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U.S. POLICY ACTION NECESSARY TO ENSURE 

ACCURATE ASSESSMENT OF THE AIR 

EMISSION REDUCTION BENEFITS OF 

INCREASED USE OF ENERGY EFFICIENCY 

AND RENEWABLE ENERGY TECHNOLOGIES

Debra Jacobson & Colin High!

INTRODUCTION

Policies encouraging energy efficiency and 

renewable energy (“EERE”) technologies need to 

ensure that the air emission reduction benefits of these 

technologies are calculated accurately. Otherwise, the 

market will not fully capture their important economic 

and social benefits. 

Federal, state, and local governments and private 

entities have developed protocols for reporting 

greenhouse gases (“GHG”) and other air emissions.1

Many of these protocols have been designed to allow 

emissions from individual sources to be aggregated into 

total emissions for an entire entity, such as a 

corporation or a governmental body, while 

! This Article was prepared by Debra Jacobson and Colin High, 

and most of its underlying research was funded by the Clean 

Energy/Air Quality Integration Initiative of the U.S. Department of 

Energy’s (“DOE”) Office of Energy Efficiency and Renewable 

Energy. Although we appreciate DOE’s strong support for our 

work, the views expressed in this Article are those of the authors 

alone. 

Ms. Jacobson is the owner of DJ Consulting, LLC and 

conducted the primary work for the Article in that capacity. She is 

also Co-Director of the Solar Institute at The George Washington 

University (“GW”), a Research Professor at GW, and a 

Professorial Lecturer in Law at GW Law School. The Solar 

Institute provided some additional support in the final stages of this 

Article. Ms. Jacobson can be contacted at 

djacobson@law.gwu.edu. 

Dr. High is Chairman of Resource Systems Group, Inc. 

(“RSG”) in White River Junction, Vermont. RSG compiled the 

initial database and designed the software used to support the Time 

Matched Marginal (“TMM”) methodology discussed in this Article 

and used in the final stages of this project. Dr. High can be 

contacted at chigh@rsginc.com 

1 See, e.g., WORLD RES. INST. & WORLD BUS. COUNCIL FOR 

SUSTAINABLE DEV., THE GREENHOUSE GAS PROTOCOL: A

CORPORATE ACCOUNTING AND REPORTING STANDARD 3 (2004)

[hereinafter GREENHOUSE GAS PROTOCOL], available at 

http://pdf.wri.org/ghg_protocol_2004.pdf. 

appropriately emphasizing consistency, transparency, 

and the use of publicly available data. When measuring 

indirect emissions from electric power generation, these 

protocols have widely relied on total output emissions 

rates (the “eGRID system average methodology”2),

which are based on information in the Emissions & 

Generation Resource Integrated Database (“eGRID”). 

The eGRID system average methodology is acceptable 

for reporting by corporations, governmental bodies, or 

other entities when they are aggregating emissions from 

all sources into an inventory.3

However, inadequate attention has been focused on 

methodologies for measuring the air emission reduction 

benefits of increased use of EERE and other lowemission technologies. This Article, presenting results 

of original research completed in 2008 and 2009, 

demonstrates that many individuals have misapplied the 

most commonly used methodology—the eGRID system 

average methodology. The eGRID system average 

methodology undervalues the GHG emission reduction 

benefits of increased use of EERE technologies in most 

regions of the country.4

This Article demonstrates that the eGRID system 

average methodology, when compared to the Resource 

System Group’s Time Matched Marginal emissions 

methodology,5 understates the carbon dioxide (“CO2”) 

and nitrogen oxide (“NOx”) emission reduction benefits 

of five EERE technologies6 by approximately 65% to 

2 As used in this Article, the term “eGRID system average 

methodology” is not a methodology “of” eGRID, but refers to a 

methodology that uses eGRID total output emission rates when 

verified, utility-specific emissions data is not available. See 

generally U.S. Environmental Protection Agency, eGRID 

Frequently Asked Questions, 

http://www.epa.gov/cleanenergy/energy-resources/egrid/faq.html 

(last visited Mar. 25, 2010) [hereinafter eGRID Frequently Asked 

Questions] (explaining that eGRID as used by EPA is an inventory 

of air emission data based on information from US electricitygenerating plants). 

3 See infra Part I.A (discussing two types of GHG protocols). 

4 See infra Part II. 

5 See infra Part I.B.3. 

6 The five technologies studied in this 2008 and 2009 research 

work are wind energy, solar photovoltaic energy (“PV”), highefficiency commercial air conditioning, high-efficiency 

commercial lighting, and LED traffic signal retrofits. See infra

Part II (introductory text). 

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165% in the two power markets studied.7 This Article 

also concludes that the emission reduction benefits of 

increased use of EERE technologies calculated with the 

Environmental Protection Agency’s (“EPA”) eGRID 

non-baseload methodology also are significantly higher 

than those calculated with the eGRID system average 

methodology.8 Recent studies across all regions 

confirm that these findings are not isolated results but 

are indicative of widespread misapplication of the 

eGRID system average methodology by many users.9

Although the eGRID system average methodology 

is generally appropriate for estimating indirect 

emissions from electricity purchases (“scope 2 

emissions”) for general emission inventory purposes,10

it does not accurately estimate the year-to-year impacts 

on GHG levels resulting from increased use of EERE 

technologies. The Climate Registry,11 hundreds of 

local governments, and others have relied on the 

eGRID system average methodology since 2008 in their 

protocols for estimating emissions from electricity 

purchases in their GHG emission inventories.12 This 

Article recommends that The Climate Registry consider 

developing an additional protocol and registry during 

the 2010 planned revision of its General Reporting 

Protocol to more accurately reflect the emission 

reduction benefits from increased use of EERE 

technologies by states and other entities. 

Similarly, EPA should consider developing an 

additional protocol in its mandatory GHG reporting rule 

to fully value emission reduction benefits from 

increased use of EERE technologies. Specifically, 

regulatory agencies should use a methodology that 

calculates emission reductions at marginal units.13

Ideally, these emission reductions should be calculated 

on an hourly basis. 

7 The study focused on the PJM Interconnection and the Upstate 

New York power markets. See infra Part II. 

8 See infra Part I.B.2 and Table 1. 

9 See infra Part II. 

10 THE CLIMATE REGISTRY, THE GENERAL REPORTING PROTOCOL 

FOR THE VOLUNTARY REPORTING PROGRAM, VERSION 1, at 97

(2008), available at 

http://www.theclimateregistry.org/downloads/GRP.pdf. 

11 Id. at 8, 99.

12 See infra Part III.A. 

13 Marginal units are fossil fuel-fired units whose power output 

varies with the overall level of power demand over time. See 

WORLD RES. INST. & WORLD BUS. COUNCIL FOR SUSTAINABLE 

DEV., GUIDELINES FOR QUANTIFYING GHG REDUCTIONS FROM 

GRID-CONNECTED ELECTRICITY PROJECTS 13-14, 54-57 (2007) 

[hereinafter QUANTIFYING GHG REDUCTIONS], available at 

http://pdf.wri.org/GHGProtocol-Electricity.pdf. 

Finally, this Article recommends addressing these 

problems in a cost-effective manner. An investment of 

limited funds by the Department of Energy (“DOE”), 

EPA, or state agencies to create an enhanced database 

of marginal electric generating units would enable 

government agencies and businesses to more accurately 

evaluate the cost-effectiveness of EERE programs in 

reducing emissions. 

I. BACKGROUND

A. Role of EERE in Reducing Air Emissions from 

Electric Power Generation 

The electric power sector is responsible for 

approximately forty percent of the CO2 emissions in the 

United States14 as well as significant direct and indirect 

emissions of methane and other greenhouse gases.15

According to leading energy experts, the electric power 

sector requires a dramatic transformation to meet 

national targets for GHG emission reductions over the 

next several decades.16 Adopting energy efficiency and 

zero or low-carbon emission technologies, such as 

renewable energy, is a key part of this transformation.17

For example, the International Energy Agency has 

stated that a “global revolution is needed in ways that 

energy is supplied and used,” and it has cited energy 

efficiency and renewable energy as central to this 

energy revolution.18 Building and product efficiency 

measures, such as high-efficiency lighting and 

appliances, and dramatic increases in the use of wind 

and solar energy, are among the high priority 

technologies cited by the Agency.19 In addition, many 

promising strategies for reducing GHG emissions from 

14 ENERGY INFO. ADMIN., ANNUAL ENERGY OUTLOOK 2010,

EARLY RELEASE OVERVIEW 11 (2009), available at 

http://www.eia.gov/oiaf/aeo/pdf/overview.pdf.

15 See U.S. ENVTL. PROT. AGENCY, eGRID 2007, YEAR 2005

SUMMARY TABLES 1 (2008) [hereinafter EGRID SUMMARY 

TABLES], available at 

http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2007

V1_1_year05_SummaryTables.pdf.

16 See INT’L ENERGY AGENCY, LAUNCHING AN ENERGY 

REVOLUTION IN A TIME OF ECONOMIC CRISIS: THE CASE FOR A 

LOW-CARBON ENERGY TECHNOLOGY PLATFORM 3 (2009), 

available at 

http://www.iea.org/G8/docs/Energy_Revolution_g8july09.pdf.

17 Id.

18 INT’L ENERGY AGENCY, ENERGY TECHNOLOGY PERSPECTIVES 

2008: SCENARIOS AND STRATEGIES TO 2050 IN SUPPORT OF THE G8

PLAN OF ACTION 1-7 (2008). 

19 Id. 

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the transportation sector involve advanced energy 

technologies, such as plug-in electric hybrid vehicles 

and electric cars, which reduce petroleum use through 

electrification.20

Implementing these strategies will increase the 

importance of ensuring the accurate assessment of the 

effects of EERE technologies on the electric grid, 

particularly hourly and seasonal differences in GHG 

emissions. In order to comprehensively report GHG 

emissions and to measure cost-effectiveness, it is 

necessary to improve the accuracy and lower the costs 

of methodologies applied by state and local 

governments and the private sector to estimate the 

emission reduction benefits of increased use of EERE 

technologies.21

Other air quality goals, including reducing ozone 

levels, also can be advanced by increasing use of EERE 

technologies and practices.22 Many of these air quality 

problems are most serious during certain seasons, such 

as the summer ozone season, and certain hours of the 

day, such as summer afternoons.23 Federal, state, and 

local agencies benefit from accurate and low-cost 

methodologies to estimate the impact of EERE 

strategies in the air quality planning process.24

In all energy sectors, it is important not only to use 

improved methods but also to obtain agreement on 

consistent protocols for quantification and reporting 

GHG emissions.25 The need for more accurate 

methodologies intensified when the EPA adopted the 

first mandatory GHG reporting rules last year.26

20 INT’L ENERGY AGENCY, TRANSPORT, ENERGY, AND CO2:

MOVING TOWARDS SUSTAINABILITY, EXECUTIVE SUMMARY 32-33 

(2009), available at 

http://www.iea.org/Textbase/npsum/transport2009SUM.pdf. 

21 See infra Part I.B.

22 U.S. ENVTL. PROT. AGENCY, GUIDANCE ON STATE 

IMPLEMENTATION PLAN (SIP) CREDITS FOR EMISSION REDUCTIONS 

FROM ELECTRIC-SECTOR ENERGY EFFICIENCY AND RENEWABLE 

ENERGY MEASURES 1 (2004) [hereinafter GUIDANCE ON STATE 

IMPLEMENTATION PLAN]. 

23 DEBRA JACOBSON & COLIN HIGH, ROLE OF ENERGY 

EFFICIENCY PROGRAMS IN BUILDINGS IN ADDRESSING AIR 

QUALITY AND GREENHOUSE GAS REDUCTION GOALS 1-3 (2008)

[hereinafter ROLE OF ENERGY EFFICIENCY PROGRAMS], available 

at 

http://apps1.eere.energy.gov/wip/pdfs/tap_webcast_20080717_fact

_sheet.pdf. 

24 See infra Part III. 

25 See generally id. (discussing technological improvements that 

would enhance federal and local air quality programs). 

26 Mandatory Reporting of Greenhouse Gases, 74 Fed. Reg. 

56,260 (Oct. 30, 2009) (to be codified at 40 C.F.R pt. 98); see also

infra Part III.B. 

Moreover, the EPA is now considering options to 

expand its GHG registry to include indirect emissions 

from electricity purchases.27

There are currently two types of protocols relating 

to GHG emissions from the use of electric power. 

Confusion about the appropriate use of these two types 

of protocols lies at the heart of the issues discussed in 

this Article. 

The first type of protocol is an emissions inventory 

to provide accounting of GHG emissions for a 

corporation or other entity.28 The protocol is designed 

to facilitate reporting by the owners or operators of 

individual units, such as business plants and 

government buildings, so that these emissions can be 

aggregated for an entire corporation or governmental 

unit.29 This protocol typically requires reporting of 

both indirect emissions and direct emissions.30 Indirect 

emissions, also known as Scope 2 emissions, are air 

emissions that result from activities, such as electricity 

purchases, of one entity that occur at sources owned or 

controlled by another entity, such as an electric 

generating plant.31 In contrast, direct emissions, also 

known as Scope 1 emissions, are air emissions from 

sources owned or operated by the reporting entity, such 

as GHG emissions from the smokestacks owned by a 

power generating company.32

The Greenhouse Gas Protocol of the World 

Resources Institute and the General Reporting Protocol

of The Climate Registry are examples of emissions 

inventories that track both scope 1 and scope 2 

emissions.33 Electricity emission factors from the 

eGRID system average database are often used under 

these protocols to represent the amount of GHGs 

27 Mandatory Reporting of Greenhouse Gases, 74 Fed. Reg. at 

56,288. 

28 GREENHOUSE GAS PROTOCOL, supra note 1, at 3. 

29 Id.

30 Id. at 26-27. Under reporting standards developed by the 

World Resources Institute and the World Business Council for 

Sustainable Development, operational boundaries have been 

established with respect to direct emissions and indirect emissions 

to assist entities in better managing the full spectrum of GHG risks 

and opportunities for reducing such risks. For many companies, 

indirect emissions from purchased electricity represent one of the 

largest sources of GHG emissions and the most significant 

opportunity to reduce these emissions. Id. 

31 Id. at 25-27. 

32 Id. at 27. 

33 Id. at 25; THE CLIMATE REGISTRY, supra note 10, at 1. The 

eGRID system average methodology is typically used in cases 

where no verified, utility-specific data is available. See infra Part 

I.B.1 for a fuller discussion of the eGRID system average database 

and calculations. 

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emitted per unit of electricity consumed.34 These 

emission factors are “usually reported in units of 

pounds of GHGs per kilowatt-hour or megawatthour.”35

A second type of protocol has been developed to 

quantify reductions in GHG emissions that result from 

projects that either generate low-carbon electricity or 

reduce the consumption of electricity transmitted over 

electric power grids.36 The most well-known protocol 

in this area is the Guidelines for Quantifying GHG 

Reductions from Grid-Connected Electricity Projects,

developed by the World Resources Institute (“WRI 

Guidelines”).37 The WRI Guidelines are intended for 

use by two primary groups: (1) “project developers 

seeking to quantify GHG reductions outside the context 

of a particular GHG offset program or regulatory 

program”; and (2) “designers of initiatives, systems, 

and programs that incorporate grid-connected GHG 

projects.”38

The basic approach set forth by the WRI Guidelines 

involves the calculation of both an “operating margin” 

and a “build margin.”39 According to the WRI 

Guidelines, the operating margin is defined as 

“electricity generation from existing power plants 

whose output is reduced in response to a project 

activity.”40 The WRI Guidelines use EERE 

technologies as the primary examples of such project 

activities.41 The WRI Guidelines state that: 

[Operating margin] emissions are 

estimated using methods that attempt to 

approximate the emissions from the 

specific power plants whose operation is 

displaced. In theory, this estimation 

requires identifying which power plants 

are providing electricity at the margin . . 

. during the times that the project activity 

is operating. . . . Generation provided or 

avoided by the project activity may 

therefore affect a different marginal 

resource in each hour. . . . [E]stimating 

[operating margin] emissions can be a 

complex and data intensive task, 

34 GREENHOUSE GAS PROTOCOL, supra note 1, at 99. 

35 Id. 

36 See QUANTIFYING GHG REDUCTIONS, supra note 13, at 4. 

37 Id.

38 Id. at 6.

39 Id. at 11-16. 

40 Id. at 13.

41 Id. at 11. 

matching a project activity’s output to 

the marginal generating sources in each 

hour. In practice, a diversity of 

estimation methods can be used that vary 

in their complexity and accuracy.42

A spectrum of methodologies thus exist that have 

been used (and misused)43 to estimate the operating 

margin. At one end of the spectrum are imprecise tools 

that estimate the impact of EERE measures on the 

average mix of emissions in the grid.44 On the other 

end of the spectrum are methodologies, such as 

computer-based “hourly dispatch” models, that “capture 

a high level of detail on the specific electric generating 

units displaced by [EERE] projects or programs.”45

These dispatch models can be expensive and “difficult 

for non-experts to evaluate,”46 and are therefore too 

advanced for most state and local governments. In 

addition, these dispatch models are generally 

proprietary,47 which means that the assumptions used in 

calculating emission reduction benefits are not 

transparent to third parties. 

B. Methodologies for Calculating Emission 

Reductions from EERE Technologies 

Grid-connected EERE technologies, such as wind 

power, have zero direct air emissions48 and displace 

emissions from fossil fuel-fired electric power 

generation.49 These emission reductions occur because 

42 Id. 

43 See infra Part I.B. 

44 STEVEN R. SCHILLER, NATIONAL ACTION PLAN FOR ENERGY 

EFFICIENCY MODEL ENERGY EFFICIENCY PROGRAM IMPACT 

EVALUATION GUIDE 6-5 (2007), available at

http://www.epa.gov/cleanenergy/documents/evaluation_guide.pdf. 

45 Id. at 6-7.

46 Id. 

47 Id. 

48 DEBRA JACOBSON & COLIN HIGH, U.S. DEP’T OF ENERGY 

OFFICE OF ENERGY EFFICIENCY & RENEWABLE ENERGY,

COMPARISON OF ALTERNATIVE AVOIDED EMISSIONS 

METHODOLOGIES APPLIED TO SELECTED NORTHEAST POWER 

MARKETS 4, available at 

http://www.mwcog.org/uploads/committeedocuments/bf5dwFtb20081216140014.pdf. 

49 QUANTIFYING GHG REDUCTIONS, supra note 13, at 11-12. In 

our Article, EERE is generally used to refer to energy efficiency 

and zero-emission renewable electric power generation, including 

solar energy, wind energy, geothermal energy, and tidal energy. 

Other renewable energy technologies, such as biomass, waste-toenergy, and landfill gas, do have some direct GHG emissions from 

combustion and other processes. 

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of the way the electric power system works. EERE 

technologies have zero fuel costs and very low 

incremental operating costs. In other words, when 

renewable generation produces power, electricity 

supplies from other sources generally will be reduced or 

not brought on-line.50 For example, “a wind-generated 

kilowatthour displaces a kilowatthour that would have 

been generated by another source—usually one that 

burns a fossil fuel. . . The wind-generated kilowatthour 

therefore avoids the fuel consumption and emissions 

associated with that fossil-fuel kilowatthour.”51 When 

available, EERE technologies generally will displace 

generation at facilities with higher operating costs and 

varying output over time.52

The specific mix of coal, oil and gas-fired power 

units that will be displaced by EERE technologies 

varies significantly among states and regions of the 

country.53 Some states, such as West Virginia and 

Pennsylvania, rely on coal plants for a majority of their 

generation, and in those States, coal units are displaced 

by EERE technologies during some seasons and times 

of day.54 In comparison, natural gas units are more 

typically displaced in other states and regions, such as 

California and New England.55

EERE technologies “almost never displace nuclear 

power on the electric grid. Nuclear power plants are 

normally operated as baseload generators that run at full 

capacity (unless there is a planned or unplanned outage) 

because of low operating costs.”56

In addition, EERE technologies generally do “not 

reduce hydroelectric power on the grid because of its 

low operating costs and flow constraints.”57 Although 

the operators of hydroelectric plants may shift the 

timing of their generation as a result of renewable 

energy use or energy savings, the “[t]otal generation at 

50 Id. at 11. 

51 Michael Milligan et al., Wind Power Myths Debunked, IEEE

POWER & ENERGY MAG., Nov.-Dec. 2009, at 89, 94. 

52 DEBRA JACOBSON & COLIN HIGH, NAT’L RENEWABLE ENERGY 

LAB. SUBCONTRACTOR REPORT #SR-500-42616, WIND ENERGY 

AND AIR EMISSION BENEFITS: A PRIMER 9-10 (2008), available at

http://www.windpoweringamerica.gov/pdfs/policy/wind_air_emiss

ions.pdf. For more information on the complexities of analyzing 

avoided emissions under cap-and-trade programs, see id. at 12-17. 

53 Id. at 10. 

54 Id. 

55 Id. 

56 Id.

57 Id.

such hydroelectric plants is generally not reduced on 

average.”58

Finally, the total amount of emission reductions 

resulting from EERE technologies also varies by time 

of day and season.59 For example, emission reductions 

are highest in the middle of the day because energy 

efficiency savings, particularly from high-efficiency 

commercial air conditioners, “are greatest at the hours 

of the day with the highest temperatures and the highest 

electricity demand in offices and other commercial 

buildings.”60 Similarly, the energy savings and air 

emission reduction benefits of high!efficiency air 

conditioners are “concentrated in the summer months” 

whereas “high!efficiency refrigerators and dishwashers 

provide year!round energy savings.”61

In this Article, we review three common 

methodologies used for quantifying emission reductions 

from increased use of EERE technologies, along with 

specific examples to illustrate the range of results. The 

three methodologies produce data with the following 

parameters: eGRID system average emission rates, 

eGRID non-baseload emission rates, and Time Matched 

Marginal (“TMM”) emission rates. 

Proprietary electric grid system dispatch models 

also have been used as a basis for calculating marginal 

emission rates from EERE technologies.62 These 

models are not reviewed because they are proprietary, 

and as a result, the costs are often prohibitive. They also 

are not sufficiently transparent or replicable to be 

suitable for use in public accounting of air emissions. 

1. EPA eGRID System Average Methodology 

The eGRID database is maintained by the EPA and 

is “a comprehensive inventory of environmental 

attributes of electric power systems” in the United 

States.63 According to EPA, eGRID is “[t]he 

58 Id. The operating schedule of hydroelectric plants also may be 

limited by environmental constraints. Id.

59 ROLE OF ENERGY EFFICIENCY PROGRAMS, supra note 23, at 1-

4. See also Press Release, PJM Interconnection, PJM Reports New 

Carbon Dioxide Emissions Data (Mar. 25, 2010) [hereinafter PJM 

Press Release], available at http://www.pjm.com/~/media/aboutpjm/newsroom/2010-releases/20100325-pjm-reports-new-carbondioxide-emissions-data.ashx (attached graphic and tables 

comparing CO2 emission rates of marginal generating units 

compared to CO2 emission rates of system average generating 

units for 2005 to 2009). 

60 Id. at 3. 

61 Id. at 1. 

62 See supra text accompanying note 45-47. 

63 eGRID Frequently Asked Questions, supra note 2.

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preeminent source of air emissions data for the electric 

power sector. . . .”64 The eGRID database is derived 

from: 

[A]vailable plant-specific data for all 

U.S. electricity generating plants that 

provide power to the electric grid and 

report data to the U.S. government. 

eGRID integrates many different federal 

data sources on power plants and power 

companies, from three different federal 

agencies: EPA, the Energy Information 

Administration (EIA), and the Federal 

Energy Regulatory Commission 

(FERC).65

This database contains air emissions data for several 

GHGs, including CO2, methane (“CH4”) and nitrous 

oxide (“N2O”).66 It also provides data for the following 

criteria and hazardous air pollutants: NOx, sulfur 

dioxide (“SO2”), and mercury (“Hg”).67

The eGRID database is provided in a web-based 

application, eGRIDweb, as well as in Microsoft Excel 

format.68 The database is publicly accessible on the 

EPA’s web site.69 The EPA funds a contractor70 to 

compile various types of data, including aggregated 

emissions data by state, electric generating company, 

parent company, power control area, eGRID subregion, 

North American Electric Reliability Corporation 

(“NERC”) region, and total U.S. levels.71 “Total 

emissions and emission rates, and total generation and 

resource mix are displayed for each of these levels.”72

The two most important eGRID output emission rates 

are the system average emission rates (also called “total 

output emission rates”) and the non-baseload emission 

rates.73

64 Id.

65 Id. 

66 Id. 

67 Id.

68 SUSY ROTHSCHILD & ART DIEM ET AL., THE VALUE OF EGRID

AND EGRIDWEB TO GHG INVENTORIES 1 (2009). 

69 See U.S. Environmental Protection Agency, eGRID, 

http://www.epa.gov/egrid (last visited Mar. 25, 2010) [hereinafter 

eGRID].

70 See E.H. PECHAN & ASSOC., INC., EGRID EMISSIONS AND 

GENERATION RESOURCE INTEGRATED DATABASE VERSION 1.0

USER’S MANUAL (2009). 

71 eGRID, supra note 69.

72 Id. 

73 See ROTHSCHILD & DIEM ET AL., supra note 68, at 5-7. 

The system average emissions rate is calculated by 

dividing the total annual emissions of a particular 

pollutant74 from all units in a region or power system 

(i.e., within the relevant grid boundary) by the total 

energy output of those units over the year.75 This 

system, or grid average, includes all units that report 

data to the government, including nuclear power plants, 

hydroelectric plants, and other zero-emission sources as 

well as fossil fuel-fired generation units.76 The system 

average emission rates rely on information from electric 

generation companies required to provide emissions 

data to both the EPA, including data from Continuous 

Emissions Monitors (“CEMs”), and to the Energy 

Information Administration.77

The EPA reports the system average emission rates 

for the NERC regions and eGRID subregions shown in 

Figure 1.78 The twenty-six eGRID subregions are 

subsets of the NERC regions.79

Figure 1: Map of U.S. EPA eGRID Subregions. 

The most recent eGRID database available includes 

emissions data for calendar year 2005.80 The eGRID 

system average emission rates for each eGRID 

subregion vary considerably depending on the amount 

74 Measurements are typically in pounds. See SCHILLER, supra

note 44, at 6-5. 

75 See id. Total energy output of units over a year is typically 

measured in Megawatt-hours or MWh.

76 See E.H. PECHAN & ASSOC., INC., THE EMISSIONS &

GENERATION RESOURCE INTEGRATED DATABASE FOR 2007

TECHNICAL SUPPORT DOCUMENT (2008), available at 

http://www.epa.gov/RDEE/documents/egridzips/eGRID2007Tech

nicalSupportDocument.pdf.

77 See id. at 4.

78 EGRID SUMMARY TABLES, supra note 15, at 6, 10. 

79 eGRID Frequently Asked Questions, supra note 2; see 

ROTHSCHILD & DIEM ET AL., supra note 68, at 1.

80 See eGRID, supra note 69. 

   :       

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of zero emission sources, such as nuclear and 

hydroelectric power, in the subregion.81 Analyses 

conducted by Resource Systems Group, Inc. (“RSG”) 

reveal that the system average emission rates are the 

lowest of the three methods discussed here in most 

eGRID subregions.82

The methodology for calculating indirect emissions 

from electricity purchases, as adopted by The Climate 

Registry, relies on system average emission rates (when 

verified, utility-specific data is not available).83 As a 

result, this methodology implicitly values emission 

reduction benefits of EERE technologies at the same 

emissions rate as the system average rate. Under the 

system average methodology, year-to-year changes in 

electric power purchases resulting from increased use 

of EERE technologies are accounted for by calculating 

the difference between the indirect emissions from 

electricity purchases in successive years based on the 

eGRID system average emission rates.84

However, according to the WRI Guidelines, 

”because calculating a simple average is significantly 

less precise than other methods,” the system average 

approach “should only be used where other methods are 

not practicable [for calculating the operating margin for 

EERE projects].”85 As discussed below, the eGRID 

system average methodology has been found to 

significantly understate the emission reduction benefits 

of increased use of EERE technologies in most 

regions.86

2. eGRID Non-Baseload Average Methodology 

The eGRID non-baseload methodology is based on 

an understanding of the way in which increased EERE 

81 The EPA eGRID summary tables provide information on the 

generation resource mix (percent of generation from coal, oil, 

natural gas, other fossil fuels, biomass, hydro, nuclear, wind, solar, 

geothermal, and other) in each of the eGRID subregion. These 

summary tables also provide information on the eGRID emissions 

rates in each of these regions. A comparison of these tables 

demonstrates that the eGRID system average emission rates are 

lower when a subregion or region has a high percentage of nuclear 

and hydro resources in the generation mix. See EGRID SUMMARY 

TABLES, supra note 15, at 6, 10. 

82 See infra Part II.

83 THE CLIMATE REGISTRY, supra note 10, at 99.

84 See id. at 99-100. 

85 QUANTIFYING GHG REDUCTIONS, supra note 13, at 55. 

86 See discussion infra Part II. See also PJM Press Release, supra

note 59 (attached graphic and tables comparing CO2 emission rates 

of marginal generating units compared to CO2 emission rates of 

system average generating units for 2005 to 2009).

technology use displaces fossil fuel-fired electric 

generation.87 This type of methodology focuses on 

electric generating units whose output varies according 

to the overall level of power demand (the “load” level) 

in the regional power market.88 These load-following 

generating units are generally fossil fuel-fired, and 

include generating units usually classified as 

intermediate (shoulder) load and peak load plants.89

These units are also called “marginal units” because 

they provide electricity “at the margin,” and their 

output varies depending on the load.90 Baseload units 

are generally excluded but may function as marginal 

units during certain hours and seasons of the year.91

For example, some coal plants operate as intermediate 

plants in the PJM92 Interconnection power market 

during certain times of the year.93 Figure 2 highlights 

the distinction between baseload and non-baseload 

(shoulder/intermediate and peak load) generating 

units.94

Figure 2: Baseload and Non-Baseload Generating Units

Source: National Conference on State Legislatures, 

2005.95

87 See supra Part I.B.1. 

88 QUANTIFYING GHG REDUCTIONS, supra note 13, at Annex A.1. 

89 Matthew Brown et al., NAT’L CONFERENCE OF STATE 

LEGISLATURES, ENERGY AND AIR QUALITY: THE POWER INDUSTRY 

AND AIR QUALITY 6-7 (2005).

90 QUANTIFYING GHG REDUCTIONS, supra note 13, at 55. 

91 JACOBSON & HIGH, supra note 52, at 10. 

92 PJM is a regional transmission organization supplying 

Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New 

Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, 

West Virginia and the District of Columbia. See PJM: Who We 

Are, http://www.pjm.com/about-pjm/who-we-are.aspx (last visited 

Mar. 25, 2010). 

93 JACOBSON & HIGH, supra note 52, at 10.

94 Brown et al., supra note 89, at 6. 

95 Id.

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UALITY 6-7 (2005).

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SON & HIGH, supra note 52, at 1

is a regional transmission

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ra note 13, at Annex A.1.

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TY: THE POWER INDUSTRY

ra note 13, at 55.

10.

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83 THE CLIMATE REG

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85 QUANTIFYING GHG

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, at 55.

Release, supra

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010).

SON & HIGH, supra note 52, at 1

et al., supra note 89, at 6.

Maryland, Michigan, New

nia, Tennessee, Virginia,

mbia. See PJM: Who We

-we-are.aspx (last visited

10.

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The major difference between the eGRID system 

average methodology and the eGRID non-baseload 

methodology is the group of generating units that is 

considered in calculating the avoided emissions rate. 

Whereas the eGRID system average methodology 

considers the emissions of all generating units 

operating on the regional or subregional power grid, the 

non-baseload calculation excludes all generation from 

resources that are not fossil fuel-fired.96 Thus, “[p]lants 

with 100% hydro, nuclear, wind, solar, and/or 

geothermal generation are removed from the nonbaseload calculation.”97 In addition, “[no] 

generation at plants with high capacity factors (0.8 and 

greater) is considered in the calculation of the nonbaseload emissions rate.”98 Thus, the non-baseload 

average emission rates do not include consideration of 

emissions from baseload units99 such as nuclear power 

plants, hydroelectric power plants, and many coal-fired 

plants.100

As highlighted by a sample of subregional power 

markets in the 2005 eGRID Subregion Emission Rates 

table, the eGRID non-baseload emission rate is 

significantly higher than the system average rate in 

most regions101 because it excludes zero-emission 

nuclear and hydropower units and renewable energy 

generation.102

96 See E.H. PECHAN & ASSOC., INC., supra note 76, at 15. 

97 Id. 

98 Id. According to the technical document, the non-baseload 

average is calculated from a percentage of each plants emissions 

and generation (depending upon capacity factor) that combusts fuel 

and has a capacity factor of less than 0.8. Id. at 15. 

99 Id. 

100 Id. Nuclear, hydroelectric, and coal plants are considered 

baseload plants because their capacity factor is greater than 0.8. 

Id.

101 See EGRID SUMMARY TABLES, supra note 15, at 6.

102 E.H. PECHAN & ASSOC., INC., supra note 76, at 15. 

Table 1: Comparison of eGRID Non-Baseload 

Methodology and eGRID System Average 

Methodology for Selected eGRID Subregions103

Comparison of the eGRID database reveals that the 

eGRID non-baseload emissions rate is higher than the 

eGRID system average rate in twenty out of the twentysix eGRID subregions.104 Experts involved in the 2005 

eGRID database confirmed this conclusion in a recently 

published paper, stating that “in general, with few 

exceptions, the non-baseload values are larger than the 

total output emission rates.”105

The eGRID non-baseload average emission rates for 

eGRID subregions are also provided by the EPA on its 

website and in its summary reports.106 The EPA’s 

Green Power Partnership appropriately relies on the 

eGRID non-baseload average methodology in 

calculating emission reductions resulting from the use 

of renewable energy generation.107 This EPA program 

has developed a calculator tool that incorporates the 

eGRID non-baseload methodology.108

103 EGRID SUMMARY TABLES, supra note 15, at 6.

104 EGRID SUMMARY TABLES, supra note 15, at 6.

105 ROTHSCHILD & DIEM ET AL., supra note 68, at 7. 

106 Id.

107 See U.S. Environmental Protection Agency, Green Power 

Partnership, http://www.epa.gov/greenpower/index.htm (last 

visited Mar. 5, 2010) [hereinafter Green Power Partnership]. The 

Green Power Partnership is a voluntary EPA program that supports 

the organizational procurement of green power by offering expert 

advice, technical support, tools and resources. For purposes of this 

program, green power is defined as electricity produced from a 

subset of renewable resources, such as solar, wind, geothermal, 

biomass, and low-impact hydro. See id.

108 See U.S. Environmental Protection Agency, Green Power 

Equivalency Calculator, 

Subregional

Power 

Market

Non-Baseload 

Methodology

(lb. CO2

/MWh)

System 

Average 

Methodology 

(lb. 

CO2/MWh)

Midwest

(MROW) 2,158.79 1,821.84

Midwest

(SRMW) 2,101.16 1,830.51

Mid-Atlantic 

(RFCE) 1,790.50 1,139.07

California 

(CAMX) 1,083.02 724.12

Southwest 

(AZNM) 1,201.44 1,311.05

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3. Time-Matched Marginal Emissions 

 Methodology (“TMM”) 

Over the course of several years, Resource Systems 

Group (“RSG”) has developed a methodology for 

estimating air emissions avoided by the increased use of 

EERE technologies. This TMM emissions 

methodology differs from the eGRID system average 

approach in several fundamental ways. 

First, the TMM methodology is based on emissions 

monitoring data for each of the 8,760 hours of the year 

for generating units that submit hourly data to EPA. In 

comparison, the eGRID database focuses on annual

averages of emissions for grid-connected electric 

generators.109 The significance of this difference is 

underscored by the National Action Plan for Energy 

Efficiency, a coalition of the DOE, EPA, and more than 

fifty other agencies and organizations.110 The National 

Action Plan’s Model Energy Efficiency Program 

Evaluation Guide, released in November 2007, 

emphasizes that the eGRID system average approach to 

estimating avoided emissions has several major 

limitations.111 The first major limitation is summarized 

as follows: 

[One] shortcoming of this approach 

is that energy efficiency savings tend to 

vary over time, such as savings from an 

office lighting retrofit that only occurs 

during the workday. Using an annual 

average emission factor that lumps 

daytime, nighttime, weekday, and 

weekend values together can skew the 

actual emissions benefits calculation.112

The second difference between the TMM 

methodology and the eGRID system average 

methodology is that the TMM methodology focuses on 

the actual generating units expected to be displaced 

when renewable energy generation or energy savings 

take place. Nuclear units and hydropower units are thus 

generally not considered by the TMM methodology 

when calculating avoided emissions. 

http://www.epa.gov/greenpower/pubs/calculator.htm (last visited 

Mar. 5, 2010). This tool is only available to Partnership members. 

109 See supra Part I.B.1. 

110 SCHILLER, supra note 44. 

111 Id. at 6-5. 

112 Id.

In comparison, the eGRID system average 

methodology considers nuclear and hydroelectric units 

in calculating avoided emissions, even though such 

units are rarely displaced by increased use of EERE 

technologies.113 The National Action Plan’s Program 

Evaluation Guide views the inclusion of nuclear and 

hydroelectric units as a shortcoming of the 

methodology: 

A shortcoming of this [system 

average] approach is that it does not 

account for the complexity of regional 

power systems. . . . In many regions, the 

marginal units displaced by energy 

efficiency programs can have very 

different emissions characteristics from 

the base load units [such as nuclear and 

hydropower] that dominate the average 

emissions rate.114

The eGRID non-baseload methodology also shares 

the first limitation of the eGRID system average 

methodology because it is based on the annual average 

avoided emissions,115 instead of a more accurate hourly 

measure. However, it differs from the system average 

methodology and shares a benefit of the TMM 

methodology because it focuses on the non-baseload 

units that are actually displaced when EERE technology 

comes online.116

Thus, RSG’s TMM methodology provides a 

marginal rather than a system average emission rate. In 

addition, it uses a time-matching approach that is 

specific to the EERE technology under consideration. 

The TMM methodology is based on the following 

steps: 

1. Estimating the hourly electric power generation 

for each fossil fuel-fired unit in a specific power market 

area; 

2. Identifying the marginal fossil fuel-fired units at 

each hour by using the hourly generation to identify 

units that follow the total load at each hour. Based on 

this information, RSG’s TMM method estimates 

average emission rates of the marginal units based on 

their incremental contribution to the load; 

3. Using data to compile a profile of the energy 

savings or energy generation on an hourly basis over 

113 See Part I.B.

114 SCHILLER, supra note 44, at 6-5. 

115 E.H. PECHAN & ASSOC., INC., supra note 76, at 15. 

116 Id.

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the 8760 hours of the year. This profile is prepared for 

a particular technology, such as wind power or highefficiency commercial air conditioning, and for a 

particular region;117

4. The “time-matching” occurs when the load 

profile is matched for a specific technology on an 

hourly basis against the marginal emissions profile for 

the same hours; 

5. The avoided emission rates can be used to 

produce, in Microsoft Excel format, a calculator that 

provides total avoided emissions from annual 

generation or savings, using either project-specific 

profiles or default regional profiles. This calculator also 

can be used to estimate avoided emissions on a monthly 

or seasonal basis. 

Although RSG pioneered the TMM methodology, 

at least one other prominent energy consulting firm has 

used a conceptually similar approach. In July 2008, 

Synapse Energy Economics published a report for the 

EPA using an hourly approach to estimate avoided 

emissions based on hourly EPA CEM data from 

intermediate and peak load generating units.118 This 

report, entitled Analysis of Indirect Emissions Benefits 

of Wind, Landfill Gas, and Municipal Solid Waste 

Generation, calculates avoided emissions for the three 

selected renewable energy technologies in each of the 

EPA’s twenty-two eGRID subregions in the continental 

U.S.119 The regional, hourly power profiles for each of 

the three renewable resources are combined with the 

hourly indirect emissions factors to yield annual 

indirect emissions benefits for each type of resource for 

each eGRID subregion.120

Researchers at the Laboratory for Energy and the 

Environment at the Massachusetts Institute of 

Technology (“MIT”) also have studied the emission 

reductions from another renewable energy technology, 

solar photovoltaics (“PV”), using a methodology 

similar to the TMM method.121 The MIT study relies 

117 The recommendations in Part IV, infra, highlight the need for 

additional work to refine these load profiles and to make them 

publicly available. 

118 See BRUCE BIEWALD ET AL., U.S. ENVTL. PROT. AGENCY,

OFFICE OF RESEARCH AND DEV., ANALYSIS OF INDIRECT 

EMISSIONS BENEFITS OF WIND, LANDFILL GAS, AND MUNICIPAL 

SOLID WASTE GENERATION i (2008), available at

http://www.synapse-energy.com/Downloads/SynapseReport.2008-

07.EPA.EPA-Indirect-Emissions-Benefits.06-087.pdf. 

119 Id.

120 Id.

121 See THE ANALYSIS GROUP FOR REG’L ENERGY 

ALTERNATIVES, EMISSIONS REDUCTIONS FROM SOLAR 

PHOTOVOLTAIC (PV) SYSTEMS 1-4 (2004), available at

on “matching up PV generation with associated 

changes in unit generation on an hourly basis.”122 It

focuses on the fossil fuel units offset by PV generation 

in each region and each hour of the day.123

Moreover, the MIT study expresses serious 

concerns about the system average approach.124 The 

researchers stressed that “[t]his report also challenges 

the simple averages approach on the ground that there 

is no evidence that average . . . emission rates occurring 

when PV is generating are representative of the fossil 

units that respond to changes in load, to be met by 

dispatchable generation.”125

Finally, the DOE’s Loan Guarantee Program Office 

(“LGPO”) has adopted the RSG TMM methodology as 

a component in its review of loan guarantee 

applications for EERE projects.126 The LGPO is 

applying the methodology in its evaluation of 

comparative GHG and air emission reduction benefits 

of projects competing for loan guarantees.127

II. FINDINGS FROM COMPARISON OF THREE AVOIDED

EMISSIONS METHODOLOGIES 

In 2008, RSG conducted an analysis of results from 

the three different methodologies that measure the 

emissions impact of increased use of EERE 

technologies. The analysis focused on five EERE 

technologies: high-efficiency commercial lighting, 

high-efficiency commercial air conditioning, LED 

traffic lights, solar PV, and wind energy. In each case, 

RSG compared the avoided emissions from a specific 

technology using its TMM methodology, the eGRID 

system average methodology, and the eGRID nonbaseload methodology. RSG analyzed both the PJM 

http://web.mit.edu/agrea/docs/MIT-LFEE_2004-003a_ES.pdf. 

122 Id. at 1-2.

123 Id. at ES-1. 

124 Id. at 1-4. 

125 Id.

126 The LGPO subcontracted with RSG to conduct the analysis of 

GHG and air emission reduction benefits on the electric grid 

resulting from alternative technologies competing for loan 

guarantees, and RSG is applying its TMM methodology in this 

process. See generally RSG, Inc., Avoided CO2 Emissions, 

http://www.rsginc.com/avoided-co2-emissions-2/ (last visited Mar. 

25, 2010). The DOE required applicants to set forth the hourly 

load profiles for electric generation or savings under their 

technologies as part of the loan guarantee application process. See 

LOAN GUARANTEE PROGRAM OFFICE, U.S. DEP’T OF ENERGY,

ELECTRICAL PROFILES—GENERATION, PURCHASES, SAVINGS AND 

TRANSFERS WORKSHEET (2008), available at

http://www.lgprogram.energy.gov/lew.xls. 

127 Id. 

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Interconnection power market and the Upstate New 

York power market. 

The findings of this analysis are as follows: 

1. Avoided air emission benefits for the five EERE 

technologies for CO2 and NOx are from 65% to 165% 

or more higher using the TMM methodology when 

compared to the eGRID system average method; 

2. Avoided air emissions benefits for the five 

EERE technologies for CO2 are from approximately 

fifteen percent higher using the TMM methodology 

when compared to the eGRID non-baseload 

methodology; 

3. Within the two power market areas studied, the 

emission reduction benefits associated with the five 

different EERE technologies varied by only up to three 

percent. The variability is mainly associated with the 

degree of seasonal variation in electricity savings or 

additional renewable energy generation. 

4. In all cases, the results for NOx are more 

complicated and more variable than the results for CO2.

This complexity is related to the highly variable 

emission rates for NOx resulting from large differences 

in the efficiency of pollution control technologies for 

this pollutant. In comparison, there are no pollution 

controls for CO2 on the units studied. 

Figure 3 highlights these results, and the actual data 

relating to these summary findings is set forth in the 

Appendix. 

Figure 3: Comparison of RSG TMM Avoided Emissions 

Rates for NOx and CO2 with eGRID Emission Rates for 

the Upstate New York and PJM Interconnection Power 

Markets 

New York Upstate (eGRID subregion NYUP) 

PJM Interconnection (eGRID subregion RFCE) 

The wide variation in the results poses significant 

challenges to state and local governments in preparing 

GHG inventories and conducting other GHG 

accounting.128 The following hypothetical example 

highlights the differences among the three 

methodologies. (It should, however, be noted that the 

eGRID system average methodology is not 

recommended for this type of estimation even though it 

is often inappropriately used for calculating emission 

reductions resulting from increased use of EERE 

technologies).129

This hypothetical involves a municipality in 

western New York that is tracking its GHG emissions 

and reporting under The Climate Registry protocols—

using system average emission rates for its purchase of 

electricity. Assume that the municipality decided to 

reduce its GHG emissions to meet a target in its 

Climate Action Plan and decided to install a municipal 

wind farm to power its own facilities and generate 

10,000 MWh per year. The reported avoided emissions 

from the three alternative emissions assessment 

methodologies are shown in Table 2. 

Table 2: Avoided CO2 Emissions from 10,000 MWh 

of Wind Energy Generation in Upstate New York. 

Based on 2005 Data. 

128 In this example (and for simplicity), we only have considered 

avoided emissions from operational changes in generation in the 

near-term. We have not considered the added complexity that 

additional generation may have on reducing the need to build new 

electric generating capacity in the future. 

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Under this example, the municipality’s own 

wind power project would result in a decrease in 

reported CO2 emissions to The Climate Registry of 

approximately 3,600 tons per year.131 On the other 

hand, the municipality might be a member of EPA’s 

Green Power Partnership and calculate the air emission 

benefits from its new wind farm using the eGRID nonbaseload methodology. Under this methodology, the 

emission reduction benefits claimed would be 7,571 

tons of CO2.

132 The amount of avoided CO2 emissions 

reported under this non-baseload methodology would 

thus differ substantially from the amount of avoided 

CO2 emissions calculated under the eGRID system 

average methodology. Moreover, using an hourly 

marginal emissions analysis, such as the TMM method, 

a more accurate estimate of the avoided emissions 

benefits of a municipal purchase of 10,000 MWh of 

wind power would be about 9,160 tons of CO2.

If the municipality wanted to quantify its progress 

toward a GHG reduction target, the TMM methodology 

would be most accurate in the near-term. This 

hypothetical illustrates the need for greater consistency 

and accuracy in calculating and reporting CO2 reduction 

benefits. 

The analysis described above is based on only two 

of the twenty-six NERC subregions. However, 

subsequent to the completion of the research included 

in this Article, RSG analyzed the emission reduction 

benefits of a wide range of EERE projects across all of 

the NERC regions and virtually all of the NERC 

subregions. RSG’s analysis confirms that the use of the 

TMM methodology results in significantly higher 

emission reduction benefits than using the eGRID 

system average methodology in most regions of the 

country. The comparison with the non-baseload 

131 The Climate Registry’s General Protocol does suggest the 

reporting of supplemental information about green power 

purchases in its entity-wide report on indirect emissions from 

electricity purchases. THE CLIMATE REGISTRY, supra note 10, at 

101. However, the indirect emissions that are reported as scope 2 

emissions from electricity purchases would only be reduced by 

3,600 tons. 

132 U.S. Environmental Protection Agency, eGRID2007 Version 

1.1 Year 2005 GHG Annual Output Emission Rates, 

http://cfpub.epa.gov/egridweb/ghg (last visited Mar. 23, 2010) 

(listing the “[a]nnual non-baseload output emissions rates” for 

Upstate New York (NYUP) as 1511.14 lb CO2/KWH); U.S. 

Environmental Protection Agency, Green Power Partnership, 

Green Power Equivalency Calculator, 

http://www.epa.gov/greenpower/pubs/calculator.htm (last visited 

Mar. 23, 2010). 

average is more complex, making it difficult to draw 

clear conclusions. 

Thus, this recent work further confirms that the 

eGRID system average methodology is an inappropriate 

approach for assessing the avoided emissions benefits 

of specific EERE programs and projects, which is 

implicit in how state and national registries and 

accounting programs currently estimate indirect 

emissions from electricity purchases.133 This 

conclusion was supported by EPA eGRID experts at the 

recent Energy and Environmental Conference 2010 

(commonly called the EUEC 2010) in Phoenix.134

According to a paper presented by the eGRID experts, 

the eGRID non-baseload emission rates, rather than the 

eGRID total output emission rates (system average 

rates), are recommended for use in estimating “the 

emissions benefits of reductions in grid supplied 

electricity use, especially those that are somewhat 

coincident with peak demand.”135 The experts 

specifically recommended the eGRID non-baseload 

methodology to calculate emission reductions from 

using high-efficiency air conditioning.136

III. IMPROVED METHODOLOGIES WOULD BENEFIT 

FEDERAL AND STATE CLIMATE AND AIR 

QUALITY PROGRAMS 

A. The Climate Registry’s Reliance on the eGRID 

System Average Methodology 

It is very important to promptly address the 

inappropriate use of the eGRID system average 

methodology for calculating changes in CO2 emissions 

resulting from increased use of EERE technologies. 

Unfortunately, a large majority of states and hundreds 

of local governments and private entities using GHG 

emission protocols are misapplying the system average 

avoided emissions methodology in these 

circumstances.137 Reliance on the eGRID system 

average methodology has significant ramifications 

because of the possibility that other government entities 

and the authors of national climate legislation will 

follow this precedent. 

Most importantly, the General Reporting Protocol 

of The Climate Registry—a non-profit organization 

133 See infra Part III.C. 

134 See ROTHSCHILD & DIEM ET AL., supra note 68, at 6. 

135 Id. 

136 See id. 

137 See infra notes 171-73 and accompanying text. 

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comprised of forty states and the District of 

Columbia—recommends the use of system average 

emissions rates to calculate indirect GHG emissions 

from electricity purchases if utility-specific emissions 

information is not available.138 The Climate Registry’s 

General Reporting Protocol incorporates a table that 

contains specific emission factors to apply in such 

cases—the eGRID system average.139

The General Reporting Protocol does suggest the 

reporting of supplemental information regarding green 

power purchases in its entity-wide report on indirect 

emissions from electricity purchases.140 However, this 

Protocol does not require any reductions in the level of 

indirect emissions from electricity purchases (scope 2 

emissions) reported as a result of the increased use of 

renewable energy technologies and does not require an 

additional inventory to reflect emission reductions that 

result from increased use of renewable energy 

technologies.141 The General Reporting Protocol, 

moreover, does not discuss any adjustments to account 

for reductions in energy use from major energy 

efficiency projects undertaken by an entity.142

In comparison to the TMM methodology, the 

General Reporting Protocol methodology does not 

involve marginal emissions rates, nor does it require 

time-varying profiles of specific EERE technologies.143

A companion Protocol developed by the Local 

Governments for Sustainability (“ICLEI”) in 

partnership with The Climate Registry, the California 

Climate Action Registry, and the California Air 

Resources Board, uses a similar approach.144 In

addition, as discussed below, Climate Registry 

138 See THE CLIMATE REGISTRY, supra note 10, at 99. The 

Climate Registry is a non-profit organization that supports public 

reporting of greenhouse gas emissions throughout North America 

in a single unified registry. The Protocol indicates that generatorspecific emission factors are preferred instead of eGRID data but 

allows the use of eGRID data when such generator-specific 

emission factors are not available. Even with respect to the 

generator-specific data, the Protocol does not require the use of 

marginal emissions data. See id. at 99. 

139 See id. at 99, 104. 

140 See id. at 101. 

141 See id.

142 See id. at 97-108. 

143 Compare id., with supra Part I.B.3 (discussing TMM 

methodology). 

144 LOCAL GOV’TS FOR SUSTAINABILITY ET AL., LOCAL 

GOVERNMENT OPERATIONS PROTOCOL, VERSION 1.0, at 169, 175-

76 (2008), available at http://www.icleiusa.org/actioncenter/tools/lgo-protocol-1 (using system average data in Tables 

providing default electricity emissions factors). 

protocols have been referenced in several major pieces 

of national climate legislation.145

It should be noted that The Climate Registry’s 

General Reporting Protocol “recognizes the need to 

develop a specific accounting framework for green 

power purchases in order to encourage and incentivize 

emission reduction efforts.”146 The General Reporting 

Protocol, however, asserts that “[t]here is not yet 

consensus on how to accurately and credibly track 

green power purchases in an accounting framework, 

beyond allowing Reporters to provide supplementary 

information about their green power purchases in 

annual emission reports.”147

In its General Reporting Protocol, The Climate 

Registry committed to “incorporating a framework for 

accounting for contractual purchases of electricity, such 

as green power,” as it “develop[ed] an industry specific 

protocol for the power and utility sector. . . .”148

Unfortunately, The Climate Registry did not resolve 

this issue when it issued its voluntary reporting protocol 

for the electric power sector in June 2009.149

The Climate Registry’s Protocols raise particular 

concerns about the disconnect between year-to-year 

changes in CO2 emissions reported to The Climate 

Registry, as compared to energy efficiency savings, and 

related GHG emission reductions reported by state 

energy agencies and electric utilities. For example, if 

the State of Maryland—a member of The Climate 

Registry—reports its CO2 inventory under the General 

Reporting Protocol, it is likely to underestimate the 

benefits of energy savings from high-efficiency heating 

equipment in the State for a variety of reasons, 

including the fact that the eGRID system average 

methodology does not reflect seasonal changes in 

avoided emissions.150

This issue of seasonal variation is significant 

because the avoided CO2 emissions from energy 

efficiency technologies in Maryland are greater in the 

145 See infra notes 160-67 and accompanying text. 

146 THE CLIMATE REGISTRY, supra note 10, at 101. 

147 Id. 

148 Id. 

149 THE CLIMATE REGISTRY, ELECTRIC POWER SECTOR 

PROTOCOL FOR THE VOLUNTARY REPORTING PROGRAM, ANNEX 1

TO THE GENERAL REPORTING PROTOCOL, VERSION 1.0, at 54, 64-

67, 76-77 (2009), available at 

http://www.theclimateregistry.org/downloads/2009/05/ElectricPower-Sector-Protocol_v1.0.pdf. 

150 See NAT’L ACTION PLAN FOR ENERGY EFFICIENCY, MODEL 

ENERGY EFFICIENCY PROGRAM IMPACT GUIDE 6-5 (2007), 

available at http://www.epa.gov/cleanenergy/energyprograms/napee/resources/guides.html#guide5. 

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winter months.151 The fossil fuel-fired units displaced 

in the regional power market are more often coal-fired 

units with high CO2 emissions.152 As Maryland 

implements its new EmPOWER Maryland efficiency 

initiative,153 The Climate Registry methodology is thus 

unlikely to reflect the full benefits of the new initiative. 

The eGRID system average methodology also will 

not provide an accurate comparison of the costeffectiveness of alternative GHG reduction strategies 

that rely on EERE technologies, particularly 

comparisons of actions in different regions and power 

markets. For example, the MIT study emphasized that 

the degree of reliance on higher emitting fossil fuelfired electricity in a particular power grid will be a 

greater determinant of the level of avoided emissions 

from solar photovoltaic energy than the amount of 

sunshine in a particular region.154 This fact may 

heighten the CO2 reduction value of solar energy in 

certain regions, such as the Northern Plains and the 

Tennessee Valley, which generally are not viewed as 

promising solar generation areas.155 Furthermore, the 

adoption of time-of-use pricing in electricity markets156

will require increased emphasis on time-of-use avoided 

emissions analysis if the cost-effectiveness of energy 

efficiency programs is to be properly evaluated. 

The misapplication of the eGRID system average is 

likely to have sweeping impacts because the broad 

151 Information is based on RSG Inc. TMM emissions 

methodology discussed supra Part I.B.3. 

152 Id.

153 See Maryland Energy Administration, Energy Facts and 

Programs, 

http://www.energy.maryland.gov/facts/empower/index.asp (last 

visited Mar. 25, 2010). Under Maryland’s “EmPOWER 

Maryland” initiative, the state is working with its utilities to 

require actions to reduce energy consumption by fifteen percent by 

the year 2015. Id.

154 STEPHEN CONNORS ET AL., LAB. FOR ENERGY & THE ENV’T,

EMISSIONS REDUCTIONS FROM SOLAR PHOTOVOLTAIC SYSTEMS 

ES-2, at 8-1 (2005), available at

http://solar.gwu.edu/index_files/Resources_files/MIT_Solar%20P

V%20Report2004.pdf. 

155 Id. at 6-7. 

156 Time-of-use rates provide electric consumers with rates that 

vary over time to reflect the “value and cost of electricity in 

different time periods.” The purpose of this approach is to 

encourage consumers “to use less electricity at times when 

electricity prices are high.” U.S. DEP’T OF ENERGY, BENEFITS OF 

DEMAND RESPONSE IN ELECTRICITY MARKETS AND 

RECOMMENDATIONS FOR ACHIEVING THEM: A REPORT TO THE 

U.S. CONGRESS PURSUANT TO SECTION 1252 OF THE ENERGY 

POLICY ACT OF 2005, at v, xi (2006), available at 

http://www.oe.energy.gov/DocumentsandMedia/congress_1252d.p

df.

geographic membership of The Climate Registry has 

spurred partnerships with other important organizations. 

For example, in December 2008, ICLEI announced a 

partnership with The Climate Registry to encourage 

local governments to report their emissions as members 

of The Climate Registry.157 Previously, ICLEI had 

worked with The Climate Registry to develop the Local 

Government Operations Protocol to facilitate reporting 

of GHGs by local governments.158 More than 600 local 

governments in the United States are members of the 

ICLEI organization.159

Several pieces of national climate legislation also 

have referenced The Climate Registry’s protocol. For 

example, H.R. 2454, the American Clean Energy and 

Security Act of 2009, approved by the House of 

Representatives in June 2009, references The Climate 

Registry in its provisions establishing a greenhouse gas 

registry.160 Section 713(b)(1) requires the EPA 

Administrator to issue regulations establishing a federal 

greenhouse gas registry, and subsection (b)(1)(E) 

requires the Administrator to: “[T]ake into account the 

best practices from the most recent federal, state, tribal 

and international protocols for the measurement, 

accounting, reporting, and verification of greenhouse 

gas emissions, including protocols from The Climate 

Registry and other mandatory state or multistate 

authorized programs.”161

Moreover, H.R. 2454 requires the EPA 

Administrator to explain any major differences in the 

approach between the registry established under the 

new regulations and The Climate Registry and other 

mandatory state and multistate programs.162 The 

Climate Registry is defined in the legislation as “the 

greenhouse gas emissions registry jointly established 

and managed by more than 40 States and Indian tribes 

157 Press Release, The Climate Registry and Local Gov’ts for 

Sustainability, ICLEI and the Climate Registry Partner to Track 

Rigorous GHG Emissions Accounting for Local Governments

(Dec. 2, 2008). 

158 See Open Letter from Rachel Tornek, Senior Policy Manager, 

Cal. Climate Action Registry & Garrett Fitzgerald, Dir. Of 

Programs, ICLEI — Local Govt’s for Sustainability, Local 

Government Protocol Invitation (Mar. 2, 2008), available at 

http://www.icleiusa.org/programs/climate/protocol-invitationletter/?searchterm=LocalGovernmentOperationsProtocol. 

159 ICLEI — Local Governments for Sustainability, About 

ICLEI, http://www.icleiusa.org/about-iclei (last visited Mar. 5, 

2010). 

160 See American Clean Energy and Security Act of 2009, H.R. 

2454, 111th Cong. § 713 (2009). 

161 H.R. 2454 §§ 713(b)(1), (b)(1)(E) (emphasis added).

162 H.R. 2454 § 713. 

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in 2007 to collect high-quality greenhouse gas emission 

data from facilities, corporations, and other 

organizations to support various greenhouse gas 

emission reporting and reduction policies for the 

member States and Indian tribes.”163

Similarly, the provisions of the Senate version of 

climate legislation establishing a GHG registry also 

reference The Climate Registry protocols.164 Section 

713(b)(1)(E) of the Clean Energy Jobs and American 

Power Act states that the regulations issued by the EPA 

Administrator to establish a GHG registry shall “take 

into account the best practices from the most recent 

Federal, State, tribal and international protocols for the 

measurement, accounting, reporting, and verification of 

greenhouse gas emissions, including protocols from 

The Climate Registry and other mandatory State or 

multistate authorized programs.”165

In summary, The Climate Registry is in a key 

position to develop additional protocols that more 

accurately reflect the emission reduction benefits of 

increased use of EERE technologies. The leadership of 

the Climate Registry has emphasized that corrections 

and clarifications to its General Reporting Protocol will 

be necessary as the program evolves.166 The 

organization has announced plans to release a new 

version of its General Protocol in 2010,167 and the 

Authors have sought to facilitate necessary 

clarifications by sharing the results of the methodology 

issues raised in this Article with the Climate Registry 

staff. 

B. Mandatory GHG Reporting Rules 

On October 30, 2009, the EPA published a final 

rule requiring the first-ever national mandatory 

reporting system for GHGs in the United States.168

Although the October 2009 rule only focused on direct 

emissions and did not require reporting of indirect 

emissions from electricity purchases,169 the preamble to 

163 H.R. 2454 § 713(a)(1). 

164 Clean Energy Jobs and American Power Act, S. 1733, 111th 

Cong. § 713(b)(1)(E) (2009). 

165 S. 1733 § 713(b)(1)(E) (emphasis added). 

166 THE CLIMATE REGISTRY, GENERAL REPORTING PROTOCOL 1.1

CLARIFICATIONS AND CORRECTIONS 1 (2008), available at 

http://www.theclimateregistry.org/downloads/08.11.24_GRP_Clar

ifications_and_Corrections.pdf.

167 Id. 

168 Mandatory Reporting of Greenhouse Gases, 74 Fed. Reg. 

56,260 (Oct. 30, 2009) (to be codified at 40 C.F.R. pt. 98). 

169 Id. at 56,289.

the final rule indicated that reporting of indirect 

emissions was likely to be considered in the future.170

Any methodology ultimately adopted by the EPA in a 

future rulemaking for mandatory reporting of GHG 

emissions will be extremely important in establishing a 

precedent for the treatment of indirect emissions from 

electricity purchases. 

According to the preamble, the EPA stated: 

While EPA is not collecting data on 

electricity purchases in this rule, we 

understand that acquiring such data may 

be important in the future. Therefore, 

we are exploring options for possible 

future data collection on electricity 

purchases and indirect emissions, and 

the uses of such data. Such a future data 

collection on indirect emissions would 

complement EPA’s interest in spurring 

investment in energy efficiency and 

renewable energy.171

To date, the EPA has not addressed the option of 

developing two separate inventories for indirect 

emissions—one to help aggregate reports of GHG 

emissions from individual entities and a second to 

account for changes in GHG emissions resulting from 

increased use of EERE technologies.172 However, since 

the EPA views the encouragement of EERE 

technologies as an important goal, it is essential for the 

EPA to use the appropriate methodology to accomplish 

its objective. 

Consistent with the WRI Guidelines, the EPA 

should use a marginal emissions methodology in any 

emissions inventory intended to measure reductions in 

GHGs resulting from increased use of EERE.173

Businesses and policymakers seeking a level playing 

field for EERE technologies should closely follow 

EPA’s future rulemaking activities in this area. 

Moreover, EERE technologies are not the only 

technologies affected by this methodological problem. 

The evaluation of air emissions reductions from other 

electric technologies, such as fuel cells, grid-connected 

batteries, compressed air and pumped storage and plug170 Id.

171 Id. 

172 See id.

173 See infra Part IV (providing a more extensive elaboration of 

this recommendation); see supra Part I.B.2.-3 (providing a 

description of the non-baseload and TMM methodologies). 

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developed with support from the DOE Clean 

Energy/Air Quality Integration Initiative, could fulfill 

this essential need. It has already been employed by the 

Metropolitan Washington Council of Governments 

(MWCOG), the Commonwealth of Virginia, the State 

of Maryland, and the District of Columbia.188

The MWCOG calculator, which is based on the 

RSG TMM methodology, allows the user to use 

standard load profiles for a specific renewable energy 

or energy savings technology, or project-specific load 

profiles, and to enter these profiles into an Excel-based 

spreadsheet.189 In developing its air quality plan to 

meet the eight-hour ozone standard, the MWCOG staff 

used the calculator to compute the annual or monthly 

avoided emission estimates for NOX using the hourly 

TMM emission rates that were embedded in the 

calculator.190 

An hourly marginal emissions methodology is also 

ideally suited to assess the benefits of EERE measures 

on high electric demand days. Energy savings measures 

188 The following document issued by the Metropolitan 

Washington Council of Governments (“MWCOG”) relies on the 

analysis from the TMM calculator in its chapter on voluntary 

control measures (Chapter 6) and in Appendix H. METRO. WASH.

COUNCIL OF GOV’TS, PLAN TO IMPROVE AIR QUALITY IN THE 

METROPOLITAN WASHINGTON, DC-MD-VA REGION: STATE 

IMPLEMENTATION PLAN FOR 8-HOUR OZONE STANDARD 6-61 to 6-

82, Appendix H (2007), available at 

http://www.mwcog.org/environment/air/downloads/SIP_APP/defa

ult.asp. 

The calculator tools are set forth in Appendix H of the MWCOG 

document for LED traffic lights, wind energy and the DC RPS. 

The methodology underlying the calculator tools is set forth in the 

following document contained in Appendix H of the MWCOG 

document. See COLIN HIGH & KEVIN HATHAWAY, RES. SYSTEMS 

GROUP, AVOIDED AIR EMISSIONS FROM ENERGY EFFICIENCY AND 

RENEWABLE ENERGY POWER GENERATION IN THE PJM

INTERCONNECTION POWER MARKET AREA (2007), available at 

http://www.mwcog.org/environment/air/downloads/SIP_APP/App

_H_-_RSG_Avoided_Emissions_Rept_5-23-07_Draft_Final.pdf. 

189 METRO. WASH. COUNCIL OF GOV’TS, supra note 188, at 6-61. 

190 Id. The calculator tool that was initially developed in 2007 

has been refined in 2008 and 2009 under a grant issued by the 

DOE’s Office of Energy Efficiency and Renewable Energy 

(EERE). The grant was issued to the Virginia Department of 

Mines, Minerals and Energy under the EERE’s state Energy 

Program to support a project titled “Promoting Air Quality with 

Clean Energy in the Metropolitan Washington Region.” The 

methodology has been further refined under work performed for 

the DOE’s Loan Guarantee Project Office to encompass GHGs and 

other pollutants. In view of the NOx emissions trading regime in 

effect in Maryland, the District of Columbia, and Virginia, the air 

agencies in these jurisdictions committed to retire NOx allowances 

in an amount commensurate with the amount of calculated 

emissions. See id. at Appendix H. 

that reduce summer electric use “during the days and 

hours with the highest electrical demand, such as highefficiency air conditioning and commercial lighting, are 

particularly valuable in reducing emissions of NOX—a

precursor to ground-level ozone that causes adverse 

respiratory effects in adults and children.”191 “Ozone is 

formed on hot summer days, and the hottest summer 

days also are typically the days of highest electrical 

demand—the so-called ‘high electric demand days.”192

Research has demonstrated that daily NOX

emissions on high electric demand days from fossil 

fuel-fired electric generating units in certain regions of 

the country, such as the Northeast and the Mid-Atlantic 

regions, substantially exceed emissions on more typical 

summer days.193 “This result occurs because the peak 

load generating units used on these limited number of 

days each year (generally fewer than a dozen days) are 

typically older units with limited pollution controls.”194

There is a major opportunity to provide access to 

the TMM method and calculator to state and local 

governments. States have time to develop and 

distribute the database and calculator prior to the 

submission of the SIPs in 2013 and to train state 

employees to use this tool. 

IV. RECOMMENDATIONS 

Based on the findings presented above, this Article 

offers five recommendations. Adopting these 

recommendations would substantially advance efforts 

to measure the full air emission reduction benefits of 

EERE technologies. 

First, DOE, EPA, and state air, climate, and energy 

agencies, including the Board of The Climate Registry, 

should fund an enhancement of the eGRID database to 

provide a profile of hourly marginal emissions for each 

of the 8,760 hours of the year in each of the eGRID 

subregions. This enhanced eGRID database should 

cover all power markets as well as all regions and 

subregions of the North American Electric Reliability 

Corporation. 

The enhanced eGRID database would include 

additional information based on the TMM methodology 

or a similar methodology (initially using data from 

191 ROLE OF ENERGY EFFICIENCY PROGRAMS, supra note 23, at

2. 

192 Id.

193 See NE. STATES FOR COORDINATED AIR USE MGMT., FINAL 

WHITE PAPER: HIGH ELECTRIC DEMAND DAY AND AIR QUALITY IN 

THE NORTHEAST (2006). 

194 ROLE OF ENERGY EFFICIENCY PROGRAMS, supra note 23, at 2. 

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developed with support from the DOE Clean 

Energy/Air Quality Integration Initiative, could fulfill 

this essential need. It has already been employed by the 

Metropolitan Washington Council of Governments 

(MWCOG), the Commonwealth of Virginia, the State 

of Maryland, and the District of Columbia.188

The MWCOG calculator, which is based on the 

RSG TMM methodology, allows the user to use 

standard load profiles for a specific renewable energy 

or energy savings technology, or project-specific load 

profiles, and to enter these profiles into an Excel-based 

spreadsheet.189 In developing its air quality plan to 

meet the eight-hour ozone standard, the MWCOG staff 

used the calculator to compute the annual or monthly 

avoided emission estimates for NOX using the hourly 

TMM emission rates that were embedded in the 

calculator.190 

An hourly marginal emissions methodology is also 

ideally suited to assess the benefits of EERE measures 

on high electric demand days. Energy savings measures 

188 The following document issued by the Metropolitan 

Washington Council of Governments (“MWCOG”) relies on the 

analysis from the TMM calculator in its chapter on voluntary 

control measures (Chapter 6) and in Appendix H. METRO. WASH.

COUNCIL OF GOV’TS, PLAN TO IMPROVE AIR QUALITY IN THE 

METROPOLITAN WASHINGTON, DC-MD-VA REGION: STATE 

IMPLEMENTATION PLAN FOR 8-HOUR OZONE STANDARD 6-61 to 6-

82, Appendix H (2007), available at 

http://www.mwcog.org/environment/air/downloads/SIP_APP/defa

ult.asp. 

The calculator tools are set forth in Appendix H of the MWCOG 

document for LED traffic lights, wind energy and the DC RPS. 

The methodology underlying the calculator tools is set forth in the 

following document contained in Appendix H of the MWCOG 

document. See COLIN HIGH & KEVIN HATHAWAY, RES. SYSTEMS 

GROUP, AVOIDED AIR EMISSIONS FROM ENERGY EFFICIENCY AND 

RENEWABLE ENERGY POWER GENERATION IN THE PJM

INTERCONNECTION POWER MARKET AREA (2007), available at 

http://www.mwcog.org/environment/air/downloads/SIP_APP/App

_H_-_RSG_Avoided_Emissions_Rept_5-23-07_Draft_Final.pdf. 

189 METRO. WASH. COUNCIL OF GOV’TS, supra note 188, at 6-61. 

190 Id. The calculator tool that was initially developed in 2007 

has been refined in 2008 and 2009 under a grant issued by the 

DOE’s Office of Energy Efficiency and Renewable Energy 

(EERE). The grant was issued to the Virginia Department of 

Mines, Minerals and Energy under the EERE’s state Energy 

Program to support a project titled “Promoting Air Quality with 

Clean Energy in the Metropolitan Washington Region.” The 

methodology has been further refined under work performed for 

the DOE’s Loan Guarantee Project Office to encompass GHGs and 

other pollutants. In view of the NOx emissions trading regime in 

effect in Maryland, the District of Columbia, and Virginia, the air 

agencies in these jurisdictions committed to retire NOx allowances 

in an amount commensurate with the amount of calculated 

emissions. See id. at Appendix H. 

that reduce summer electric use “during the days and 

hours with the highest electrical demand, such as highefficiency air conditioning and commercial lighting, are 

particularly valuable in reducing emissions of NOX—a

precursor to ground-level ozone that causes adverse 

respiratory effects in adults and children.”191 “Ozone is 

formed on hot summer days, and the hottest summer 

days also are typically the days of highest electrical 

demand—the so-called ‘high electric demand days.”192

Research has demonstrated that daily NOX

emissions on high electric demand days from fossil 

fuel-fired electric generating units in certain regions of 

the country, such as the Northeast and the Mid-Atlantic 

regions, substantially exceed emissions on more typical 

summer days.193 “This result occurs because the peak 

load generating units used on these limited number of 

days each year (generally fewer than a dozen days) are 

typically older units with limited pollution controls.”194

There is a major opportunity to provide access to 

the TMM method and calculator to state and local 

governments. States have time to develop and 

distribute the database and calculator prior to the 

submission of the SIPs in 2013 and to train state 

employees to use this tool. 

IV. RECOMMENDATIONS 

Based on the findings presented above, this Article 

offers five recommendations. Adopting these 

recommendations would substantially advance efforts 

to measure the full air emission reduction benefits of 

EERE technologies. 

First, DOE, EPA, and state air, climate, and energy 

agencies, including the Board of The Climate Registry, 

should fund an enhancement of the eGRID database to 

provide a profile of hourly marginal emissions for each 

of the 8,760 hours of the year in each of the eGRID 

subregions. This enhanced eGRID database should 

cover all power markets as well as all regions and 

subregions of the North American Electric Reliability 

Corporation. 

The enhanced eGRID database would include 

additional information based on the TMM methodology 

or a similar methodology (initially using data from 

191 ROLE OF ENERGY EFFICIENCY PROGRAMS, supra note 23, at

2. 

192 Id.

193 See NE. STATES FOR COORDINATED AIR USE MGMT., FINAL 

WHITE PAPER: HIGH ELECTRIC DEMAND DAY AND AIR QUALITY IN 

THE NORTHEAST (2006). 

194 ROLE OF ENERGY EFFICIENCY PROGRAMS, supra note 23, at 2. 

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Continuous Emission Monitors and other data reported 

to the EPA). As a result, it should be possible to 

compile this database for a relatively modest amount of 

funding relative to the potential benefits. Since the 

enhanced eGRID database would be extremely useful 

in obtaining air quality, climate, and energy goals by 

federal, state, and local agencies, some type of costsharing arrangement among these agencies appears 

reasonable. 

Alternatively, it seems appropriate to fund such a 

database on a longer-term basis with funds from 

allowance auction revenues generated under regional or 

national climate change legislation. This funding can 

be justified on the grounds that the data will directly 

facilitate the development of more accurate GHG 

reduction estimates. 

Second, as an interim measure pending the 

completion of the eGRID enhancement set forth in our 

first recommendation, the federal government and 

states should utilize the eGRID non-baseload emissions 

methodology for calculating the emission reduction 

benefits of increased use of EERE technologies. This 

non-baseload methodology is far more representative 

than the eGRID system average methodology and has 

already been developed by the EPA. Moreover, the 

eGRID non-baseload methodology is recommended for 

estimating emissions reductions from EERE,195 and this 

methodology has been adopted by the EPA Green 

Power Partnership to calculate the GHG emission 

reduction benefits of renewable power.196

The eGRID non-baseload methodology could 

therefore serve as a valuable interim approach. During 

the interim period before the development of an 

enhanced eGRID database, appropriate agencies should 

consider allowing state and local governments, such as 

the MWCOG, and private entities to submit marginal 

emissions calculations from other verified sources, such 

as the RSG TMM, instead of using the EPA nonbaseload emissions estimates.197

195 ROTHSCHILD & DIEM ET AL., supra note 68, at 6. 

196 See Green Power Partnership, supra note 107.

197 For example, shortly before the publication of this Article, 

the PJM Interconnection announced the availability of CO2

emissions data for marginal generating units from January 2005 to 

December 2009. The related press release stated that this data “can 

be used to estimate carbon dioxide reductions from demand 

response, energy efficiency measures and increases in emissionsfree generation.” PJM Press Release, supra note 59 (attached 

graphic and tables comparing CO2 emission rates of marginal 

generating units compared to CO2 emission rates of system 

average generating units for 2005 to 2009). 

As noted above, recent research by RSG has 

determined that the difference between the eGRID nonbaseload methodology and the TMM methodology can 

be quite significant for certain regions and technologies. 

Therefore, reliance on this eGRID non-baseload 

approach should be only an interim step. 

Third, DOE, the EPA, and other interested agencies 

and parties should consider continuing their support of 

the Northeast Energy Efficiency Partnerships (“NEEP”) 

Evaluation, Measurement, and Verification (“EMV”) 

Forum,198 and other similar efforts to develop hourly 

load profiles for specific EE technologies. The EMV 

Forum was initiated in 2008 with funding from a 

number of entities, including DOE and the EPA. There 

remains strong synergy between the proposed work of 

the EMV Forum to compile hourly load profiles of 

various energy efficiency measures with the proposed 

compilation of hourly marginal emission rates under the 

TMM and the eGRID non-baseload methodologies. 

As stated in Part I.B.3, supra, the “time-matching” 

aspect of the TMM methodology involves matching the 

load profile for a specific technology against the 

emissions profile of the marginal units.199 The 

recommendation for the eGRID enhancement would 

provide information to generate the hourly emissions 

data on the marginal units, but the NEEP process and 

other similar efforts will provide the second piece of the 

puzzle for energy efficiency resources—the load profile 

for specific energy efficiency technologies, such as 

high-efficiency commercial air conditioning and highefficiency residential lighting.200

198 See SUSAN COAKLEY ET AL., NE. ENERGY EFFICIENCY P’SHIP,

NORTHEAST EVALUATION MEASUREMENT AND VERIFICATION 

FORUM: THREE-YEAR PLAN 3 (2008), available at 

http://neep.org/uploads/EMV%20Forum/EMV_forum_plan_4.08.p

df. 

199 See supra Part I.B.3.

200 With respect to renewable energy resources, many of the load 

shape profiles are already readily available. For example, the 

National Renewable Energy Laboratory has developed the 

PVWattsTM calculator to allow researchers to easily determine the 

energy production of grid-connected “PV” energy systems in 

different regions of the U.S. and the world. National Renewable 

Energy Laboratory, Renewable Resource Data Center, PVWatts, 

http://www.nrel.gov/rredc/pvwatts (last visited Mar. 5, 2010). The 

PVWatts calculator works by creating hour-by-hour performance 

simulations that provide estimated monthly and annual energy 

production in kilowatts and energy value. Users can select a 

location and choose to use default values or their own system 

parameters for size, electric cost, array type, tilt angle, and azimuth 

angle. In addition, the PVWatts calculator can provide hourly 

performance data for the selected location. See id.

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Fourth, DOE, the EPA, and other agencies and 

entities should initiate additional research to refine the 

time-matched marginal emissions methodology. 

Although both marginal emissions methodologies are 

far more accurate than the system average methodology 

in estimating avoided emissions from EERE 

technologies, these marginal methodologies require 

additional research to overcome certain limitations 

discussed below.201 Federal and state agencies should 

consider funding this research to increase the value of 

the marginal methodologies measurement approach. 

The most valuable improvement in both the eGRID 

non-baseload and TMM methodologies would be an 

expansion of the methodologies to include the impact of 

significant grid changes in the mid-term and long-term. 

This refinement would capture changes in emissions 

caused by the construction of new power generation 

units and by the retirement of old units. 

The two eGRID methodologies and the initial TMM 

methodology (used for the analysis of the two power 

markets discussed in this Article) only capture the 

displacement of existing fossil fuel-fired units on the 

grid by EERE technologies because they are based on 

the analysis of historic data. These analyses thus reflect 

the so-called operating margin202 that dominates nearterm changes in emissions. 

However, leading experts on GHG emission 

analysis emphasize that the analysis of avoided 

emissions from grid-connected electric generation over 

several decades also must consider the impact of new 

units that may be built on the grid—the so-called build 

margin.203 Further work is needed to develop costeffective and transparent methods to capture this “build 

margin.”204

In addition, electric generation modeling experts 

suggest that it would be useful to compare the results of 

at least one leading proprietary dispatch model with the 

results of the TMM methodology.205 This comparison 

201 See infra Part I.B.3. 

202 See QUANTIFYING GHG REDUCTIONS, supra note 13, at 13. 

203 See Id. 

204 RSG has undertaken initial work related to three NERC 

subregions to expand its TMM methodology to include projections 

of the construction of new electric generating capacity based on 

construction in progress, planned additions, and projections of 

future plant additions (reflecting alternative public policy 

scenarios). 

205 Telephone Interview with Chris James, Senior Assoc., 

Synapse Energy Econ., & Jeremy Fisher, Scientist, Synapse 

Energy Econ. (Winter, 2009). 

would be useful to further validate the methodology and 

to identify any areas of necessary improvement. 

Finally, we recommend that federal and state 

regulatory agencies, as well as electric utilities and their 

trade associations, consider the GHG emission profiles 

of electric generation in evaluating charging strategies 

for electric and plug-in hybrid vehicles as well as 

energy storage technologies. The GHG emissions 

profile of electric generation varies by region, season, 

and hour of the day. Unless governmental agencies 

consider the specific emissions profile of each region, 

they may fail to develop the optimal strategy for 

reducing GHG emissions from new technologies for 

energy storage and advanced electric vehicles. 

CONCLUSION

Federal and state agencies involved in climate, air 

pollution, and energy matters should act promptly to 

ensure that accurate methodologies are used in 

calculating reductions in emissions of GHGs and other 

air pollutants resulting from increased use of EERE 

technologies on the electric power grid. In particular, 

the forty states involved in The Climate Registry should 

consider developing an additional protocol and registry 

during their planned revisions of the General Reporting 

Protocol in 2010. The proposed supplementary 

protocol should incorporate a more accurate 

methodology based on marginal emissions to account 

for the increased use of EERE technologies on the 

electric power grid. Such an approach also should be 

incorporated into EPA’s Mandatory Greenhouse Gas 

Reporting Rule and in requirements of climate 

legislation relating to GHG inventories. 

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APPENDIX 

Comparison of RSG TMM Avoided Emissions Rates for NOx and CO2 with eGRID Emission Rates for Two 

NERC Subregions 

New York Upstate (NERC subregion NYUP) 

CO2 CO2 NOx NOx

Avoided 

Emission 

Rate 

(lb/MWh)

Difference 

from eGRID 

System 

(Average %)

Avoided 

Emission 

Rate 

(lb/MWh)

Difference 

from eGRID 

System 

(Average %)

Avoided 

Emissions 

Methodology

eGRID 

Output 

System 

Average 721 0% 0.83 0%

eGRID 

NonBaseload 

Average 1,706 137% 1.82 119%

RSG 

Average 

TMM Rate 1,813 151% 2.14 158%

TMMWind 1,832 154% 2.01 142%

TMM-PV 1,805 150% 2.12 155%

TMM-AC 1,798 149% 2.20 165%

TMMLighting 1,808 151% 2.18 163%

TMMLED Traffic 

Signals 1,822 153% 2.17 161%

For the New York Upstate data, 8760 hour generation or savings 

profile was simulated based on comparable profiles for the TMM-Wind, 

TMM-PV, TMM-AC, TMM-Lighting, and TMM-LED Traffic Signals.

Note: The results are based on 2005 eGRID data and 2005 

Continuous Emission Monitors (CEM) and other 2005 emissions data 

reported to the EPA. The year 2005 is the most recent for which 

comprehensive verified emissions data is available from the EPA.

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PJM Interconnection — (NERC subregion RFCE) 

CO2 CO2 NOx NOx

Avoided 

Emission 

Rate 

(lb/MWh)

Difference 

from eGRID 

System 

(Average %)

Avoided 

Emission 

Rate 

(lb/MWh)

Difference 

from eGRID 

System 

(Average %)

Avoided Emissions 

Methodology

eGRID Output 

System Average 1,139 0% 1.63 0%

eGRID NonBaseload Average 1,790 57% 2.77 70%

RSG Average TMM 

Rate 1,888 66% 2.14 31%

TMM-Wind 1,905 67% 2.01 23%

TMM-PV 1,892 66% 2.12 30%

TMM-AC 1,870 73% 2.2 35%

TMM-Lighting 1,894 66% 2.18 34%

TMM-LED Traffic 

Signals 1,881 65% 2.17 33%

For the PJM Interconnection data, 8760 hour generation or savings profile was 

simulated based on comparable profiles for the TMM-Wind data. 8760 hour generation 

or savings profile was based on actual profiles for the region for the TMM-PV, TMMAC, TMM-Lighting, and TMM-LED Traffic Signals.

Note: The results are based on 2005 eGRID data and 2005 EPA Continuous 

Emission Monitors (CEM) and other 2005 emissions data reported to the EPA. The 

year 2005 is the most recent for which comprehensive verified emissions data is 

available from the EPA.

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