources of methane 

emissions from natural gas and oil systems that currently are not included within the U.S. GHGI,

and may potentially be excluded from other inventory estimates: 

• Abandoned wells and other derelict infrastructure

• Emissions measurements on gathering pipelines

• Emissions of methane dissolved in produced water that is released from management of 

produced water, e.g., retention ponds24

• Management of solid waste from natural gas operations, including land application

• Well work-overs that are not recompletions25

• Well testing.

 24 Methane entrained in produced water is estimated within the U.S. GHGI for completion flowback, liquids 

unloading and from ongoing water production in coal bed methane wells in two states (AL and WY). Dissolved 

methane takes longer to evolve and emit; some states have regulations that would require control of these emissions, 

so emissions estimates would need to be developed considering those regulations. 

25 All well work-overs are categorized as recompletions in the U.S. GHGI. However, work-over activities other than 

recompletions such as well bore maintenance can also be performed, which could release methane and may have 

different methane emission factors than for recompletion. In addition, it is not clear that well workovers other than 

recompletion are included in the activity factor methods used in this U.S. GHGI source category.
17

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Emissions of natural gas that are under the control of the end user (e.g., in industrial facilities or 

feeder lines), but occur prior to end use combustion, are not classified under the natural gas 

supply chain in the U.S. GHGI. Only emissions up to an end user’s meter or off-take point are 

considered a part of the natural gas supply chain. In addition, some of these emissions are also 

not included in the U.S. GHGI, and potentially also for other inventories. In the U.S. GHGI, an 

estimate of emissions of methane from incomplete combustion is included in the applicable end 

use sector’s GHG emissions (e.g., residential, commercial, vehicles), but emissions from sources 

like leaking pipes, valves and flanges that exist on the natural gas consumer’s site are not 

included. These additional potential sources of fugitive methane emissions are understudied and 

their magnitude and frequency has not yet been quantified. (One study suggesting potential 

importance of this source category is Wennberg et al. [2012]).

4.2 Inventory Representativeness

Uncertainty and inaccuracy in inventories can result from a lack of representativeness of the 

samples used to construct emissions factors for inventories. For example, the age of the 

equipment sampled in the original EPA/GRI bottom-up measurement study (which is the source 

of most emission factors used in the U.S. GHGI) (Harrison et al. 1996) may differ from 

equipment currently used. There is a potential relationship between age of equipment and 

emissions. A variant of this issue is how technologies can evolve over time in their design and 

use; emission factors for older equipment may not represent those for current ones. This issue 

can be addressed by sampling modern populations of equipment in their current usage patterns 

and developing updated emission factors from these new data. 

A strong sampling strategy can help to ensure representativeness of the samples used to construct 

emissions factors for inventories. In order to create a sample that is useful in improving 

inventories, it is critical both to sample across the range of key influencing factors of emissions, 

such as:

• Equipment type

• Equipment age

• Degree of maintenance

• Duty cycle26

• Operator practices

• Emissions controls in place.

Also, it is important to consider regional differences that may also affect emission rates. Some 

sources of regional variation include:

• Regulatory jurisdictions and the differences in requirements between them 

• Regional gas price that can influence how natural gas infrastructure is utilized and 

developed

 26 Proportion of time a component, device, or system is operated.
18

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• Geologic factors, such as gas composition, water-to-gas ratio or the gas-to-oil ratio, and 

the type of gas being extracted (shale gas, tight gas, conventional gas, or coal bed 

methane) that can influence the use of certain technologies and practices

• Age of infrastructure that can depend on when a play was developed amongst other 

factors

• Market concentration and capitalization of operators

• Climate of the infrastructure location such as temperature, elevation, and relative 

humidity.

While regional information is inherent in the data sources used in the U.S. GHGI (e.g., samples 

for emission factors taken in different regions, regional activity information), the U.S. GHGI 

does not report regionally disaggregated emission estimates except in some specific cases. To 

wit, the U.S. GHGI includes six natural gas-producing regions only for production, gathering, 

and boosting. Other supply chain segments are assessed nationally.

Another concern regarding representativeness of samples is whether facilities whose emissions 

are sampled were chosen at random from the full target population. Sampling bias can occur if a 

random sample is not achieved. Sampling bias is the systematic undersampling or non-sampling 

of sources that have emission profiles different than those sources sampled to represent a source 

category. 

Acquiring a truly random sample of industrial facilities is challenging owing to, for instance, 

issues of access. A requirement for measuring emissions from a particular component of natural 

gas infrastructure is to have access to that component. Those conducting scientific studies 

generally lack the authority to require access to industrial facilities in the oil and gas sector, and 

thus are limited to measuring facilities whose owners have volunteered a facility to be 

measured.27 In addition, safety requirements for visitors to industrial facilities necessitate some 

degree of advance notice. Questions have been raised as to whether any changes to normal 

operation were made prior to the emission measurement. It is not possible to prove sampling bias 

or measure the degree of sampling bias without access to the non-sampled population, which is 

obviously a challenging undertaking given the aforementioned access restrictions. Yet, the 

authors are unaware of a study that has accomplished a truly random sample of the full 

population of target source category. 

Recent measurement studies on several types of emissions sources suggest that emissions from a 

small number of sources represent a large fraction of total emissions from that source category, 

creating the so-called “fat tail” distribution of emissions rates (e.g., Allen et al. 2013; Allen et al. 

2015a; Allen et al. 2015b). The main concern in this context is undersampling for any given 

source category; smaller samples are unlikely to capture the rare tail of the distribution. 

Undersampling of the tail increases the likelihood that the mean of the sample will be 

downwardly biased relative to the mean of the true population (Brandt et al. 2014). In other 

words, the mean of the sample will underestimate the emissions from the source category. 

 27 The authors are only aware of one study that used compulsory sampling, which was required by the City of Fort 

Worth. This bottom-up measurement study sampled a large number of natural gas operations in the Barnett shale of 

Texas (Eastern Research Group 2011).
19

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4.3 Activity Factor Data

Most of the public and scientific attention toward improving inventories has been focused on 

improving emissions factors. However, activity factors are also critical to ensuring accurate and 

unbiased inventory estimates of emissions, and therefore also should be carefully evaluated and 

strategies for improvement considered. Analogous to the census of the U.S. population, direct 

counting of all natural gas emission sources is not possible, so activity factors are estimated 

using a variety of data sources and methods. EPA has improved accounting of wells and 

completions and identified other areas for improvement of activity factors (EPA 2015a). It is 

likely that counts of other source categories could benefit from improvement (EPA 2014a). 

4.4 Uncertainty Quantification 

Another challenge with current inventories is in robust uncertainty quantification. Emissions 

cannot be estimated with 100% accuracy; there is inevitable uncertainty. Uncertainty 

quantification not only lends credibility to estimation efforts, but can inform decision makers 

about relative confidence and help focus inventory improvement efforts for instance through an 

uncertainty importance analysis. 

Uncertainty quantification is part of rigorous reporting of measurements of methane emissions, 

and should likewise be robustly reported along with inventory estimates to enable meaningful 

comparisons. It is challenging to quantify the uncertainty of all input parameters to an inventory, 

but this should not deter vigorous efforts to do so. In fact, this endeavor will become more viable 

in the near future, enabled by recent or soon-to-be available bottom-up measurement studies 

(such as those shown in Figure 6); one of the goals of these recent studies has been to rigorously 

estimate and report uncertainty. Most efforts today include development and reporting of the full 

distribution of emission estimates which can be used to update the distributions employed in 

uncertainty quantification of inventories. Uncertainty quantification also requires robust 

information about distributions of activity factors and other sources of uncertainty. 

For several years, the U.S. GHGI has employed a Monte Carlo uncertainty quantification 

framework using IPCC-recommended Tier 2 methods (IPCC 2006).

28 It is focused on evaluating 

parametric uncertainty, considering twelve major emission source categories29 that account for 

92% of total methane emissions from the natural gas supply chain. The ranges and distributions 

tested for emission and activity factors are informed by empirical data and expert judgment; 30

the latter is especially relied upon in the case of activity factors. Parametric uncertainty (e.g., 

uncertainty in the emission and activity factors) is not the only source of uncertainty. There is 

also methodological uncertainty. Whereas U.S. GHGI methods have changed over time, the 

reported uncertainty range has not been updated since 2010 (EPA 2011; EPA 2015a). EPA plans 

to eventually update the U.S. GHGI uncertainty distributions (EPA 2015a).

 28 Baseline methane emissions for 2012 were 6.2 MMt methane and EPA’s uncertainty analysis reports the 95% 

confidence interval for methane emissions as 5.0 (- 19%) to 8.0 (+ 30%) MMt methane. 29 Though the number of source categories examined in its uncertainty analysis is reported, which categories were 

selected is not. 

30 The emission factor ranges, employing normal, lognormal, uniform, triangular, or beta probability distributions, 

are based on a combination of information documented in different data sources (e.g., EPA/GRI [1996] and EIA 

[2015]) and expert judgment.
20

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5 Recommendations for Improvements to the U.S. 

GHGI Leveraging Bottom-up and Top-down Studies

By providing a high level of granularity and repeatability, inventories have been a critical 

foundation for informing source category prioritization. Inventories, especially the U.S. GHGI,

have been frequently used in research and development as well as emissions reduction decision

making. It will be important to leverage recent and forthcoming studies to maintain the functions 

of inventories and address the many challenges to inventory accuracy in a rapidly evolving

industrial sector such as natural gas. This section focuses specifically on opportunities for 

improvement to the U.S. GHGI; to the extent other inventories use similar structure, data 

sources, and methods, the suggestions made here could be relevant to their improvement as well. 

As defined in Text Box 4-1, studies that can help to inform and improve the U.S. GHGI come 

from both top-down and bottom-up perspectives. Leveraging the multitude of these studies will 

benefit from careful analysis of any differences in design, scope and scale of each study. For 

context, Text Box 5-1 displays a summary of published top-down and bottom-up measurement 

studies. 

Bottom-up measurement studies provide estimates of emissions from particular categories of 

components, activities, or sites that can be used to develop or update the emission factors used in 

inventories. To easily do so, bottom-up studies should align either with existing inventory source 

category definitions or with available activity factor data. Top-down studies can help assess the 

accuracy of inventory estimates, though direct comparisons between these two forms of 

emissions estimates can be challenging. For instance, studies can be conducted at different 

spatial and temporal scales, and represent different sets of emission sources. Currently, top-down 

studies come at a high degree of emission source aggregation (i.e., representing emissions from a 

set of sources and sectors), providing limited feedback as to emission source category 

prioritization. Advances in downscaling top-down emission estimates have the potential to 

enhance the ability of top-down studies to aid inventory verification efforts. 

To elaborate on the sectoral alignment issue, inventories including the U.S. GHGI typically

categorize emissions by industry sectors, such as oil or natural gas. Similarly, the regulatory 

system in the United States is organized by industry sector, and thus inventory allocation of 

emissions to industrial sectors is necessary to support policy and regulatory decisions. However, 

hydrocarbons exist along a continuum of gaseous to liquid, often in the same geologic formation, 

making the task of categorizing a particular emission source as either in the oil or natural gas 

industrial sector challenging and arbitrary, with no consensus definition. This challenge is 

increasing as investment in liquids production (condensates, natural gas liquids, and oil) grows, 

increasing the scale of multi-product systems. Categorization of emissions by industry sector in 

locations where both are present creates challenges in allocating measured emissions to oil or 
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natural gas products, and also for the comparison of resulting measurements to an appropriate set 

of inventory source categories.31

 31 It should be noted that the U.S, EPA (2015a) has updated its methods for allocation. The 2014 U.S. GHGI (EPA 

2014a) categorizes GHGs emitted from oil wells also producing natural gas (i.e., associated gas wells) within the oil 

sector using the World Oil Magazine outlook for producing wells (2013). A revision for allocation in the 2015 U.S. 

GHGI (EPA 2015a) is to use a gas-to-oil production ratio greater than 100 Mcf/bbl to determine if a well is a gas 

wells rather than an oil well.

Text Box 5-1. Top-down and Bottom-up Studies 

Both top-down and bottom-up measurement studies can support inventory improvement, but in 

different ways. Figure 6 displays the locations of an exemplary set of 37 published measurement 

studies representing different segments of the U.S. natural gas supply chain. Many more studies are 

ongoing (e.g., EDF 2014). See section 7 for the numbered reference list of Figure 6.

Figure 6. Mapping of exemplary methane emission measurement studies published through 

February 2015 by type and location. See Section 7 for the numbered reference list. National 

studies and those not specific to a basin within a multi-state (AAPG-CSD) region are listed to the 

sides of the map under “United States” or “[region name]” headings. Studies conducted within 

unspecified areas within a named state are listed underneath the 2-letter state code. The portions of 

the natural gas supply chain covered by the studies are: production, boosting, and gathering: 20, 22-

29, 33, 36-39; transmission, storage, and processing: 2, 19-21, 29, 31-39; and distribution: 2, 21, 30, 

36, 37, 40, 41. [AAPG-CSD = American Association for Petroleum Geologists-Committee on Statistics 

of Drilling.]

*Only active plays relevant to current and known forthcoming studies are shown (Oil and Gas Journal 

2014). 
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EPA has stated its intention to evaluate new studies of methane emissions as they become 

available, as well as leverage resources collected through related EPA programs such as the 

GHG Reporting Program. In addition, inventory improvement efforts, such as those outlined in 

the President’s FY16 budget announced on January 14, 2015 (White House 2015), have the 

opportunity to improve the U.S. GHGI. Based on examination of available evidence and of 

current challenges to both bottom-up and top-down studies (Figure 6) as well as considering 

assumptions and limitations of inventories first described in Brandt et al. (2014), we identify a 

set of opportunities for improvement to estimates of methane emissions from the U.S. GHGI,

organized by type of study. Again, to the extent other inventories use similar structure, data 

sources, and methods, the suggestions made here could be relevant to their improvement as well. 

These recommendations can offer guidance for agencies, researchers and research-sponsoring 

organizations to consider when evaluating inventory improvement opportunities as well as 

designing studies that can help enhance inventory accuracy. 

Bottom-up Measurement and Inventory Studies 

1. Studies that fill known, prioritized gaps in current knowledge of natural gas-related 

methane emission sources, such as abandoned wells or measurements on gathering 

pipelines, can improve inventory completeness and accuracy. Another gap is leakage of 

natural gas after it is transferred to the end user, which at least one study (Wennberg et al. 

2012) suggests could be an important source of anthropogenic methane emissions. 

2. Studies that update emission factors for priority source categories can improve inventory 

accuracy. Prioritization considerations could include the degree of technological change 

since previous emissions factors were developed and contribution to total natural gas 

sector emissions. 

3. Accounting for variability of emission factor for sources within an emission source 

category, regional variation, and “fat tail” distributions requires enhanced bottom-up 

measurement study sampling strategies and inventory methods. Researchers and study 

sponsors should consider how to ensure robust sample size, strong sampling design to 

capture source variability and minimization of self-selection bias. Robustly incorporating 

heavily-skewed distributions in inventory methods could be advanced through statistical 

research on the quantitative methods underpinning inventories.

4. Inventory utilization of bottom-up measurement study results can be enhanced if the 

study has been designed to produce results that align with the existing inventory 

structure. Conversely, results from new studies might indicate areas where inventory 

structure could change to better reflect the latest insights regarding emission sources.

5. Activity factor accuracy can be improved by leveraging existing data sources (such as 

EPA’s GHGRP) or developing new data sources that can accurately, completely and 

directly (e.g., limiting use of surrogates for the activity) describe the industry’s activity. 

One consideration for changes in activity factor methods is how to maintain a consistent 

time series such that trends can be consistently assessed, balanced by the need to improve 

accuracy for today’s emission estimates. Another is to develop the empirical foundation 

for activity factor uncertainty estimation. 

6. Transparency and credibility of inventory estimates can be improved by bolstering 

uncertainty quantification of inventory models, including development of uncertainty 

assessment for emission and activity factors as methods change over time. Improving 
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inventory estimates of uncertainty relies on robust uncertainty estimates from the 

underlying emission and activity factor studies. 

7. Opportunities exist to enhance the inventory or develop derivative products to more 

easily align spatially, temporally and sectorally with atmospheric measurements. Such an 

effort could yield an improved validation function of top-down measurement studies. For 

instance, separating gathering systems from the production segment32 in inventory 

accounting and coordinating estimates of emissions from co-product systems such as colocated oil, liquids, and condensate production activities could improve alignment of 

inventory estimates to where, when and from what physical sources emit in the 

atmosphere. Developing a gridded inventory product, especially in conjunction with the 

aforementioned suggestions, could greatly improve the ability of top-down measurement 

studies to more accurately compare their results to those from the inventory. 

Top-down Measurement Studies

8. A key research need for top-down measurement studies is to improve and validate 

methods for attributing total methane concentrations in the atmosphere to their source, 

such as distinguishing between biogenic and fossil sources and then amongst the many 

fossil sources like coal, natural gas, and oil. Additionally, to aid in the attribution process, 

research is needed to better understand methane emissions from non-oil and gas sources. 

For instance, natural sources like geologic seepage share the same isotopic signature as 

anthropogenic fossil sources, and thus can confound attribution of fossil methane to fossil 

fuel infrastructure.

9. Another key research need for top-down measurement studies is strengthening methods 

to downscale the resolution of atmospheric measurements to those for specific sources 

such as individual facilities. Such advances can enhance the capability of top-down 

measurement studies to quantify emission source strength, variability, and prioritization

within a certain geographic area. One approach to achieve this goal is through pairing 

top-down and bottom-up measurement studies using various methods in the same region 

to leverage advantages of both approaches (e.g., comprehensive coverage through topdown measurements, detailed emission source specification through bottom-up 

measurements) and gain confidence in the ability of top-down methods to reflect 

emissions quantified directly.

Integration of Bottom-up and Top-down

10. Significant benefit could be gained for both bottom-up inventories and top-down 

measurements if they could be contemporaneously and systematically coordinated with 

aligned system boundaries (spatial, temporal and sectoral). Such an effort could reduce 

the consistent gap found by Brandt et al. (2014) between measurements and inventories; 

move toward the goal of top-down measurement studies capable of verification of 

inventory estimates of emissions; and increase confidence in inventory methods and their 

results that are used for policy and industrial decision making. 

 32 Proposed reporting rules in the GHGRP for gathering systems may help this differentiation of gathering and 

boosting if enacted (Federal Register 2014).
24

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29 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

7 Measurement and Inventory Study References (as 

Displayed in Figures 6 and C-1) 

Figure 6 - Top-down Measurement Studies

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15. WRAP. (2008a). Development of Baseline 2006 Emissions from Oil and Gas Activity in the

Denver-Julesburg Basin. Accessed January 

2015: http://www.wrapair.org/forums/ogwg/documents/2008-

04_'06_Baseline_Emissions_DJ_Basin_Technical_Memo_(04-30).pdf. 

WRAP. (2008b). Development of 2010 Oil and Gas Emissions Projections for the DenverJulesburg Basin. Environ International Corp. Accessed December 

2014: http://apcd.state.co.us/documents/deno308/2008-

04_2710_Projection_Emissions_DJ_Basin_Technical_Memo%2804-30%29.pdf. 

16. WRAP. (2009g). Development of Baseline 2006 and Midterm 2012 Emissions from Oil and

Gas Activity in the North San Juan Basin: Final Report. Environ International Corp. Accessed 

December 2014: http://www.wrapair.org/forums/ogwg/documents/NSanJuanBasin/2009-

09_06_Baseline_and_12_Midterm_Emissions_N_San_Juan_Basin_Technical_Memo_09-

01.pdf.

17. WRAP. (2009c). Development of Baseline 2006 Emissions from Oil and Gas Activity in the

South San Juan Basin: Final Report. Environ International Corp. Accessed December 

2014: http://www.wrapair.org/forums/ogwg/documents/SSanJuanBasin/2009-

11y_06_Baseline_S_San_JuanBasin_Technical_Memo_11-25R.pdf. 

WRAP. (2009d). Development of 2012 Oil and Gas Emissions Projections for the South San 

Juan Basin: Final Report. Environ International Corp. Accessed December 

2014: http://www.wrapair.org/forums/ogwg/documents/SSanJuanBasin/2009-

12_12%20Projections_S_SanJuan_Basin_Technical_Memo_12-08R.pdf. 

18. New Mexico Environment Department. 2006. Ozone Precursor Emission Inventory for San

Juan and Rio Arriba Counties, New Mexico. Prepared by Environ International Corp. and 

Eastern Research Group. Accessed January 

2015: http://www.wrapair.org/forums/ogwg/documents/NM_Area_Emissions_report.pdf. 

19. Texas Commission for Environmental Quality (TCEQ). (2007). Emissions from Oil and Gas

Production Facilities: Final Report. 582-7-84003. Prepared by Eastern Research Group. 

Accessed December 
37 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2014: http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/58207

84003FY0701-20090831-ergi-ei_from_old_gas_facilities.pdf. 

20. TCEQ. (2009a). Drilling Rig Emission Inventory for the State of Texas: Final Report.

Prepared by Eastern Research Group. Accessed November 

2014: http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/58207

83985FY0901-20090715-ergi-Drilling_Rig_EI.pdf. 

21. TCEQ. (2011). Appendix 8: Development of Texas Statewide Drilling Rigs Emission

Inventories for the Years 1990, 1993, 1996, and 1999 Through 2040: Final Report. 582-11-

99776. Prepared by Eastern Research Group. Accessed December 

2014: http://www.tceq.texas.gov/assets/public/implementation/air/sip/dfw/dfw_rfp_sip_2015/DF

WRFP_SIP_Appendix_8.pdf. 

22. Zavala-Araiza, D.; Sullivan, D.; Allen, D. (2014). “Atmospheric Hydrocarbon Emissions and

Concentrations in the Barnett Shale Natural Gas Production Region.” Environmental Science and 

Technology 48 (9); pp. 5314–5321. Accessed December 

2014: http://pubs.acs.org/doi/abs/10.1021/es405770h. 

23. Heath, G.; Meldrum, J.; Fisher, N. (2012) “Chapter 1: Life Cycle Greenhouse Gas Emissions

from Barnett Shale Gas Used to Generate Electricity.” In: Joint Institute for Strategic Energy 

Analysis (JISEA). Natural Gas and the Transformation of the U.S. Energy Sector: Electricity. 

Logan, J.; Heath, G.; Macknick, J.; Paranhos, E.; Boyd, W.; Carlson, K. NREL/TP-6A50-55538. 

Golden, CO: National Renewable Energy Laboratory. Accessed October 

2014: http://www.nrel.gov/docs/fy13osti/55538.pdf. 

24. EDF. (2009). Emissions from Natural Gas Production in the Barnett Shale Area and

Opportunities for Cost-Effective Improvements. Accessed December 

2014: http://www.edf.org/sites/default/files/9235_Barnett_Shale_Report.pdf. 

25. TCEQ. (2009c). 2009 Barnett Shale Phase I Inventory County Count of Sources by Profile.

Accessed January 2015: http://www.tceq.state.tx.us/airquality/point-source-ei/psei.html#barnett

26. TCEQ. (2014). Oil and Gas Emission Inventory, Eagle Ford Shale. Prepared by the Alamo

Area Council of Governments. Accessed November 

2014: http://www.aacog.com/documentcenter/view/19069. 

27. Arkansas Department of Environmental Quality (ADEQ). ( 2011a). Emissions Inventory &

Ambient Air Monitoring of Natural Gas Production in the Fayetteville Shale Region. Accessed 

January 

2015: http://www.adeq.state.ar.us/air/pdfs/fayetteville_shale_air_quality_report_tagged.pdf. 

ADEQ. (2011b). Emissions Inventory & Ambient Air Monitoring of Natural Gas Production in 

the Fayetteville Shale Region. Conference Paper. Accessed January 

2015: http://www.epa.gov/ttnchie1/conference/ei20/session6/dlyon.pdf. 
38 

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28. TCEQ. (2009b). Development of an Emission Inventory for Natural Gas Exploration and

Production in the Haynesville Shale. Draft Report. Accessed January 

2015: http://www.netac.org/UserFiles/File/NETAC/9_29_09/Enclosure_2b.pdf. 

29. U.S. Department of the Interior (DOI). (2004). Data Quality Control and Emissions

Inventories of OCS Oil and Gas Production Activities in the Breton Area of the Gulf of Mexico: 

Final Report. Prepared by Eastern Research Group. Accessed December 

2014: www.data.boem.gov/PI/PDFImages/ESPIS/2/3008.pdf. 

30. Pennsylvania Department of Environmental Protection. (2012). 2012 Natural Gas Emissions

Inventory by Source Type. Accessed January 

2015: http://www.portal.state.pa.us/portal/server.pt/community/Emission%20Inventory/21810/M

arcellus%20Inventory/1829967. 

31. Rose, Adam, Brent Yarnal, Robert Neff, Howard Greenberg, Mohammed Kharbach, and

Cheng-Hau Peng. (2003). Greenhouse Gas Emissions Inventory for Pennsylvania Phase I 

Report. Harrisburg, PA: Pennsylvania Department of Environmental Protection. Accessed 

January 

2015: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CB8QFj

AAahUKEwir7qOfqabHAhUEdT4KHQpDBOc&url=http%3A%2F%2Fwww.dli.state.pa.us%2

Fportal%2Fserver.pt%2Fgateway%2FPTARGS_0_2_903559_0_0_18%2Fgreenhousegasemissi

onsinventory.pdf&ei=vrDMVauXJ4TqQGKhpG4Dg&usg=AFQjCNGiADJ2nK4D0NCoOKIYc66Is_mqvg&bvm=bv.99804247,d.cW

w&cad=rja. 
39

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix A. U.S. GHGI Data

Figures A-1 – A-3 show U.S. GHGI estimates of methane emissions from production, gathering, 

and boosting; processing; and transmission and storage, respectively (EPA 2014a). These figures 

show fugitives and venting or combustion exhaust emissions for each emission source category. 

Many emission categories shown are aggregations of the nearly 110 source categories in the U.S. 

GHGI. As much as feasible, we based our labeling of these emission source categories on the 

terminology and categorization of emissions used in the U.S. GHGI. For clarity and consistency 

across figures we have made some modifications to the U.S. GHGI terminology.

Tables A-1 – A-4 show how methane emission estimates from the U.S. GHGI are processed for 

use in Figures A-1 – A-3 and Figure 4. Data processing involved transformation of units of 

measure, application of emission reductions from policies and voluntary industry actions (EPA 

2014a), and aggregation of emission source categories to simplify the figures.
40 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table A-1. U.S. GHGI Estimates of Natural Gas Methane (CH4) Emissions in 2012 from the Production and Gathering and Boosting 

Segment of the Natural Gas Supply Chain. Light orange cells indicate where we have summed emissions reductions from voluntary and 

required emission reductions reported in U.S. GHGI. Dark orange cells show emission reduction we calculated from the “Other Production” 

emission reductions reported in the U.S. GHGI. 33 Green cells indicate that the U.S. GHGI directly reported net emissions for this category.

Emission 

Type Emission Source Category PotentialEmissions

Emissions

Reductions Net Emissions Figure A-1 Emission Source Categories

Aggregated Net 

Emissions

Gg CH4/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr

Fugitives

Non-Associated Gas Wells (less wells with hydraulic fracturing) 18 0.46 -0.25 0.21

Onshore wells & Sep. 3.9

Gas Wells with Hydraulic Fracturing 35 0.89 -0.47 0.42

Field Separation Equipment - Heaters 33 0.83 -0.44 0.39

Field Separation Equipment - Separators 107 2.7 -1.4 1.3

Field Separation Equipment - Dehydrators 33 0.82 -0.43 0.39

Field Separation Equipment - Meters/Piping 106 2.7 -1.4 1.3

Small Reciprocating Compressors 71 1.8 -0.94 0.83

Large Reciprocating Compressors 15 0.39 -0.21 0.18 Recip. compres. stations 1.0

Large Reciprocating Stations 1 0.03 -0.01 0.01

Pipeline Leaks 176 4.4 0.00 4.4 Pipeline leaks 4.4

Vented

Gas Well Completions without Hydraulic Fracturing 0.00 0.00 0.00 0.00

Drilling, compl. & work. 5.5

Gas Well Work-overs without Hydraulic Fracturing 0.50 0.01 -0.01 0.01

Hydraulic Fracturing Completions and Work-overs that Vent 192 4.8 0.00 4.8

Flared Hydraulic Fracturing Completions and Work-overs 3.9 0.10 0.00 0.1

Hydraulic Fracturing Completions and Work-overs with RECs 10 0.26 0.00 0.26

Hydraulic Fracturing Completions and Work-overs with RECs that flare 11 0.27 0.00 0.27

Well Drilling 1 0.02 -0.01 0.01

Produced Water from Coal Bed Methane Wells in Powder River 47 1.2 -0.63 0.55 CBM produced water 0.7 Produced Water from Coal Bed Methane Wells in Black Warrior 13 0.32 -0.17 0.15

Pneumatic Device Vents 1,208 30 -22 8.4 Pneum. devices 8.4

Chemical Injection Pumps 67 1.7 -0.07 1.6 Chem. inject. pumps 1.6

Kimray Pumps 388 9.7 -5.1 4.6 Kimray pumps 4.6

Dehydrator Vents 121 3.0 -0.97 2.1 Dehydrators 2.1

Condensate Tanks without Control Device 188 4.7 -1.3 3.4 Conden. tanks 4.1 Condensate Tanks with Control Device 38 0.94 -0.25 0.69

Gas Engine Exhaust 266 6.6 -3.5 3.1 Gas engine exhaust 3.2

Liquids Unloading (with plunger lifts) 119 3.0 0.00 3.0 Liquids unloading 6.8 Liquids Unloading (without plunger lifts) 155 3.9 0.00 3.9

Vessel Blowdown 0.70 0.02 -0.01 0.01

Blowdowns & upsets 0.24 

Pipeline Blowdown 2.8 0.07 -0.04 0.03

Compressor Blowdown 2.7 0.07 -0.04 0.03

Compressor Starts 6.1 0.15 -0.01 0.14

Pressure Relief Valves 0.7 0.02 -0.01 0.01

Mishaps 1.5 0.04 -0.02 0.02

Fugitives and 

Vented

Shallow Water Gas Platforms (Gulf of Mexico & Pacific) 266 6.6 -3.5 3.1 Offshore wells 3.4 Deepwater Gas Platforms (Gulf of Mexico & Pacific) 23 0.58 -0.31 0.27

Acronyms: CBM = coal bed methane; CH4 = methane; chem. inject. = chemical injection; compl. = well completions; cond. = condensate; Gg = 

gigagrams; MMt CO2e = million metric tonnes carbon dioxide equivalent; pneum. = pneumatic; REC = reduced emission completions; recip. 

compres. = reciprocating compressor; sep. = field separation equipment; work. = well workovers. 

Source: EPA 2014a

33 The emission reductions reported in the table include an allocation of the “Other Production” emission reduction made in proportion to the contribution of an 

emission source category to total potential emissions for Production, Gathering and Boosting.
41

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure A-1. U.S. GHGI estimates of natural gas methane emissions in 2012 from the production and gathering and boosting segment of 

the natural gas supply chain. Acronyms: CBM = coal bed methane; CH4 = methane; chem. inject. = chemical injection; compl. = well 

completions; cond. = condensate; MMt CO2e = million metric tonnes carbon dioxide equivalent; pneum. = pneumatic; recip. compres. = 

reciprocating compressor; sep. = field separation equipment; work. = well workovers. 

Source: EPA 2014a34

 34 Figure A-1 is created from aggregated U.S. GHGI data shown in Table A-1 in Appendix A.
42

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table A-2. U.S. GHGI Estimates of Natural Gas Methane (CH4) Emissions in 2012 from the Processing Segment of the Natural Gas 

Supply Chain. Light orange cells indicate where we have summed emissions reductions from voluntary and required emission reductions 

reported in U.S. GHGI. Dark orange cells shows the emission reduction we calculated from the “Other Processing” emission reduction category 

reported in the U.S. GHGI. 35

Emission 

Type Emission Source Category PotentialEmissions

Emissions

Reductions Net Emissions Figure A-2 Emission Categories

Aggregated Net 

Emissions

Gg CH4/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr

Fugitives

Processing Plants 34 0.84 -0.28 0.56 Processing plants 0.56

Reciprocating Compressors 442 11 0.00 11 Reciprocating compressors 11

Centrifugal Compressors (wet seals) 238 5.9 -2.0 4.0 Centrifugal compressors 4.7 Centrifugal Compressors (dry seals) 44 1.1 -0.37 0.73

Vented

Gas Engine Exhaust 187 4.7 -0.15 4.5 Gas E&T exhaust 4.6 Gas Turbine Exhaust 5.3 0.13 -0.04 0.09

AGR Vents 13 0.33 0.00 0.33 AGR units 0.33

Kimray Pumps 5.0 0.13 -0.04 0.08 Kimray pumps 0.08

Dehydrator Vents 31 0.77 -0.64 0.13 Dehydrators 0.13

Pneumatic Devices 1.9 0.05 -0.02 0.03 Pneumatic devices 0.03

Blowdowns 47 1.2 -0.40 0.79 Blowdowns 0.79

Acronyms: AGR = acid gas removal; CH4 = methane; E&T = engine and turbine; Gg = gigagrams; MMt CO2e = million metric tonnes carbon 

dioxide equivalent

Source: EPA 2014a

 35 The emission reductions reported in the table include an allocation of the “Other Processing” emission reductions made in proportion to the contribution of an 

emission source category to total potential emissions from the Processing segment.
43 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure A-2. U.S. GHGI estimates of natural gas methane emissions in 2012 from the processing segment of the natural gas supply chain.

Acronyms: AGR = acid gas removal; CH4 = methane; ET = engine and turbine; MMt CO2e = million metric tonnes carbon dioxide equivalent.

Source: EPA 2014a36

36 Figure A-2 is created from aggregated U.S. GHGI data shown in Table A-2 in Appendix A.
44

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table A-3. U.S. GHGI Estimates of Natural Gas Methane (CH4) Emissions in 2012 from the Transmission and Storage Segment of the

Natural Gas Supply Chain. Light orange cells indicate where we have summed emissions reductions from voluntary and required emission 

reductions reported in U.S. GHGI. Dark orange cells indicate emission reductions we calculated through an allocation procedure from a more 

general category. 37 Red cells indicate where we used a different allocation procedure to calculate the reported emission reductions.38 Purple cells 

indicate that for the aggregation of net emissions, we allocated “compressor station” fugitives between reciprocating and centrifugal compressors 

in proportion to total net emissions.

Emission 

Type Emission Source Category PotentialEmissions

Emissions

Reductions Net Emissions Figure A-3 Emission Categories

Aggregated Net 

Emissions

Gg CH4/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr

Fugitives

Compressor Stations (T] 111.00 2.78 -0.48 2.30 - - Compressor Stations (S) 52.00 1.30 -0.22 1.08

Compressor Stations (T) - Reciprocating Compressor 773.30 19.33 -0.01 19.32 Reciprocating 

compressor stations 25.81 Compressor Stations (S) - Reciprocating Compressor 150.20 3.76 0.00 3.75

Compressor Stations (T) - Centrifugal Compressor (wet seals) 232.80 5.82 -1.00 4.82

Centrifugal 

compressor stations 6.35 Compressor Stations (S) - Centrifugal Compressor (wet seals) 22.30 0.56 -0.10 0.46

Compressor Stations (T) - Centrifugal Compressor (dry seals) 15.00 0.38 -0.06 0.31

Compressor Stations (S) - Centrifugal Compressor (dry seals) 6.50 0.16 -0.03 0.13

Storage Wells 12.90 0.32 -0.06 0.27

Pipelines and storage 

wells 2.26 Pipelines Leaks 3.30 0.08 -0.01 0.07

Meters & Regulators - Transmission Interconnect 75.60 1.89 -0.33 1.56

Meters & Regulators - Farm Taps & Direct Sales 17.50 0.44 -0.08 0.36

LNG Storage Stations 10.60 0.27 -0.05 0.22

LNG compressor 

stations 1.50

LNG Storage Reciprocating Compressors 40.10 1.00 -0.17 0.83

LNG Storage Centrifugal Compressors 13.80 0.35 -0.06 0.29

LNG Import Terminal Stations 1.20 0.03 -0.01 0.02

LNG Import Terminal Reciprocating Compressors 5.60 0.14 -0.02 0.12

LNG Import Terminal Centrifugal Compressors 1.40 0.04 -0.01 0.03

Vented

Dehydrator Vents (T) 2.10 0.05 -0.01 0.04 Dehydrators 0.13 Dehydrator Vents (S) 4.00 0.10 -0.02 0.08

Engine (T) Exhaust 235.30 5.88 -2.85 3.03

E-T-G exhaust 3.56

Turbine (T) Exhaust 1.30 0.03 -0.01 0.03

Engine (S) Exhaust 20.30 0.51 -0.25 0.26

Turbine (S) Exhaust 0.20 0.01 0.00 0.00

Generator (Engines) Exhaust 11.50 0.29 -0.05 0.24

Generator (Turbines) Exhaust 0.00 0.00 0.00 0.00

Pneumatic Devices (T) 221.30 5.53 -0.35 5.18 Pneumatic devices 6.05 Pneumatic Devices (S) 42.30 1.06 -0.18 0.87

Pipeline Maintenance 184.80 4.62 -2.50 2.12 Upsets 2.12

Compressor Station (T) 151.10 3.78 -0.65 3.13 Compressor stations 3.72 Compressor Station (S) 28.90 0.72 -0.12 0.60

LNG Storage Engines Exhaust 2.70 0.07 -0.01 0.06

LNG Storage Turbines Exhaust 0.00 0.00 0.00 0.00 LNG E&T exhaust 0.26

LNG Storage Station 5.90 0.15 -0.03 0.12

 37 The emission reductions reported in the table include an allocation of the “Other Transmission and Storage” emission reductions made in proportion to the 

contribution of an emission source category to total potential emissions for Transmission and Storage segment.

38 The emission reductions reported in the table include an allocation of emission reductions for “Reciprocating Engines” to transmission stations’ and storage 

facilities’ emission reductions in proportion to their contribution to total potential emissions for all reciprocating compressors in this segment. An analogous 

procedure was used for allocating emissions reductions for “Engine Exhaust”. 
45

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Emission 

Type Emission Source Category PotentialEmissions

Emissions

Reductions Net Emissions Figure A-3 Emission Categories

Aggregated Net 

Emissions

Gg CH4/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr

LNG Import Terminals Engines Exhaust 3.60 0.09 -0.02 0.07

LNG Import Terminals Turbines Exhaust 0.00 0.00 0.00 0.00

LNG Import Terminals Station 0.60 0.02 0.00 0.01

Acronyms: CH4 = methane, E&T = engine and turbine; E-T-G = engine, turbine and generator; Gg = gigagrams; LNG = liquefied natural gas; 

MMtCO2e = million metric tonnes carbon dioxide equivalent; T = transmission; S = storage.

Source: EPA 2014a
46 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure A-3. U.S. GHGI estimates of natural gas methane emissions in 2012 from the transmission and storage segment of the natural 

gas supply chain. Acronyms: CH4 = methane, ET = engine and turbine; ETG = engine, turbine and generator; LNG = liquefied natural gas; 

MMtCO2e = million metric tonnes carbon dioxide equivalent.

Source: EPA 2014a39

39 Figure A-3 is created from aggregated U.S. GHGI data shown in Table A-3 in Appendix A.
47 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table A-4. U.S. GHGI estimates of natural gas methane (CH4) emissions in 2012 from the distribution segment of the natural gas supply 

chain. Light orange cells indicate the U.S. GHGI had an emission reduction due to existing control measures. Dark orange cells indicate that the 

U.S. GHGI had an “Other Distribution” emission reduction that we allocated to each emission source category in proportion to its potential 

emissions relative to total potential emissions.

Emission 

Type Emission Source Category PotentialEmissions

Emissions

Reductions Net Emissions Figure 4 Emission Categories Aggregated Net Emissions

MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr MMt CO2e/yr

Fugitives

Mains—Cast iron 149 3.7 0.00 3.7 Cast iron main lines 3.7

Mains—Unprotected steel 135 3.4 -0.14 3.2 Unprotected steel main & service 

lines 6.3

Services—Unprotected steel 128 3.2 -0.13 3.1

Mains—Protected steel 29 0.72 -0.03 0.69

Protected steel main & service lines 1.9

Services Protected steel 51 1.3 -0.05 1.2

Mains—Plastic 126 3.2 -0.13 3.0

Plastic pipe & service lines 3.2

Services—Plastic 8.1 0.20 -0.01 0.19

Services—Copper 4.9 0.12 0.00 0.12 Copper service lines 0.12

Meters & regulators >300 105 2.6 -0.11 2.5

Meters & regulators (city gates) 12

Meters & regulators 100-300 203 5.1 -0.21 4.9

Meters & regulators <100 4.9 0.12 0.00 0.12

Regulators >300 104 2.6 -0.10 2.5

Regulator-vault >300 0.50 0.01 0.00 0.01

Regulators 100-300 78 2.0 -0.08 1.9

Regulator-vault 100-300 0.20 0.01 0.00 0.00

Regulators 40-100 6.0 0.15 -0.01 0.14

Regulators-vault 40-100 0.40 0.01 0.00 0.01

Regulators <40 0.30 0.01 0.00 0.01

Residential customer meters 99 2.5 -0.10 2.4 Customer meters 2.5 Commercial/industry customer meters 4.1 0.10 0.00 0.10

Vented

Pressure relief valve releases 1.2 0.03 0.00 0.03

Pipeline blowdown 2.3 0.06 0.00 0.06 Blowdowns & upsets 0.95

Upsets/mishaps (dig-ins) 35 0.89 -0.02 0.87

Acronyms: CH4 = methane, Gg = gigagrams; MMt CO2e = million metric tonnes carbon dioxide equivalent; M&R = meters and regulators.

Source: EPA 2014a

Gg CH4/yr
48

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix B. Inventory Studies

Figure B-1 shows other regional and state emission inventories in the United States. Inventories 

including GHG emissions as well as other emissions are included. As illustrated below, most 

inventories focused on criteria air pollutants like nitrous oxides and particulate emissions. Most 

GHG inventories are also focused on a small geographic area.

Figure B-1. Emission inventory studies through May 2014, aggregated by the type of emissions

included and by location. See Section 7 for the numbered inventory study reference list. National 

studies are listed to the side of the map. Studies conducted within unspecified areas are shown labeled 

by states in darker grey. The portions of the natural gas supply chain covered by the studies are: 

production, gathering, and boosting: all studies; transmission, storage and processing: 1, 3, 4, 23, 24, 27, 

31; and distribution: 1, 3, 23, 31. 

* Non-GHGs include VOCs, HAPs, PM, and others. 

** Only active plays relevant to current and known forthcoming studies are shown (Oil and Gas Journal

2014).