Roadmap for Incorporating Energy 

Efficiency/Renewable Energy

Policies and Programs into State and Tribal 

Implementation Plans

Appendix I: Methods for Quantifying Energy 

Efficiency and Renewable Energy Emission 

Reductions
I-1

EPA-456/D-12-001j

July 2012

Roadmap for Incorporating Energy Efficiency/Renewable Energy

Policies and Programs into State and Tribal Implementation Plans

Appendix I: Methods for Quantifying Energy Efficiency and Renewable Energy Emission

Reductions

By:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Outreach and Information Division

Research Triangle Park, North Carolina

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Outreach and Information Division

Research Triangle Park, North Carolina
I-2

ACKNOWLEDGMENTS

We would like to acknowledge substantial contributions from members of an inter-office EPA 

team that included the Office of Atmospheric Programs, the Office of Policy Analysis and 

Review, the Office of General Counsel and Regions 1 and 6. This document also reflects 

comments received from a number of stakeholders, including state and local air quality 

agencies.
I-3

Contents

FIGURES ...................................................................................................................................................................... I-4

TABLES ........................................................................................................................................................................ I-5

SECTION I.1: PURPOSE ............................................................................................................................................... I-6

SECTION I.2: GETTING STARTED: IMPORTANT FACTORS........................................................................................... I-6

Availability of Staff and Budgetary Resources........................................................................................................ I-6

Energy Data Availability and Relationship between Air and Energy Agencies ....................................................... I-6

Potential Magnitude of Emission Reductions......................................................................................................... I-7

Multi-Jurisdictional Collaboration .......................................................................................................................... I-7

SECTION I.3: STEPS FOR QUANTIFYING EMISSIONS OF ENERGY EFFICIENCY AND RENEWABLE ENERGY POLICIES 

AND PROGRAMS......................................................................................................................................................... I-8

Step 1: Develop a Future Projected Baseline Emissions Inventory ........................................................................ I-8

Step 2a: Estimate the Energy Savings of Energy Efficiency and Combined Heat and Power Policies and ProgramsI10

Energy Efficiency............................................................................................................................................... I-10

Combined Heat and Power............................................................................................................................... I-11

Sources of Information for Energy Efficiency and Combined Heat and Power Policies and Programs............ I-12

Step 2b: Estimate the Energy Impacts of Demand Response Policies and Programs.......................................... I-13

Step 2c: Estimate the Renewable Energy Generated from Policies .................................................................... I-14

Important Considerations................................................................................................................................. I-15

Step 3: Understand How EE/RE Policies and Programs Impact Emissions in a Nonattainment Area.................. I-15

Determining the Geographic Boundary of Emissions Analysis......................................................................... I-16

Steps to Analyze Energy Efficiency/Renewable Energy Policy and Program Impacts ...................................... I-16

Step 4: Choose an Approach to Quantify Avoided or Displaced Electric Generating Unit Emissions .................. I-17

Four Emission Quantification Approaches ....................................................................................................... I-18

Suggested Quantification Approaches for Each State and Tribal Implementation Plan Pathway.................... I-31

Important Considerations for Emission Quantification Approaches................................................................ I-31

Managing Uncertainty ...................................................................................................................................... I-32

References................................................................................................................................................................ I-34
I-4

FIGURES

Figure 1: Steps for Quantifying Emissions of Energy Efficiency and Renewable Energy Policies and Programs....... I-8

Figure 2: Resources for Emissions Inventory Development ...................................................................................... I-9

Figure 3: Resource for Emissions Projections.......................................................................................................... I-10

Figure 4: Emissions Quantification Approaches ...................................................................................................... I-18

Figure 5: eGRID2010 Subregion Representational Map .......................................................................................... I-20

Figure 6: Emissions Quantification Using an eGRID Approach ................................................................................ I-21

Figure 7: Sample Curve for Relating Displacement to Capacity Factors.................................................................. I-22

Figure 8: Steps for Historical Hourly Emissions Rate Approach............................................................................... I-26
I-5

TABLES

Table 1: How to Choose an Emissions Quantification Approach............................................................................. I-19

Table 2: Allocating Displaced Energy Using the Capacity Factor Approach............................................................. I-24

Table 3: Suggested Emissions Quantification Approaches for Each Pathway ......................................................... I-31
I-6

SECTION I.1: PURPOSE

The purpose of this appendix is to help jurisdictions determine the best emissions

quantification approach when accounting for energy efficiency/renewable energy (EE/RE)

policies and programs in state implementation plans/tribal implementation plans (SIPs/TIPs). 

This appendix starts with an overview of the important factors to consider prior to quantifying 

emission of EE/RE policies and programs. Then, the emission quantification process is 

described with links to resources, tools and key information to help state, tribal and local

agencies select an appropriate emissions quantification approach. After reviewing this 

appendix, jurisdictions should understand the methods and options for quantifying EE/RE 

policies and programs in their SIP/TIP. 

SECTION I.2: GETTING STARTED: IMPORTANT FACTORS

Important factors to consider before quantifying emissions of EE/RE policies and programs 

include:

Availability of staff, budgetary resources, and energy data;

Energy data availability and air agency’s relationship with state, tribal and local energy 

experts;

Potential magnitude of emission reductions for the electric generating unit (EGU) sector 

associated with EE/RE policies and programs; and

Whether to engage in multi-jurisdictional collaboration. 

Understanding these factors upfront will aid the decision making process for how to account for 

emission reductions in a SIP/TIP. 

Availability of Staff and Budgetary Resources

Understanding the range and capability of the emission quantification approaches will help a 

jurisdiction judge the level of resources needed to address analytical questions on the 

emissions impact of EE/RE policies and programs. It is possible to estimate the emission 

impacts of EE/RE policies and programs with low staff resources. Basic approaches are easy to 

employ and are often useful in justifying more effort if there is a significant emission reduction 

potential. Methods that are more sophisticated could mean increased staff time, air-energy 

trainings and monetary resources devoted to paying for fees associated with proprietary energy 

models. Reviewing the existing tools and resources that complement each emission 

quantification approach described in this appendix is a good first step to determining the level 

of effort needed to quantify the emission impacts of EE/RE policies and programs

Energy Data Availability and Relationship between Air and Energy Agencies

A significant piece of information necessary for quantifying the emissions impact of EE/RE 

policies and programs is data on energy impacts from state energy offices, utility commissions,

or other data sources. The strength of relationship between a state’s air agency and its energy

agencies may influence whether the appropriate type of data is available for a SIP/TIP emissions 

analysis. For example, some approaches will require hourly energy impacts information, while 
I-7

others only need annual or season energy impact information. It is possible that, even in cases 

where relationships between air and energy agencies are strong, the types of data needed for 

specific emissions analysis may not be available at the level of detail compatible with the 

desired emissions quantification approach. In these cases, air agencies may need to rearrange 

the data to use it with a specific approach or choose the emissions quantification approach that 

is compatible with the available energy impacts information. Reviewing this appendix will help 

determine what energy impacts information will be needed depending upon the state, tribal or 

local air agency’s policy objectives and analytical questions. 

Potential Magnitude of Emission Reductions

One of the most important objectives in understanding the potential magnitude of emission 

reductions of EE/RE policies and programs is comparing the potential emission reductions for a 

particular pollutant resulting from an EE/RE policy or program to existing emissions for that 

pollutant:

1) From the EGU sector in the state, 

2) From all sources in that state, and 

3) In a nonattainment area for which the SIP/TIP is intended. 

This comparative analysis will help illuminate how large an impact the EE/RE policies and 

program may potentially have in an area relative to other contributors. Generally, state, tribal

and local air agencies inventory emissions on an annual basis to review the emission reduction 

potential of different control strategies. However, EE/RE policies affect EGUs that operate 

marginally at shorter time scales. Therefore, a typical annual emissions profile of the EGU 

sector will not be completely indicative of the emission reduction potential of EE/RE policies 

and programs within a jurisdiction. Some emissions quantification approaches provided in this 

appendix, such as the capacity factor and hourly emissions approaches, can be used at the 

same time as the emissions inventory development process to help determine the emission 

reduction potential of EE/RE policies and programs over shorter time periods without 

expending large resources. 

Multi-Jurisdictional Collaboration

Since power plants are interconnected through the larger electric power system, to fully 

understand the emissions impact of an EE/RE policy, it is often important to consider

collaborating with neighboring states to develop joint EGU analyses. Understanding the goals 

and intentions of neighboring state, tribal and local air agencies could be an important factor in 

deciding the most appropriate emissions quantification approach. For example, a highly 

resource intensive approach for one state may make sense if multiple states are contributing 

resources. Including collective EE/RE policy goals may also increase the emissions reduction 

potential within the region. In many cases, a collective quantification effort would enhance 

efficiencies in costs and would better capture cross state benefits in the air quality planning 

process.
I-8

SECTION I.3: STEPS FOR QUANTIFYING EMISSIONS OF ENERGY 

EFFICIENCY AND RENEWABLE ENERGY POLICIES AND PROGRAMS

There are four general steps state, tribal and local agencies should consider when quantifying 

emission reductions of new EE/RE policies and programs (see Figure 1). (If all of the EE/RE 

policies and programs sought for inclusion in the SIP/TIP process are included in baseline 

emission projections, then steps 2,3 and 4 are not needed.) 

Step 1: Develop a Future Projected Baseline Emissions Inventory

As a part of the SIP/TIP process, a jurisdiction must prepare an emissions inventory that 

documents the emissions in a selected base year for the EGUs within the area of analysis (e.g., 

state-level or multi-state level emission inventories). The information necessary to develop an 

Figure 1: Steps for Quantifying Emissions of Energy Efficiency and Renewable Energy Policies and 

Programs
I-9

emission inventory for the EGU sector includes electricity generation data, and emission factors 

to convert estimates of energy use into emissions or measured reports from continuous 

emissions monitoring (CEMS) equipment. For both SIPs/TIPs and developing a future projected 

baseline emission inventory, a bottom-up emissions inventory is the most appropriate and 

involves collecting activity data, emissions profiles and pollution control device information for 

each EGU in the state, tribal area, locality, or region. Developing a future projected baseline 

emissions inventory provides a comprehensive assessment of emissions and details regarding 

spatial and temporal attributes that are required for air quality modeling. (See Figure 2 for 

resources on emissions inventory development.)

Emissions relevant to SIPs/TIPs include all six criteria air pollutants, including precursors to 

ozone (nitrogen oxides (NOx) and volatile organic compounds (VOC)), as well as carbon 

monoxide (CO), sulfur dioxide (SO2), lead (Pb), and coarse and fine particulate matter (PM). 

Figure 2: Resources for Emissions Inventory Development

A baseline emissions projection forecasts what emissions will occur in the future, in the 

absence of additional policies. This baseline projection is a reference case that includes “on the 

books” EE/RE policies or programs, against which the impacts of new policies and programs are 

measured. When state, tribal or local agencies take into account certain EE/RE policies and 

programs in developing a baseline emissions projection, then such activities are considered 

already included in the SIP/TIP, as part of the SIP/TIP baseline pathway, and additional emission 

reductions cannot be granted through another SIP/TIP pathway. Only those policies and 

programs not included in the future baseline may be included in the subsequent steps. In other 

words, if a state, tribal or local agency incorporates the EE/RE policies and programs in the SIP 

baseline emissions projections, then any emission reductions from those EE/RE policies and 

Emissions Collection and Monitoring Plan 

System (ECMPS)

• The EPA system for reporting of 

emissions data, monitoring plans, and 

certification data.

• It replaces the Emission Tracking System 

that previously served as a repository of 

SO2, NOx, and cabon dioxide (CO2)

emissions data from the utility industry.

• The EPA collects data annually in fiveminute, hourly, daily, monthly and 

annual intervals from continuous 

emissions monitors at all large U.S. 

power plants.

• Available at: 

http://camddataandmaps.epa.gov/gdm/ 

Emissions & Generation Resource 

Integrated Database (eGRID)

• This free, publicly available software

from EPA has data on annual SO2, NOx, 

and CO2, and mercury (Hg) emissions for 

most power plants in the United States.

• It also provides annual average nonbase load emission rates, which may 

better characterize the emissions of 

load-following resources. 

• Available at: http://www.epa.gov/egrid 
I-10

EPA Emissions Inventory Improvement Program Technical Report 

Series, Volume X: Emissions Projections. 

•This is guidance that provides information and procedures to 

state and local agencies for projecting future air pollution 

emissions for the point, area, and mobile source sectors.

•It describes data sources and tools states can use for emissions 

projections.

•Available at: 

http://www.epa.gov/ttn/chief/eiip/techreport/volume10/x01.pdf 

programs are already counted and cannot be “double counted” in other parts of the SIP/TIP. 

(Refer to Figure 3 for resources for emissions projections and Appendix E for more information 

on the SIP/TIP baseline emission projections pathway.)

State, tribal and local agencies can project future emissions based on historic trends and 

expectations about the impact of numerous factors, including projections of population growth 

and migration, economic growth, fuel availability and prices, and technological progress. Some 

of these factors will rely on 

data about electricity 

imports and exports, power 

plant construction or 

retirement, transmission 

constraints and 

environmental regulations.

 

The underlying 

assumptions for the 

baseline emissions 

projection include an

electricity demand forecast. 

The forecast documents the historical, current, and projected pattern of energy demand in the 

state or region, as well as how future generation projections will meet forecasted demand. 

Step 2a: Estimate the Energy Savings of Energy Efficiency and Combined Heat 

and Power Policies and Programs

Energy Efficiency

The purpose of this step is to determine the energy savings from the specific EE policy/program, 

which can then be used to estimate any associated emission reductions from the power sector.1

Energy savings refer to the expected reduction in energy demand that are the result of a

specific EE policy and/or program. For SIP/TIP purposes, energy savings can be a reduction in 

the base year energy demand, and/or a reduction in the projection of future energy demand. 

The impacts of EE policies or programs are typically measured in kilowatt-hours (kwh) or 

gigawatt-hours (GWhs) and are generally reported on an annual basis, but some EE programs

may be available in smaller time intervals, such as a seasonal or hourly scale. 

A general approach for determining the amount of energy saved for EE policies and programs, 

although each EE policy and/or program will have individual factors to be considered. The 

general approach is:

 

1

Estimated savings from EE policies and programs can be calculated in many different ways. State energy 

regulators typically have methods for estimating energy savings in your jurisdiction. Methods may vary in 

precision and rigor. Energy regulators can provide the estimates of the energy savings in your jurisdiction from the 

EE policies and programs they oversee.

Figure 3: Resource for Emissions Projections
I-11

Step A: Determine the baseline energy usage subject to the EE policy and/or program.2

Step B: Determine the projected energy use after implementation of the EE policy 

and/or program.

Step C: Subtract the result of Step A from the result from Step B. The result yields the 

projected energy savings due to the EE policy and/or program.

For later verification purposes, state, tribal and local agencies may need to collect and compare

the evaluated (e.g., ex-post)3estimate of energy savings to initial forecast impacts. This added 

step can result in more accurate estimates of energy savings and associated emission 

reductions. The appropriate level of energy savings verification depends primarily on the SIP 

pathway selected. Other factors to consider are the magnitude of projected energy impact and 

the nature and type of efficiency program under consideration (for more information, consult

the appendices on each SIP pathway). 

Combined Heat and Power 

Combined heat and power (CHP) is an efficient method of providing energy services (electricity 

and thermal energy, including cooling) to the end user. Instead of purchasing electricity from 

the grid and simultaneously burning fuel in an on-site boiler or furnace to produce needed 

thermal energy, an industrial or commercial user can use a CHP plant on site to provide both 

services in one energy efficient step. CHP’s inherent efficiency and the elimination of demand 

for electricity from the power system with associated transmission and distribution losses can 

provide significant energy savings and lower emissions that can be accounted for in a SIP/TIP. 

Calculating the net energy savings from CHP requires estimating the expected reduction in the 

amount of electricity generated by an existing utility system as a result of the specific CHP

policy and/or program and subtracting from that the incremental fuel used by CHP systems 

over and above the existing boilers and/or furnaces that were replaced. The resultant savings 

can reduce current energy demand, future demand, or both. The purpose of this step is to 

determine the energy saving impacts of a specific CHP policy/program. 

For determining the amount of energy saved for CHP policies and programs, although each 

policy and/or program will have individual factors to be taken into account, the general 

approach is as follows:

Step A: Determine the baseline forecast of energy use for the activity subject to the CHP

policy and/or program.

Step B: Determine the projected energy use after implementation of the CHP policy 

and/or program. The projected energy use must reflect:

o The reduction in energy use at the utility power plant due to the reduction in 

demand caused by the generation of power on-site.

 

2

EPA (2010c), Chapter 2.

3

Evaluations of ratepayer funded energy-efficiency programs – sometimes referred to as evaluation, 

measurement, and verification (EM&V) – provide retrospective estimates of energy savings. They are typically 

conducted on an annual basis by state Public Utility Commissions (PUCs). It is important to note that energyefficiency forecast data – not ex-post EM&V data – are used to calculate avoided-emissions for SIP purposes.
I-12

Sources of Information on Energy 

Impacts of EE/CHP Policies

•State Public Utility Commissions

•State or Local Energy Offices

•Energy Information Administration

•Electric Grid Operators

•Regional Transmission Organizations

•Independent Service Organizations

•Utilities 

o The incremental increase in fuel use at the industrial or commercial facilities that 

the CHP system uses over and above the boilers and/or furnaces that CHP 

replaced.

Step C: Subtract the result of Step A from the result from Step B. The result yields the 

projected energy savings due to the CHP policy and/or program.

For later verification purposes, state, tribal and local agencies may need to collect and compare 

the evaluated (e.g., ex-post) estimate of energy and fuel savings to initial forecast impacts. This 

added step can result in more accurate estimates of energy and fuel savings as well as

associated emission reductions. The appropriate level of verification depends primarily on the 

SIP pathway selected. Other factors to consider are the magnitude of projected energy impact 

and the nature and type of CHP program under consideration. (See the SIP Pathway 

appendices for more information.)

Operational and performance data for CHP technologies are also important factors in this 

analysis. This information is available from the EPA Combined Heat and Power Partnership, 4

National Renewable Energy Laboratory (NREL), the Department of Energy’s (DOE) eight 

Regional Clean Energy Application Centers5and universities and other energy associations that 

promote or conduct research on the applications of CHP.

Sources of Information for Energy Efficiency and Combined Heat and Power Policies 

and Programs 

Energy experts within a jurisdiction can help state, tribal and local air agencies understand 

current and future policy opportunities, as well as obtain estimates of the kilowatt-hour (KWh) 

impacts (projected and historical) 

from the EE/CHP policy/program of 

interest. The EPA recommends 

starting with the public utility 

commission staff and energy offices.

These agencies are typically 

responsible for the evaluation, 

measurement and verification 

(EM&V) of EE programs, and the 

tracking of RE generation. If 

jurisdictions need further 

information, the Energy Information 

Administration (EIA) and electric grid operators can also be good sources. For example, a large 

utility that controls the dispatch of resources within the area of analysis could be an electric 

grid operator and could provide electricity demand and supply forecasts. If a state, tribal or 

local jurisdiction is located within a regional transmission organization (RTO) or an independent 

system operator (ISO), these entities can also be helpful resources. These organizations can

help develop the energy impacts information or serve as a resource for assistance in developing 

 

4

For more information, go to: http://www.epa.gov/chp/. 

5

For more information, go to: http://www1.eere.energy.gov/industry/distributedenergy/racs.html. 
I-13

the energy savings or generation estimates for particular EE/CHP policies or programs. Refer to 

Appendix B, for more information on the roles and responsibilities of these organizations.

When gathering information from energy organizations in a state or region, air agencies will 

want to ask specific questions about the policy or program design characteristics. .

6

The 

questions below are intended to help air agencies ask questions up front to avoid confusion and 

ensure the energy savings information is provided in the proper SIP/TIP context. 

Program period: What year does the policy/program producing the energy savings start

and end?

Anticipated compliance or penetration rate: How many utilities will achieve the target 

or standard called for? How many consumers will invest in new EE/CHP equipment 

based on the initiative? How will this rate change over the time period?

Compliance assurances: Are there compliance penalties, monetary incentives or other

assurances included in the policy or program design?

Annual degradation factor: How quickly will the performance of the EE measure 

installed become less efficient?

Transmission and distribution loss (T&D): Is there an increase or decrease in T&D losses 

that would require adjustment of the energy savings estimate?

Past Performance: What is the success rate of existing or past EE/RE policies and 

programs?

Evaluation, Measurement and Verification (EM&V): Which programs have EM&V 

procedures in place, and how will energy savings be measured and verified over time?

Step 2b: Estimate the Energy Impacts of Demand Response Policies and 

Programs

“Demand response” is a broad term encompassing a range of programs designed to reduce 

electricity demand during peak periods of electricity use. Demand response initiatives range 

from programs that provide customer incentives for voluntary or mandatory load curtailment 

based on contractual arrangements, to dynamic pricing structures that charge higher rates for 

energy consumed during peak periods. 

Demand response policies and programs can effectively reduce emissions during “high electric 

demand days.”

7

 For example, hot summer days are conducive to ground-level ozone 

formation, and air conditioning loads on such days are often major contributors to electricity 

demand spikes. At the same time, some EGUs called “peaking units” only operate during 

periods of peak demand when the electric grid requires maximum generating capacity, and 

could be high-emitting sources. Peaking units may lack NOx controls because they have low 

 

6

EPA (2010c), p. 42.

7 On the hot, hazy days of summer, higher demand for electricity can result in a dramatic increase in ozoneforming air pollution. These are called “high electric demand days” or HEDD.
I-14

emissions on a seasonal basis, even if hourly emissions are high during periods when they are in 

use. 

Since demand response programs target peak demand hours, air agencies will want to collect 

hourly energy savings data, during peak hours, to translate the energy savings from these 

programs into emission reductions. Demand response programs may not always provide 

emission reduction benefits. For instance, some demand response programs relieve demand 

from the electric grid by replacing that generation with back-up generators that emit emissions.

(e.g., diesel generators) To properly account for the emission impact of demand response 

programs in a SIP/TIP, emission calculations should account for the net emissions (e.g., 

emission reductions from the electricity grid and any emission increases from back-up 

generation). 

To avoid a net emissions increase that can result from the use of emissions-intensive sources of 

backup power generation, state, tribal and local agencies should combine demand response 

efforts that focus on passive approaches, such as cycling down equipment to curtail electricity 

demand, with efforts to promote clean backup power that can be an effective strategy for 

achieving this objective. Some program administrators have addressed this issue by including 

requirements for the types of load reductions that are eligible for demand response incentives. 

Step 2c: Estimate the Renewable Energy Generated from Policies

This step determines how much energy would be displaced by the RE policy and/or program, 

including less polluting sources of new energy, such as cogeneration and fuel cells. In general, 

for renewable sources, state, tribal and local agencies will need data showing the total amount 

of energy provided to the grid by the RE source. Keep in mind, for later verification purposes, 

data on the amount of renewable energy generated may need to be collected and compared to 

original estimates.

Operational and performance data for renewable technologies are also important factors in this 

analysis. This information is available from National Renewable Energy Lab (NREL), as well as 

universities and other RE associations that promote or conduct research on the applications of 

RE. In addition, generation-related data and RE potential information can be obtained from 

many sources, including:

State energy offices 

Utility Integrated Resource Planning filings

Public utility commissions

ISOs

North American Electric Reliability Corporation (NERC)

EPA’s Emissions & Generation Resource Integrated Database (eGRID) 8

DOE’s EIA

DOE’s NREL

 

8

For more information, go to: www.epa.gov/egrid.
I-15

Important Considerations

The jurisdiction’s analysis of RE policy and program impacts needs to account for how various

RE resources generate energy at different times of the day and year. For example, wind power 

facilities may be unable to provide energy during high ozone episodes due to stagnation 

conditions, as compared to solar power facilities. Land-based wind energy facilities have to be 

sited in locations where winds speeds are sufficiently strong to produce power on as many days 

as possible, which can be in locations outside of the urban corridor where air quality tends to 

be the poorest. Nevertheless, these remote RE facilities may affect emissions levels at fossilfired EGUs that are positioned upwind of jurisdictions with poor air quality. Any air-quality 

analysis of RE policy impacts should account for these emissions impacts. Off-shore wind 

generation – which may be closer to coastal load centers and areas of poor air quality – should 

likewise be analyzed to determine how generation and emissions from surrounding EGUs are 

affected, including the magnitude of such impacts. 

Step 3: Understand How EE/RE Policies and Programs Impact Emissions in a 

Nonattainment Area

Air quality planners need to determine the displaced emissions attributable to each applicable 

EGU in order to understand how those emission reductions will improve the air quality in the 

nonattainment area.9 Emission reductions cannot be assigned to the nonattainment area 

based on where the EE/RE policy is implemented. Rather, a jurisdiction should assign emission 

reductions based on where the displacement of electrical generation will likely occur. All 

emission quantification approaches described below, except for the eGRID non-baseload 

emissions rate approach, can allocate emission reductions to an individual EGU or power plant. 

To determine if an EE/RE policy affects a nonattainment area, air agencies should (1) estimate 

which EGUs and EE/RE policy or program will likely affect generation and (2) use the emission 

rates at those EGUs. 

For example, if the nonattainment area imports a significant amount of electricity from 

locations outside and downwind of the area, reduced demand from EE could result in less 

electricity being imported, rather than reduced production (and consequently reduced 

emissions) within the nonattainment area, or in areas affecting its air quality. Conversely, if the 

energy savings reduce emissions at upwind sources, then the measure may produce some air 

quality benefits to the area. (For more details, see the section below on determining the 

geographical area where emissions occur.)

After state, tribal and local agencies quantify the emission reductions of EE/RE 

policies/program, as described in step four of this appendix, the state should use the 

appropriate air quality model to evaluate the extent to which reductions will improve air quality 

 

9

The current policy with respect to taking credit for emission reductions outside nonattainment areas for purposes 

of Reasonable Further Progress in ozone SIPs is as follows: RFP credit can be taken for VOC and NOx emission 

reductions within 100 kilometers (km) and 200 km, respectively, outside the nonattainment area under certain 

circumstances. This policy is currently under reconsideration. See “Reasonable Further Progress Requirements for 

the 1997 8-Hour Ozone National Ambient Air Quality,” 75 Federal Register 80420-80425, 80421, 

http://www.gpo.gov/fdsys/pkg/FR-2010-12-22/pdf/2010-32139.pdf,.
I-16

in the nonattainment area from the selected EE/RE policy and/or program. The EPA has 

separate modeling guidance documents to help state, tribal and local agencies choose the best 

air quality model for SIPs/TIPs.10

Determining the Geographic Boundary of Emissions Analysis 

Determining the appropriate geographical boundaries for an emissions quantification analysis 

requires an understanding of the electric power grid relative to the nonattainment area and 

EE/RE policy of interest. (Refer to Appendix B for more information on the electric grid.) A 

state, tribal or local agency will need to choose the most appropriate boundary for their 

analysis based on key factors:

Whether adjacent states are analyzing EE/RE policies within the same NERC region11

The location of the area in which the EE and/or RE policy is being implemented

Whether there are any transmission constraints where the EE/RE policy is being 

implemented

How energy is transferred within the state and across state boundaries

Determining the location of the emission reductions that occur at fossil fuel fired generation 

units is challenging because electricity from numerous generators is fed into an electrical grid 

from which many different consumers at various locations will draw power. There typically is 

no direct connection between a specific facility generating electricity and the end user of that 

electricity. Understanding how the electric grid operates within a jurisdiction is the first 

important step in making an approximation as to which units would be affected by a certain 

EE/RE policy and program. 

In areas of the country where several states in close proximity to one another implement EE 

and/or RE policies and programs, it may be advantageous for these states to work together in 

conjunction with their electric grid operation, ISO/RTO and EPA regional office to identify the 

overall impact of the EE/RE policy and programs on the electrical grid in the future. Ideally, 

such a process will yield a technically valid solution that attributes the emission reductions from 

decreased reliance on fossil fuel fired EGUs in an equitable manner between the states, and 

also ensures that double counting of emission reductions does not occur. 

The EPA understands that conducting this type of analysis may be beyond the means of the 

jurisdictions that implement these EE/RE policies and programs. Accordingly, EPA encourages

any state, tribal and local agencies that need assistance with this to contact the relevant EPA 

regional office for assistance.

Steps to Analyze Energy Efficiency/Renewable Energy Policy and Program Impacts 

Determining the location of the fossil fuel fired units that can operate less as EE or RE becomes 

available can be a complex task, particularly when the efficiency or renewable resources are 

located outside the nonattainment area that seeks to use the emissions reduction from that 

 

10 For more information, go to: http://www.epa.gov/ttn/scram/aqmindex.htm.

11 For more information, go to: http://www.eia.gov/cneaf/electricity/page/prim2/figure7.html.
I-17

reduced generation for SIP/TIP purposes. The following three steps can be used to analyze 

EE/RE policies and programs:

Step 1: Review historical analysis to determine the location of the fossil fuel fired EGUs 

that could reduce their output as EE or RE resources were made available. This 

information should already exist at the balancing authority or reliability organization 

such as an, ISO or RTO that oversees the electrical grid for the area or the utility that is 

complying with the EE or RE policy. 

Step 2: Understand how the grid has responded in the past as efficiency or renewable 

resources have come on-line in order to develop planning assumptions for how the grid 

will respond in the future. 

Step 3: Obtain and review the results from existing dispatch modeling conducted by the 

grid operator of the balancing authority, ISO or RTO. The grid operators have the most 

pressing need to accurately determine the impact that EE or RE resources will have on 

the future operation of the electrical network. 

The next section will describe four different emission quantification approaches that state, 

tribal and local agencies can consider. 

Step 4: Choose an Approach to Quantify Avoided or Displaced Electric 

Generating Unit Emissions

State, tribal and local agencies will need to choose the most appropriate emission 

quantification approach based on the agency’s policy objectives. Each approach answers

different analytical questions, has varying levels of rigor, assumptions, resource requirements, 

data needs, and varied temporal and spatial scales of emission outputs. They all are intended 

to quantify the avoided or displaced emissions from fossil fuel generation as a result of EE/RE 

policy/program implementation.12 

The level of sophistication of each emission quantification approach is inversely related to the 

degree of uncertainty inherent in each approach. Basic approaches apply simple assumptions 

and tend to have a higher degree of imprecision compared to more sophisticated approaches. 

Figure 4 shows how the level of sophistication of those approaches increases as you go from 

straightforward emissions calculations to complex modeling.

Basic approaches, such as eGRID emission rates, are generally more appropriate for preliminary 

screening analyses or to be used for analyzing the incorporating of emission reductions in 

specific SIP/TIP pathways, such as the weight of evidence (WOE) or emerging/voluntary 

measures SIP pathway (Refer to Appendix H for the WOE pathway and Appendix G for the 

emerging/voluntary measures pathway). More sophisticated approaches, such as energy 

modeling can simulate how the system of EGUs will change generation and emissions given 

specific assumed conditions and capture multiple assumptions for multiple factors such as:

 

12 Lifecycle emissions are not addressed in this appendix and do not apply to quantifying emissions for SIP/TIP 

purposes. 
I-18

electricity imports, exports, fuel prices, unit availability, and technology performance. (Refer to 

Appendix B for more information on how the electric grid operates) Therefore, more 

sophisticated approaches are most appropriate for quantifying emissions for the baseline 

emissions projection and control strategy pathways. (Refer to Appendix E for information on 

the baseline emissions projection pathway and Appendix F for information on the control 

strategy pathway.)

Four Emission Quantification Approaches

This section explains four emission quantification approaches, including how each approach 

works, when to use it, the advantages and limitations, available tools to support each, and an

emission quantification example13. The approaches are:

Basic approach: eGRID sub region “non-base load” emission rates 

Basic approach: capacity factor emission rates 

Midrange approach: historical hourly emission rates

Sophisticated approach: energy models

Agencies will have different policy objectives and associated analytical questions associated 

with incorporating EE/RE policies and programs in their SIPs/TIPs. Some of the policy objectives 

and analytical questions state, tribal and local agencies may want to address can be satisfied by 

one or more of the quantification approaches. Table 1 provides analytical questions commonly 

asked during the SIP/TIP process and the corresponding emissions quantification approach. 

 

13 Emission quantification examples are currently under development. For more information on specific SIP/TIP 

examples, go to Appendix K.

Figure 4: Emissions Quantification Approaches
I-19

Table 1: How to Choose an Emissions Quantification Approach

Analytical Questions Emission 

Quantification 

Approaches

Energy 

Data Needs

Emissions

Outputs

Pathways

What is the relative 

magnitude of SO2 or NOx 

emission reductions of an 

EE/RE policy or program 

in my jurisdiction?

eGRID region 

non-baseload

emission rates 

approach

Annual or season 

energy impacts 

(megawatt hours 

(MWh))

eGRID regional

average nonbase load

emissions 

WOE 

Which EGUs in my state 

are on the margin and 

how much will emissions 

be displaced on a 

seasonal or annual basis? 

Capacity factor 

emission rates

approach

Annual or seasonal 

energy impacts 

(MWh)

EGU emissions 

of marginal 

units on an 

ozone season 

or annual basis

WOE,

Emerging/ 

Voluntary 

Measures, or 

Control 

Strategy

Pathways

How can I quantify hourly 

emission reductions? 

How much are emissions 

reduced during peak 

electricity demand? 

Historical hourly 

emission rates

approach

Hourly energy 

impacts, peak 

and/or base load 

effects (MW or 

MWh)

Quantify EGU 

hourly 

emission rates 

and hourly 

emission 

reductions

Baseline, WOE,

or Control 

Strategy 

Pathways

How will EGU emissions 

change in future years? 

How can I compare 

baseline emissions 

forecasts and emissions 

of new EE/RE policies? 

How can I simulate 

emission changes of 

EE/RE policies when also 

subject to cap and trade 

program(s)?

Energy models 

approach 

Hourly, seasonal or 

annual energy 

impacts depending 

upon model 

capabilities (MW or 

MWh)

Average 

emissions 

based on 

dispatch 

order, hourly 

emissions or 

seasonal 

emissions

WOE,

Emerging/ 

Voluntary 

Measures, or 

Control 

Strategy 

Pathways

Basic Approach: eGRID Sub region “Non-Base Load” Emission Rates 

This approach entails a simple calculation. An agency would multiply the amount of generation 

or electricity consumption displaced by the EE/RE policy/program by the “non-base load” 

emission rate indicated for a specific pollutant in an eGRID subregion. The non-base load 

emission rate for an eGRID subregion represents an average emission rate for the EGUs that are 

likely to be displaced by an EE/RE policy and program. 

What is eGRID?

The EPA’s eGRID is a data source that provides information on the environmental 

characteristics of almost all electric power generated in the U.S. The eGRID includes 
I-20

operational data such as total annual emissions, emission rates, generation, resource mix, 

capacity factors, and heat input.14 

The eGRID includes emissions of GHGs , NOx, and SO2, and Hg. The eGRID emissions data are 

associated with the generation of electricity, not with the consumption of electricity; therefore, 

the values do not account 

for transmission and 

distribution losses, 

imports and 

exports across

eGRID subregions

(or any other 

geographic area), 

transmission 

constraints within 

any geographic 

area, or life cycle 

emissions at EGUs

(e.g., emissions 

from the 

extraction, 

processing, and 

transportation of 

fuels).

The eGRID 

subregions are 

identified and 

defined by EPA, using the NERC regions and power control areas as a guide. An eGRID 

subregion is often, but not always, equivalent to an Integrated Planning Model (IPM)15 sub 

region. The 26 eGRID subregions in eGRID2010 are subsets of the NERC regions as shown in 

Figure 5. 

When to use the eGRID approach

The EPA recommends that state, tribal and local agencies use this approach as a method for 

estimating potential emission reductions for the WOE SIP pathway. Alternatively, the approach 

can be used as a preliminary, screening analysis before carrying out a more sophisticated 

analysis that could be used under another SIP/TIP pathway submission or to justify a more 

sophisticated analysis under the WOE pathway. 

 

14 For more information, go to: http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html. 

15 The IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector. 

It provides forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies while 

meeting energy demand and environmental, transmission, dispatch, and reliability constraints. 

Figure 5: eGRID2010 Subregion Representational Map

Source: 

http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2010V1_1_year07_Summa

ryTables.pdf>
I-21

Non-base load emission rate (lb/MWh)

x

(1/1-grid loss factor)

x

Reduced consumption or supply in energy of EE policy and 

program (MWh)

x

(2,000lbs/1 short ton conversion for criteria pollutants)

=

Tons of emissions reduced from EE/RE policy and program

Jurisdictions can estimate the emissions reduction potential for SO2 emissions, ozone season or 

annual NOx emissions in their region by applying eGRID subregion emission factors to the EE/RE 

policy energy impacts. This method requires minimal resources, simple assumptions, and 

provides a rough estimate of average emissions at a regional scale. This approach is most 

appropriate when EGUs within the eGRID subregion contribute to air quality in the 

nonattainment area, either by being in or upwind to the nonattainment area.) 

How does this approach work?

State, tribal and local agencies can use the eGRID subregion non-base load output emission 

rates to estimate emission reductions of EE/RE policies and programs that reduce consumption 

of grid-supplied electricity. In particular, eGRID will help planners figure out the non-baseload

output emission rates. This type of rate is useful to understand because it is associated with 

emissions from the EGUs most likely to be displaced when EE/RE policies and programs are 

implemented. These emissions data are derived from plant level data and are aggregated up to 

the eGRID subregion level.16

This basic eGRID method entails a simple calculation where a jurisdiction would multiply the 

amount of generation or 

electricity sales displaced by the 

EE/RE policy/program by the 

“non-base load” emission rate 

indicated for a specific pollutant 

in an eGRID subregion.

17

 The

non-base load emission rate for 

an eGRID subregion represents 

an average emission rate for the 

EGUs that are likely to be 

displaced by an EE/RE policy and 

program. This method is 

recommended to help state, 

tribal and local agencies estimate 

the relative magnitude of 

emission impacts from a 

potential EE/RE policy or program by using the equation in Figure 6. 

What are the advantages and limitations of the eGRID approach?

Advantages:

Easy “back of the envelope” calculation

Non-baseload output emission rates gives a basic understanding of how much EGU 

emissions could likely be avoided or displaced

 

16 EPA (2010a).

17 Grid loss factors should be included in this calculation. EPA (2010c).

Figure 6: Emissions Quantification using an eGRID Approach
I-22

eGRID data is derived from reported data submitted to EPA and the Energy Information 

Administration

Limitations:

Future looking EGU capacity is not represented 

o Some EGUs in base year may have already or will shut down in future years. 

The eGRID approach does not show which EGUs could have its emissions displaced

Information is from 2007 and is generally on a three year time lag

The eGRID only accounts for generation within a specific area and does not include 

information about imports/exports of electricity (except for state level net imports)

o Approach assumes EE/RE policies will affect all non-baseload plants 

proportionally to each plant’s non-base load generation.

What tools and resources are available for this approach?

The eGRID contains information in several formats, including spreadsheets, and summary 

tables in Portable Document Format (PDF) format.

18

 

Basic Approach: Capacity Factor Emission Rates 

This approach is based on the assumption that a power plant’s capacity factor is an indicator for 

the amount of generation subject to 

displacement. This approach makes 

general assumptions based on the 

capacity factor of a power plant to 

show, which power plants may 

reduce generation with additional 

EE/RE policies or programs. Capacity 

factor information can be found n 

the eGRID or a state, tribal or local 

agency can use their own capacity 

factors from other sources. This 

approach does not account for the 

complex nature of the electrical grid 

such as electricity imports, exports,

and transmission constraints.

What is a capacity factor? 

A power plant’s capacity factor is a 

ratio in a given period of time: "the 

actual electricity produced by a 

generating unit" to "the electricity

that could have been produced at continuous full-power operation."19

 

18 For more information on eGRID, go to: http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html. 

19 For more information, go to: http://www.eia.doe.gov/electricity/page/prim2/charts.html.

Figure 7: Sample Curve for Relating Displacement to 

Capacity Factors

Source: http://www.synapseenergy.com/Downloads/SynapseReport.2005-07.PQA-EPA.DisplacedEmissions-Renewables-and-Efficiency-EPA.04-55.pdf
I-23

The capacity factor of a power plant can be used as a proxy for how likely the power plant is to 

be displaced by an EE/RE policy or program. Figure 7 shows an example of a displacement 

curve, or a “rule of thumb,” that relates the likelihood that a power plant or unit’s output would 

be displaced to its capacity factor. Baseload plants, such as nuclear units or large coal-fired 

plants, have capacity factors close to 1, so they are represented on the lower right hand side of 

Figure 7. The capacity factor approach assumes that these plants will displace zero percent of 

their generation from the addition of EE/RE policies and programs. As a power plant’s capacity 

factor gets closer to zero, the likelihood that a plant will displace generation increases. For 

example, the capacity factor approach assumes that peak load plants with low capacity factors,

such as combustion turbines are represented on the upper left hand side of Figure 7, and 

therefore, are much more likely to be displaced.

When to use the capacity factor approach

State, tribal and local agencies could use a capacity factor approach to incorporate EE/RE 

policies and programs in their SIP/TIP as a control measure, as long as future generation 

characteristics, exports, and imports are included in the analysis. The approach could also be 

used in an analysis of the benefits of EE/RE policies and programs in support of a WOE 

demonstration or voluntary/emerging measure pathway. This approach is most appropriate to 

answer the questions such as: 

What is the relative dispatch order of the power plants within a state or region?

Which power plants are on the margin and what are the emission rates of those plants?

Estimating the emission impacts of energy efficiency policies or policies or solar policies or 

programs that generally affect non-baseload power plants more so than baseload power plants

would be the appropriate type of EE/RE policy or program to apply for this approach (e.g., using 

this approach for specific RE technologies, such as on-shore wind, that displace baseload EGUs 

would not produce satisfactory results). 

How does this approach work?

This approach is based on the assumption that a power plant’s capacity factor is an indicator for 

the amount of generation and emissions subject to displacement by an EE/RE policy/program. 

This approach makes general assumptions about power plant generation and displacement 

based on an power plant’s historical annual or seasonal generation. By using a general rule of 

thumb shown in Figure 7, the impacts of EE/RE policies and programs are allocated to each 

power plant’s or unit’s annual or seasonal emission rate. 

For example, the approach assumes that power plants with low capacity factors (operating at 

equal to or less than 20 percent of capacity) are most likely to be displaced by the EE/RE 

policy/program and power plants with high capacity factors (operating at equal to or greater 

than 80 percent of maximum capacity) would not be displaced by the EE/RE policy/program. 

The power plants with capacity factors between 20-80 percent are displaceable using a linear 

relationship shown in Figure 7. 
I-24

Seasonal Variation Considerations

While the annual capacity factors described above are helpful in determining which power 

plants are likely to displace generation by EE/RE programs/policies, EPA recommends using 

seasonable capacity factors when possible for summer-time ozone because annual capacity 

factors ignore seasonal weather variations. For example, many combustion turbines only 

operate during summer daytime hours in a typical year. Using an annual capacity factor would 

incorrectly allocate displaced emissions to these units during seasons when they are not 

operating. If you are assessing the impact of EE/RE programs on ground-level ozone, the EPA’s 

eGRID has ozone season capacity factors available for this type of analysis. 

Table 2 illustrates how an example could work in practice for an efficiency program projected to 

save 1,000 MWhs pear year. There are seven generating units in this hypothetical power 

system, labeled A through G in the first column. The second column shows the percentage of 

each unit’s production that could be displaced by the efficiency program, based on the rule of 

thumb from Figure 7. The third column shows each unit’s actual generation in the historical 

year being used. The fourth column shows the amount of energy that could be displaced at 

each unit, which is the second column multiplied by the first column. The fifth column shows 

the percentage of the saved energy that is allocated to each unit. This is done by dividing the 

displaceable energy for each unit by the total available displaced energy (e.g., Unit A’s displaced 

energy is 50,000 MWhs, which is 6.5 percent of the total 768,100 MWhs displaceable energy) 

and the sixth column shows the MWhs displaced at each generating unit (column five 

multiplied by 1,000 MWhs). The final step would be to multiply the MWhs displaced in Column 

6 with the appropriate emission rates for each unit. 

Table 2: Allocating Displaced Energy Using the Capacity Factor Approach

Unit

(1)

Percentage 

Displaceable 

(2)

Historical 

Generation (MWh)

(3)

MWhs 

Displaceable

(4)

Percentage of Energy 

Saved Allocated to 

Unit

(5)

MWhs Displaced 

(6)

A 100% 50,000 50,000 6.5% 65

B 82% 65,000 53,000 6.9% 69

C 79% 120,000 94,800 12% 123

D 48% 500,000 240,000 31% 312

E 22% 1,500,000 330,000 43% 430

F 0% 1,800,000 0 0% 0

G 0% 2,000,000 0 0% 0

Totals 6.035,000 768,100 100% 1,000

Source: http://www.synapse-energy.com/Downloads/SynapseReport.2005-07.PQA-EPA.Displaced-EmissionsRenewables-and-Efficiency-EPA.04-55.pdf

What are the advantages and limitations of this approach?

Advantages:

Emissions can be assigned to each power plant or generating unit

Relatively easy calculation if infrastructure is set up
I-25

Simple way to get a relative sense of the marginal unit in area of analysis

Limitations:

This is a simplified approach assuming power plants are operating the same throughout 

the year or ozone season

Emissions estimates are approximate, based on annual or seasonal capacity factors

o Does not account for maintenance and outages 

Imported and exported power is not considered

Assumes power plant generation characteristics are the same in the base year and 

future year.

Assumes a general rule of thumb where all energy savings or generation affect all 

peaking units first, which is not always true with some EE programs or RE technologies 

(e.g., street lighting programs, wind power)

What tools and resources are available for this approach?

The EPA is providing a Capacity Factor Emissions Calculator (CFEC) for state, tribal and local

agencies to use for this approach. The CFEC uses eGRID annual and ozone season capacity 

factors for the power plants represented in this database. Jurisdictions can follow the 

instructions in this appendix and the CFEC when using this emission quantification approach.20 

Midrange Approach: Historical Hourly Emission Rates

This approach requires technical manipulation of data about historical generation, including 

load and emission rates to determine EGU dispatch order and marginal emissions rates. By 

applying this approach, state, tribal and local agencies will understand where EGUs lie within 

the dispatch order for each hour, day, and month of a historical year. Depending upon where 

geographical boundaries are set, this approach may not account for electricity imports, exports,

and transmission constraints. And understanding these things will be useful in quantifying 

emissions in a SIP/TIP because the electricity grid is a large interconnected system that crosses 

nonattainment area and state boundaries. For example, if an EGU in a nonattainment area is 

exporting it’s generation outside of the nonattainment area, then EE/RE policies implemented 

within the nonattainment area will have little effect on the EGU’s emissions. 

The EPA has information state, tribal and local agencies can use for this approach. The EPA 

collects generation, emissions and heat input data in hourly intervals from CEMS for all large 

EGUs subject to EPA’s trading programs.21 State, tribal and local agencies can use reported 

hourly generation and emissions data for each EGU to derive hourly emission rates for one hour 

or group of hours. For example, 2 p.m. to 6 p.m. in the Electric Reliability Council of Texas

(ERCOT) NERC region22 is a typical group of hours that represents times of peak demand. 

 

20 Capacity factors are located in the eGRID excel file called: eGRID2010V1_1_year07_PLANT.xls. EPA (2010b).

21 For more information, go to: 

http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=emissions.prepackaged_select

22 The Electric Reliability Council of Texas manages the flow of electricity in Texas. For more information, go to: 

http://www.ercot.com/about/. 
I-26

Taking into account the emission rates from the EGUs that typically operate during these hours 

would represent the potential emission reduction during peak hours within ERCOT. 

When to use historical hourly emission rate approach

By applying this approach state, tribal and local agencies will understand (for every hour or 

segment of hours of an historical year) which EGUs are base load (operating all hours of the 

day), which EGUs are load following (EGUs that ramp up or down depending upon demand),

and which EGUs are peaking units (EGUs that only operate at high demand periods). This 

approach is most appropriate to answer the questions such as: 

How much emissions are reduced in blocks of hours, or during periods of peak electricity 

demand?

How much emissions are reduced for demand response policies or programs? 

Agencies can use an historical hourly emissions rate approach to incorporate EE/RE policies and 

programs in their SIP/TIP as a control measure, as long as future generation characteristics, 

exports, and imports are included in the analysis. The approach could also be used in an 

analysis of the benefits of EE/RE policies and programs in support of a WOE demonstration or 

baseline analysis. 

How does this approach work?

The state, tribal or local agency first needs to evaluate how the EE/RE policy reduces load or 

displaces generation in the area of analysis. Most importantly, the agency needs to identify if 

the EE/RE policy impacts peak hours and/or base load energy use. From this, air quality 

planners will be able to determine which EGUs within the dispatch order will be affected by the 

policies. 

It is possible for multiple EE/RE policies/programs to affect both the base load and peak hours 

of a day. In that case, the agency should add the programs “bottom up” to obtain an aggregate 

level of energy savings and generation on an hourly basis and then apply their impacts to the 

predicted displaced EGUs.23 Refer to Figure 8 for steps associated with this approach.

 

23 Synapse (2005).

Figure 8: Steps for Historical Hourly Emissions Rate Approach

Step 1: 

Collect EGU 

hourly 

information 

from EPA, 

DOE or 

other 

Step 2: 

Analyze 

EGU 

dispatch 

order and 

hourly 

emissions

Step 3: 

Identify 

"hourly 

segments 

of EGU 

emission 

rates"

Step 4: 

Apply 

hourly 

generation 

or savings 

profile of 

EE/RE

Step 5: 

Quantify 

hourly 

emission 

changes of 

new EE/RE 

program
I-27

What are the advantages and limitations to the approach?

Advantages:

Reported data is easy to find on EPA’s website on an hourly, daily, and quarterly basis

EGU emission rates from any group of hours can be derived from the hourly data

Emission impacts of different RE technologies can be compared against other RE 

technologies (e.g., wind vs. solar) and EE policies and programs

Limitations:

Setting up hourly emissions database can be resource intensive if infrastructure is not in 

place

Future EGU capacity changes are not represented.

Only EGUs subject to EPA’s national reporting requirements are represented in EPA’s 

hourly EGU database24

Energy import and export exchanges and transmission constraints are not captured

What tools and resources are available? 

The EPA has available hourly emissions and generation data.25 EGUs greater than 25 

megawatts (MW) must report hourly emissions data to EPA on quarterly basis to comply with 

EPA’s National Acid Rain Program.

26

The Mid-Atlantic Regional Air Management Association (MARAMA) completed an hourly 

emissions analysis of the states in the Northeast and Mid-Atlantic region.27 The purpose of the

analyses was to assist states in the Northeast and mid-Atlantic regions with addressing policy

relevant questions concerning emissions during periods of peak electricity demand.

The Washington Council of Governments used a time-matched marginal emissions approach 

that matches certain EE or RE technologies or measures with historical hourly emissions 

information from the EPA hourly database. The emissions tool is applicable for the 

Virginia/Maryland/ Washington, DC area.28 

 

24 For more information, go to: http://www.epa.gov/airmarkets/.

25 For more information, go to: http://www.epa.gov/airmarkets/.

26 For more information on the hourly emissions and generation information, go to: 

http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=iss.progressresults

27 For more information, go to: 

http://www.marama.org/RegionalEmissionsInventory/2007hourlypoint/FinalDoc_mar2011_Analysis_of_Hrly_CA

MD_Emissions_Data.pdf.

28 For more information, go to: http://www.mwcog.org/environment/air/EERE/default.asp. 
I-28

Sophisticated Approach: Energy Models

This approach employs dynamic simulation models that forecast which EGUs will be displaced 

in the future based on inputs and assumptions in the model. Energy models can produce 

emissions output information at various levels and account for the complex interactions of the 

grid such as, transmission constraints, import/export dynamics, fuel prices, air pollution control 

equipment and wide range of energy policies, and environmental regulations. 

What is an energy model? 

Energy models require extensive underlying data and complex formulation that represents the 

engineering and economic decisions made by the energy system. They capture the complex 

interactions within the electricity market and simulate what might happen given a set of 

assumptions. These models are equipped to:

Simulate energy transfers among different regions

Optimize system dispatch from EGUs

Incorporate transmission constraints, forced outages, environmental regulations, plant 

retirements, new generation, and limitations on specific power plants (e.g., ramp rates, 

start-up constraints minimum down time)

Dispatch models

Dispatch models specifically replicate least-cost system dispatch, with the lowest cost resources 

dispatched first and the highest cost last. Dispatch models determine which generating units 

are displaced and when they are displaced based on economic and operating constraints. 

Capacity expansion models

Capacity expansion models (also called system-planning models) can examine the potential 

long-term impacts on the electric sector or upon the entire energy system of an EE/RE policy 

and program. These models predict how the electric system will evolve over time; including 

what capacity will be added through the construction of new generating units and what units 

will be retired, in response to changes in new regulations, demand, and prices. 

When to use an energy model approach

If a jurisdiction wants to incorporate EE/RE policies and programs in their SIP/TIP in the baseline 

emissions projection pathway or as a control strategy, using an energy model, dispatch model, 

capacity expansion model, or comparable model, is recommended. Either energy model or an 

alternative emission projection tool can handle multiple iterations of different EE/RE 

policy/program scenarios, therefore, state, tribal and local agencies can use a single emission 

quantification method to compare emissions of a baseline emissions projections and varying 

policy cases with new EE/RE policies. 

Dispatch models are generally good to use for analysis of one to five years into the future 

especially when the future EGU fleet is not changing substantially. An hourly dispatch model 

can be used to determine hourly emission rates, which can then be aggregated by time period 
I-29

and applied to a range of EE/RE policies and programs according to their production 

characteristics. 

Capacity expansion models forecast future generation and retirements, as well as the dynamic 

fluctuation within the electric grid and are generally useful to use for analysis five to 30 years 

into the future. This approach is most appropriate to use when an EE/RE policy or program is at 

a large enough scale to completely change electric system operations (e.g., EE/RE policy 

transforms the electric sector market or future EGU operational characteristics)

How does this approach work?

Dispatch models

Energy modeling experts or consultants normally operate dispatch models. These models 

determine which EGUs operate on the “margin” in the electrical power system (typically the 

most expensive unit needed to meet demand is the “marginal EGU” in a given time period). 

States can use hourly dispatch or energy models to determine hourly marginal emission rates 

(pounds/kWh), which can then be aggregated by time period and applied to a portfolio of 

programs used to achieve the EE/RE policy requirement. 29

There are important considerations when using dynamic simulation models such as dispatch 

models. Since this method can be less transparent than other methods, jurisdictions should 

inform the EPA regional office of important input assumptions for any dispatch or energy model 

used to measure displaced emissions. 

The following information should accompany a state, tribal or local agency’s SIP/TIP submittal 

under this pathway for any quantification of emission reductions using a dispatch or similar 

type of model:

Type and amount of energy savings/generation information used

o Specify if peak (MW), annual (MWh), seasonal, and/or hourly load information was 

applied for EE/RE policy 

Fuel prices assumed for all fuels and technologies

Emission rates for each applicable EGU

What are the advantages and limitations of dispatch models?

Advantages:

Electricity transfers are well represented

Can provide very detailed estimations about specific plant and plant type effects

Highly detailed, geographically specific hourly emissions data at the EGU level

Can simulate emission changes for jurisdictions subject to cap and trade programs

Limitations:

The model is only as good as the assumptions

 

29 EPA (2010c), pp. 69-70.
I-30

All dispatch models are proprietary and require more resources than other approaches

Expertise in energy modeling is normally recommended

Future changes in electric system are not factored into model 

Capacity expansion models

Energy modeling experts or consultants normally operate capacity expansion models. These

models are typically used for longer-term studies (e.g., five to 20 years), where the impacts are 

dominated by long-term investment and retirement decisions. They are also typically used to 

evaluate large geographic areas. This method involves allowing the model to predict what will 

likely happen to the electric energy resource mix based on costs of new technology, growth, 

existing fleet of generating assets, environmental regulations (current and planned), and 

considering dispatch both with and without the new clean energy resource.30

The following documentation should accompany a state, tribal or local agency’s SIP/TIP

submittal under this pathway for any quantification of emission reductions using a capacity 

expansion model or similar type of model:

Fuel price forecasts, EGU retirements, and EE/RE regulatory requirements (e.g.,

renewable portfolio standards)

Plant type and emission rates of assumed new generation for all applicable future years

Information on whether model outputs were validated or calibrated against actual data 

or another projection model

What are the advantages and limitations of capacity expansion models?

Advantages:

This is the most sophisticated way to capture how the electrical grid will react to EE/RE 

policies

Future EGU generation and retirements are represented

Provides very detailed estimations about specific plant and plant type effects

Highly detailed geographically data at EGU or emission unit level

Can simulate emission changes for jurisdictions subject to cap and trade programs.

Limitations:

The outputs of energy models are only as good as the assumptions

Energy models are proprietary, require significant resources to run than other 

approaches, and can be data intensive

Input assumptions for capacity expansion models can be difficult to discern due to the 

proprietary nature of the models

Expertise in energy modeling is normally recommended

Hourly emissions information is not always available

 

30 EPA (2010c), p. 71-72.
I-31

What tools and resources are available? 

There are many different energy models available. Dispatch models include: PROSYM, 

PROMOD, and Ventyx Market Analytics. Capacity expansion or planning models include: 

NEMS, IPM, and ENERGY 2020.

Suggested Quantification Approaches for Each State and Tribal Implementation Plan 

Pathway

The EPA is providing suggested emission quantification approaches state, tribal and local 

agencies can use as guidelines when accounting for emission impacts of EE/RE policies and 

programs within a certain SIP/TIP pathway (see Table 3). These approaches are suggestions 

only, so a jurisdiction can choose to use an alternative approach, not listed here, that has 

comparable rigor and emission results. Before getting too deep into any EE/RE emissions 

analysis, contact the air program in an EPA regional office31 to discuss options for the emission 

quantification approach that is appropriate for the EE/RE policies and programs at hand.

Table 3: Suggested Emissions Quantification Approaches for Each Pathway

Pathway Suggested Quantification Methods32

Baseline Emissions Projection Pathway Energy models approach

Historical hourly emission rate approach

Alternative emissions projection tools or analysis

Control Strategy Pathway Energy models approach

Historical hourly emission rate approach

Capacity factor approach

Emerging/

Voluntary Measures Pathway

Capacity factor approach

WOE Pathway Energy models approach

Historical hourly emission rate approach

Capacity factor approach

eGRID subregion non-base load emission rates

 

Important Considerations for Emission Quantification Approaches

All emission quantification approaches should address the following key information:

Defining geographic boundaries at the appropriate regional level (see section below)

Identifying EGU dispatch order

Accounting for electricity imports/exports

Transparency of data sources and analytical steps 

Recognition of any transmission constraints

Environmental regulations affecting EGUs 

If a state, tribal or local agency is not using an energy modeling approach, the following 

information should be specifically addressed in any SIP/TIP submission:

 

31 For more information, go to: http://www.epa.gov/aboutepa/where.html.

32 The quantification methods are only suggestions. Please note that the capacity factor and historical hourly 

emissions approach are under a third-party peer review process as of June 2012.
I-32

Future Generation

If the projections for EE/RE policies and programs extend out more than five years, then, for 

more sophisticated analyses, a state, tribal or local agency should develop assumptions for how 

future generation will change over time. The jurisdiction should examine each nonattainment 

area33 and assign emission rates to new units expected to come online or exclude planned 

retired plants in the jurisdiction’s future emission rates. There are multiple organizations that 

project how EGUs will meet future demand and react to new environmental regulations. The

EPA recommends obtaining projections of future EGU growth from EPA, EIA, electric grid 

operators, RTOs, ISOs, or NERC.

It is also important to consider which new resources may be entering an area and whether 

there are plans for transmission upgrades. Energy efficiency resources can help an area avoid 

the need for new or upgraded transmission lines. Depending upon the region, upgrades could 

encourage further development of RE, or may permit greater access by older, high-emitting 

sources that may be more likely to run if the new transmission is built.

Energy Exports and Imports

The EGUs located in the area of analysis may import or export significant amounts of energy. 

The first step in addressing electricity transfers is to determine whether there has been 

significant movement in recent years between the area of analysis and other areas. The 

following data sources are available for electricity import/export information: 

Data on total generation and export/import percentages will indicate whether the area 

is a net importer or exporter, as well as the magnitude of transfers relative to total 

generation34

Most system operators (balancing authorities, RTOs /ISOs) release information annually 

about generation, loads, and interchange on their system

Long-term power purchase agreements that underlie exports and import transfer 

information

If EGUs collectively in an area of analysis are net exporters or importers, then the next step is to 

determine if the transfer level follows a daily load pattern, a seasonal load pattern or is a 

consistent source of energy to an outside importer throughout the year. Once typical energy 

transfers are characterized, the dispatch order in the area of analysis should be adjusted to 

account for these transfers within the relevant time frames.35 

Managing Uncertainty

All SIP/TIP emission reduction measures have some level of uncertainty, whether it comes from 

the uncertainty associated with the emissions factors for certain sources, the level of 

 

33 For more information on areas not meeting the National Ambient Air Quality Standards, go to: 

http://www.epa.gov/oar/oaqps/greenbook/.

34 For more information, go to: http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html.

35 Synapse (2005).
I-33

compliance with existing SIP/TIP measures, or air quality modeling for an attainment 

demonstration. By using conservative assumptions, appropriate discount factors or verification 

techniques, emission reductions from EE/RE policies and programs can be applied for SIP/TIP

purposes. The EPA recognizes that there will likely always be some level of uncertainty 

regarding the exact quantity and location of emission reductions resulting from EE or RE

measures. However, in many cases EPA also believe that you can apply existing tools with 

sufficient rigor to be able to quantify estimated emission reductions with acceptable certainty 

to allow the reductions to be credited. 
I-34

References

EPA (2010a). eGRID Technical Support Document. Available online at

<http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2010TechnicalSupportDocumen

t.pdf>

EPA (2010b). eGRID Table in: eGRID2010V1_0_STIE_USGC. Available online at

<http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html>

EPA (2010c). Assessing the Multiple Benefits of Clean Energy. September 2011. Available online at

<http://www.epa.gov/statelocalclimate/resources/benefits.html> 

Synapse Energy Economics, Inc. (2005). Methods for Estimating Emissions Avoided by Renewable Energy

and Energy Efficiency. July 8, 2005. Available online at <http://www.synapseenergy.com/Downloads/SynapseReport.2005-07.PQA-EPA.Displaced-Emissions-Renewablesand-Efficiency-EPA.04-55.pdf>
35

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Outreach and Information Division

Research Triangle Park, NC

Publication No. EPA-456/D-12-001j

July 2012