From: Culligan, Kevin [mailto:Culligan.Kevin@epa.gov] 

Sent: Thursday, May 29, 2014 9:14 PM 

To: Higgins, Cortney 

Cc: Wayland, Robertj 

Subject: FW: several more TSDs 

Cortney – The Existing State Plans TSD is actually now an appendix to state plan considerations – 

we sent you a version with edits on 5-16. Will make sure it is correctly referenced in the preamble. 

From: Culligan, Kevin 

Sent: Friday, May 16, 2014 5:49 PM 

To: Higgins, Cortney 

Cc: Wayland, Robertj 

Subject: several more TSDs 

Note – we’ve also included a memo on efficiency improvements on oil/gas referenced in the new 

version of the preamble. I am still tracking down the Treasury contact. 

<< State Plan Considerations TSD_04-10-14_InteragencyCommentsUnderEO12866_EPA 

Responses.docx >> 

<< State Plan Considerations TSD-Appendix_04-03-14_InteragencyCommentsUnderEO12866 

__revised by EPA 5-16-14.docx >> 

<< TechMemo - HRI at OilGas NGCC CT 05162014.docx >> 

<< GHG Abatement Measures Chapter 5 TSD Interagency Comments EPA Response May 15 

PM.docx >> 
Technical Support Document (TSD) for 

Carbon Pollution Guidelines for Existing Power Plants: 

Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric 

Utility Generating Units 

Docket ID No. EPA-HQ-OAR-2013-0602 

State Plan Considerations 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

June 2014 

 
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Table of Contents 

I. Introduction ........................................................................................................................3 

II. Description of State Plan Pathways .................................................................................4 

A. Direct Emission Limits .............................................................................................6 

B. Portfolio Approach ....................................................................................................8 

III. Enforceability Considerations under Different State Plan Scenarios .........................12 

A. Parties Regulated by the State other than Affected EGUs ......................................13 

B. Private or Public Third-Party Entity not Regulated by the State ............................15 

C. State Agency, Authority, or Entity ..........................................................................16 

D. Multi-State Approaches ..........................................................................................17 

IV. Incorporating End-Use Energy Efficiency and Renewable Energy Programs and 

Measures under a Rate-Based Approach ......................................................................19 

A. Concept of Adjusting EGU CO2 Emission Rates based on the Effects of End-Use 

Energy Efficiency and Renewable Energy ..................................................................19 

B. Approaches for Adjusting EGU CO2 Emission Rates ............................................20 

C. Methods and Tools for Quantifying Avoided CO2 Emissions from End-Use Energy 

Efficiency and Renewable Energy .........................................................................23

V. Quantification, Monitoring, and Verification of End-Use Energy Efficiency and 

Renewable Energy Programs and Measures .................................................................32 

A. Quantification, Monitoring, and Verification for End-Use Energy Efficiency .....35 

B. Quantification, Monitoring, and Verification for Renewable Energy Measures ...45 

VI. Reporting and Recordkeeping for End-Use Energy Efficiency and Renewable 

Energy Programs and Measures ....................................................................................71 

A. Reporting for End-Use Energy Efficiency Programs and Measures .....................72 

B. Reporting for Renewable Energy Programs and Measures ...................................74 

VII. Treatment of Interstate Emission Effects ......................................................................79 

A. Background ............................................................................................................81 

B. Summary of Possible Approaches for Treatment of Interstate Emission Effects..83 

VIII. Appendix ...........................................................................................................................92 

 
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I. Introduction

As discussed in the preamble, in section VIII.F, the EPA is proposing to give states broad 

discretion to develop state plans that best suit their circumstances and policy objectives. In 

developing its plan, a state will need to make a number of decisions that will require careful 

consideration, in order to ensure that its plan both meets a state’s policy objectives and is 

approvable by EPA. The preamble, in section VIII.F, identified several key decision points and 

factors that states should consider when developing their plans. In this section of the preamble, 

the EPA also raised a number of considerations for how it will apply the proposed general plan 

approvability criteria to different types of state plan approaches. This includes a number of 

considerations related to appropriate approaches, methods, and materials that are submitted for 

state plan components in an approvable plan, under different types of state plans. This technical 

support document (TSD) explains and discusses these considerations in depth, and elaborates 

options proposed in the preamble where relevant. Topics addressed in this TSD include: 

 Description of state plan pathways 

o Provided as context for the discussion of applied considerations that follows 

 Enforceability considerations under different plan scenarios 

o Summary of potential enforceable mechanisms under different state plan 

scenarios 

 Incorporating energy efficiency and renewable energy (EE/RE) requirements and 

programs in state plans, including: 

o Options for adjusting EGU CO2 emission rates based on the effect of EE/RE 

requirements and programs 

o Methods for estimating avoided CO2 emissions that result from EE/RE 

requirements and programs, for use in projecting emission performance under a 

state plan and in ex post adjustment of CO2 emission rates during plan 

implementation

 Quantification, monitoring, and verification of EE/RE requirements and programs 

o Survey of quantification, monitoring, and verification under existing state and 

utility EE/RE requirements and programs 

Comment [A1]: 1.This TSD should echo the

emphasis in the preamble on the role of the

ISO/RTO or other planning authority with respect

to understanding the system/reliability

implications of a state plan design on the grid and

about including “guidance” to states about

utilizing flexibility to address events (including

retirements) that may occur during the

compliance period while ensuring grid reliability.

EPA response:

A companion TSD, Projecting EGU CO2 Emission

Performance in State Plans, addresses the

potential role of ISO/RTO in facilitating state plan

development in a respective grid region, including

the role that an ISOs/RTOs could play in providing

analytical support to states during plan

development.

Deleted: 

Comment [A2]: The discussion of avoided CO2

calculations conflates two separate issues:  

their use as part of an ex ante determination of

whether a state plan (such as a portfolio approach)

will achieve a given level of emissions (or emissions

rate) as part of an equivalency determination and

their use ex post to ‘adjust’ emissions. It would help

clarify things if this distinction were discussed. With

regards to the ex post use, all emissions are already

measured at the stack, so an avoided emissions

calculation is unnecessary and would constitute

double counting of emissions reductions.

EPA response:

We have added text here in response to this

comment. The referenced TSD section addresses

both considerations.
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o Discussion of possible approaches for minimum requirements or guidance for 

quantification, monitoring, and verification for EE/RE requirements and programs 

included in state plans, building off existing state processes and infrastructure 

o Discussion of areas where supplemental information would be useful for 

estimating avoided CO2 emissions from EE/RE requirements and programs 

 Reporting and recordkeeping for responsible parties subject to EE/RE requirements or 

implementing EE/RE programs included in state plans 

o Survey of reporting and recordkeeping under existing state and utility EE/RE 

requirements and programs 

o Discussion of possible approaches for EE/RE reporting requirements for state 

plans, building off existing state processes and infrastructure 

o Discussion of areas where supplemental information would be useful for 

estimating avoided CO2 emissions from EE/RE requirements and programs 

 Treatment of interstate emission effects 

o Further elaboration of proposed approaches and alternatives discussed in the 

preamble 

It should be noted that the preamble discusses, and solicits comment on, legal issues 

concerning whether CAA section 111(d) authorizes some of the types of state plans described in 

this TSD and whether it authorizes state plans to include some of these types of measures. This 

TSD does not further discuss those legal issues, but solely for the purpose of describing all of the 

available types of state plans and measures, assumes that all of them are authorized under CAA 

section 111(d). 

II. Description of State Plan Pathways

As discussed in the preamble, the EPA is proposing a state plan approach that could 

accommodate a diverse set of state requirements, programs, and measures, through two basic 

approaches – direct emission limits and a portfolio approach. These two basic approaches 

provide four distinct state plan “pathways” under CAA section 111(d). These pathways include: 
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 Rate-based CO2 emission limits applied to affected EGUs; 

 Mass-based CO2 emission limits applied to affected EGUs; 

 A state-driven portfolio approach 

 A utility-driven portfolio approach 

Under this flexible approach, a state plan could include a combination of measures that reduce 

CO2 emissions at affected EGUs through the application of emission limits as well as measures 

that involve actions within the interconnected electricity system that reduce utilization at affected 

EGUs and thereby avoid EGU CO2 emissions. Examples of these latter measures include, among 

others, end-use energy efficiency resource standards and renewable energy portfolio standards, 

as well as certain components of utility integrated resource plans. A state could either rely solely 

on CO2 emission limits that are enforceable against affected EGUs or, alternatively, rely on a 

portfolio approach, which would include those limits as well as other enforceable measures. 

Table 1 provides practical examples of possible state plan approaches under each of these 

pathways, which are discussed in more detail below. 

This section elaborates the different plan approaches that are discussed in section VIII.B 

of the preamble, to provide context for the applied considerations that are discussed throughout 

the remainder of the TSD. In particular, some of these considerations apply to different types of 

state plan approaches. For example, section III of this TSD addresses enforceable legal 

mechanisms that might apply under different types of state plans, such as utility- and state-driven 

portfolio approaches, which would apply enforceable obligations to different entities. Section IV 

of this TSD applies to state plans that implement rate-based CO2 emission limits for affected 

EGUs that also provide for the adjustment of CO2 emission rates based on the effect of 

enforceable end-use energy efficiency and renewable energy requirements and programs that are 

incorporated in a plan. Section V of this TSD addresses quantification, monitoring, and 

verification of end-use energy efficiency and renewable energy programs and measures. 

Considerations addressed in this section would apply to states implementing a rate-based 

emission limit approach that provides for adjustment of CO2 emission rates based on the effect of 

end-use energy efficiency and renewable energy, as well as states implementing utility- or statedriven portfolio approaches that incorporate end-use energy efficiency and renewable energy 
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requirements and programs. Likewise, section VI of this TSD addresses considerations for 

reporting and recordkeeping for end-use energy efficiency and renewable energy requirements 

and programs, which would apply for these types of state plans. 

Table 1. Different Illustrative Plan Approaches 

 

A. Direct Emission Limits 

The first basic state plan approach is CO2 emission limits that apply directly to affected 

EGUs, and includes two pathways: 1.) rate-based CO2 emission limits applied to affected EGUs; 

and 2.) mass-based CO2 emission limits applied to affected EGUs. For both types of emission 

limits, end-use energy efficiency and renewable energy measures that avoid EGU CO2 emissions 

could be a major component of a state’s overall strategy for cost-effectively reducing EGU CO2

emissions. 

1. Rate-Based CO2 Emission Limits Applied to Affected EGUs 

Comment [A3]: It would be helpful to have the

equivalent formula for rate‐based equivalency. In

particular, all of the above considerations apply to

the numerator of this formula. In addition, the

contributions to the denominator should be

specified. Specific questions include:

Does generation from new NGCC units

count? New nuclear or CCS?

What amount of generation from existing

clean units (including non‐hydro RE, hydro,

nuclear, etc.) is included in the denominator?

How are uprates dealt with?

Is the EE generation included in the

denominator gross savings or net savings?

EPA response:

This topic is addressed in a companion TSD,

Projecting EGU CO2 Emission Performance in State

Plans. We have made revisions in that TSD in

response to similar comments in that document.

The referenced section in this TSD describes

different types of possible state plans.
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Rate-based emission limits would apply a lb CO2/MWh emission limit to affected EGUs. 

Depending on a state’s approach, compliance flexibility could be provided through different 

mechanisms, such as averaging among affected sources, or the use of tradable credits for avoided 

CO2 emissions resulting from end-use energy efficiency and renewable energy measures as 

discussed below. In the case of the latter approach, such credits could be used by an affected 

EGU to adjust its CO2 emission rate when demonstrating compliance with a rate-based emission 

limit.1

 Rate-based emission limits could be implemented on a state-by-state basis, or through 

multi-state averaging or trading. Rate-based emission limits might also be a component of a 

portfolio approach (described below), where the emission rate limit would not assure full 

achievement of the required level of emission performance specified for affected EGUs in the 

state plan, in which case the emission limit would be supplemented with other enforceable 

measures. 

Rate-based emissions limits could incorporate end-use energy efficiency and renewable 

energy measures that avoid EGU CO2 emissions, through an administrative adjustment by the 

state or tradable crediting system.2

 These adjustment credits could be used by an affected EGU to 

comply with the rate-based emission limit, by adjusting the unit’s reported CO2 emission rate.3

Under this approach, end-use energy efficiency and renewable energy measures that avoid EGU 

CO2 emissions would be enforceable components of a state plan. These actions would need to be 

enforceable components of a state plan to provide assurance that a sufficient amount of 

adjustment credits will be available to facilitate EGU compliance with the emission rate limit, 

                                                            

1

 Section IV of this TSD discusses possible approaches for such adjustments. 2

 Under a tradable credit system, a state would issue adjustment credits based on avoided CO2 emissions achieved 

through end-use energy efficiency and renewable energy measures. EGUs could then apply these tradable 

adjustment credits when demonstrating compliance with a rate-based CO2 emission limit. These tradable credits 

might be issued to affected EGUs for free or through purchase. Alternatively, end-use energy efficiency and 

renewable energy actions undertaken by private parties (including EGU owners and operators) might be eligible for 

the issuance of adjustment credits. Under an administrative adjustment approach, a state program administrator 

might administratively adjust an affected EGU’s CO2 emission rate based on avoided CO2 emissions achieved 

through state end-use energy efficiency and renewable energy deployment programs or utility compliance with 

energy efficiency resource standards or renewable energy portfolio standards. These adjustments might be 

apportioned to affected EGUs based on a predetermined metric. Affected EGUs would then begin with an adjusted 

CO2 emission rate when considering additional actions necessary for compliance with the rate-based CO2 emission 

limit. 

3

 Section IV of this TSD discusses possible approaches for such adjustments. 
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and that end-use energy efficiency and renewable energy measures that generate adjustment 

credits are properly quantified, monitored, and verified.4

2. Mass-Based CO2 Emission Limits Applied to Affected EGUs 

Mass-based emission limits would apply either an individual limit on CO2 tons emitted 

from an affected EGU or establish a finite CO2 emissions budget for a group of affected EGUs. 

The latter approach is typically implemented through a tradable allowance system. 

With mass-based emission limits, end-use energy efficiency measures that avoid EGU 

CO2 emissions could be a major component of a state’s overall strategy for cost-effectively 

reducing EGU CO2 emissions, but would be complementary to the enforceable state plan (i.e., 

not included as enforceable measures in a state plan). These actions could be used to help a state 

cost-effectively achieve the CO2 emissions limits, or to achieve other policy goals, but CO2

emissions performance would be assured through the enforceable limit on mass emissions from 

affected EGUs. 

B. Portfolio Approach 

The second basic state plan approach uses a portfolio of actions, in which a state plan 

includes multiple programs and measures that are designed to achieve either a rate-based or 

mass-based emissions performance goal for affected EGUs. This approach includes two 

pathways: 1.) a state-driven portfolio approach; and 2.) a utility-driven portfolio approach. A 

portfolio approach would include emission limits for affected EGUs along with other enforceable 

end-use energy efficiency and renewable energy measures that avoid EGU CO2 emissions. A 

portfolio approach could be state-driven or utility-driven, depending on the utility regulatory 

structure in a state. 

In general, a portfolio approach is distinguished from an emission limit approach by the 

fact that achievement of the full level of required emission performance for affected EGUs 

                                                            

4

 Section V of this TSD addresses considerations for quantification, monitoring, and verification of end-use energy 

efficiency and renewable energy measures. 

Comment [A4]: It would be very helpful to have

the explicit formula that is the basis for determining

mass‐based equivalence. In particular, the precise

list of sources whose emissions count needs to be

specified. Specific questions include:

Do emissions from new NGCC units count?

Do emissions from new CCS units count?

Do emissions from units with a capacity

factor of less than 1/3 or other uncovered

sources count?

How will biomass emissions be incorporated

when the final guidance on this is available?

EPA response:

This topic is addressed in a companion TSD,

Projecting EGU CO2 Emission Performance in State

Plans. We have made revisions in that TSD in

response to similar comments in that document.

The referenced section in this TSD describes

different types of possible state plans.
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specified in the plan is not ensured through the application of direct emission limits that apply to 

affected EGUs. 

A portfolio approach would include both direct emission limits that apply to affected 

EGUs and other indirect measures that avoid EGU CO2 emissions. Under a portfolio approach, 

end-use energy efficiency and renewable energy measures that avoid EGU CO2 emissions would 

be enforceable components of a state plan. This would be necessary because the emission limit 

applied directly to affected EGUs would not assure full achievement of the required level of 

emission performance specified in the state plan.5

As discussed below, due to differences in state utility regulatory structure, a portfolio 

approach implemented in a restructured state with retail competition will likely look quite 

different from one implemented in a state with vertically integrated, regulated electric utilities. 

This includes the process for developing the portfolio approach, the mechanisms for 

implementing it, the responsible parties, and the regulatory and legal relationships among parties 

and state regulators. 

1. State-Driven Portfolio Approach 

 A state-driven portfolio approach – rather than a utility-driven approach – is more likely 

to be adopted in a state with a restructured electricity sector. In these states, rate-regulated 

electric utilities have typically divested electric generation assets and there is often also retail 

competition where non-utility entities can supply retail customers with electricity.6

 Electric 

distribution utilities in these states typically purchase electricity from competitive wholesale 

markets. As a result, utilities in these states typically do not engage in a utility integrated 

resource planning (IRP) process. IRP processes, which are a natural fit for implementing a 

                                                            

5

 Under a portfolio approach, either a rate-based or mass-based emission limit might be applied. Such plans might 

also include application of a direct emission limit to a subset of affected EGUs. Both scenarios would necessitate 

inclusion of supplemental measures, such as end-use energy efficiency and renewable energy, or other measures that 

directly apply to affected EGUs (e.g., repowering or retirement of one or more high-emitting EGUs), in order to 

achieve the required level of emission performance for affected EGUs specified in the state plan. 

6

 In some restructured states, the holding company that owns the distribution utility may also own generating assets 

which are operated on a competitive basis. In these instances, there typically are legal provisions in place that 

mandate the independent management and operation of generation and distribution assets. 
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portfolio approach, are much more common in states with regulated vertically integrated utilities 

that own and operate electric generating assets. 

In restructured states, policies for increasing end-use energy efficiency and renewable 

energy are often established through regulations that apply to electric distribution utilities, such 

as end-use energy efficiency resource standards and renewable portfolio standards. Many of 

these states have also established independent non-profit entities to administer end-use energy 

efficiency and renewable energy deployment programs funded through regulated electricity 

rates. 

Under a state-driven portfolio approach a mix of entities might have enforceable 

obligations under a state plan. This includes owners and operators of affected EGUs subject to 

direct emission limits, as well as electric distribution utilities, private or public third-party 

entities, and state agencies or authorities that administer end-use energy efficiency and 

renewable energy deployment programs or are subject to portfolio requirements. 

2. Utility-Driven Portfolio Approach 

A state with vertically integrated, state-regulated electric utilities is more likely to adopt a 

utility-driven portfolio approach. In such states, private utilities own and operate electric 

generation, transmission, and distribution systems necessary to supply retail customers with 

electricity.7

 These utilities are overseen by state public utility commissions that approve utility 

capital investments and oversee utility operations, and allow utilities to recover approved 

investments and operating costs, along with a specified financial rate of return, through regulated 

retail electricity rates. State utility commissions often require regulated utilities to engage in an 

integrated resource planning (IRP) process, which seeks to identify the least-cost set of resources 

available to provide electricity to retail customers over a multi-year period, and often includes 

evaluation of measures such as end-use energy efficiency, demand-side management, and 

                                                            

7

 In some instances these regulated utilities may supplement electric generation with purchases through wholesale 

electricity markets and may also sell surplus generation in these wholesale markets on a competitive basis. 
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renewable energy.8

 Once an IRP is approved by a state public utility commission, a utility’s cost 

recovery and rate of return is often linked to identified measures and metrics in the IRP.9

Under a utility-driven portfolio approach, a vertically integrated utility would develop 

and implement a portfolio of measures designed to meet the rate-based or mass-based emission 

performance level for its affected EGUs specified in the state plan. This plan would likely be 

developed and approved through an IRP-like process overseen by the state public utility 

commission. If there is more than one rate-regulated electric utility in the state, the state might 

apportion the state emission performance level for affected EGUs among utilities. 

A utility plan under this approach might rely heavily on end-use energy efficiency and 

renewable energy actions, but also might focus on direct actions at affected EGUs, such as 

repowering to fire a lower-carbon fuel or retirement of high-emitting units. Such plans might 

also include direct emission limits on affected EGUs, or implementation of an environmental 

dispatch approach that incorporates CO2 emission rate into the dispatch protocol used by the 

utility to schedule electric generation.10

Under a utility-driven portfolio approach, the entire suite of obligations under the plan 

would be enforceable against the utility company, which would also be an owner and operator of 

affected EGUs. If there are other affected EGUs in the state that are not owned and operated by a 

vertically integrated utility, a state plan might need to include other measures that address CO2

emission performance by these affected EGUs. 

A similar approach could be taken by municipally owned utilities or utility cooperatives, 

which often also engage in an IRP process. However, state public utility commissions (PUCs) 

                                                            

8

 Depending on the state, an IRP process may also assess factors such as fuel diversity and environmental 

performance, among others, when identifying the least-cost mix of resources for a utility. 9

 In some cases, an IRP prescribes or authorizes specific actions. In others, an IRP serves as a guide for the utility 

and the public utility commission when evaluating acquisition or implementation of specific utility resources or 

programs. In such cases, the specific resource or program is approved through a PUC order that authorizes or

requires actions and identifies performance obligations. These orders may or may not be fully consistent with 

provisions in an IRP.

10 Vertically integrated utilities, even if they operate within the footprint of a competitive wholesale electricity 

market, may self-schedule generation assets.
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often do not regulate these utilities. As a result, implementation of a portfolio approach by these 

entities would introduce practical enforceability considerations under a state plan. 

III. Enforceability Considerations under Different State Plan Scenarios 

As discussed in the preamble of the proposal, in section VIII.F.1, a state plan must 

include enforceable measures. To ensure that its plan is enforceable, a state will need to: 

 Identify in its plan the entity or entities responsible for meeting compliance and other 

enforceable obligations under the plan 

 Include mechanisms for demonstrating compliance with plan requirements or 

demonstrating that other binding obligations are met 

 Provide a mechanism(s) for legal action if affected EGUs are not in compliance with plan 

requirements or if other entities fail to meet enforceable plan obligations 

As discussed in the preamble, responsible entities in an approvable state plan may include 

an owner or operator of an affected EGU, other entities with responsibilities assigned by a state, 

or the state itself. Other entities might include an entity that is regulated by the state, such as an 

electric distribution utility, or a private or public third-party entity. State responsibility might 

include obligations that are assumed directly by a state agency, authority, or other state entity to 

carry out aspects of a state plan. 

While this approach provides states with broad discretion to develop plans that best suit 

their circumstances and policy objectives, assigning responsibility to other parties regulated by 

the state, private or public third-party entities, or state entities raises enforceability 

considerations. This section discusses how the general enforceability approach discussed in the 

preamble, and described above, might apply in practice under different state plan approaches. 

This section describes possible scenarios of responsible entities and legal mechanisms 

and approaches that might be used to address enforceability considerations under different types 

of state plans. These scenarios were developed to capture the range of entities that are currently 

implementing end-use energy efficiency and renewable energy deployment programs in states, or 

are subject to states requirements such as end-use energy efficiency resource standards (EERS) 
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or renewable portfolio standards (RPS). For each of these examples, this section describes 

current legal relationships between these entities and the state, and discusses possible legal 

instruments that might provide the state with the authority to ensure that obligations in a state 

plan are met and to address failure to meet those obligations. The mechanisms discussed take 

different forms, but would specify the three elements described above: obligations, compliance 

demonstration, and enforcement mechanisms. This section also discusses enforceability 

considerations in cases where states act jointly through a multi-state plan. 

A. Parties Regulated by the State other than Affected EGUs 

One likely state plan scenario involves inclusion of enforceable obligations for stateregulated entities other than affected EGUs. An example of a state-regulated entity that is not an 

owner or operator of affected EGUs may be an electric distribution utility.11 These entities are 

typically regulated by a state public utility commission. An example of an enforceable state plan 

measure that might apply to an electric distribution utility is a compliance obligation under a 

state end-use energy efficiency resource standard (EERS) or renewable portfolio standard (RPS), 

or implementation of incentive programs for the deployment of end-use energy efficiency and 

renewable energy technologies. 

Another example is where a vertically integrated, state-regulated utility implements a 

portfolio of enforceable actions under a state plan, which may include actions that apply directly 

to affected EGUs as well other actions such as end-use energy efficiency and renewable energy 

deployment programs. While vertically integrated utilities may own and operate affected EGUs, 

some of the measures implemented may require different enforceability mechanisms than an 

emission limit applied to an affected EGU. 

 

                                                            

11 Electric distribution utilities are also often referred to as local distribution companies (LDCs). Here we refer to an 

electric distribution utility as an entity that owns and operates an electric distribution network but is not the owner 

and operator of EGUs. As discussed further, vertically integrated utilities own and operate electric distribution 

networks as well as EGUs. 
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1. Electric Distribution Utility with Obligations to Meet an EERS or RPS Pursuant 

 to State Regulations 

EERS and RPS requirements are typically implemented through state regulations, but 

may also be implemented through a public utility commission order. State EERS and RPS 

regulations provide legal instruments generally comparable in enforceability to regulatory 

emission limits applied to EGUs. These regulations typically specify compliance obligations, 

reporting, and enforcement. However, many state RPS regulations and some EERS regulations 

include alternative compliance payment (ACP) provisions that provide the utility with the option 

of making a payment in lieu of full compliance with the portfolio requirement. Thus, state EERS 

and RPS mandates may not guarantee achievement of a given level of end-use energy efficiency 

or renewable energy deployment during a plan performance period.12

2. Vertically Integrated Electric Utility with Obligations under a State-Approved 

Integrated Resource Plan 

A utility integrated resource plan (IRP) may include a number of direct and indirect 

actions that affect EGU CO2 emissions, and may also include compliance with EERS and RPS 

regulations. Broadly, IRPs may prescribe or authorize actions for which utilities can recover 

capital investments and operating costs through regulated retail electricity rates.13 This creates 

strong financial incentives for implementing an action, but may not mandate an action. 

For a state plan under this scenario, an enforceability consideration is whether an IRP, 

and related public utility commission orders, must include additional requirements to implement 

                                                            

12 We note that some direct emission limits for CO2 emissions include somewhat similar provisions. For example, 

both the RGGI state CO2 emission budget trading programs and the California GHG emission budget trading 

program include cost containment provisions where more emission allowances are made available to affected 

sources at certain allowance price thresholds. In both these instances, such relevant characteristics of the state 

regulations would need to be taken into account when projecting the CO2 emission performance that will be 

achieved by affected EGUs under the state plan. 

13 In some cases, an IRP prescribes or authorizes specific actions. In others, an IRP serves as a guide for the utility 

and the public utility commission when evaluating acquisition or implementation of specific utility resources or 

programs. In such cases, the specific resource or program is approved through a PUC order that authorizes or 

requires actions and identifies performance obligations. These orders may or may not be fully consistent with 

provisions in an IRP. 
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certain actions, beyond denial of rate recovery or a change to utility tariffs if a utility fails to 

meet specified obligations in the IRP. If so, this may require state legislation to provide 

additional authority to state public utility commissions in some states, or confer additional 

authority to other agencies (e.g., a state environmental agency). 

B. Private or Public Third-Party Entity not Regulated by the State 

Another state plan scenario involves public or private third-party entities with 

enforceable obligations under a state plan. A private or public third-party entity could be a utility 

entity that is not regulated by a state public utility commission, such as a municipal utility or a 

utility cooperative.14 It could also be a private non-profit entity established to administer end-use 

energy efficiency and renewable energy deployment programs.15 In most cases, since they often 

expend electricity ratepayer funds, such non-profit entities are created by state legislation and 

overseen by state public utility commissions or state-regulated private utilities. 

An appropriate legal instrument or agreement applicable to such entities included in a 

state plan might include legal arrangements similar to those currently used to establish 

independent entities that expend electricity ratepayer dollars in multiple states. For entities not 

subject to state oversight, such a mechanism might also include mechanisms where an entity 

voluntarily submits to the authority of a state, pursuant to state statutory or regulatory authority 

specified in a state plan. Such agreements might be attached to a funding source. For example, 

the entity would voluntarily submit to such authority as a condition of receiving certain funds, 

such as state appropriated funds or funds collected through state-regulated electricity rates. 

Alternatively, a municipal utility or utility cooperative might voluntarily submit to state authority 

as a condition of the state agreeing to let the entity implement a portfolio approach, in lieu of the 

application of certain direct CO2 emission limits for affected EGUs owned and operated by such 

entities through a state regulation. In some cases, new state statutory authority might be enacted 

                                                            

14 Here, “not regulated” refers to regulation of electricity rates by a state public utility commission. To the extent 

that such entities are owners and operators of affected EGUs, these EGUs may be subject to state environmental and 

other regulations. In some cases these utility entities are also subject to state EERS and RPS regulations, as specified 

under state law. 

15 Examples include the Energy Trust of Oregon, the Delaware Sustainable Energy Utility, and Efficiency Vermont. 
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to support a state plan, specifying enforceable obligations for these private or public third-party 

entities under the plan. 

An additional consideration is whether such legal instruments or agreements, if related to 

a renewable energy or end-use energy efficiency deployment program, should specify a stable 

budget authority and funding source through each plan performance period. Such authority and 

funding might be necessary to ensure that suitable funds are made available to achieve the level 

of energy savings or renewable energy deployment projected in the state plan, which may be 

necessary to achieve the level of emission performance by affected EGUs that is projected will 

be achieved through implementation of the plan. 

C. State Agency, Authority, or Entity 

 

This state plan scenario involves a state entity with an enforceable obligation in a state 

plan. For example, state authorities in some states implement renewable energy and end-use 

energy efficiency deployment programs.16 In this scenario, the requirement for the state entity 

would be an enforceable component of the state plan. 

One type of legal arrangement that might be applied under this scenario is legislation 

directing state executive branch agencies or independent state authorities to follow through on 

obligations under a state plan. 

Such legislation might provide independent legal authority under state law to compel 

executive branch actions, or actions by independent state authorities under the plan, if 

obligations are not met. Depending on the form of legislation, this could also provide citizens 

with the ability to compel state action under state law, if obligations are not met under a state 

plan.17

                                                            

16 A prominent example is the New York State Energy Research and Development Authority (NYSERDA), which 

administers energy efficiency and renewable energy deployment programs. 

17 We note that under the CAA, measures included in an approved 111(d) state plan would be federally enforceable 

by EPA, and that citizens would also have the ability to file citizen suits to compel enforcement of state plan 

obligations, under CAA Section 304 (42 U.S. Code Section 7604). 
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An additional consideration is whether such legal arrangements, if related to a renewable 

energy or end-use energy efficiency deployment program, should also specify a stable budget 

authority and funding source through the plan performance period, or other provisions, to ensure 

that programs are implemented as projected under the state plan. 

D. Multi-State Approaches 

 As discussed in the preamble, in section VIII.F.1, multi-state approaches introduce crosscutting enforceability considerations. 

For these multi-state approaches, states would demonstrate emission performance from 

affected EGUs in aggregate jointly with partner states. For states participating in a multi-state 

approach, the individual state performance goals in the emission guidelines would be replaced 

with an equivalent multi-state performance goal. For example, states taking a rate-based 

approach would demonstrate that all affected EGUs subject to the multi-state plan achieve a 

weighted average CO2 emission rate that is consistent, in aggregate, with an aggregation of the 

state-specific rate-based CO2 emission performance goals established in the emission guidelines 

that apply to each of the participating states. If states were taking a mass-based approach, 

participating states would demonstrate that all affected EGUs subject to the multi-state plan emit 

a total tonnage of CO2 emissions consistent with a translated multi-state mass-based goal. This 

multi-state mass-based goal would be based on translation of an aggregation of the state-specific 

rate-based CO2 emission performance goals established in the emission guidelines that apply to 

each of the participating states. 

1. Multi-State Emission Budget or Rate-Based Emission Trading Programs 

 

The Regional Greenhouse Gas Initiative (RGGI) is an example of a multi-state approach 

to regulation of CO2 emissions. Through this initiative, nine states are currently implementing 

coordinated CO2 emission budget trading programs. The program works as a coordinated 

regional whole through a shared emission and allowance tracking system and allowance auction 

process, but is implemented in accordance with materially consistent stand-alone state 

regulations and individual statutory authority. These regulations recognize CO2 allowances 

issued by other participating states for use by affected EGUs when complying with each state’s 
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emission limitation, but contain all the necessary components to administer the program 

requirements on an individual state basis.18 As a result, while the initiative is implemented 

regionally, each CO2 emission budget trading program regulation is enforceable against affected 

EGUs at the state level and functions as a discrete program. 

As a result, a multi-state emission budget trading program approach, such as RGGI, is 

enforceable in practice at the state level. A multi-state rate-based emission trading program could 

also be established in much the same manner as a multi-state emission budget trading program, 

and could therefore be enforceable at the state level.19

2. Multi-State Portfolio Approaches 

 

A multi-state portfolio approach could introduce novel enforceability considerations. If it 

were based on interdependent emission reduction strategies among states that are not tied to 

emission limits that directly apply to affected EGUs, the emission performance of affected EGUs 

in one participating state may be dependent, in part, on actions taken in other participating states. 

If a state (or states) failed to implement commitments under the multi-state plan, this raises the 

question of whether the EPA should address non-performance of one or more participating states 

in the context of failure to achieve the required level of multi-state emission performance under 

the plan, or instead enforce actions at the individual state level for those states that are failing to 

meet commitments under the multi-state plan. 

 

                                                            

18 The emission limitation consists of a requirement to submit CO2 allowances equal to reported CO2 emissions 

during a compliance period. While states have individual emission budgets, representing the total number of 

allowances issued for a given year that are available for allocation, there are no individual state emission limits. The 

CO2 emission constraint is regional, based on the sum of state CO2 emission budgets. 19 Enforceability would be contingent, in part, on states having comparable enforcement mechanisms. 
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IV. Incorporating End-Use Energy Efficiency and Renewable Energy Programs and 

 Measures under a Rate-Based Approach 

A. Concept of Adjusting EGU CO2 Emission Rates based on the Effects of End-Use 

 Energy Efficiency and Renewable Energy 

As discussed in the preamble, in section VIII.F.3, the EPA is proposing that RE and 

demand-side EE requirements, programs, and measures may be incorporated into a rate-based 

plan approach.20 Measures that avoid CO2 emissions from affected EGUs, such as quantified and 

verified end-use energy savings and renewable energy generation, could be used to adjust the 

CO2 emission rate of an affected EGU when demonstrating compliance with a rate-based CO2

emission limit. Alternatively, a state could use the effect of such measures as a basis for 

administratively adjusting the average CO2 emission rate of affected EGUs when demonstrating 

achievement of the required emission rate performance level in a state plan. 

 Under this approach, affected EGUs could comply with a rate-based CO2 emission limit 

through actions at the EGU,21 as well as through the use of credits22 for actions that occur 

elsewhere in the interconnected electricity system that avoid CO2 emissions from affected EGUs. 

If a state is implementing a portfolio approach, then the state could administratively adjust the 

average CO2 emission rate of affected EGUs through a similar process when demonstrating 

achievement of the required emission rate performance level in the state plan. 

 This section explores in depth the mechanics and implications of the different possible 

approaches for adjusting CO2 emission rates that are summarized in the preamble. Aspects of 

this discussion may apply to both retrospective demonstration of CO2 emission performance 

                                                            

20 The EPA is also proposing that RE and demand-side EE measures could be used under a mass-based portfolio 

approach in an approvable state plan. However, the focus of this section is limited to application of such measures 

under a rate-based approach. 

21 This could include an individual affected EGU or group of affected EGUs if a rate-based averaging or trading 

approach is used. 

22 These credits could be tradable, or represent non-tradable credits administratively apportioned to affected EGUs. 

This latter approach effectively represents an administrative adjustment applied by the state. 
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achieved by affected EGUs under an approved state plan and projections of CO2 emission 

performance by affected EGUs included in a submitted state plan. 

B. Approaches for Adjusting EGU CO2 Emission Rates 

As discussed in the preamble, credits or adjustment to an EGU CO2 emission rate, based 

on the effect of RE and demand-side EE programs and measures, might represent avoided MWh 

of electric generation or avoided tons of CO2 emissions.23 If adjustment or credits represent 

avoided MWh, they would be added to the denominator of the lb CO2/MWh emission rate when 

determining an adjusted lb CO2/MWh emission rate. If adjustment or credits represent avoided 

CO2 emissions, they would be subtracted from the numerator when determining an adjusted lb 

CO2/MWh emission rate. The approach chosen could affect the amount of credit or adjustment 

provided for RE and demand-side EE programs and measures. These implications are discussed 

below. 

1. Adjustment of CO2 Emission Rate based on Avoided MWh 

One approach is to adjust an EGU’s CO2 emission rate based on avoided MWh of 

generation from an EGU, or cohort of EGUs, resulting from RE and demand-side EE programs 

and measures.24 A MWh crediting or adjustment approach implicitly assumes that the avoided 

CO2 emissions come directly from the particular affected EGU (or group of EGUs) to which the 

adjustment or credits are applied. It assumes, in effect, that an additional emission-free MWh is 

being generated by that respective EGU, and that the RE or demand-side EE measure reduces 

CO2 emissions from that individual EGU or group of EGUs.25 In practice, the average or 

marginal CO2 emission rate in the power pool or identified region – representing the avoided 

CO2 emissions from the generating sources being displaced by a MWh of energy savings or a 

                                                            

23 The preamble solicits comment on the appropriateness of these different approaches, which are further elaborated 

in this section. 

24 It should be noted that this was the process used by EPA for incorporating end-use energy efficiency, renewable 

energy, and nuclear energy when calculating state CO2 emission performance goals for affected EGUs. As discussed 

in the preamble, states may have flexibility to use a different approach when demonstrating the effect of these 

measures in a rate-based state plan. 

25 As a result, the assumed avoided CO2 emissions from an individual MWh of energy savings or generation from 

renewable energy will differ based on the reported CO2 emission rate of the individual EGU to which the MWh is 

applied as an adjustment to its MWh output. 
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MWh of renewable energy generation – could differ significantly from the calculated avoided 

CO2 emissions derived by adjusting the MWh output of an affected EGU. The following 

examples highlight these concepts. 

Example 1: Assume an EGU with a stack emission rate of 1,500 lb CO2/MWh generates 

1,000 MWh. Also assume that 1,000 emission-free MWh credits for the effect of demand-side 

EE measures are added to the denominator when calculating the EGU’s adjusted CO2 emission 

rate. The adjusted CO2 emission rate is 1,500,000 lb CO2 divided by 2,000 MWh, which equals a 

CO2 emission rate of 750 lb CO2/MWh. In this example, each MWh credit represents 750 lb of 

avoided CO2 emissions. 

Example 2: The same calculation applied to an affected EGU with a 2,000 lb CO2/MWh 

rate would yield a different result. In this instance the adjusted emission rate is 2,000,000 lb CO2

divided by 2,000 MWh, which equals a CO2 emission rate of 1,000 lb CO2/MWh. In this 

example, each MWh credit represents 1,000 lb of avoided CO2 emissions. 

2. Adjustment of CO2 Emission Rate based on Avoided CO2 Emissions 

An alternative approach is to provide an adjustment to the CO2 emission rate of an EGU, 

or cohort of EGUs, based on the estimated CO2 emissions that are avoided in the power pool or 

identified region as a result of RE and demand-side EE programs and measures. This approach 

acknowledges that the avoided CO2 emissions may come from the electric power pool or other 

identified region as a whole, rather than an individual EGU. The avoided CO2 emissions are 

determined based on the MWh saved or generated, multiplied by a CO2 emission rate for the 

power pool or region. 

This CO2 emission rate could be based on the average or marginal emission rate in the 

power pool, region, or state. A marginal avoided emission rate represents the generation that is 

displaced at the margin for every MWh saved or generated through an RE or demand-side EE 

program or measure. An average avoided emision rate is based on either all fossil generation in a 

region or total generation. This approach assumes that every MWh saved or generated equally 

displaces generation from every generator in a power pool or region. 
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Another approach that has been suggested by some stakeholders is crediting or 

adjustment based on the level of the rate-based emission limit for affected EGUs (or the overall 

rate-based level of emission performance for affected EGUs specified in a state plan). For 

example, under this approach, if the emission rate limit were 1,500 lb CO2/MWh, one MWh of 

energy savings or renewable energy generation would result in a credit or adjustment 

representing 1,500 lb of CO2 that could be subtracted from the numerator of an EGU’s CO2

emission rate. This approach assumes that affected EGUs jointly emit CO2 from the stack on an 

average basis at this required compliance level (i.e., consistent with a “closed” averaging 

system), and that a MWh saved or generated avoids CO2 emissions from affected EGUs at this 

average compliance level.26

3. Other Considerations 

Some of the CO2 emissions avoided through RE and demand-side EE measures may be 

from non-affected EGUs. This may result because affected EGUs are only a subset of the fossil 

fuel-fired EGU fleet, which also includes (or will include) existing non-affected fossil fuel-fired 

EGUs, such as simple-cycle combustion turbines used to meet peak demand, as well as new 

fossil fuel-fired EGUs subject to emission standards under CAA section 111(b). Furthermore, as 

the fleet capital stock slowly turns over, affected EGUs under section 111(d) will comprise a 

slowly shrinking subset of the total fossil fuel-fired EGU fleet. These dynamics may need to be 

addressed in a state plan when crediting or adjusting CO2 emission rates of affected EGUs based 

on the effects of RE and demand-side EE measures. The approaches described in more detail 

later in this section may be able make these distinctions between affected and non-affected fossil 

fuel-fired EGUs. 

 

                                                            

26 This outcome might be expected in a closed averaging system, without the use of crediting or adjustment for the 

avoided emission effects of RE and demand-side EE measures. In this instance an EGU that emitted above the 

compliance rate would need to average its performance with (or submit tradable credits obtained from) an EGU that 

performed below the required rate. On balance, all EGUs subject to the emission rate limit would perform at or 

below the compliance rate. 
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C. Methods and Tools for Quantifying Avoided CO2 Emissions from End-Use Energy 

 Efficiency and Renewable Energy 

1. Introduction 

There are a number of approaches for quantifying the avoided CO2 emissions resulting 

from end-use energy efficiency and renewable energy (EE/RE) programs, requirements and 

measures27 in the electric sector. These approaches range from the application of basic avoided 

emission rates to using sophisticated electric sector models.28 Annual average avoided emission 

rates have often been used for rough approximations of CO2 emissions avoided from reduced 

electric energy use.29 An annual average avoided emission rate assumes that EE/RE programs 

and measures reduce electric generation from all generating types on a proportional basis 

consistent with the generation mix in a region. A marginal emission rate represents the emission 

rate of an electric generating unit (EGU) or cohort of EGUs likely to be displaced by EE/RE 

measures (i.e., a marginal avoided emissions rate), based on the last unit(s) to come online to 

meet electricity load and the first unit(s) to be brought offline when electricity load is reduced. 

The primary question underlying estimating CO2 emissions reductions from EE/RE 

measures is the determination of which electric generators will be displaced (i.e., cease 

generation or reduce generation output) in the presence of incremental EE/RE. This section 

briefly describes a range of avoided emission rates approaches, their underlying assumptions, 

and considerations associated with the use of different avoided emission rate approaches. 

2. Average Emission Rate Approach 

An average emission rate (typically expressed in tons of CO2/MWh) is usually 

understood to mean the average of all generators’ emissions rates, weighted by annual 

generation. The rate (ravg) is calculated as: 

                                                            

27 As used here, the term “EE/RE measures” refers to any program actions or regulatory requirements that result in 

the increased use of energy-efficient equipment or practices, or renewable energy generating resources. 

28 Electric sector models include simulation dispatch models (i.e. production-cost models) and capacity expansion 

planning models. 

29 See, for example, EPA “Household Carbon Footprint Calculator,” available at 

http://www.epa.gov/climatechange/ghgemissions/ind-calculator.html. 
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ݎ௔௩௚ ൌ ∑௜ ݁௜

∑௜ ݃௜

Where: 

e = annual emissions (tCO2) from all sources i and 

g = annual generation (MWh) from all sources i. 

Because the average emission rate puts the sum of all generation in the denominator (as MWh), 

including nuclear, hydroelectric, and renewable generating resources, the rate fundamentally 

assumes that EE/RE reduces all generating types by an equal proportion, regardless of their type 

or contribution to the margin.30 EGUs are generally dispatched on an economic merit order, 

where the least-cost EGUs (on a variable cost basis) are dispatched first, and higher cost 

resources are dispatched later. Since nuclear, hydro, wind, and solar resources operate at a very 

low cost,31 they are generally dispatched before most fossil units.32 Under current and historical 

operating conditions, there are few circumstances in which non-fossil resources reduce output in 

the presence of lower demand. As a result, in states with a moderate contribution of non-emitting 

resources to total generation, the average emission rate may be lower than could reasonably be 

expected for reductions from EE/RE programs and measures.33 Conversely, in states that have 

significant baseload fossil generation and few non-emitting resources, a state-average emission 

rate may reflect an emission rate that is too high. This may occur by incorporating emissions 

from coal-fired EGUs that are less likely to reduce generation with a reduction in electricity load, 

compared with other lower-emitting fossil fuel-fired EGUs. 

                                                            

30 In other words, a two-percent reduction in energy use would cause all EGUs in the system to reduce generation 

output by two percent, including coal, gas, nuclear, hydro, and renewable generating resources. 31 For representative variable costs for new EGU, see supporting documentation for the Electricity Market Module 

of the Annual Energy Outlook (AEO) 2013, table 8.2., “Cost and performance characteristics of new central station 

electricity generating technologies”. Geothermal, wind, and solar generating resources are assumed to have a 

variable operating cost of $0/MWh. Hydroelectric generating resources have an assumed variable operating cost of 

$2.60/MWh and incur no fuel cost, while nuclear generating resources have an assumed variable operating cost of 

$2.10/MWh (exclusive of fuel costs). In contrast, gas and coal units are assumed to have variable operating costs 

from $3-$7/MWh (exclusive of fuel costs. 

32 The exception is rare curtailment events for renewable resources when more energy is produced than required, and 

due to operational constraints, where wind turbines are stalled to maintain system energy balance. Renewable energy 

generators, such as wind and solar, are sometimes referred to as “non-dispatchable” resources, since the renewable 

energy resource is intermittent and these generators cannot be called upon to run when the renewable energy 

resource is unavailable. However, here we refer to economic dispatch of these resources at times when the 

renewable energy resource is available. In these instances, these generators are often one of the first resources to be 

called upon, due to their low variable operating costs. 

33 Counting hydroelectric, nuclear, and renewable resources in the denominator would render the emissions rate too 

low, and thus the avoided emissions smaller than actually realized. 
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3. Marginal Emission Rate Approach 

A marginal emission rate represents the emission rate of the EGU or cohort of EGUs 

likely to be displaced by EE/RE (i.e., an avoided emission rate). A marginal unit is the highestcost unit dispatched at any point in time.34 Under most circumstances, and at any given time, the 

marginal unit is the last unit to be brought online to meet electricity demand and the first unit to 

be brought offline when electricity load is reduced. Due to constraints on generating unit ramp 

rates35 and transmission availability, it is not uncommon for multiple units to be dispatched 

incrementally simultaneously, thus creating a cohort of marginal units. Marginal unit(s) change 

on a moment-to-moment basis, determined by load requirements and the variable cost of each 

unit available to generate another unit of power. A marginal unit can either be a unit brought 

online to meet load or may be an EGU that is already operating, but that is dispatched at a greater 

level of output to meet load. 

The marginal emission rate (rmarginal) can be expressed for any given hour (t) as a function 

of the difference between two distinct cases – the reference case (i.e., in the absence of 

incremental EE/RE programs and measures) and the change case, where the EE/RE programs 

and measures have been implemented. A formula describing this rate is written as follows: 

ݎ௟௔௡௜௚௥௔௠,௧ ൌ ∑௜ ݁௥௘௙,௜,௧ െ ∑௜ ݁ாாோா,௜,௧

∑௜ ݃௥௘௙,௜,௧ െ ∑௜ ݃ாாோா,௜,௧

Where: 

eref and eEERE = emissions (tCO2) from EGUs i in hour t in the reference case (ref) and the change 

case (EERE), respectively; and 

gref and gEERE = generation (MWh) from EGUs i in hour t in the reference case (ref) and the 

change case (EERE), respectively. 

                                                            

34 See EPA’s Roadmap for Incorporating EE/RE Policies and Programs in State Implementation Plans: Appendix B, 

available athttp://www.epa.gov/statelocalclimate/documents/pdf/overview_paper_4-28-2011.pdf 35 Ramp rate refers to the ability of an EGU to respond to increasing load, based on its ability to increase output. 

Ramp rates are typically determined by technology type, with some technologies, such as combustion turbines, able 

to more quickly increase output in response to increasing load. 
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The magnitude of the EE/RE program and the EE/RE load impact shape36 is a key 

element in determining marginal emissions reductions. In order to obtain a valid estimate of the 

emission reduction effect of an EE/RE program, an annual marginal avoided emission rate 

should be calculated that reflects the EE/RE program’s load impact shape and magnitude. This 

annual marginal avoided emission rate may then be applied to other EE/RE programs with 

similar load impact shapes and magnitudes. The marginal emission rate is highly time sensitive, 

as are the impacts of EE/RE programs and measures. For example, the EGU that is on the margin 

and is most likely to be displaced from EE/RE in each hour of the year. In addition, different 

EE/RE programs may have very different load impact shapes,reducing energy requirements in 

different hours of the year. The interaction between the pattern of EGUs on the margin and the 

load impact shape of EE/RE programs and measures results in a specific marginal avoided 

emission rate. Applying an annual marginal avoided emission rate calculated based on the impact 

of one specific set of EE/RE programs and measures to another set of EE/RE programs and 

measures that is substantively different in timing or magnitude of energy savings or generation 

(i.e., with a different load impact shape and magnitude) may result in erroneous results (i.e. the 

assumption that the wrong EGUs are displaced).37

Several mechanisms have been proposed to estimate the marginal emission rate without 

the use of a formal electric dispatch simulation model. These mechanisms rely on historic hourly 

generation and emissions data, collected by EPA’s Clean Air Markets Division, to estimate 

hourly marginal emissions rates for a past historical year. The benefit of these mechanisms is that 

they are simple to apply, but (a) are difficult to verify or validate without the benefit of a formal 

model and (b) rely on historical data and patterns of dispatch, which may not represent future 

patterns of dispatch. 

                                                            

36 The load impact shape of an EE/RE program or measure is the hourly (or, if necessary, daily or seasonal) pattern 

of either energy savings from an EE program or measure, or generation from an RE generator that is supplied to the 

grid. 

37 For example, an EE program focused on measures that reduce peak electricity demand, such as more efficient air 

conditioning, or a solar RE program, may result in significant reductions in electricity use from the grid during peak 

demand hours, and little electricity use reduction during overnight “trough” hours. In contrast, an industrial EE 

program may result in a relatively constant electricity load reduction over most hours. An EE program focused on 

peak reduction measures may reduce generating output from primarily peaking units, while an EE program targeting

EE measures with a more constant load impact shape may significantly reduce baseload generation during overnight 

trough hours.
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4. Methods and Tools for Estimating Avoided CO2 Emission Rates and Avoided 

 CO2 Emissions 

4.1. Calculation Tool Method 

The EPA has developed a user-friendly tool to estimate the emission reduction impacts of 

EE/RE requirements, programs and measures. The “AVoided Emissions and geneRation Tool”

(AVERT)38 was developed to help air quality planners quantify NOx and SO2 emission impacts, 

as specified in EPA’s Roadmap for Incorporating EE/RE Programs in NAAQS SIPs.39 AVERT 

can also be used to quantify the displaced CO2 emissions of EE/RE measures within the 

continental United States. 

The AVERT method uses historical hourly emissions rates based on recent the EPA data 

on fossil fuel-fired EGUs’ hourly generation and emissions reported through EPA’s Acid Rain 

Program.40 This method couples historical hourly generation and emissions with the hourly load 

reduction profiles of EE/RE programs and measures to determine hourly emissions reductions on 

the margin. AVERT can be used to estimate EE/RE-related emissions reductions in a current or 

near-future year. However, AVERT estimates for current or future years are based on historical 

behavior rather than projected economic behavior. As a result, AVERT does not use projections 

of future fuel or electricity market prices that affected EGU dispatch, and is therefore not an 

appropriate tool for longer-term projections. 

Users of AVERT can analyze how the different load profiles of a variety of EE programs 

and measures, as well as wind and solar technologies, affect the magnitude and location of CO2

emissions at the county, state, and regional level. AVERT has a flexible framework with a 

simple user interface designed specifically to meet the needs of state air quality planners and 

other interested stakeholders. 

                                                            

38 More information about AVERT, including documentation and a user’s manual, is available at 

(http://www.epa.gov/avert). 

39 See Appendix I of the Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and Programs, 

available at http://epa.gov/airquality/eere/pdfs/appendixI.pdf), for details about how this approach can be used in 

the different NAAQS SIP pathways. 40 http://ampd.epa.gov/ampd/.

Deleted: 
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AVERT may be used to derive marginal emission reductions from historical generation 

and emissions data, which can be used to derive a marginal avoided CO2 emission rate. 

However, AVERT does not quantify average emissions rates. AVERT approximates historical 

dispatch behavior using a statistical algorithm. It does not represent transmission constraints, or 

significant changes in grid structures or future economic conditions. To estimate a recent 

historical marginal CO2 emissions reduction from existing EE/RE programs or measures, a user 

would input the MWh related to results from EE/RE programs or measures (representing either 

MWh of electricity savings or MWh of generation) in a representative historical baseline year as 

a positive increment to electricity load, and record the emissions incrementally added in the 

tool.41 The calculated marginal CO2 emission rate is the incremental CO2 emissions added by 

AVERT, based on historical EGU dispatch patterns, divided by the incremental MWh of EE/RE 

savings or generation input to AVERT as an increase in electricity load. This approach reflects 

the marginal impact of EE/RE measures based on historical recorded patterns of emissions and 

generation.42

4.2. Electricity Sector Modeling Method 

Quantification of avoided CO2 emissions from EE/RE requirements, programs, and 

measures can be achieved through retrospective modeling approaches. Models can be used to 

calculate avoided CO2 emissions by comparing actual realized EGU CO2 emissions to projected 

EGU CO2 emissions that would have occurred in a historical reference case that does not include 

implementation of the EE/RE that is being evaluated. The appropriate choice of model depends 

on the look-back period. For short look-back periods of one to three years, an electricity system 

simulation dispatch model can determine the marginal generation contribution to emissions in a 

                                                            

41 Because AVERT represents a historical baseline year, EE and RE programs that occurred in a past year are 

already recorded by the statistics of AVERT. To estimate the emissions impacts of historic EE/RE programs, a user 

would increase the load by the EE/RE increment to estimate what generation and emissions would have been in the 

reference case look-back year. 

42 AVERT reflects fuel and emission control technologies to the extent they have influenced dispatch during the 

base year chosen. However, AVERT cannot change dispatch based on future economic or regulatory conditions, 

such as expected fuel prices, emission allowance prices, or specific emissions limits. AVERT should not be used for 

this type of analysis. When used to review historical data, AVERT will capture the impact of historical fuel prices 

and other impacts on the variable cost of production, but cannot capture specific emission limits. 
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historical reference case (i.e. absent incremental EE/RE programs).43 Over the short term, 

simulation dispatch models properly account for EGU economic dispatch considerations, such as 

fuel and emission allowance prices, and operational constraints, such as ramp rates, outages, and 

heat rate curves. Look-back periods beyond three to five years would benefit from use of a 

utility-scale capacity expansion and dispatch planning model to understand the change in build 

out of new generating capacity, as well as transmission and distribution infrastructure, and its 

impact on generation between the historical reference case and actual realized historical 

outcome. 

Electricity System Simulation Dispatch Models 

Quantifying the CO2 emissions reductions achieved by EE/RE measures through 

modeling of a near-term look-back period requires a counterfactual historical reference case 

model run, which examines how the electric system would have operated in the absence of the 

EE/RE emission reduction measures under consideration. The emissions projected in this model 

run may be compared against actual realized emissions during the historical period. The lookback model should be calibrated by running the same model with the EE/RE measures in place 

and comparing the outcome of that model against realized generation and emissions at an 

appropriate spatial scale.44

Simulation dispatch models can be readily run for historical years provided they are 

loaded with the accurate input assumptions, including actual historic fuel costs, emission 

allowance prices, and transmission constraints. While these models will not choose economically 

optimal EGU retrofit or retirement decisions, they will provide a change in EGU dispatch and the 

associated change in emissions across a large region in a more detailed manner than capacity 

expansion planning models. 

                                                            

43 “Reference case” here refers to the case in which additional incremental carbon emissions reduction mechanisms 

are not employed, in this case resulting from EE/RE programs and measures.

44 Generally, simulation models will not capture exact output of individual EGUs relative to reality due to a variety 

of factors, including outages and other non-economic considerations not reflected in the model. Therefore, a 

comparison at an individual EGU scale may not be meaningful, but aggregate emissions at a state or regional scale 

should be expected to be comparable. 
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Simulation dispatch models may be most relevant as part of ex-post plan reporting, for 

estimating the avoided CO2 emissions from affected EGUs that occurred as a result of EE/RE 

measures included in a plan, during a specified plan reporting period. 

Utility-Scale Capacity Expansion and Dispatch Planning Models 

Quantifying the CO2 emissions reductions achieved by EE/RE measures through 

modeling over a longer-term look-back period requires a counterfactual historical reference case 

model run, which examines how the electric system would have operated and have been built 

out, in the absence of the EE/RE measures under consideration. The critical difference between 

this type modeling approach and the use of a simulation dispatch model is the assessment of 

changes made at the “build margin” – i.e., new additions to generating capacity that may have 

been avoided or compelled, or retirements of existing units that may not have occurred in the 

reference case. The emissions projected in this model run may be compared against actual 

realized emissions during the historical period. The look-back model should be calibrated by 

running the same model with the EE/RE measures in place and comparing the outcome of that 

model run against actual realized generation and emissions at an appropriate spatial scale. 

Capacity expansion and dispatch planning models could be run for a longer-term 

historical period to better reflect what the electricity system would have looked like in the 

absence of the EE/RE measures. When EE or RE resources have been added over the course of 

three to five years, these models will reflect how these resources have avoided new power plants, 

retrofits, or fuel switch decisions. Some models may also be able to reflect avoided transmission 

investments. 

Capacity expansion and dispatch planning models may be more relevant than simulation 

dispatch models for projecting the emission performance that will be achieved by affected EGUs 

under a plan. As discussed below, these models are able to assess both the “operating” and 

“build” margins that impact EGU CO2 emissions as the result of EE/RE measures. 

 
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5. Considerations Associated with use of an Avoided Emissions Rate 

Operational versus Build Margin 

Avoided emission rates approaches that do not include a capacity expansion model all 

assume that EE/RE measures only impact the operational margin – i.e., they impact moment-tomoment operations of EGUs in the existing grid. While this is true, deployment of EE/RE over a 

period of years may have impacts beyond the operational margin. Deployment of significant 

EE/RE measures over time often impacts decisions to build new fossil generating capacity, retire 

existing aging generating capacity, and make different decisions about transmission expansion. 

These different decisions about what infrastructure to build or retire are known as the “build 

margin,” and may ultimately have a greater impact on long-term emissions outcomes than the 

operational margin. 

While a new wind farm brought online next year has an impact on patterns of generation 

from existing fossil EGUs, decadal-scale planning associated with a policy meant to encourage 

EE/RE has an impact on the choice of whether or not to pursue the construction of new fossil 

generating capacity. A utility pursuing aggressive EE/RE programs may avoid the construction 

of new fossil generating capacity and expansion of transmission and distribution capability, and 

may even allow the utility to retire non-economic generating units no longer required for 

generation or reliability purposes. 

The use of an avoided emissions rate calculated based on the operational margin alone 

may fail to represent the impact of EE/RE measures that occur at the build margin. This 

differentiation is particularly important in regions where the pursuit of EE/RE measures may 

result in the retirement of high-emitting, non-economic generating assets. The use of a capacity 

expansion and dispatch planning model would help accurately assess avoided emissions due to 

changes at both the build and operational margins. 

 
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Quantifying In-State versus Out-of-State Emissions Reductions 

The electric system is not confined by state boundaries, and emissions displaced by 

EE/RE measures may occur over a wide geographic area,45 including outside of the state that 

implemented the EE/RE measures. Without the use of a simulation dispatch model (or, under 

some circumstances, use of a tool such as AVERT)46 it can be challenging to attribute emissions 

reductions that occur within the implementing state versus emissions reductions that occur across 

state boundaries. If a state sought to recognize the effect of EE/RE measures and quantified 

emissions reductions using an approach that did not account for avoided emissions at an 

appropriate spatial scale, it may inappropriately account for emissions reductions that occur in 

other states, or conclude that all emissions reductions occur within the state’s boundaries.47 In 

interconnected areas in which some states pursue mass-based emissions reductions approaches 

through their state plans while other states pursue rate-based approaches that include adjustment 

or crediting for avoided CO2 emissions from implementation of EE/RE measures, avoiding 

double-counting of emission effects among states will be an important consideration when 

quantifying the avoided emissions resulting from EE/RE measures. These considerations are 

discussed below in section VII of this TSD. 

V. Quantification, Monitoring, and Verification of End-Use Energy Efficiency and 

Renewable Energy Programs and Measures 

As discussed in the preamble, in section VII.F.4, a key consideration for state plans is the 

process and requirements for quantifying, monitoring, and verifying the effect of renewable 

                                                            

45 See California Energy Commission, Emissions Reductions from Renewable Energy and Energy Efficiency in 

California Air Quality Management Districts (2011), available at 

(http://ucciee.org/downloads/CAEmissionsReductions.pdf). 46 AVERT captures the magnitude of in-state versus out-of-state emissions reductions, but may not be an accurate 

assessment tool for edge-cases –i.e. states that fall near the boundaries of the AVERT regions – because AVERT 

does not capture inter-regional transmission. 

47 In the simplest case, a state without any fossil generating capacity that implements EE/RE measures would cause 

emissions reductions in neighboring states, and none within its own borders. Similarly, a state with significant, 

predominantly baseload fossil generation may also cause little in-state emissions reductions from implementation of 

EE/RE measures because those emissions reductions may occur at marginal generators located outside of the state. 

Conversely, a state with significant marginal fossil generating capacity might realize reductions in emissions from 

EE/RE measures implemented in other states. These complications may arise regardless of if a state is a net importer 

or net exporter of energy. The key differentiation is if the state’s EGUs are generally on the margin relative to 

interconnected EGUs in neighboring states.

Comment [A5]: 2.Can renewable energy that

is physically located in a state with a mass‐based

policy be used for out‐of‐state compliance in a

state with a rate‐based policy?

EPA response:  

This question is related to treatment of interstate

emission effects, which is addressed later in

section VII of the TSD.

Comment [A6]: 3.Commenters suggest

separating the RE and EE EM&V discussion

completely into separate sections. They are

different issues. For EE, the Uniform Methods

Project was developed to provide a national

approach to EM&V. Commenters suggest citing

UMP as an optional but preferred approach to

EM&V and are willing to provide assistance.   

EPA response:

This section is divided into two subsections that

address EE and RE separately. We have maintained

both within a single major section of the TSD

because state plans containing either enforceable

RE or EE measures would be subject to a similar

requirement to include an EM&V plan for those

measures in the state plan.

We have highlighted the role of the Uniform

Methods Project (UMP) in subsequent edits later in

this section.
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energy and demand-side energy efficiency measures that result in electricity generation or 

savings.48 In the preamble, the EPA proposes that a state plan that includes enforceable RE and 

demand-side EE measures must include an evaluation, measurement, and verification (EM&V) 

plan that explains how the effect of these measures will be determined in the course of plan 

implementation. An EM&V plan will specify the analytic methods, assumptions, and data 

sources that the state will employ during the state plan performance periods to determine the 

energy generation and energy savings related to RE and demand-side EE measures. As discussed 

in the preamble, an EM&V plan would be subject to EPA approval as part of a state plan. In the 

preamble, the EPA also discusses its intent to develop guidance for acceptable EM&V methods 

that could be incorporated in an approvable EM&V plan included as part of an approvable state 

plan. The EPA seeks comment in the preamble on the critical features of such guidance, 

including scope, applicability, and minimum requirements, as well as the appropriate basis for 

and technical resources used to establish such guidance, including existing state and utility 

protocols and existing international, national, and regional consensus standards or protocols. This 

section further elaborates these considerations discussed in the preamble, with individual subsections addressing RE and demand-side EE programs and measures. 

The appropriate type of EM&V for RE and demand-side EE programs and measures will 

depend on the state plan approach. For states implementing a mass-based portfolio approach, the 

effect of renewable energy and demand-side energy efficiency requirements, programs, and 

measures in helping to achieve the required level of CO2 emission performance under a state 

plan will be directly evident in reductions in the monitored CO2 emissions from affected EGUs.49

In effect, the overall impact of these measures could be tracked through CO2 emission 

monitoring, reporting, and record-keeping requirements applied to affected EGUs. However, for 

states implementing a rate-based plan approach, an approvable plan will need to include 

                                                            

48 In particular, this consideration applies to states implementing a rate-based emission limit approach that provides 

for adjustment of CO2 emission rates based on the effect of end-use energy efficiency and renewable energy, as well 

as states implementing utility- or state-driven portfolio approaches that incorporate end-use energy efficiency and 

renewable energy requirements and programs. 

49 There could be exceptions, for example where a state plan includes acknowledgement of avoided CO2 emissions 

that occur outside state borders as a result of state plan measures. See section VII of this TSD for further discussion 

of the treatment of interstate effects. 

Comment [A7]: EPA: Note, this revised text

conforms with revisions made to the preamble, at

section VIII.F.4, in response to interagency

comments. We have also made additional changes

throughout this section to conform with the

revised preamble text at section VIII.F.4.

Deleted: 

Deleted: .seeks comment on the suitability of 

current state and utility quantification, monitoring, 

and verification approaches for end-use energy 

efficiency and renewable energy in the context of an 

approvable state plan, and on whether harmonization 

of state approaches, or supplemental actions and 

procedures, should be required in an approvable state 

plan. The preamble also discusses generally whether 

the EPA should establish good practice guidance for 

quantification, monitoring, and verification of 

renewable energy and demand-side energy efficiency 

measures included in an approvable state plan, and 

solicits comment on the content of such guidance.

Deleted: quantification, monitoring, and 

verification
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quantification, monitoring, and reporting requirements related to RE and demand-side EE 

requirements, programs, and measures incorporated in a state plan. 

Utilities and states have conducted ongoing EM&V of end-use energy efficiency and 

renewable energy measures and programs for several decades. These evaluations, which include 

quantification, monitoring and verification of results, generally rely upon a well-defined set of 

industry-standard practices and procedures. However, measurement approaches vary by state 

based on multiple factors, including the measure and program type being evaluated, the level and 

nature of regulatory oversight, the degree of state and utility experience with these measures and 

programs, and the overall magnitude of program impacts. 

This section of the TSD discusses current state and utility evaluation, monitoring, and 

verification approaches for end-use energy efficiency and renewable energy programs and 

mandates. This section also discusses the potential suitability of these approaches in the context 

of an approvable state plan, and whether harmonization of state approaches, or supplemental 

actions and procedures, might be warranted in an approvable state plan. In particular, this section 

discusses considerations related to the establishment of requirements and guidance for 

quantification, monitoring, and verification of end-use energy efficiency and renewable energy 

measures for an approvable state plan. It also discusses the possible appropriate basis for and 

resources used to establish such requirements and guidance. This discussion includes 

consideration of existing state and utility protocols, as well as any international, national, and 

regional consensus standards or protocols. 

This section also discusses the types of end-use energy efficiency and renewable energy 

measures and programs for which EM&V of results is relatively straightforward. Such 

approaches might be subject to streamlined review of EM&V protocols included in an 

approvable plan, provided that such protocols are applied in accordance with EPA requirements 

and guidance. For example, many utilities have implemented a similar core set of end-use energy 

efficiency and renewable energy measures and programs for utility customers. For these types of 

measures and programs, a substantial base of experience has been established nationally for 

quantification, monitoring, and verification of measure and program outcomes. 

Deleted: evaluation

Deleted: good practice guidance or 

Deleted: , if the EPA were to take this approach

Deleted: quantification, monitoring, and 

verification

Deleted: quantification, monitoring, and 

verification

Deleted: industry best practices
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In the preamble, at section VIII.F.4, the EPA notes that it is not proposing to limit the 

types of RE and demand-side EE programs and measures that may be included in a state plan. 

However, less established types of measures and programs, such as new and innovative demandside EE programs that seek to alter consumer and building occupant behavior may pose 

quantification and verification challenges. Still other types of measures, such as state energyefficient appliance standards and building codes, have not typically been subject to similar 

evaluation of energy savings results. These types of approaches may have substantial impacts, 

but may require additional documentation of EM&V methods in accordance with EPA guidance, 

including development of appropriate quantification, monitoring, and verification protocols if 

they do not currently exist. 

 

Deleted: Oother

Deleted: those
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A. Quantification, Monitoring, and Verification for End-Use Energy Efficiency 

1. Introduction 

For rate-based state plans, a key element of the plan is a demonstration of how the state, 

and related entities with enforceable obligations under the plan, will measure and verify energy 

savings to be achieved the through the implementation of end-use energy efficiency measures 

incorporated in the plan.50 In the context of demand-side energy efficiency programs currently 

overseen by state PUCs, this function is typically addressed through an evaluation, measurement, 

and verification plan (EM&V plan). This section discusses current state and utility EM&V 

practice for end-use energy efficiency programs and discusses considerations related to 

acceptable EM&V plans and evaluation approaches for a state plan under CAA section 111(d). 

2. Background on Evaluation, Monitoring, and Verification (EM&V) of Energy 

 Efficiency Measures 

From the time that demand-side energy efficiency (EE) emerged as an important energy 

strategy in the 1970s,51 efforts to evaluate the impacts of EE actions have been critical to their 

success, credibility, and expansion. Starting with measurement and verification (M&V) of 

individual projects, these efforts have evolved to the point where there is now a mature and 

rigorous evaluation, measurement, and verification (EM&V) industry. This industry includes 

many professional firms, protocols and guidelines, training and certification programs, 

regulatory oversight, and established conferences with a rich library of published reports and 

publically available data and analyses. 

State agencies responsible for planning, implementing, and evaluating demand-side 

energy efficiency programs and policies utilize EE savings values, as follows:52 

                                                            

50 In this section we use the term “end-use energy efficiency measure” or “energy efficiency measure” to refer to an 

end-use energy efficiency requirement (such as an EERS), and energy efficiency program, or individual installed 

energy efficiency measure, such as installation of an energy-efficient air conditioner through an energy efficiency 

program. 

51 National Energy Program Fact Sheet on the President's Program, April 20, 1977. 

52 State and Local Energy Efficiency Action Network. 2012. Energy Efficiency Program Impact Evaluation Guide. 

Prepared by Steven R. Schiller, Schiller Consulting, Inc., available at http://www.seeaction.energy.gov. 

Deleted: possible

Deleted: protocol
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 Projected savings: values reported by a program implementer or administrator before the 

efficiency activities are completed 

 Claimed savings: values reported by a program implementer or administrator after the 

efficiency activities have been completed, prior to independent evaluation of savings 

 Evaluated savings: values reported by an independent third-party evaluator after the 

efficiency activities and an impact evaluation have been completed. 

 Both claimed and evaluated energy savings involve real-time and/or retrospective 

assessments of the performance and implementation of an energy efficiency program or a 

portfolio of programs. Important impacts for evaluation include energy and demand savings and 

non-energy benefits (e.g., avoided emissions, health benefits, job creation and local economic 

development, energy security, transmission and distribution benefits, and water savings). Impact 

evaluations also support cost-effectiveness analyses aimed at determining the value of energy 

efficiency programs to utility customers and identifying relative program costs and benefits of 

energy efficiency compared to other energy resources, including both demand- and supply-side 

options. 

Regardless of how the energy savings of an energy efficiency measure are determined, all 

energy savings values are estimates of savings and not directly measured. Savings are 

determined by comparing energy use after an energy efficiency project or measure is installed 

(the reporting period) with what is assumed to be the energy use in the absence of the project or 

measure (the “counterfactual” scenario, or baseline). Savings therefore depend critically on 

baseline assumptions, which are necessarily estimated with varying degrees of accuracy. Figure 

1-1 illustrates this concept. 
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Figure 1. Energy Use Before, During, and After an Energy Efficiency Project is Installed53

3. Current EM&V Practices in States 

3.1 EM&V for Demand-Side Energy Efficiency Programs Overseen by State PUCs 

Current practice with EM&V for demand-side energy efficiency programs in the U.S. is 

primarily defined by state utility commission (PUC)54 requirements for customer-funded 

efficiency programs. The level of PUC oversight varies from state to state, but this oversight 

process has generated the majority of the industry guidance and protocols for documenting 

energy savings from energy efficiency programs. Typically, impact evaluation reports are 

prepared based on the requirements established by PUCs and submitted (usually annually) for 

PUC review, approval, and use in resource planning and performance assessment. According to a 

recent survey, most states (79 percent) rely on independent consultants and contractors to 

conduct evaluations, while some states (21 percent) use utility and/or government agency staff.55 

                                                            

53 Ibid. 

54 In some states these government entities are referred to as a public service commission (PSCs) or board of public 

utilities (BPU), as well as other names. 

55 Kushler, M.; Nowak, S.; Witte, P. , A National Survey of State Policies and Practices for the Evaluation of 

Ratepayer-Funded Energy Efficiency Programs, American Council for an Energy-Efficient Economy (ACEEE) 

(February 2012), Report Number U122 Available at http://www.aceee.org/ research-report/u122. 
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The range of EM&V budgets varies significantly between states, typically from two 

percent to six percent of total energy efficiency program expenditures.56 The average EM&V 

budget in 2011 was about 3.6 percent of program expenditures. Reasons for this disparity may 

include the fact that as states expand energy efficiency programs, they may implement more 

complex programs, which require additional EM&V.57 EM&V effort in states also typically 

increases as the magnitude of program expenditures and energy savings impacts increase, and as 

states and utilities gain experience in implementing energy efficiency programs. 

States at the low end of this EM&V expenditure range typically rely heavily on deemed 

savings approaches, which are a common and relatively low-cost strategy for documenting 

energy savings. Deemed savings are measure-specific stipulated values based on historical and 

verified data (in some cases using the results of prior EM&V studies). Unlike other EM&V 

approaches, with deemed savings there are no – or very limited – measurement activities. 

Instead, only the quantity of energy efficiency measures implemented is verified (e.g., number of 

motors installed correctly, number of energy-efficient air conditioners that were purchased using 

a program rebate). The verified installed energy efficiency measures are then multiplied by the 

estimated (or deemed) energy savings per measure to derive energy savings for each measure 

and energy savings for the total number of measures installed through an energy efficiency 

program. The use of deemed energy savings is only considered appropriate for efficiency actions 

with well-known characteristics. A variant of this approach is the deemed savings calculation, 

which involves the use of one or more agreed-upon (stipulated) engineering algorithms used to 

generate energy and/or demand savings associated with energy efficiency measures. These 

calculations may include predetermined assumptions for one or more parameters in the 

algorithm, but typically require users to input data associated with the actual installed measure 

into the algorithm. 58 

                                                            

56 Wallace, P.; Forster, H.J. (2012). State of the Efficiency Program Industry Budgets, Expenditures, and Impacts 

2011. Consortium for Energy Efficiency. www.cee1.org/files/2011 CEE Annual Industry Report.pdf . 57 Expansion of energy efficiency programs may also lead to a reduction in EM&V costs per unit of energy savings, 

as programs achieve economies of scale and experience in conducting EM&V activities.

58 Examples of equipment types that are commonly evaluated using deemed savings values and calculations include 

energy-efficient washing machines, computer equipment, and refrigerators, and lighting retrofit projects with wellunderstood operating hours. For deemed savings calculations, evaluators collect information about the actual 

Comment [A8]: The opposite effect can also be

seen: additional programs mean more experience

with EM&V and hence reduced costs.

EPA response:

We have added a footnote in response to this

comment.
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The deemed savings values, themselves, are typically centrally located in a “Technical 

Reference Manual” (TRM). The content and format of these TRMs vary, but in most cases 

consists of a database of standardized, state- or region-specific algorithms (deemed calculations) 

and associated energy savings estimates (deemed savings values) for energy efficiency measures. 

TRMs also include various data assumptions, sample calculations, and other inputs that the state 

uses to develop energy savings values for the range of energy efficiency programs in place. The 

benefits to energy efficiency program administrators of using a TRM include reduced EM&V 

costs and greater certainty regarding projected, claimed, and evaluated energy savings values 

(see definitions above). There are about 20 states currently using TRM databases.59

It should be noted that TRM values for individual energy efficiency measures are not 

always formally vetted in a regulatory process, although this is a good practice.60 A recent 

survey of TRMs found that deemed energy savings values for comparable energy efficiency 

measures vary across states and regions.61 The reasons for these variations include the use of 

different calculation methodologies, technical assumptions, and input variables. Some of these 

differences are expected based on relevant differences in weather and baseline assumptions (e.g., 

existing building stock and common practices vary from one state to another). However, other 

differences are related to out-of-date input assumptions and calculation errors. In the context of 

state plans, this variation, and in particular data quality issues with some TRMs, raises 

consideration of whether complete reliance on existing TRM resources for state plans is prudent 

and appropriate, including how such reliance could or should be circumscribed. 

 In addition to the use of deemed savings, states on the higher end of the EM&V 

expenditure range rely to a greater extent on a variety of direct measurement approaches for 

documenting energy savings. Rather than mandating which EM&V methods must be used in a 

                                                                                                                                                                                               

installed measures -- such as hours of usage, wattage, and/or equipment capacity -- and combine this with 

predetermined assumptions in the algorithm. 

59 State and Local Energy Efficiency Action Network, Scoping Study to Evaluate Feasibility of National Databases 

for EM&V Documents and Measure Savings, prepared by Jayaweera, T.; Haeri, H.; Lee, A.; Bergen, S.; Kan, C.; 

Velonis, A.; Gurin, C.; Visser, M.; Grant, A.; Buckman, A.; The Cadmus Group Inc. (June 2011), available at 

http://www.eere.energy. gov/seeaction/pdfs/emvscoping__databasefeasibility.pdf. 60 State and Local Energy Efficiency Action Network, Energy Efficiency Program Impact Evaluation Guide, 

prepared by Steven R. Schiller, Schiller Consulting, Inc. (2012), available at http://www.seeaction.energy.gov . 61 Ibid. 
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particular situation, PUCs typically allow utilities and other program administrators to select 

from a range of appropriate EM&V approaches that are consistent with standard practice in the 

energy efficiency industry. EM&V analyses and calculations are then carried out, in most cases 

by an independent, third-party evaluator, through a process that is unbiased, uses technically 

rigorous methods, effective peer review, and is subject to public review and comment. In 

addition, energy savings are frequently certified by the PUC as compliant with requirements 

defined in a pre-approved EM&V plan. 

 EM&V requirements in states with the most experience implementing and overseeing 

energy efficiency programs are typically based upon the following industry best practices: 

 Use of one or more of the industry-standard EM&V protocols or guidelines (listed 

below), as well as the use of deemed savings values for well-understood energy 

efficiency programs and measures 

 Consideration of local factors, such as climate, building type, and occupancy. 

 Involvement of stakeholders and solicitation of expert advice regarding EM&V processes 

and resulting energy savings impacts. 

 Conduct of EM&V activities (e.g., direct equipment measurements, application of 

deemed savings, and reporting of impacts) on a regular basis. 

 Provision of interim and annual reporting of achieved energy savings. 

 Despite this well-defined and generally accepted set of industry best practices, many 

states with energy efficiency programs use different input values and assumptions (e.g., net 

versus gross savings, run-time of equipment, measure lifetime) in applying these practices. This 

can result in significant differences in claimed energy savings values for similar energy 

efficiency measures between states, even when the same measure type is installed under 

otherwise identical circumstances. In response to a growing awareness of this lack of cross-state 

comparability, policy makers, regulatory agencies, and other stakeholders are increasingly 

advocating for the use of common evaluation approaches across jurisdictions. Several national 

and regional EM&V efforts have emerged to promote collaboration and information sharing 

across states. These initiatives include the Northeast Energy Efficiency Partnership’s (NEEP) 
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EM&V Forum, which is active in New England, New York, and the Mid-Atlantic, and the 

Pacific Northwest’s Regional Technical Forum (RTF). Both efforts aim to promote multi-state 

coordination in EM&V practices, make EM&V results more transparent and publicly available, 

and support the adoption of similar definitions, methods, and input assumptions.62 

 In addition to stakeholder efforts to promote EM&V collaboration and information 

sharing, a growing number of EM&V protocols and guidelines, some of which have recently 

been developed, are being used in the U.S. to promote greater consistency of measurement 

techniques and methodologies: 

a.

b. U.S. DOE Uniform Methods Project (UMP) Protocols64

c. International Performance Measurement and Verification Protocol (IPMVP)65

d. Federal Energy Management Program (FEMP) M&V Guidelines66

e. American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 

Guideline 14, Measurement of Energy and Demand Savings67

f. California Evaluation Protocols68

g. ISO New England (ISO-NE) Manual for Measurement & Verification of Demand 

Resources69

                                                            

62 For a list of EM&V resources, including more information about these regional EM&V collaboratives, see 

http://www1.eere.energy.gov/seeaction/evaluation.html. 64 U.S. Department of Energy (DOE), Uniform Methods Project (April 2013), available at: 

http://energy.gov/eere/about-us/initiatives-and-projects/uniform-methods-project-determining-energy-efficiencyprogram-savings. 65 Efficiency Valuation Organization (EVO), International Performance Measurement and Verification 

Protocol (2012), available at: http://www.evo-world.org/. 66 FEMP, M&V Guidelines: Measurement and Verification for Federal Energy Projects, Version 3.0 (April 2008), 

available at: https://www1.eere.energy.gov/femp/pdfs/mv_guidelines.pdf. 67 American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), Guideline 14: 

Measurement of Energy and Demand Savings (June 2002), available at: 

https://gaia.lbl.gov/people/ryin/public/Ashrae_guideline14-

2002_Measurement%20of%20Energy%20and%20Demand%20Saving%20.pdf. 68 California Public Utilities Commissions (CPUC), California Energy Efficiency Evaluation Protocols: Technical, 

Methodological, and Reporting Requirements for Evaluation Professionals (2006), available 

at: http://www.calmac.org/events/EvaluatorsProtocols_Final_AdoptedviaRuling_06-19-2006.pdf.

Moved down [1]: International Performance 

Measurement and Verification Protocol 

(IPMVP)63¶

Moved (insertion) [1]
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h. PJM Energy Efficiency Measurement and Verification Manual70

i. State and Local Energy Efficiency Action Network, Energy Efficiency Program Impact 

Evaluation Guide71

j. U.S. DOE Superior Energy Performance Measurement and Verification Protocol for 

Industry72

 While many states are currently relying upon these protocols and guidelines, other states 

and regional organizations (e.g., ISOs and RTOs) take the additional step of specifying accuracy 

and uncertainty requirements for energy savings estimates. For example, ISO-NE requires that 

energy efficiency bids into its Forward Capacity Market (FCM) ensure that impact evaluations 

achieve ±10 percent statistical precision at the 80% confidence interval (see below for more on 

FCMs). 

 

 Regardless of the evaluation approach followed, the majority of state PUCs and energy 

efficiency program administrators aim to strike a balance between the transaction costs of 

EM&V activities (i.e., expense, time, staff effort) and the resulting reliability, validity, and 

usefulness of the estimated energy savings results. The appropriate balance between EM&V 

costs and the rigor of EM&V – and the related certainty of energy savings estimates – is often 

determined based on the type of program (including program purpose and goals), level of 

program expenditures, and magnitude of anticipated energy savings. 

3.2 EM&V for Energy Efficiency Measures used in ISO Forward Capacity Markets

 

Two Independent System Operators (ISOs) responsible for operating regional electricity 

grids and overseeing wholesale electricity markets in certain regions of the country have 

                                                                                                                                                                                                69 ISO-New England (ISO-NE), Measurement and Verification of Demand Reduction Value from Demand 

Resources (2013), available at http://www.iso-ne.com/rules_proceds/isone_mnls/MMVDR/index.html. 70 PJM, Manual 18B: Energy Efficiency Measurement & Verification (2010), available at 

http://www.pjm.com/~/media/documents/manuals/m18b.ashx. 71 U.S. DOE State and Local Energy Efficiency Action Network (SEE Action), Energy Efficiency Program Impact 

Evaluation Guide (December 2012), available 

at: http://www1.eere.energy.gov/seeaction/pdfs/emv_ee_program_impact_guide.pdf. 72 U.S. DOE, Superior Energy Performance Measurement and Verification Protocol for Industry (2012), available 

at: http://superiorenergyperformance.energy.gov/pdfs/sep_mv_protocol.pdf. 

Comment [A9]: EPA: Added based on

interagency comment.

Comment [A10]: This approach is not separate

from or inconsistent with the listed guides. Please

clarify the language.

EPA response:

We have made edits in response to this comment

to clarify this point.
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established forward capacity markets (FCMs) that pay suppliers to ensure sufficient electric 

generating capacity is available to meet future peak electricity demand. In operating these 

markets, ISO New England (ISO-NE) and PJM both allow demand-side energy efficiency 

programs and other demand-side resources to compete directly with electric generators to meet 

the regional capacity needs. One requirement for utilities and other energy efficiency program 

administrators seeking to bid into the market is to submit an evaluation plan. ISO acceptance of 

this evaluation plan “qualifies” energy efficiency programs and projects as prospective market 

resources. The evaluation plan specifies the amount of energy and demand savings to be 

delivered over the contract period (typically three years into the future), and documents how the 

requirements of the ISO’s M&V standards manual will be satisfied. 

Based on experience to-date, states bidding their energy efficiency programs into both the 

ISO-NE and PJM forward capacity markets are typically subjected to EM&V requirements that 

go beyond the evaluation already conducted for the purpose of meeting PUC requirements. This 

is because, while PUCs typically require evaluation protocols and procedures for documenting 

the cost-effectiveness of annual energy (MWh) savings, FCMs require the measurement and 

verification of capacity (MW) savings during specific peak demand hours. In addition to the 

evaluation of energy savings, a separate set of measurement techniques and data collection 

protocols are required to document peak demand reduction impacts. Furthermore, attaining the 

level of statistical precision and confidence described above typically requires additional 

sampling73 than is required by PUCs. One consideration is whether EM&V requirements in ISO 

capacity markets include components that would facilitate better estimation of avoided CO2

emissions related to energy efficiency programs include in state plans. 

For the states located in ISO-NE and PJM, the common evaluation requirements for FCM 

participation have created an impetus for regional collaboration on EM&V practices. New 

England and Mid-Atlantic states continue to work together to establish consistent evaluation 

protocols through the creation of an “EM&V Forum,” which is convened by the Northeast 

                                                            

73 With additional sampling requirements, energy efficiency program evaluators are typically required to measure a 

larger percentage of the energy efficiency measures installed within the total population of all measures installed 

through an energy efficiency program. 
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Energy Efficiency Partnerships (NEEP) and supports common evaluation methods, reporting 

metrics, and cost sharing on research studies.74 The Forum also serves as a venue for information 

exchange to address common EM&V challenges encountered with FCM participation. 

                                                            

74 More information about the EM&V Forum is available through Northeast Energy Efficiency Partnerships, at 

https://neep.org/emv-forum/index. 
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3.3 EM&V for Programs and Policies Not Typically Overseen by PUCs 

In contrast to energy efficiency programs overseen by PUCs, EM&V is less common for 

other types of energy efficiency requirements or programs, especially for minimum energy 

efficiency requirements that do not involve the expenditure of electricity ratepayer dollars. 

Examples include building energy codes, appliance efficiency standards, various energy 

efficiency financing programs, behavioral change programs, and market transformation 

programs that target both the suppliers of energy-efficient products and increasing consumer 

demand for those products. While these approaches often have substantial impacts in reducing 

energy use, they may also face EM&V challenges. In some cases, appropriate evaluation 

protocols and approaches have not been developed for some programs and measures. In cases 

where appropriate EM&V methods do exist, there may also be less experience applying them.75

4. Considerations for EM&V of End-Use Energy Efficiency Programs and Measures in 

 State Plans 

A key consideration for state plans is the process and requirements for EM&V of RE and 

demand-side EE measures that result in electricity generation or savings. As described in the 

preamble, at section VII.F.4, the EPA intends to develop guidance on acceptable methods that 

can be incorporated in an EM&V plan included as part of an approvable state plan. Critical 

features of such EM&V guidance, including scope, applicability, and minimum criteria, are 

discussed in this section. 

4.1 Accuracy of Energy Savings Estimates 

To document and verify that avoided CO2 emissions from energy efficiency programs 

and measures are real and persistent, impact evaluation must be rigorous and transparent. Impact 

assessment should also consider the appropriate balance between certainty of results and the 

EM&V costs to achieve a specified level of certainty. Because energy savings data are estimates, 

their use as part of the basis for determining the avoided CO2 emissions resulting from energy 

                                                            

75 Examples of such EM&V methods are described in the PNNL report, “Building Energy Codes Program: National 

Benefits Assessment: 1992-2040.”; and in Meyers et al, “Energy and Economic Impacts of U.S. Federal Energy and 

Water Conservation Standards Adopted From 1987 through 2012” LBNL-6217E. 

Deleted: may

Deleted: many currently

Comment [A11]: This text may overemphasize

EM&V challenges for this subset of measures. For

example, extensive analysis goes into appliance

standards, both ex ante to set the standard and ex

post when standards are reviewed for possible

update.

EPA response:

We have made edits to this text in response to this

comment.
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efficiency programs and measures in a state plan will depend upon the level of accuracy of this 

information. Therefore, evaluation results should be reported as “expected values”—that is, 

energy savings values are expected to be correct within an associated range of certainty. Key 

considerations for EM&V of energy efficiency programs and measures in state plans are similar 

to those faced in the design of any program evaluation approach: (1) the level of certainty that is 

required given a program’s objectives and requirements, and (2) how achievement of that 

 
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necessary level of certainty is balanced with the amount of effort (e.g., resources, time, money) 

used to obtain that level of certainty. 

4.2 EM&V Technical Considerations for State Plans 

Using energy efficiency requirements, programs, and measures as an emission reduction 

approach in state plans requires consideration of several evaluation-related technical 

considerations. The following sections describe these considerations. 

4.2.1 Qualifying Demand-Side Energy Efficiency Actions 
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States are currently implementing a wide range of demand-side energy efficiency 

requirements, programs, and measures. However, EM&V of some of these programs and 

measures are associated with greater levels of measurement precision and certainty than others, 

based in part on the EM&V procedures currently in place. For example, energy efficiency 

programs subject to PUC oversight and review are frequently evaluated using rigorous 

evaluation procedures that are based upon several decades of research and experience. These 

energy efficiency programs are subject to a quasi-judicial, public, and transparent review 

process, which can lead to adoption of EM&V protocols that convey a relatively high level of 

certainty for EM&V results. 

In contrast, other energy efficiency requirements and measures, some of which may result 

in very significant and cost-effective energy savings (e.g., building energy codes, local tax 

credits, EE loan programs, etc.), are not subject to PUC oversight and typically have comparably 

fewer EM&V requirements. There are also some newer EE program designs (e.g., certain 

behavior-based programs and some market transformation programs76) for which rigorous 

EM&V methodologies or a track record of energy savings persistence do not yet exist.77 

                                                            

76 Behavior-based energy efficiency programs aim to affect consumer energy-use behaviors in order to 

achieve energy and/or peak demand savings. Techniques to measure the impacts of these program designs 

are emerging and currently under development. Market transformation programs are characterized by 

strategic intervention in a market to address market barriers and market failures to accelerate market 

adoption of energy-efficient technologies and practices, and create lasting market change. While market 

transformation approaches can have very high energy savings impacts, by creating sustained deployment 

of energy-efficient technologies and practices, evaluation of these strategies is challenging due to the 

involvement of numerous market players and the multi-year timeframe for achieving energy savings.77 We 

note that EM&V approaches and protocols for behavior-based end-use energy efficiency programs do exist, but they 

have not been widely applied. For examples, see State and Local Energy Efficiency Action Network (SEE Action), 

Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency Programs: 

Issues and Recommendations (2012), Prepared by A. Todd, E. Stuart, S. Schiller, and C. Goldman, Lawrence 

Berkeley National Laboratory. Available at 

http://www1.eere.energy.gov/seeaction/pdfs/emv_behaviorbased_eeprograms.pdf. 

77 We note that EM&V approaches and protocols for behavior-based end-use energy efficiency programs do exist, 

but they have not been widely applied. For examples, see State and Local Energy Efficiency Action Network (SEE 

Action), Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency 

Programs: Issues and Recommendations (2012), Prepared by A. Todd, E. Stuart, S. Schiller, and C. Goldman, 

Deleted: various

Comment [A12]: One commenter suggests

behavior based programs have been used for

decades. Evaluation approaches exist for them, for

example,

http://www1.eere.energy.gov/seeaction/pdfs/emv_

behaviorbased_eeprograms.pdf.

EPA response:

We have made edits to this text in response to this

comment and have added a footnote referencing

the existence of EM&V protocols for behavior‐

based programs.

Deleted: ¶
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As discussed in the preamble, while the EPA does not intend to limit the types of RE and 

demand-side EE measures and programs that can be included in a state plan – provided that 

supporting EM&V is rigorous, complete, and consistent with EPA requirements and guidance – 

the level and type of documentation required by EPA in an approvable state plan may depend on 

whether EM&V practices for that type of program or measure are well established. One option 

for organizing these variations in EM&V practices is with a qualitative hierarchy, as follows: 

 EM&V procedures and protocols well established – for example, rebate and direct 

install programs for appliances, HVAC, and lighting equipment 

 EM&V procedures and protocols moderately well established – for example, 

building codes and standards 

 EM&V procedures and protocols less well established – for example, building 

disclosure and labeling programs 

Table 2 provides an illustrative characterization of the relative level of EM&V 

uncertainty for different types of energy efficiency programs and measures under this approach. 

This information is illustrative and generalized. In particular, there are numerous exceptions to 

the categorization of the relative uncertainty of EM&V results for different types of programs 

and measures listed in Table 2. 

Table 2. Illustrative Characterization of EM&V Procedures and Protocols for 

 Common Energy Efficiency Programs and Polices78 

EM&V Procedures and EM&V Procedures and EM&V Procedures and 

                                                                                                                                                                                               

Lawrence Berkeley National Laboratory. Available at 

http://www1.eere.energy.gov/seeaction/pdfs/emv_behaviorbased_eeprograms.pdf. 

78 This table is intended as a generalized description, based upon numerous conversations with professional energy 

efficiency program evaluators. It should be noted that there are states with building energy codes, behavior 

programs, and market transformation programs that are well documented and subject to rigorous EM&V. In 

addition, such characterizations will change over time, as EM&V approaches for new and innovative programs and 

measures become standard practice.79 This includes energy efficiency programs and measures for which there is 

significant experience with EM&V, a robust set of existing EM&V studies and reports, and relatively 

straightforward EM&V approaches. 

Deleted: W

Deleted: 4.1

Comment [A13]: The three tier approach for

relative establishment of EM&V approaches could

be problematic. Some of the approaches in the

middle column on page 47, such as building

commissioning and CHP, have well established

procedures. The discussion seems to omit EM&V

currently used by the ESCO industry. If a tiered

approach were used, it would need to be updated

over time as new approaches become standard

practice.  Has EPA considered that and how it will be

done?   

EPA response:

We have made edits to this subsection and table 2

in response to this comment. We have also added

text acknowledging the need to update such

categorization over time as EM&V becomes

standard practice for different types of EE

programs and measures. Finally, we note that the

text states that Table 2 is intended to be

illustrative only, not a definitive indication of the

degree to which EM&V protocols are established

for different types of EE programs and measures.

Deleted: 4.1

Deleted: ¶
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Protocols Well Established Protocols Moderately Well 

Established 

Protocols Less Well 

Established 

 Direct install incentive 

programs for building 

equipment (retrofits and 

new construction), 

including: 

o lighting 

o heating, 

ventilation, and 

air conditioning 

(HVAC) 

o refrigeration 

o motors 

 Building commissioning 

and retro-commissioning 

 Combined heat and 

power (CHP) 

installations/retrofits 

 Electrical distribution 

system and transmission 

system upgrades 

 Building energy codes 

(requirements and incentive 

programs for new 

construction, remodels) 

 State government 

building/operations 

programs (procurement, 

design standards, etc.) 

 Product-specific mid-stream 

and upstream market 

transformation programs 

 Industrial energy efficiency 

new construction or retrofits 

 General education 

programs for consumers, 

contractors, distributers, 

suppliers 

 Targeted training 

programs 

 Building labeling and 

disclosure programs 

 Point-of-sale product 

sales 

 Targeted consumer 

behavior programs 

As discussed in the preamble, at section VIII.F.4, EPA is proposing to allow a wide (or 

unlimited) set of energy efficiency program and measure types in state plans, as long as the 

energy savings are adequately documented according to rigorous EM&V methods and 

appropriate state regulatory oversight. Recognizing these variations in EM&V procedures and 

protocols, one option for EM&V requirements and guidance for state plans is to streamline 

review of EM&V plans for a pre-defined list of well-understood program types for which 

evaluation is straightforward and energy savings results are subject to a relatively low level of 

uncertainty.79 Other programs and measures with less well developed EM&V approaches would 

require greater documentation in state plans of EM&V methods that will be applied. This 

proposed approach is intended to maximize state flexibility and accommodate the full range of 

state energy efficiency programs, while simultaneously maintaining EM&V rigor and 

                                                            

79 This includes energy efficiency programs and measures for which there is significant experience with EM&V, a 

robust set of existing EM&V studies and reports, and relatively straightforward EM&V approaches. 

Deleted: <#>Building commissioning and 

retro-commissioning ¶

Deleted: <#>Combined heat and power (CHP) 

installations/retrofits¶

Deleted: or

Deleted: limit the eligible types of energy 

efficiency programs and measures that could be 

included in a state plan to
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transparency. As discussed in the preamble, at section VIII.F.4, EPA is also taking comment on 

the option of limiting the eligible types of energy efficiency programs and measures that could 

be included in a state plan to a pre-defined list of well-understood program types for which 

evaluation is straightforward and energy savings results are subject to a relatively low level of 

uncertainty.

4.2.2 Avoided Transmission and Distribution (T&D) Losses from End-Use Energy 

 Efficiency Measures 

In general, the difference between the amount of electricity input to the transmission 

system by an EGU and the amount ultimately delivered to an end-user constitutes transmission 

and distribution (T&D) line losses.80 According to EIA data, nationally, annual electricity 

transmission and distribution losses are equivalent to about seven percent of the electricity that is 

input to the transmission system in the United States. For every unit of energy use avoided at the 

end-use site, energy efficiency also avoids the losses that would otherwise occur as electricity is 

delivered to consumers through the T&D system.81 Many state PUCs are aware of this 

additional benefit of demand-side energy efficiency programs, and actively credit program 

energy savings results to account for program contributions to avoided line losses, albeit using a 

range of measurement approaches and calculations. A consideration for EM&V requirements 

and guidance for state plans is whether to account for avoided T&D losses, and how to do so in a 

consistent manner across states. 

                                                            

80 The T&D system includes all the power lines and related equipment used to deliver electricity from an electric 

generating plant to an end-use site. Along the way, some of the supplied by the generator is lost due to the resistance 

of the wires and equipment that the electricity passes through, as well as reactive power losses in alternating current 

systems due to inductance and capacitance. Most of this lost energy is converted to heat. The magnitude of losses in 

the T&D system depends on the physical characteristics of the system in question, as well as how it is operated. 81 U.S. EPA, Assessing the Multiple Benefits of Clean Energy: A Resource for States (February 2010), available at: 

http://epa.gov/statelocalclimate/documents/pdf/epa_assessing_benefits_ch3.pdf 

Deleted: An alternative approach is

Deleted: EPA is proposing to allow a wide (or 

unlimited) set of energy efficiency program and 

measure types in state plans, as long as the energy 

savings are adequately documented according to 

rigorous EM&V methods and appropriate regulatory 

oversight. The former option potentially reduces 

measurement uncertainty, while the latter approach 

may provide more flexibility. 

Deleted: in
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4.2.3 Reported Energy Savings Values 

Energy savings results for energy efficiency programs are often expressed in terms of 

annual MWh of savings per year. However, for an assessment of the associated avoided CO2

emissions impacts, it may be useful to utilize time-differentiated (i.e., hourly, seasonal) energy 

savings data.82 Information about the timing of energy savings has direct implications for 

estimating the avoided CO2 emissions that result from an efficiency program or portfolio of 

programs. The temporal energy savings profile that results from the application of energyefficient technologies and practices to different end-uses can vary significantly. For example, air 

conditioner programs save energy primarily on hot summer days, whereas a refrigerator program 

saves energy every hour of the year. Time-differentiated information related to electricity 

generation is useful in estimating avoided CO2 emissions. In particular time-differentiated data is 

necessary to estimate the marginal avoided CO2 emissions related to electric generation, as 

discussed above in section IV.C.3. 

In practice, state PUCs around the country have substantially different requirements and 

recommendations for evaluating and reporting time-differentiated energy savings. Some energy 

efficiency program administrators report annual energy savings impacts, where savings are 

typically based on either tracking data (i.e., data used to estimate savings for planning purposes) 

or evaluated (i.e., ex-post) savings data. Other programs supplement reporting of annual energy 

savings with data on the timing of energy savings, which can be used to identify the marginal 

EGU or cohort of marginal EGUs that would have provided generation in the absence of the 

energy savings. This supplemental data can be used to more accurately estimate avoided CO2

emissions that result from program energy savings. Due to improved evaluation software and 

data availability, it is increasingly common for energy efficiency program EM&V plans to 

include calculation of estimated seasonal or even hourly energy savings as part of the program 

evaluation process. A consideration for EM&V requirements and guidance for state plans is the 

extent to which time-differentiated data on energy savings from energy efficiency programs is 

available, and whether states can readily acquire such data and information for use in 

implementing their state plans. 

                                                            

82 This is often referred to as the “load shape” of energy savings achieved through an energy efficiency measure. 

Deleted: or
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 A related consideration is metrics reported for electricity savings. The primary metric 

required to understand the avoided CO2 emissions impacts of energy efficiency programs and 

measures is annual MWh of energy saved. In addition, a secondary metric is MW demand 

reduction impacts, which may be desirable because it is helpful in identifying the marginal EGU 

or cohort of EGUs, in particular units on the build margin. This information is necessary to 

estimate the marginal avoided CO2 emissions related to electric generation, when reporting on 

avoided CO2 emissions achieved through energy efficiency programs and measures during a plan 

reporting period, as discussed above in section IV.C.3. Identifying EGUs on the build margin, 

based on estimates of MW demand savings that will be achieved through the implementation of 

energy efficiency programs and measures included in a state plan, may also be useful in 

projecting emission performance by affected EGUs that will be achieved under the state plan. 

4.2.4 Savings Definitions: Net and Gross Savings 

As described above, state PUCs typically specify whether energy efficiency program 

administrators are required to report either gross or net energy savings, or both. Gross savings 

are the change in energy use (MWh) and demand (MW) that results directly from programrelated actions taken by program participants, regardless of why they participated in a program. 

Net savings refer to the change in energy use and demand that is directly attributable to a 

particular energy efficiency program.83 Reporting of net savings helps a PUC ensure that energy 

efficiency program budgets are being used to promote technologies and practices that are not 

otherwise being adopted in the marketplace. A consideration for EM&V requirements and 

guidance for energy efficiency requirements and programs included in state plans is whether 

                                                            

83 Calculations of net energy savings involve excluding energy efficiency measures undertaken by "free riders" (i.e., 

EE program participants who receive a program rebate even though they would have taken the efficiency action 

anyway), or adjusting energy savings estimates to account for these effects. Free riders increase program costs 

without producing additional energy savings impacts beyond what would have occurred in the absence of the EE 

program. Free ridership may also be addressed in the setting of baselines that are used to calculate energy savings. 

Estimates of net energy savings may also involve an assessment of "free drivers" (sometimes referred to as 

"spillover effects"). These are individuals who do not directly participate in an EE program, but who undertake 

efficiency actions in response to program activity (e.g., marketing/advertising, greater availability of energyefficient equipment in a marketplace as the result of EE programs). Accurate estimation of free ridership and 

spillover effects is complex, especially in areas of the country with robust energy efficiency markets with multiple 

non-program influences competing for customers’ attention.

Deleted: of
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required reporting of energy savings should be specified on either a gross or net basis, or both, to 

promote national consistency in measuring the impact of energy efficiency measures across state 

plans.84

4.2.5 “Measure Life” and Persistence of Savings

Measure life and persistence of energy savings describe the ongoing effects of an 

installed energy efficiency measure, including the retention of the measure (i.e., is it still in 

place) and the performance degradation of that measure, which reduces a measure’s achieved 

energy savings over time. Typically, program administrators estimate the impact of energy 

efficiency programs in terms of first-year savings (in MWh), plus the cumulative MWh savings 

realized from that program (or measure) over an assumed “measure lifetime”. Depending on the 

mix of energy efficiency measures and their assumed measure lives, these energy-savings 

benefits may extend from 10 to 15 years, or more into the future from the point of measure 

installation. In practice, evaluators determine measure lifetimes on the basis of engineering 

judgment, manufacturer specifications, and some empirical field studies. These values are 

frequently entered into PUC-managed state technical databases for ongoing and repeated use in 

evaluation studies. For state plans, a key consideration for EM&V is whether measure life and 

persistence values for energy efficiency measures documented by states are accurate, up-to-date, 

and consistent with those utilized in other states (after accounting for appropriate non-energy 

factors, such as weather and building occupancy type). 

5. Options for EM&V Requirements and Guidance for State Plans 

As EPA develops guidance on acceptable evaluation methods that can be incorporated in 

an EM&V plan included as part of an approvable state plan, the agency (as discussed in the 

preamble at section VIII.F.4) is seeking comment on the appropriate basis for and technical 

resources used to establish such guidance, including consideration of existing state and utility 

                                                            

84 If both gross and net savings were required to be reported, this would increase the transparency of reported energy 

savings estimates, but only one savings value would be used to evaluate the effect of energy efficiency programs and 

measures on CO2 emissions from affected EGUs. 
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protocols, as well as existing international, national, and regional consensus standards or 

protocols, as described in this section. 

As summarized in the preamble, and discussed in more depth in this section, utilities and 

states have conducted ongoing evaluation of end-use energy efficiency and renewable energy 

measures and programs for several decades. These evaluations, which include quantification, 

monitoring and verification of results, generally rely upon a well-defined set of industry-standard 

practices and procedures. As a result, existing state and utility EM&V requirements and 

processes generally provide a solid foundation for minimum EM&V requirements that can be 

utilized by the EPA in the development of EM&V requirements and guidance for state plans. 

However, measurement approaches vary by state based on multiple factors, including the 

measure and program type being evaluated, the level and nature of regulatory oversight, the 

degree of state and utility experience with these measures and programs, and the overall 

magnitude of program impacts. Due to this variation in state EM&V approaches, as well as the 

specific objectives of a state plan under CAA section 111(d), harmonization of state EM&V 

approaches, or inclusion of supplemental EM&V actions and procedures, may be warranted in an 

approvable state plan.

As discussed previously, current state EM&V practices involve aligning the level of 

EM&V effort (i.e., rigor, reliability, validity, and uncertainty of energy savings estimates) with 

the appropriate level of certainty of evaluation results, while taking into consideration the 

magnitude of energy efficiency program impacts. This approach is consistent with the objective 

of achieving environmental results, ensuring minimum levels of cross-state consistency, and 

supporting and encouraging the use of energy efficiency requirements, programs, and measures 

in state plans. To advance these objectives, the EPA could take several possible approaches for 

documenting energy efficiency savings from measures in state plans. 

Options for EM&V requirements and guidance for state plans that incorporate energy 

efficiency requirements, programs, and measures include: 

Deleted: might

Deleted: established

Deleted: best 

Deleted: require the

Deleted: to be aligned

Deleted: required

Deleted: and

Deleted: These best practices are

Deleted: credible

Deleted: e

Deleted: e

Deleted: state 

Deleted: support

Deleted: the following process considerations are 

relevant for documenting

Deleted: or

Comment [A14]: Options listed omit a “middle

of the road” approach such as the Uniform Methods

Project.  

EPA response:

We have included a “middle‐of‐the‐road” option in

response to this comment and have included the

Uniform Methods Project (UMP) as an example of

such an option.
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 Establishing specific EM&V requirements with a level of defined rigor – such as a 

required minimum level of precision and accuracy (see discussion of ISO forward 

capacity markets above) – for all energy efficiency programs and measures 

 Establishing specific EM&V requirements for certain types of widely used energy 

efficiency programs and measures – such as those addressed by DOE’s Uniform Methods 

Project (UMP) – while establishing a generalized EM&V approach that states can apply 

to programs that are relatively new, innovative, or untested

 Establishing a set of generalized, process-oriented EM&V requirements that apply to all 

energy efficiency programs and measures, while providing flexibility to customize 

EM&V approaches, as appropriate for different types of programs and measures, 

provided that EM&V meets these minimum requirements 

At one end of this spectrum, establishing program-specific EM&V requirements and an 

associated level of rigor for EM&V provides certainty to states in terms of required energy 

savings documentation, and does so in a manner that ensures a consistent level of EM&V rigor 

across all state plans. However, this approach may require significant effort by the EPA to 

establish such requirements, and could potentially duplicate state efforts currently under way to 

harmonize EM&V practices. This approach may also limit the variety of valid EM&V 

approaches applied at the state level, and by extension the types of energy efficiency programs 

and measures that could be included in a state plan. It could also inhibit the development of 

innovative EM&V approaches that improve the accuracy of energy savings estimates. At the 

other end of the spectrum, if only generalized, process-oriented EM&V requirements and 

guidance are established, then a state has maximum flexibility, but also faces somewhat greater

uncertainty about whether the EM&V approach included in a state plan will be approved by the 

EPA. This could increase the transaction costs incurred by states during the development of their 

plans, and could possibly delay the full implementation of energy efficiency programs 

incorporated in state plans. 

Alternatively, a middle-ground approach involves a combination of specific EM&V 

criteria for common energy efficiency program and measure types, along with generalized 

Deleted: ;

Deleted: Establishing no EM&V requirements 

at all, beyond basic requirements to quantify and 

verify energy savings, and allowing states to 

establish their own EM&V protocols;

Formatted: Indent: Before: 0.5", No bullets

or numbering

Comment [A15]: Under a rate‐based approach,

this is risky and should not be allowed (states with

lax EM&V could potentially generate false efficiency

credits that lower their compliance costs).  For rate‐

based approaches, EPA should consider requiring

strong national EM&V standards for efficiency (and

indicating its preference for this approach here),

drawing from DOE’s uniform methods project and

other state of the art approaches.  On the other

hand, for mass‐based approaches, EPA should

consider allowing states to use their own EM&V for

efficiency.  

EPA response:

We have deleted discussion of this option in

response to this comment.

Deleted: minimum specified

Deleted: , and 

Deleted: no specific 

Deleted: increased

Deleted: proposed

Comment [A16]: Though this comment

bookends the options, a lack of EM&V requirements

would be extremely problematic.

EPA response:

We have deleted discussion of this option in

response to this comment and the previous

comment.

Deleted: In the middle of the spectrum,

Deleted: some minimum
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guidance for emerging program designs and measures. Such an approach would provide some 

level of certainty regarding acceptable EM&V approaches in state plans, while maintaining a 

certain degree of flexibility for states to determine an appropriate mix of EM&V approaches,

given the types of energy efficiency programs and measures included in their plan. 

In addition, one option for supplementing either approach described above is to prescribe 

who can conduct EM&V activities and prepare energy savings documentation, and to specify 

their needed qualifications. This approach is analogous to professional certification requirements 

in the accounting and engineering fields, in which a minimum level of credibility, rigor, and 

accountability is imparted to the services provided by qualified individuals and firms. Criteria for 

eligible evaluators might include a demonstration of independence from those implementing or 

administering the energy efficiency programs and measures (i.e., identification and mitigation of 

potential conflicts of interest) and required minimum levels of training, experience, or 

certification. This approach recognizes that the qualifications, integrity, and independence of 

those conducting EM&V of energy efficiency programs and measures, and preparing energy 

savings estimates, is critical to assuring best-practice EM&V. However, such requirements alone 

may not ensure sufficient evaluation rigor. 

5.1 Use of EM&V Protocols 

Establishing requirements and guidance for EM&V of energy efficiency programs and 

measures included in state plans may involve: 

 Relying on existing EM&V infrastructure and protocols, most of which have been 

established for utility-customer funded energy efficiency programs overseen by state 

PUCs 

 Establishing new protocols and procedures 

Relying on existing approaches has the benefit of utilizing existing resources that can be 

relatively quickly ramped up for use in state plans. However, existing state and utility EM&V 

infrastructure and protocols may not be applicable to the full range of energy efficiency 

programs and measures that a state may want to include in its plan. In addition, because existing 

Deleted: could be specified for all energy 

efficiency programs and measures, using general 

guidance, and/or establishing EM&V criteria for 

specific widely used energy efficiency programs and 

measures

Deleted: States would have

Deleted: about

Deleted: as well as

Deleted: some

Deleted: in establishing appropriate

Deleted: for

Deleted: A supplemental approach, in addition to 

those

Deleted: ,

Deleted: or
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state and utility EM&V infrastructure and protocols were established to support the goals of state 

energy efficiency programs, in current form they may not adequately support the level of EM&V 

required for state plans under CAA section 111(d). In particular, this may include the form and 

precision of energy savings data and reporting necessary to evaluate avoided CO2 emissions that 

result from energy efficiency programs and measure included in state plans. 

 5.2 EPA Review of EM&V Plans as Part of the State Plan Review Process 

As discussed in the preamble, at section VIII.F.4, the EPA is proposing that a state plan 

must include an EM&V plan, which is subject to approval by EPA, as part of plan review and 

approval. Under this approach, the EPA would review these EM&V plans as part of its review of 

submitted state plans. One option is that an approvable EM&V plan could rely primarily on 

state- or utility-level EM&V plan review and approval processes, consistent with established 

EPA requirements and guidance for EM&V, with open public involvement and state lead-agency 

approval. Using existing state EM&V plan review processes may better ensure that energy 

savings estimates are transparent, peer reviewed, and address stakeholder input. Using state 

processes also minimizes duplication of state and the EPA requirements, and balances the need 

for EM&V credibility and rigor with an interest in encouraging the deployment of cost-effective 

energy efficiency programs and measures through incorporation in state plans. 

5.3 General Quality Standards for EM&V Rigor and Accuracy 

 Since existing state EM&V process vary, EM&V guidance established by EPA may 

need to identify and establish minimum criteria for EM&V rigor, accuracy and reliability, and 

quality control. Requirements could be (a) a single set of requirements that apply to all energy 

efficiency programs and measures in all states or (b) a variable or flexible set of requirements 

with increasing levels of EM&V effort and rigor depending on the relative degree of uncertainty 

of energy savings from the energy efficiency programs and measures in a state plan. For 

example, well understood energy efficiency measures with a higher degree of energy savings 

Deleted: 

Deleted: Development of EM&V plans that are 

included in a state plan

Deleted: of energy savings reported. Under this 

approach, states would develop EM&V plans as part 

of the state plan development process. The EPA 

would then review these EM&V plans as part of its 

review of a submitted state plan.

Deleted: However, this approach could create 

uncertainty for states about the approvability of a 

proposed EM&V approach in a state plan, and could 

also reduce the consistency and comparability of 

EM&V approaches used in state plans. At the same 

time, existing state EM&V processes currently vary 

in the level of regulatory oversight, which could 

necessitate establishment of minimum criteria for 

EM&V plans.

Deleted: the EPA

Deleted: requirements
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certainty might require a lower level of EM&V effort, while measures with greater complexity or 

uncertainty of energy savings effects would require greater EM&V effort.85

 For simple, well-understood and straightforward energy efficiency programs and 

measures, such as lighting retrofits, EPA guidance might specify only verification that measures 

were installed and the use of deemed energy savings values (i.e., lower EM&V effort level). In 

contrast, EPA guidance might specify more detailed EM&V (i.e., a higher level of EM&V effort) 

for less well-understood or more complex energy efficiency programs and measures, such as 

behavior programs and market transformation programs.

 A prescriptive EM&V approach in EPA guidance for different types of energy efficiency 

programs and measures would provide states with certainty while supporting a consistent level of 

EM&V rigor across all states. A flexible EM&V approach, based on an individual assessment of 

the measurement uncertainty related to the energy efficiency programs and measures included in 

a state plan could provide states with greater flexibility when selecting energy efficiency 

programs and measures. However, the lack of a prescribed EM&V approach in EPA guidance 

could increase uncertainty about the approvability of different plan approaches. 

6. EM&V Documentation in a 111(d) State Implementation Plan 

EM&V documentation will be an important component of state plans that incorporate 

energy efficiency programs and measures, because transparency and reproducibility increase 

overall confidence in reported energy savings results. A crucial component of EPA’s proposed 

approach for evaluating energy efficiency programs and measures included in a state plan is a 

requirement that state plans that include enforceable energy efficiency and renewable energy 

measures must include an EM&V plan for these measures. These EM&V plans would specify

how achieved energy savings will be retrospectively evaluated at appropriate increments during 

the plan period. Decisions about the level of EM&V documentation that is necessary in a state 

                                                            

85 For the purposes of this discussion, lower, medium, and high levels of “EM&V effort” are intentionally 

indeterminate. One possibility is that “lower EM&V effort” could refer to greater reliance on deemed savings 

values, smaller sample sizes for measured savings, fewer direct measurements, and proportionately greater reliance 

on ex-ante estimates. “Medium” and “high” levels of EM&V effort could require incrementally more effort in each 

of these areas. Other interpretations of this concept are possible. 

Deleted: S

Deleted: require

Deleted: , while

Deleted: , would require more detailed EM&V 

(high level of EM&V effort).

Deleted: bed

Deleted: all

Deleted: this EM&V documentation is an 

Deleted: , which

Deleted: ies a priori
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plan must consider tradeoffs between provision of more information and greater transparency, 

and the level of EM&V effort required. Excessive documentation requirements may not add 

value in terms of transparency, but may discourage the inclusion of cost-effective energy 

efficiency options in state plans. However, two basic criteria for EM&V documentation should 

be applied in state plans: 

 Energy savings documentation should be provided at a level of detail that allows for 

recalculation of program energy savings totals; and 

 EM&V information in state plans should be provided in a consistent manner across states 

to allow for comparison, benchmarking, and more efficient review of plans by the public 

and EPA 

6.1 Illustrative Example of an EM&V Plan for End-Use Energy Efficiency Programs 

 Measures in a State Plan 

The following is an example of a possible outline of the types of information that might 

be included in an EM&V plan for energy efficiency programs and measures included in a state 

plan. An EM&V plan would specify how EM&V activities will be conducted and reported for 

relevant energy efficiency programs and measures during a state plan performance period. An 

EM&V plan could apply to utility energy efficiency programs that are incorporated into a state 

plan on a stand-alone basis, and might also apply to such programs when used to meet 

mandatory energy efficiency requirements, such as an EERS, that are incorporated into a state 

plan. 

Who Will Document Savings and When

 Name of the organization that will prepare evaluated energy savings reports 

 Relationship of the organization to the subject energy efficiency program(s) and program 

administrator(s) 

 Schedule of when the reports will be prepared and what period of time they will cover 

 Name of the state or regional government entity, or non-governmental entity, which will 

review and certify the evaluated savings 

Deleted: and their

Comment [A17]: Incomplete sentence – please

clarify.   

EPA response:

This was a typo, as this bullet was originally

combined with the second bullet. Thank you for

spotting.
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 How evaluated energy savings reports will be made publicly available and what the 

primary use of the reports will be 

 

Documentation Procedures

 List of energy savings metrics to be reported (e.g., annual MWh, monthly MWh, hourly 

MWh, average MW), and whether gross or net savings, or both, will be reported 

 Name of impact evaluation protocols, guidance documents, and other methods that will 

be followed in preparing evaluated energy savings reports 

 Description of the range of uncertainty for energy savings estimates indicated in 

evaluated energy savings reports, including sources of uncertainty 

 Description of assumptions concerning availability of data and data collection methods 

 Indicate how the following issues, if applicable, will be addressed in the claimed and 

evaluated savings estimates: 

o Inclusion of estimates of avoided electricity transmissions and distribution losses 

o Adjustment of gross savings estimates to net savings estimates (if applicable 

o Sources of uncertainty 

 
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B. Quantification, Monitoring, and Verification for Renewable Energy Measures 

For rate-based state plans, a key element of the plan is a demonstration of how the state, 

and related entities with enforceable obligations under the plan, will measure and verify electric 

generation that is achieved through the implementation of renewable energy measures 

incorporated in the plan.86 This section discusses current state and utility quantification, 

monitoring, and verification practices for renewable energy measures, and discusses 

considerations related to possible acceptable quantification, monitoring and verification 

approaches for a state plan under CAA section 111(d). 

States and utilities use a variety of policy instruments to increase the production and use 

of renewable energy. The principal mechanisms include: renewable portfolio standards (RPSs), 

feed-in tariffs (FITs), tax incentives (e.g., property tax exemptions; production-based tax 

incentives; etc.), financial assistance programs (e.g., grants, loans, and other direct financial 

assistance based on generating capacity or investment level), and other policies (R&D support; 

manufacturing incentives; workforce training; net metering; etc.). Experience to date indicates 

that RPSs have led to the vast majority of the increase in renewable energy generating capacity 

and generation resulting from state policies. However, FITs and production-based tax incentives 

have been among the most important incentives used by states and utilities to help achieve RPS 

requirements, as well as to spur additional production and use of renewable energy. Further, 

these types of programs rely on measurable electric generation as the basis for compliance or 

incentive payments. As a result, the following discussion focuses on quantification, monitoring, 

and verification mechanisms related to these state policies. Other types of programs (e.g., certain 

grant and rebate programs) may not currently quantify electric generation output from funded 

renewable energy projects. However, if such programs were modified to require the collection of 

such data, many of the quantification, monitoring, and verification considerations discussed in 

this section would also generally apply. 

                                                            

86 In this section we use the term “renewable energy measure” to refer to a renewable energy requirement (such as 

an RPS), a renewable energy deployment program, or individual installed renewable energy measures, such as 

installation of a solar photovoltaic system through a renewable energy deployment program. 
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1. Renewable Portfolio Standards 

A RPS is designed to increase the amount of renewable energy a distribution utility or 

load-serving entity provides to retail electricity customers.87. This increased customer demand in 

turn increases the production of renewable energy to meet demand. To achieve compliance with 

a RPS, an increasing share of a distribution utility’s electricity retail sales is required to be 

produced or acquired from renewable energy resources and delivered to customers. To verify 

compliance, RPSs have been complemented by tracking systems for renewable energy 

generation and use. These tracking systems account for the growing amount of renewable energy 

that is produced for obligated retail sellers as well as large and small retail energy consumers that 

purchase renewable energy on a voluntary basis.88

The point of regulation for state RPSs is typically investor-owned electric distribution 

utilities, because most RPS apply to entities under the jurisdiction of state PUCs. In a number of 

states, municipally-owned utilities and electric cooperatives are exempt from state RPS, have 

lower RPS requirements, or are required to develop their own renewable energy procurement 

targets. Additionally, some states have created separate renewable energy requirements for each 

of their affected distribution utilities. 

The absolute amount of renewable energy that each distribution utility is obligated to 

deliver will vary, with requirements in the form of a fixed amount of renewable energy (either 

MWh or MW of capacity) or percentage of retail sales. 

There are several pathways that affected distributed utilities typically have to meet state 

RPS requirements, including building and operating renewable energy generating capacity, 

purchasing electricity from renewable energy generators, and purchasing the attributes from 

renewable energy generation. Many state RPSs take this latter approach. Rather than requiring 

                                                            

87 In states that have restructured their electricity sectors and introduced retail competition, entities other than a 

utility may supply electricity to a retail customer. These entities use a regulated distribution utility’s network to 

deliver electricity to a retail consumer. These entities either generate their own electricity or contract for supply from 

wholesale electricity market participants. In this section, we use the term “distribution utility” to refer broadly to 

both local distribution companies (LDCs) and other load-serving entities that supply electricity to retail customers. 

88 “Voluntary” renewable energy purchases, as used here, refers to renewable energy purchases in addition to the 

renewable energy required by RPSs. 
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each distribution utility to generate electricity from its own renewable energy facilities or 

purchase electricity from a renewable facility owned by others, many states require distribution 

utilities to acquire renewable energy certificates (RECs) that represent the attributes of the unit of 

renewable electricity produced.89 

By allowing REC trading, many states have created markets for RECs based on specific 

state RPS requirements. Renewable energy generators can sell RECs as another product bundled 

with the underlying power they produce or sell RECs separately to different customers. Once the 

RECs are separated from the power generated, the power has no attributes associated with it and 

is considered generic or “null” power.90 

There are a number of key aspects of RPS design and implementation that affect the 

quantification, monitoring, and verification of renewable energy generation used to meet a RPS: 

 Eligible renewable energy resources. While most RPS-eligible resources in most states 

will result in avoided CO2 emissions from fossil fuel-fired EGUs, some RPS-eligible 

resources in some states are responsible for greenhouse gas (GHG) emissions or do not 

meet common definitions of renewable energy (e.g., waste coal, coal-bed methane, and 

fuel cell operation using fossil-fuel feedstocks). 

 Existing and new resources. RPS-eligible resources can include facilities that began 

operation prior to the enactment of the RPS and, more importantly, prior to the proposal 

of the emission guidelines by EPA. 91

                                                            

89 RECs are contractual instruments that convey ownership of the attributes of a unit of energy generated, but do not 

represent the energy itself. The attributes conveyed with RECs include information about the generator, such as: 

type of resource (e.g., wind), plant-level air emissions (if any), geographic location, nameplate capacity (MW), 

commercial operation date, ownership, and the eligibility for RPS compliance or voluntary market certification. 90 These distinctions are typically made as part of utility disclosure to customers about the energy resource mix and 

emissions of the electricity used by a customer that was supplied from by the distribution utility. “Generic power” or 

“null power” refers to energy that has no generation attributes or descriptive information (i.e., the remaining system 

mix of power, after assignment of specified power to different utility customers, through power purchase contracts 

or purchase of generation attributes through RECs). For electricity labeling, disclosure to customers, or other market 

claims, generic or null power is typically assigned the attributes of the remaining system mix of power, after the 

assignment of attributes as described above. 

91 In the preamble, the EPA is proposing that, for an existing state requirement, program or measure, a state may 

apply toward its required emission performance level the emission reductions that existing state programs and 
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 Scope of coverage. In some states a RPS applies to all retail sales in a state, but in others 

only a subset of retail sales (e.g., only investor-owned utility retail sales) are subject to a 

RPS. 

 Credit multipliers. Some states provide additional incentives for specific eligible 

resources in the form of bonus credit toward compliance in their RPS accounting 

framework. For example, these states may favor certain resources (e.g., distributed solar 

PV), or locally-important resources or technologies. The MWh produced or renewable 

energy certificates (RECs) related to MWh production from such facilities may be 

counted twice or three times toward compliance with a RPS in such states. However, 

these credit multipliers and bonuses are not an accurate representation of the amount of 

renewable energy generation that is attributable to a RPS. For the purpose of quantifying 

the amount of renewable energy produced as a result of a RPS included as a measure in a 

state plan, only the actual renewable energy generation used to comply with an RPS is 

relevant. 

 Banking. Some states permit the carryover of renewable energy produced in one year to 

satisfy RPS requirements in a subsequent year. Accounting for year-to-year carryover 

should be addressed in a state plan, in order to determine the renewable energy 

generation that occurred in a respective reporting year or compliance period. 

 Alternative compliance payments (ACP). Many states allow a compliance alternative 

which requires obligated entities to pay a predetermined fee to the state for each MWh of 

RPS shortfall. Although these ACP payments may be directed to programs to promote the 

deployment of renewable energy technologies, these payments are not equivalent to 

renewable energy generation and should not be accounted as such. 

 Interstate issues. While treatment of interstate emission effects is discussed in detail in 

section VII, for quantification, monitoring, and verification of renewable energy 

                                                                                                                                                                                               

measures achieve during the plan period due to actions taken after the date on which the emission guidelines are 

proposed (i.e., from June 2014 onward). 
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generation under a RPS it is important to note that most states allow use of eligible 

renewable energy resources located in other states to satisfy the state RPS requirements.92

Considerations related to the quantification, monitoring, and verification of renewable 

energy generation used to meet a RPSs depends on the design and implementation of the RPS. 

Distribution utilities subject to a RPS may meet their RPS obligations by building and operating 

their own renewable energy generating facilities, entering into bilateral contracts with other 

parties to purchase renewable energy, and participating in the REC market. Each compliance 

method has specific implications for the quantification, monitoring, and verification of 

renewable energy generation used to meet RPS obligations. Implications under these different 

pathways are discussed below. 

Build renewable generating facilities

In many states, utilities with RPS obligations may build, own, and operate their own 

renewable energy generating facilities. This pathway is often used by vertically integrated 

utilities subject to a RPS. For large renewable energy generating facilities, production is 

measured through a revenue-grade utility meter as it enters the grid at the point of 

interconnection. This meter is subject to the same verification standards as for any other 

generator participating in the wholesale market. 

Some utilities with RPS obligations also build, own, and operate smaller distributed 

renewable energy generating facilities. Smaller generators, such as residential rooftop solar PV 

systems of less than 10 kW capacity, often don’t have discrete metering of their total 

generation.93 State RPS requirements may permit these distributed generators to qualify for use 

in meeting utility RPS obligations based on an engineering estimate of their renewable energy 

generation output , provided the distributed generators are registered with a REC tracking system 

and the generation output is verified according to tracking system and RPS rules. 

                                                            

92 This may also include international renewable energy resources, such as Canadian hydroelectric and wind energy 

resources, which may be used to comply with some state RPSs. 

93 Metered data for these PV systems may only track net electricity supply to or from the grid, representing either 

surplus generation that is not used to serve on-site electricity load or additional net electricity supplied from the grid 

if the system does not meet a building’s total electricity load. 

Comment [A18]: Suggest including this

clarification in the preamble.

EPA response:

This is discussed in the preamble, in the section

discussing treatment of interstate emission effects

(section VIII.F.6).
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Bilateral contract model 

Under the bilateral contract model, distribution utilities with RPS obligations contract 

with renewable energy generators for supply. These contracts typically specify a delivery amount 

in MWh over a specified contract period. These supply contracts may be short- or long-term, 

may specify generation from certain renewable energy EGUs, and may be solicited through an 

RFP or entered into through negotiation. Quantification of renewable energy generation (in 

MWh) is accomplished through the use of a revenue-grade meter that measures the flow of 

electricity from the generator into the transmission grid. A contract may also stipulate an 

adjustment to the metered MWh generation data to account for transmission losses that occur 

between the point of injection of electricity to the transmission grid and the point of receipt at a 

utility transmission or distribution system. A renewable energy supply contract also generally 

addresses the ownership of the RECs related to the renewable energy generation. Under such a 

contract, purchase of the RECs should accompany the purchase of the electricity, in order for the 

utility to satisfy its RPS obligations through the contract.94 

State RPS compliance processes may provide for PUC review of supply contracts, 

including inspection of meters and verification of electricity delivery from the generator to the 

utility distribution network through a specified contract path (e.g., through evidence of 

transmission rights held or scheduled). The purchasing utility also reports their purchase and 

delivery of RPS-compliant renewable energy pursuant to the contract to the state agency 

responsible for RPS enforcement, typically the state PUC or state energy office. Verification is 

accomplished by audit of electricity supply contracts along with REC tracking system reports of 

RECs held by the utility and submitted for retirement by the tracking system administrator. Some 

state RPS require that electricity from qualifying renewable energy sources be produced within 

the state or a specified grid region, or if outside the state or specified grid region, that electricity 

be delivered to the state or grid region. In such cases, the verification of electricity delivery is 

typically done by the REC tracking system administrator before issuing RECs for the imported 

energy. Verification can be provided, for example, by demonstration of scheduled delivery 

                                                            

94 This ensures that multiple parties are not using the same MWh of renewable energy generation to comply with 

their RPS obligations. 
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through the ISO or RTO serving the state or region, through demonstration that the seller holds 

transmission rights for delivery or possession of NERC tags for the energy.95 Bilateral contracts 

also typically require certification by the seller that attributes related to the sold electricity have 

not been and will not be otherwise sold, retired, claimed, represented as part of energy sold 

elsewhere, or used to satisfy obligations in another jurisdiction. 

REC model

Under the REC model, renewable energy generators register their EGU with a renewable 

energy tracking system, which have been established by several regional groupings of states, as 

well as a few individual states.96 The registration process collects data about the generator’s 

attributes: type of resource (e.g., wind), plant-level emissions, geographic location, nameplate 

capacity (MW), commercial operation date, ownership, and the eligibility for RPS compliance or 

voluntary market certification. After the generator is registered, revenue-meter data is 

transmitted to the tracking system. Meter accuracy is verified for renewable energy generators in 

the same manner as for any other generator participating in wholesale electricity markets. 

Each MWh of renewable energy generation reported to the tracking system by a 

registered generator results in the issuance of a REC, with its own unique serial number and 

information about the generator, location, resource type, and the month in which the MWh was 

generated, and the month or quarter in which the certificate was issued. The renewable energy 

                                                            

95 A NERC Tag, sometimes referred to as an E Tag, is an electronic tag that is used to track wholesale electricity 

transactions that involve the transfer of electricity across or through control areas. NERC Tags allow transmission 

system operators to track electricity transactions in real time in order to assess any potential reliability implications 

of scheduled power transactions. NERC Tags define the physical path of an electricity transaction from the point of 

generation to the point of receipt, and also define the financial path, including all parties to a transaction. All 

wholesale electricity transactions that will result in the transfer of electricity from one control area to another, or that 

involve transfers through a control area, must be accompanied by a NERC Tag. Based on a real-time assessment of 

NERC Tags, system operators can curtail transfers if reliability issues would arise as a result of the transfer. NERC 

tags are issued through an electronic system in accordance with specifications established by the North American 

Electric Reliability Corporation (NERC). 

96 Currently, nine regional renewable energy certificate (REC) tracking systems operate in different regions of 

North America, including: Texas Renewable Energy Credit Program, run by the Electric Reliability Council of 

Texas (ERCOT), NEPOOL Generation Information System (GIS), PJM Generation Attribute Tracking System 

(PJM-GATS), Western Renewable Energy Generation Information System (WREGIS), Midwest Renewable Energy 

Tracking System (M-RETS), North American Renewables Registry (NARR), Michigan Renewable Energy 

Certification System (MIRECS), Nevada Tracks Renewable Energy Credits (NVTREC), and North Carolina 

Renewable Energy Tracking System (NC-RETS). For more information see 

http://apps3.eere.energy.gov/greenpower/markets/certificates.shtml?page=3 . 
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generator can then sell the renewable electricity as a bundle (both the commodity electricity and 

the associated REC) or unbundle the RECs from the electricity and sell the two products 

separately. Other market participants, such as brokers, REC marketers, and load-serving entities 

also maintain accounts with the tracking system so that REC electronic transactions can be 

recorded within the tracking system platform. The system tracks each REC through these 

transactions and ultimately “retires” the REC when the final purchaser designates it for 

retirement. Retirement could result from the REC being used to satisfy a state RPS, or as a result 

of a voluntary buyer retiring the REC to demonstrate that they had purchased and used 

renewable energy to meet their electricity demand. 

Because the tracking system follows the REC from the point of issuance to retirement, 

including all interim transactions, it minimizes the opportunity for renewable energy to be 

double-counted across, for example, two different state RPSs, or between two voluntary 

purchasers. In recent years, the various tracking systems have developed interchange standards 

so that RECs generated within one tracking system can be transferred to and used within another 

tracking system.97 Note that not every potential interchange possibility is currently supported, 

and that many states have additional eligibility restrictions within their RPS that may limit the 

use of RECs related to electric generation from distant locations. These include requirements in 

some state RPSs for the seller to hold firm transmission rights for delivery of the accompanying 

electricity from the renewable energy generator into the respective ISO/RTO system or grid 

system in which a state is located. 

Small distributed generators

Smaller distributed generators, such as residential rooftop solar PV systems of less than 

10 kW capacity, often don’t have discrete metering of their total generation. State RPS 

requirements may permit these distributed generators to qualify for use in meeting utility RPS 

obligations based on an engineering estimate of their renewable energy generation output , 

provided the distributed generators are registered with a REC tracking system and the generation 

output is verified according to tracking system and RPS rules. Where such projects are third-

                                                            

97 Additional linkages between tracking systems are being established. More information can be found at 

http://www.narecs.com/resources/registries/ 
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party owned and operated, the project developer will own the RECs and factor their revenue 

value into their pricing offered to the site host for electricity supply. Note also that many utilitysponsored renewable energy incentive programs stipulate that all RECs resulting from the project 

must be transferred from the generation owner to the utility as a condition of participation in the 

incentive program. These RECs can then be used by the utility to meet its RPS obligations, or 

can be sold to other parties. 

State agency role 

In many states, the PUC or its equivalent is responsible for establishing the detailed rules 

and procedures that obligated parties must follow to comply with a RPS. The PUC is usually 

responsible for receipt and review of obligated parties’ periodic compliance reports, imposing 

compliance penalties as needed, and for evaluating the impacts of the program on energy costs, 

generation diversity, and market operations. The list of eligible resources and MWh requirements 

are often set through state legislation, but these decisions may also be delegated to the PUC for 

study and promulgation through regulations of commission orders. 

In some states the energy office may be responsible for certifying the eligibility of 

specific generators to participate in the RPS and for making siting determinations. In New York, 

for example, the New York State Energy Research and Development Authority (NYSERDA) is 

responsible for the centralized procurement of the renewable energy needed to meet the RPS for 

all of the state’s investor-owned utilities. 

2. Feed-in Tariffs 

Feed-in tariffs (FITs) are offered by some individual electric distribution utilities and 

some states for renewable energy systems that meet eligibility criteria.98 Under a FIT, the utility 

offers to purchase specific kinds of electricity (e.g., solar) from sellers at posted prices or under a 

published pricing formula for a specified period of time. FITs typically have caps on the amount 

                                                            

98 There are several states that offer FITs, including California, Hawaii, Maine, Oregon, Rhode Island, South 

Carolina, Vermont and Washington, as well as numerous electric utilities. 

Deleted: Public Utilities Commission (

Deleted: )
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of renewable energy that will be purchased by a utility (in MWh of energy or MW of capacity). 

FITs may also include customer impact caps for the tariff as a whole (in total dollars spent or in 

specified retail rate impacts allowable), and may also have limits on the size of any participating 

renewable energy generator from which the utility will purchase electricity through the FIT. FITs 

may also have pricing formulas that are differentiated by resource or that change through time as 

specified benchmarks are achieved (e.g., MW of renewable energy generating capacity subject to 

the FIT, amount of electricity purchased through a FIT as a percentage of utility sales, or retail 

rate impact level reached). Typically, the tariff treats all similarly-situated generators in a 

consistent manner. 

Quantification of renewable energy generation output under a FIT is accomplished 

through the use of a revenue-grade meter to measure the generator’s injection of electricity into 

the grid. The utility’s tariff will typically specify the minimum performance characteristics 

and/or certifications that a meter must meet in order to be used on its system. Utilities retain the 

right to inspect and test the calibration of meters connected to their systems. As the utility will be 

paying the generator each month based on the meter reading, it is in the utility’s interest to 

ensure that the meter is reading precisely and accurately through time. 

Both the utility and the state will need to consider the ownership of the environmental 

attributes arising from the renewable energy generation purchased by a utility through a FIT, and 

whether the renewable energy can be counted toward RPS compliance. If renewable energy 

generation purchased by a utility through a FIT may be counted toward RPS compliance, then it 

should not be counted separately as another renewable energy program in a state plan. 

State agency role 

FITs are usually authorized by state statutes that specify which utilities must offer a FIT, 

eligibility criteria (e.g., renewable energy resource type, location, project MW generating 

capacity limits), and sometimes overall program targets (e.g., total installed MW of generating 

capacity subject to the FIT). State statutes may also specify whether the utilities offering a FIT 

will receive the RECs related to purchased electricity generation output for use in complying 

with a state RPS, and whether customers receiving FIT payments may also receive incentives 

under other utility and state programs. Typically state statutes leave implementation details to the 

Deleted: e
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PUC (or other utility governing body, if applicable, for municipal and cooperative utilities), but 

may provide guidance on what to consider in setting FIT payment levels. Based on this statutory 

authority, PUCs develop detailed rules governing implementation, which can include payment 

levels and contract length. PUCs may direct the affected utilities to develop standard contracts 

with all the terms and conditions spelled out, and these standard contracts must be approved by 

the PUC. As with state RPS, the PUC is responsible for receipt and review of the utility’s 

periodic status reports, approving changes to a tariff if needed, and evaluating the impacts of the 

tariff on retail prices, generation diversity, and electricity system operations. 

Several FITs are offered by distribution utilities not overseen by PUCs, such as municipal 

utilities and rural electric cooperatives. These utilities have a variety of governance structures 

(e.g., municipal government, cooperative board of directors). The utility governing bodies in 

these situations will be responsible for receipt and review of the utility’s status reports, taking 

corrective action if needed, and evaluating the impacts of the tariff. 

3. State Tax Incentives 

Several states offer a variety of tax incentives to promote the production and use of 

renewable energy. These currently include sales tax exemptions for certain kinds of equipment 

(e.g., PV panels), property tax abatement for improvements to a building or facility related to the 

asset value of the renewable energy generating system, and income tax credits for the 

installation of renewable energy systems based on capacity or investment level. Several states 

provide a renewable energy production tax credit based on the amount of renewable energy 

generated. This approach is useful because it results in a measureable quantity of renewable 

energy electricity generation. 

With a production-based tax incentive, the renewable energy generator might claim a tax 

credit for each MWh of qualifying renewable energy generation within the state. One design 

consideration for a production-based tax incentive that affects quantification of renewable energy 

generation output is whether the electricity must be sold to a third party as opposed to being used 

by the site host. In the former case, a revenue-grade utility meter would be present at the point of 

interconnection to the electricity grid, which provides measurement of MWh generation output 
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for tax compliance purposes. Site-host use might necessitate the installation of an additional 

meter within the project site to permit reliable measurement of the renewable energy generator’s 

output. 

State agency role

Currently, existing state tax policies are primarily under the authority of the state revenue 

agency. The state revenue agency might have the primary responsibility for establishing the rules 

for production-based tax incentives, although it may seek advice from state energy agencies 

regarding the technical aspects of renewable energy generator operation and the behavior of 

energy markets. The renewable energy generator might claim the tax incentive through the state 

tax collection process and report MWh generation to claim the tax credit. The revenue agency 

could receive tax filings from the owner and operators of renewable energy generators and be 

responsible for determining whether the taxpayer’s claim for tax incentives is supported by the 

MWh generation evidence. Assuming the state revenue authority retains its ability to audit the 

taxpayer’s return, it could verify claimed MWh generation. 

As with FITs, the renewable energy generation resulting from production-based tax 

incentives might be used for RPS compliance. If that were to become the case, then it should not 

be counted separately in a state plan from MWh generation used to comply with a state RPS. 

4. Options for Quantification, Monitoring, and Verification of Renewable Energy 

 Measures in State Plans 

As summarized in the preamble, and discussed in more depth in this section, utilities and 

states have conducted ongoing evaluation of renewable energy measures and programs for 

several decades. These evaluations, which include quantification, monitoring and verification of 

results, generally rely upon a set of standard practices and procedures. In addition, states have 

designed and implemented REC tracking systems to facilitate compliance with state RPS. This 

resource provides the ability to track the location and attributes of renewable energy generators, 

and the electric generation from these generators, as well as the parties that use RECs for 

compliance with state RPS. As a result, existing state and utility requirements and processes for 

quantification, monitoring, and verification of renewable energy programs and measures 
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generally provide a solid foundation for minimum requirements and guidance for EM&V for RE 

measures in state plans that are established by the EPA. 

The programs discussed above (RPS, FIT, and performance-based tax incentives) all 

require quantification, monitoring, and verification of electricity generation from renewable 

energy generators, as well as provisions of other key information, to determine eligibility and 

track program activity or compliance with regulatory requirements (if applicable). Quantification 

of electricity generation is typically through the use of revenue-quality meters,99 or engineering 

estimates for small distributed generators. These data are essential to program management, 

verification of compliance or payments, budget control, and tracking progress toward goals. For 

example, PUCs overseeing compliance with state RPS receive compliance information from 

each obligated utility, including retail sales, compliance status based on MWh of electricity 

generated by eligible utility-owned renewable energy sources, or electricity or RECs purchased 

from eligible renewable energy generators, which may also consider application of multipliers or 

alternative compliance payments.100 PUCs overseeing utility FIT and state agencies overseeing 

performance-based tax incentives managers receive reports containing MWh of electric 

generation from qualified electric generators that received payments under either a FIT or tax 

incentive. These data are essential for normal program management and accountability. 

Current state data requirements under RPS, FIT, and production-based tax incentives are 

tailored to the objectives of these programs and facilitating effective regulatory oversight. 

Typically, avoiding CO2 emissions, while considered a relevant co-benefit, is not a primary 

objective of these regulations and programs. As a result, additional information and reporting 

may be necessary to accurately quantify the avoided CO2 emissions associated with the 

renewable energy generated through an RPS, FIT, or production-based tax incentive that is 

included in a state plan. 

The following types of information will increase the accuracy and verifiability of avoided 

CO2 emission estimates related to renewable energy requirements, programs, and measures 

                                                            

99 “Revenue-quality meter” refers to a meter used for billing purposes in wholesale electricity markets, which 

typically need to meet ISO or RTO precision requirements or other specifications. 

100 Compliance status will also consider the application of credit multipliers or alternative compliance payments, if 

relevant under an RPS. 

Deleted: or

Deleted: might be

Deleted: for state plans
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included in a state plan. For example, information on the location of the renewable energy 

generation (e.g., in-by state or within a specified grid region) of the renewable energy generation 

used for compliance with state requirements and programs would be helpful in determining 

avoided CO2 emissions. Information about the location of electric generators that supplied 

electricity generation that was imported to a state or grid region, will also be important. Timedifferentiated information related to electricity generation is also useful in estimating avoided 

CO2 emissions. In particular time-differentiated data is necessary to estimate the marginal 

avoided CO2 emissions related to electric generation. Such time-differentiated data could be 

based on metering or engineering estimates for a technology type that indicate the typical 

generation profile for the renewable energy resource. This could include time differentiation on 

an hourly, daily, or seasonal basis. If RECs can be banked and used or RPS compliance at a later 

time than the year in which the electricity generation related to the REC occurred, information 

about the quantity and vintage of RECs from prior year(s) generation that is used for RPS 

compliance will also be useful. 

VI. Reporting and Recordkeeping for End-Use Energy Efficiency and Renewable 

 Energy Programs and Measures

As discussed in the preamble, in section VIII.F.5, reporting and recordkeeping for enduse energy efficiency and renewable energy requirements and programs will be an important 

component of certain types of sate plans. If a state plan incorporates renewable energy and 

demand-side energy efficiency requirements and programs under a rate-based approach or 

implements a mass-based portfolio approach with such measures, reporting and recordkeeping 

requirements for an approvable plan would differ from those applicable to an affected EGU. For 

example, these requirements may include compliance reporting by an electric distribution utility 

subject to an end-use energy efficiency resource standard (EERS) or renewable portfolio 

standard (RPS). They may also include reporting by a vertically integrated utility implementing 

an approved integrated resource plan. In the latter instance, the utility may also be the owner and 

operator of affected EGUs, but additional reporting of quantified effects of renewable energy and 

demand-side energy efficiency measures under the utility plan would be necessary to 

demonstrate emissions performance under the state plan. In other instances, a state agency or 
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entity or a private or public third-party entity may be implementing programs and measures that 

support the deployment of clean energy technologies that are incorporated in a state plan. In each 

of these instances, reporting of program compliance or program outcomes is a necessary part of 

an approvable plan to demonstrate performance under the plan. 

In the preamble, the EPA seeks comment on appropriate reporting and recordkeeping 

requirements for entities implementing end-use energy efficiency and renewable energy 

programs included as enforceable measures in a state plan, or for entities subject to requirements, 

such as an EERS or RPS, that are included as an enforceable state plan measure. This section 

provides examples of current reporting and recordkeeping under state energy efficiency 

requirements and programs, such as EERS, RPS, and utility and state deployment programs for 

energy efficiency and renewable energy. The section then examines the suitability of these 

reporting and recordkeeping practices as potential approaches in an approvable state plan. 

A. Reporting for End-Use Energy Efficiency Programs and Measures

Reporting requirements and time frames (i.e. how often reports are required) for entities 

implementing energy efficiency programs and measures are key considerations for state plans. In 

a state-regulatory context with PUC oversight, impact reports are the mechanism by which 

utilities and other program administrators document energy (MWh) and demand (MW) savings. 

These reports serve as basis for PUC review of total achieved energy savings relative to program 

goals or regulatory requirements, as well as for determining financial performance incentives for 

utilities, where they exist. 

In most states, impact reporting is initially conducted at the level of efficiency 

“programs” (each consisting of numerous “project” installations or efficiency measures 

occurring at individual homes, commercial buildings, or industrial facilities). Program level data 

are then aggregated to the “portfolio” level to capture the full impact of energy efficiency 

investments occurring under a PUC’s jurisdiction for the timeframe of interest. 

Impacts reports are typically submitted annually, but in some cases program 

administrators also provide interim (e.g., quarterly) reports. This added step can help inform 
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progress towards goals, as well as provide for corrections in cases where evaluated (ex-post) 

energy savings are not achieving projected or claimed (ex-ante) savings levels. 

The information provided below presents a range of common reporting elements and 

common practices implemented by state PUCs. These reporting elements and practices – which 

raise important considerations for the reporting of energy-efficiency impacts in state plans – 

include: 

 State EM&V guidelines, protocols, and/or framework utilized, where applicable 

 Energy efficiency policy or program information reported to PUCs in annual reports: 

o Short description of the policies or programs implemented 

o Implementation schedules and timeframes 

 Energy-savings impacts reported to PUCs in annual reports: 

o Incremental annual and lifetime MWh savings for reporting (in the case of an 

energy efficiency program) or compliance years ( in the case of utility compliance 

with a multi-year Energy Efficiency Resource Standard) 

o Peak demand (MW) impacts (reported in many, but not all states) 

 Verification documentation, which shows that installation of energy efficiency measures 

occurred, and the installed measures are capable of generating energy savings 

 EM&V process followed: 

o Date and location of on-site facility visits and field observations 

o Description of public process for review of overall EM&V approach, EM&V 

plan, and EM&V results 

o Information about evaluators: 

 Name of firms and individuals performing EM&V activities, and 

qualifications 

 Certification that evaluators were selected through a public bid process, 

and are third-parties unaffiliated with efficiency program administrators or 

the state government 
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 EM&V methods used: 

o Deemed savings values - name, date, and public location of technical reference 

manual (TRM)101 used for deemed savings values 

o Direct measurement approaches - description of the measurement approaches and 

reference to the EM&V protocols, standards, and guidance documents used 

 Other documentation: 

o Data about the quantity of measures/projects on which the full program-level 

energy savings impacts are based (i.e., information describing the sample size and 

sampling procedures used) 

o Whether net or gross energy savings102 are estimated, definitions used for gross 

and net savings, and the basis for gross to net calculations, if applicable 

o Avoided transmission and distribution (T&D) impacts assumptions, if applied 

The energy savings EM&V reporting elements listed above vary from state to state in 

terms of type and level of documentation, and reports are provided in different formats from state 

to state. This significant variation among states in reporting contents and format raises several 

considerations for states utilizing demand-side energy efficiency programs in state plans. One is 

whether the reporting processes, timeframes, and documentation required by state PUCs, 

described above, are sufficient and appropriate in the context of state plans. Another 

consideration is whether lead state agencies that oversee energy efficiency programs should be 

required to certify reported energy efficiency savings impacts on behalf of the state, potentially 

including certification that the values are appropriate and conservative, and meet their approval. 

A final consideration is whether and how energy savings impact reports are made available for 

public input and comment prior to finalization, recognizing that impact reports in many states 

                                                            

101 A technical reference manual (TRM) is a document consisting of predetermined savings values, assumptions, 

methods, and calculation approaches for conducting EM&V of state demand-side energy efficiency programs. Most 

states with robust efficiency programs rely on a TRM. 

102 Gross savings are the change in energy use and/or energy demand that results directly from program-related 

actions taken by program participants, regardless of why they participated in the program. Net savings refer to the 

change in energy use and/or energy demand that is directly attributable to a particular energy efficiency program. 
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exist but are not easily located or widely accessible by the public, nor are they provided in 

consistent formats from state to state. 

B. Reporting for Renewable Energy Programs and Measures 

1. Typical Reporting and Compliance Requirements under State RPS 

Each state has different reporting and compliance requirements for its RPS, but all states 

with mandatory RPS require obligated entities to provide compliance reports to the state PUC or 

equivalent state oversight agency. Compliance obligations are typically specified in authorizing 

legislation, regulations, or PUC orders. Compliance is typically on an annual basis, and includes 

a list of required reporting elements. Some states also require distribution utilities to provide an 

implementation plan describing how they will comply with the state RPS rules in the future. 

Data requirements for reporting may vary based on the design and implementation of a 

RPS. However, for nearly all state RPS requirements, annual compliance report data is based on 

measurable electric generation results and verified through tracking system data.103 In some 

states, compliance reports may also include state-level projections of renewable energy 

generation resulting from current or proposed state RPS policies. 

The most common form of tracking system for RPS compliance is a regional or state 

REC tracking system or registry. These systems track RECs for both the compliance and 

voluntary markets. RECs are typically provided with a unique identification number and may be 

certified by a third-party verifier. Annual compliance reports containing REC data typically 

include the number of RECs the utility or load-serving entity procured and retired, what 

renewable energy generators supplied the RECs, and how much the utility spent on procuring the 

RECs. 

2. Typical Reporting for Renewable Energy Deployment Programs104 

                                                            

103 Some state RPS require electric utilities to procure a specified amount of renewable energy generating capacity, 

rather than supply a specified number of MWh or percentage of electricity supply from renewable energy generation 

to retail customers. 

104 In this section, “deployment programs” refers to incentive programs and market transformation programs 

designed to accelerate the market deployment of renewable energy technologies. 
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Renewable energy deployment programs involve the provision of a payment or credit for 

a renewable energy project, or for a quantified amount of electricity generation, in the case of 

performance-based incentives. Qualification of eligible projects and payment for qualifying 

electric generation (or related attributes) require reporting of electric generation and other project 

for each specific program. Program administrators use this information to track program progress 

and report to PUCs or other oversight entities. The summary below addresses typical reporting 

required for utility-administered programs, as well as programs administered by non-profit 

entities and state agencies and authorities. 

Reporting for utility administered renewable energy incentive programs 

Some utilities offer incentives to electricity consumers to accelerate the deployment of 

renewable energy technologies, such as rebates, feed-in tariffs, and net metering programs. As 

mentioned previously, it is easier to quantify the renewable electricity generation resulting from 

some programs than it is for others. Utilities administering FITs, for example, will track the 

number of customer contracts, resource type, capacity of each contracted project, MWh 

generated, and utility expenditures for that generation under the tariff. In contrast, utilities 

administering a rebate or loan program are more likely to track the number of customer 

participants, the type and size of the projects, the cost of the projects, and the amount of rebates 

paid or loans provided. Measuring the electric generation output of these projects may not 

necessary to evaluate program status. 

Net-metering is a renewable energy incentive program that is based on performance, but 

where the total output of the net-metered device is often unknown. Utilities reporting on netmetering programs track the number of participating customers, the type and size of net-metered 

systems, and overall net-metered capacity. However, the gross amount of electricity generated 

may not be known if a single bi-directional meter is used. Such meters only record net electricity 

withdrawn from the grid or net electricity production supplied to the grid during an identified 

time period. Utilities and the customers where the renewable energy generating system is located 

may not necessarily know the total electric generation from the renewable energy system, unless 

two meters are installed – one to measure total output from the customer-sited system, and 

another to measure the total electricity purchased from the utility. 
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Many of these renewable energy deployment programs are developed as part of 

requirements by PUCs, and therefore utilities must provide reporting on a routine basis to the 

PUC about program expenditures and outcomes (typically quarterly or annually). These records 

should be readily accessible to states for estimating the impacts of their renewable energy 

deployment programs included in a state plan. However, some utility incentive programs are 

administered by distribution utilities that are not regulated by a state PUC (e.g., municipal and 

cooperative electric utilities). In these instances program reporting data may not be readily 

available to a state, unless separately required by a state if such programs are included in a state 

plan. 

Renewable energy deployment programs may be designed and implemented under the 

auspices of a utility integrated resource plan (IRP).105 IRPs document how a utility will meet 

forecasted annual peak and energy demand over a defined period of time through a combination 

of supply-side and demand-side resources. IRPs are typically mandated through state legislation 

or PUC orders and may include renewable energy generation planning, particularly as it relates 

to compliance with in-state requirements. IRPs are typically submitted on an annual basis to a 

state utility commission and/or other state entity, and address a multi-year period (e.g., 10 years 

is a typical period for an IRP). IRPs are a good resource for tracking and forecasting utility 

renewable energy developments within a state. Though they may not include renewable energy 

production data, data included in IRPs may help states project renewable energy generation 

trends in subsequent years, and are a good resource for the development of state plans. 

Reporting for renewable energy incentive programs administered by non-profit or state 

entities 

State renewable energy financial incentives are typically administered by a PUC, state 

revenue agency, other state agency (e.g., state energy office), or a private non-profit or for-profit 

entity contracted by a state agency. These programs may include an administrative process for 

                                                            

105 IRPs are typically required in states with vertically integrated electric utilities, but are less common in states that 

have deregulated their electricity sectors and introduced competition for the supply of electricity to retail customers. 
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pre-qualification, which in some instances may be competitive (e.g., performance-based 

contracts) or available on a first-come, first-serve basis (e.g., capped production tax incentive). 

Applications to incentive programs are good sources of data, and program administrators usually 

compile data from approved applications to track program status. Additional reporting data is 

also typically required to receive the incentive. For example, a production-based tax incentive is 

calculated based on the amount of electricity generated by a renewable energy installation, which 

may be tracked and verified through utility or third-party metering protocols. These reports, 

which are currently used for internal reporting for budgetary control and performance evaluation, 

and to track other performance metrics for regular public program reporting, could form much of 

the basis for reporting under state plans for such measures. 

3. Considerations for Reporting Requirements for Renewable Energy Measures 

 in State Plans 

State renewable energy requirements, such as RPS and FITs, and incentive programs 

typically include robust reporting requirements. For nearly all state RPS requirements, annual 

compliance report data is based on measureable electric generation, using revenue-quality 

meters, and verified through tracking system data.106 Other requirements and programs that 

provide performance-based payments and incentives, such as FITs, net metering, and 

performance-based tax incentives, also require reporting of metered generation output.107 State 

and utility incentive programs where payment of incentives is not based on electric generation 

may not currently be sufficient for reporting under a state plan. Additional reporting 

requirements may be necessary if these programs are included as enforceable measures in a state 

plan.

In addition to the reporting states currently require for renewable energy requirements 

and programs, supplemental reporting information or adjustments may be necessary for state 

                                                            

106 Small distributed renewable energy systems, such as those below 10 kW in capacity, are often allowed to use 

engineering estimates to determine annual output. 

107 Some of these programs, such as net metering, may also require supplemental reporting in order to track total 

generation that avoids CO2 emissions. In many instances, net metering programs only track the net electricity 

supplied to a customer or supplied to the grid by the renewable energy system, rather than total generation output. 

Deleted: 
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plans to demonstrate the avoided CO2 emissions associated with these requirements, programs, 

and measures. States may need to require additional reporting detail, such as the location of 

renewable energy generating units that supplied output used to comply with a state RPS. 

Additional reporting detail about when renewable energy was generated may also be valuable for 

estimating the avoided CO2 emissions from renewable energy generation, especially if a 

marginal avoided emission rate approach is used. This includes reporting of the typical 

generating profile of a renewable energy generating unit, group of units, or renewable energy 

resource type.108 For distributed renewable energy resources, reporting of the MW capacity of 

generating systems that are installed as a result of state requirements or programs during a 

reporting period would also be useful for estimating avoided CO2 emissions. These distributed 

resources, since they are located “behind” the utility meter at a customer location, have a similar 

effect in reducing the demand for electricity supplied from the grid as end-use energy efficiency 

measures. 

Example reporting requirements that provide sufficient data for estimating the avoided 

CO2 emissions from renewable energy requirements and programs might include the following: 

 Metered MWh generation, using a revenue quality meter, or estimates of annual output 

for small systems below 10 kW in capacity 

 MW capacity of “behind-the-meter” distributed renewable energy generating systems 

added during a reporting period as the result of a state program 

 For renewable energy resources reported, including through REC data, the typical 

generating profile of a renewable energy generating unit, group of units, or renewable 

energy resource type 

 For REC data, information including the following generator attributes: type of resource 

(e.g., wind), plant-level emissions, geographic location, nameplate capacity (MW), 

commercial operation date, ownership, and the eligibility for RPS compliance or 

voluntary market certification 

                                                            

108 This might include information about the seasonal or daily generating profile of the generating unit or renewable 

energy resource type. 
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VII. Treatment of Interstate Emission Effects 

Programs and measures in a state plan, such as RE and demand-side EE measures, may 

affect the emission performance of the interconnected electricity system beyond a state border. In 

addition, many state measures allow for actions in neighboring states to meet the in-state 

requirement, or explicitly address CO2 emissions in neighboring states. For example, many state 

renewable portfolio standards allow for generation by qualifying renewable energy sources in 

other states to count toward meeting the state portfolio requirement. Some states also apply CO2

emission requirements related to the generation of power purchased by regulated utilities, 

including power imported from out of state. 

As discussed in the preamble to the proposal, in section VIII.F.6, the EPA recognizes the 

complexity of accounting for interstate effects associated with measures in a state plan in a 

consistent manner, to minimize the likelihood of double counting. The EPA also realizes that 

interstate effects on CO2 emissions from affected EGUs could be attributed in different ways in 

the context of an approvable state plan. This section discusses in more detail the options and 

alternatives for treatment of interstate CO2 emission effects presented in the preamble. These 

options and alternatives could be applied to both projections of plan performance and 

demonstration of achieved emission performance under a plan. These options and alternatives 

may not be mutually exclusive – in some instances states could apply different approaches, 

without introducing the potential for double counting of emission effects. One option presented 

could lead to double counting of emission effects, and we highlight these aspects of this option in 

the discussion below. 

In general, the options and alternatives address different possible state plan scenarios, and 

consider the range of interstate approaches that states are currently using to implement electricity 

sector policies, such as multi-state emission budget trading programs and regional renewable 

energy certificate markets for state RPSs. The options and alternatives reflect possible 

accounting approaches for interstate emission effects under CAA section 111(d) that could 

potentially align with these current state programs and measures that we anticipate states may 

want to include in a state plan. 
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A. Background 

Electricity flows across state lines. Often electricity load centers (i.e., areas of high 

electricity demand) in one state are supplied in part by generating units in another state. As a 

result, some states are net exporters or importers of electricity on an annual basis. Reducing 

electricity load through improved end-use energy efficiency (e.g., through state energy efficiency 

programs) or deploying new renewable energy electric generating capacity (e.g., through a state 

RPS) therefore can result in CO2 emission effects that are realized outside the state that 

implements the regulation or program that produces the effects. Reducing electricity demand or 

increasing available electric generating capacity also often impacts the economic dispatch curve 

and locational economics that are used to dispatch EGUs on a regional basis. As a result, state 

end-use energy efficiency and renewable energy regulations and programs often have regional 

effects on electricity generation and avoided CO2 emissions. In addition, many state regulations 

explicitly address CO2 emissions in neighboring states, or allow for actions in neighboring states 

to meet an in-state regulatory requirement. For example, many state RPS allow for generation in 

other states to count toward meeting a utility portfolio requirement. 

End-use energy efficiency actions reduce electricity load, and ultimately impact electric 

generation. In some instances improving end-use energy efficiency will reduce electric 

generation nearby a load center (e.g., in the case of a load pocket with limited access to 

electricity transmission capacity). In such cases, it may be feasible to directly link in-state enduse energy efficiency programs and measures to avoided CO2 emissions from specific in-state 

EGUs. More often, reduction of electricity load will impact EGU dispatch across a regional 

generation control area, based on factors such as power plant economics and electricity 

transmission capability, and could also impact flows between control areas.109 In these cases, 

state end-use energy efficiency programs and measures will affect electricity generation in the 

state that reduces load, as well as in neighboring states. 

                                                            

109 Often these dispatch economics differ by location, based on electricity demand, transmission constraints, and 

generation economics of individual power plants necessary for meeting demand (e.g., in competitive wholesale 

markets, these factors are represented through locational marginal prices (LMPs), which determine dispatch). 
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State RPS regulations also impact electricity generation at a regional level. Over time, 

state RPSs result in the introduction of new, incremental renewable energy generating capacity to 

regional generation control areas, which affects EGU dispatch at the regional level. State RPSs 

are typically applied to electric distribution utilities as a percentage of sales (e.g., a specified 

percentage of delivered electricity must come from qualifying renewable energy sources). Many 

state RPSs do not require the qualifying renewable energy electric generation to take place 

within the state, or even be delivered into the state, but instead require that the renewable energy 

be supplied within (or delivered into) the ISO/RTO in which the state resides. Often, utility 

compliance with state RPS is through the submission of renewable energy credits (RECs), which 

represent the attributes of renewable energy generation but not the actual electricity generated. 

As a result, in many cases the intent of the state policy is often to affect the characteristics of the 

regional electric generation mix, rather than the state generation mix. 

The approach to implementing an RPS may differ in vertically integrated, cost-of-service 

states where the distribution utility also owns electric generating capacity and dispatches 

generation resources within its service territory. In such cases, the RPS may require a utility to 

increase its renewable energy generating capacity, rather than supplying a percentage of the 

electricity it delivers to retail customers from renewable energy sources to meet load. A number 

of state RPS also include “carve-outs” or “set-asides” where a portion of the renewable energy 

supplied to retail customers must come from renewable energy generating capacity located inside 

the state. Most of these carve-outs are for distributed solar photovoltaic generating capacity, 

which are located at the point of customer end-use (e.g., rooftop mounted solar PV on residential 

homes and commercial buildings).110 Distributed solar PV capacity often provides benefits to the 

electric distribution system by improving distribution system reliability and avoiding the need for 

distribution system capacity upgrades. This type of distributed renewable energy generation has 

effects similar to end-use energy efficiency, as it reduces the customer electricity load that must 

                                                            

110 A number of RPS also have carve-outs for other renewable energy resources, typically those where there is a 

significant renewable energy resource located in the state. An example is carve-outs for offshore wind energy in 

coastal states. 
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be met through large EGUs connected to the grid.111 Regardless of the approach taken, state RPS 

regulations typically have impacts on EGU dispatch, and related avoided CO2 emissions, beyond 

the state border. 

B. Summary of Possible Approaches for Treatment of Interstate Emission Effects 

As discussed in the preamble, the EPA is proposing a set of approaches for addressing 

interstate emission effects that result from the implementation of state plans that incorporate enduse energy efficiency and renewable energy programs. The preamble also solicits comment on 

additional alternatives. The proposed approaches in the preamble include: 

 For EE programs and measures: 

o A state may take into account in its plan only those CO2 emission reductions 

occurring in the state that result from demand-side energy efficiency programs 

and measures implemented in the state. 

o States participating in multi-state plans would have the flexibility to distribute the 

CO2 emission reductions among states in the multi-state area. 

o States could jointly demonstrate CO2 emission performance by affected EGUs 

through a multi-state plan in a contiguous electric grid region, in which case 

attribution among states of emission reductions from demand-side energy 

efficiency measures would not be necessary. 

 For RE programs and measures: 

o Consistent with existing state RPS policies, a state could take into account all of 

the CO2 emision reductions from renewable energy programs and measures 

implemented by the state, whether they occur in the state and/or in other states. 

o States participating in multi-state plans would have the flexibility to distribute the 

CO2 emission reductions among states in the multi-state area. 

o States could jointly demonstrate CO2 emission performance by affected EGUs 

through a multi-state plan in a contiguous electric grid region, in which case 

                                                            

111 Distributed solar PV is also typically peak coincident, meaning it provides its greatest electric generation output 

at times of peak system electricity demand. At many times of the day and year, solar PV systems supply electricity 

back to the grid, when PV system output exceeds building electricity demand. 
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attribution among states of emission reductions from renewable energy measures 

would not be necessary 

This section surveys the range of potential approaches that could be applied for 

individual state plans, as well as approaches that could be applied on a regional basis. The 

surveyed approaches include those proposed, as well as alternatives. These basic approaches, 

including variants of some approaches, include: 

 State may only claim the impact of a measure in reducing in-state EGU CO2 emissions 

For plan measures such as end-use energy efficiency and renewable energy regulations 

and programs, estimating the avoided CO2 emissions from in-state versus out-of-state 

EGUs could be addressed through modeling, other analytical tools, or proxy metrics (e.g., 

net import factor). 

 State that implements the measure claims the emissions reduction benefit 

Under this approach, the state that implements the measure (e.g., end-use energy 

efficiency and renewable energy regulations or programs, or an emission limit that 

addresses out-of-state generation) claims the avoided CO2 emissions, regardless of where 

they occur. 

 Cooperative multi-state accounting 

Multiple states are allowed to mutually agree to how they will distribute avoided CO2 

emissions from state plan measures (e.g., end-use energy efficiency and renewable 

energy regulations or programs, or an emission limit that addresses out-of-state 

generation) across their respective EGU fleets. Avoided CO2 emissions are distributed 

among states by agreed formula they derive – an accounting “credit” in one state for 

out-of-state avoided CO2 emissions is complemented by an accounting “debit” in 

the other state where the avoided CO2 emissions occurred (i.e., through an increase in 

reported CO2 emissions or CO2 emission rate). 

 Tradable regional EE/RE credit market 

This is a variant of the multi-state accounting approach, which could be applicable where 

multiple states in a region are implementing rate-based state plans. Under this approach, 

state end-use energy efficiency and renewable energy regulations and programs that meet 
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EM&V guidelines or requirements are allowed to generate credits, based on MWh of 

energy savings or renewable energy generation. These credits, which are denoted in 

avoided tons of CO2 or avoided MWh, could be used by affected EGUs toward 

demonstration of compliance with state rate-based CO2 emission limits within a 

designated region. EE/RE credit issuance could be on a project and/or program basis. 

State accounting for interstate emission effects would be addressed through the credit 

market and determined based on credits held by affected EGUs. 

 Regional demonstration by states of EGU emission performance 

States are allowed to regionally demonstrate emission performance by affected EGUs. 

Stets jointly demonstrate emission performance for affected EGUs, in terms of total CO2 

emissions (under a mass-based multi-state plan) or weighted average CO2 emission rate 

(under a rate-based multi-state plan). 

 The EPA jointly assesses regional performance achieved in aggregate by all individual 

state plans in a grid region 

The EPA assesses interstate effects on a regional basis during the plan review process. 

The EPA requires states to agree to an interstate attribution process only if necessary (i.e., 

if regional performance falls short of the aggregated identified performance levels for 

affected EGUs in individual state plans). Alternatively, the EPA requires plan revisions if 

regional performance falls short of the aggregated regional performance level (i.e., the 

aggregated identified performance levels for affected EGUs in individual state plans). 

Table 3 includes illustrative examples of the application of some of the different 

approaches for addressing interstate emission effects summarized above. The table explains how 

these approaches might be applied in different state plan contexts. The illustrative examples 

consider the range of approaches states are currently using to implement electricity sector 

policies, all of which interstate effects, such as multi-state emission budget trading programs, 

regional renewable energy certificate markets used for state RPS compliance, and end-use 

energy efficiency programs. 

Table 3. Applied Examples of Intersate Emission Effects Attribution Approaches 
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1. State May Claim the Impact of a Measure on CO2 Emissions from Affected EGUs 

 Within its Borders 

Under this approach, the effect of a state measure could be applied to help demonstrate 

emission performance by affected EGUs in the state if it has the effect of avoiding CO2

emissions from those in-state EGUs. This could be done regardless of whether the action taken to 

implement the state measure occurs within or outside the state. For example, renewable energy 

generation that occurs outside the state as a result of a state renewable portfolio standard 

obligation would still be a valid state plan action, provided the out-of-state renewable energy 

generation has the effect of avoiding CO2 emissions from affected EGUs inside the state. As 

another example, for a state that is a net importer of electricity, improvements in demand-side 

EE and related reductions in electricity demand may reduce the need for generation from both 

affected in-state and out-of-state EGUs. These electricity demand reductions could be applied to 

help demonstrate emission performance by affected EGUs in the state if the reduction in 

electricity demand has the effect of avoiding CO2 emissions from those in-state EGUs. 
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Estimating the effect of RE and demand-side EE measures on in-state versus out-of-state 

EGU CO2 emissions could be addressed through modeling, other analytical tools, or proxy 

metrics such as a net import factor. 

Modeling could be used to assess the interstate effects of state measures on EGU CO2

emissions, both for projections of emission performance under the plan and ex post 

demonstration of performance achieved. Under this approach, both projected plan performance 

and performance achieved is assessed on a state-by-state basis. 

 Ex ante projections of plan performance 

 To project the effect of EE/RE measures under a state plan, a dispatch model would be 

applied to a grid region to estimate the marginal or average avoided CO2 emissions impact of the 

plan measures on a state-by-state basis within the region. To the extent that a state’s EE/RE 

measures were projected to avoid CO2 emissions from its own in-state EGUs, these effects could 

be applied to meet the required level of CO2 emission performance for affected EGUs in the state 

plan. (See section IV.C of this TSD for a full discussion of using a dispatch modeling approach 

to projected avoided CO2 emissions that will be achieved through a plan.) 

Ex post demonstration of plan performance

To assess the state-by-state avoided CO2 emissions that result from the implementation of 

a plan, a dispatch model would also be applied to a grid region, on a retrospective “look-back” 

basis. This modeling would assess the avoided CO2 emissions resulting from reported MWh of 

energy savings and MWh of reported renewable energy generation, as a result of implementation 

of EE/RE measures in the plan. Under a rate-based plan approach, modeled estimates of avoided 

CO2 emissions, based on reported EE savings and RE generation, could be applied through an 

administrative adjustment by the state program administrator or through the issuance of tradable 

EE/RE credits within the state. For ex post demonstration under a mass-based plan approach, 

performance would be determined based on reported stack CO2 emissions from affected EGUs—

no further analysis would be necessary. (See section IV.C of this TSD for a full discussion of 

using a modeling look-back approach to estimate avoided CO2 emissions.) 
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Use of simplified proxy metrics to apportion effects among states

Simplified metrics, such as a net electricity import or export factor, might also be applied 

to assess the impact of state actions in avoiding CO2 emissions from in-state affected EGUs. For 

example, in a state that imports 30% of electricity on average during a year, energy savings from 

EE measures might be multiplied by a factor of 0.70.112 Avoided CO2 emissions might then be 

calculated by multiplying this adjusted energy savings number by the average or marginal CO2

emission rate for affected EGUs in the state. This type of approach could be employed in both 

projections of plan performance and ex post demonstrations of performance. However, this 

method would be subject to uncertainty, as electricity net imports may vary significantly on an 

annual basis, due to changes in system dispatch. (See section IV.C of this TSD for a discussion 

of dispatch dynamics that affect avoided CO2 emissions.) 

This approach would avoid double counting of emission effects of state measures among 

states. However, it could reduce incentives for states to employ measures that have a systemwide, regional effect in reducing EGU CO2 emissions. In effect, because an adjustment factor 

would be applied to energy savings under this approach, a net importer state would need to 

achieve greater energy savings through end-use energy efficiency requirements and programs to 

achieve a ton of avoided CO2 emissions under its plan than a state that is not a net importer. 

 

                                                            

112 In this instance, it would be assumed that 30% of the reduction in electricity load resulted in avoided CO2

emissions from out-of-state EGUs that serve electricity load in the importing state. 
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2. State that Implements the Measure Claims the Emission Effects 

Under this approach, the state that implements the measure (e.g., an EERS or RPS, or an 

emission limit that addresses the attributes of purchased electricity from out-of-state generation) 

claims the avoided CO2 emissions, regardless of where they occur. 

If the avoided CO2 emissions from state plan measures at the regional level are greater 

than avoided emissions from affected EGUs within the state, these interstate effects would need 

to be accounted for and applied to affected EGUs within the state. This could be achieved 

through an administrative adjustment by the state, or through a tradable credit system that is 

limited to affected EGUs in the state. 

Under an administrative adjustment approach, out-of-state avoided emissions would be 

applied to the in-state EGU fleet by the state program administrator when determining average 

fleet CO2 emission rate or tonnage CO2 emissions. Under a tradable credit approach, credits 

would be issued for all avoided CO2 emissions resulting from applicable state plan measures, 

without regard to where the avoided emissions occurred. Since the tradable credit system would 

be limited to affected EGUs in the state, use of the credits by affected EGUs when demonstrating 

compliance with a rate-based emission limits would functionally apply the avoided CO2

emissions to the state that was responsible for the measure. 

This approach provides a clear policy signal and incentives that reward state actions that 

reduce EGU CO2 emissions on a system-wide, regional basis. However, this approach, absent 

cooperative accounting among states in a grid region, as described below, will likely lead to 

double counting of emission impacts among states, which could reduce the overall emissions 

reductions achieved through state plans on a national basis under CAA section 111(d). We also 

note below that other approaches could also provide incentives for a regional, system-based 

approach to achieving CO2 emissions reductions from affected EGUs, without raising the 

prospect of double counting of emission effects among state. 

 
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3. Cooperative Multi-State Accounting of Interstate Emission Effects 

Under this approach, multiple states would be allowed to mutually agree on how they 

will distribute avoided CO2 emissions from RE and demand-side EE measures across their 

respective EGU fleets. Avoided CO2 emissions would be distributed among states according to a 

formula that they specify. Based on this agreed formula, each state would adjust its demonstrated 

emission performance by affected EGUs accordingly. In effect, a “credit” for out-of-state 

emission effects in one state would be complemented by a “debit” for such effects in another 

state. 

This approach provides states with discretion about how to attribute interstate effects, 

based on their situations and policy preferences in a grid region. Importantly, this approach also 

avoids the potential for double counting of interstate emission effects among states. However, 

this approach is premised on regional collaboration among all states in a grid region. Not all 

states in a grid region may be willing to cooperate in implementing such an accounting approach. 

4. Tradable Regional EE/RE Credit Market 

Under this approach, RE and demand-side EE actions that meet applicable quantification, 

monitoring, and verification requirements would be issued tradable credits that could be applied 

by affected EGUs to their reported CO2 emission rates when demonstrating compliance with an 

emission limitation in a state plan.113 A credit issued in one state could be used by an affected 

EGU in another state toward meeting its respective rate limit.114

A regional credit market would be premised on agreement among states that credits 

issued throughout a region could be used in multiple states. The distribution among different 

states of usage of the credits would be determined by economic factors such as credit prices and 

                                                            

113 These credits might be denoted in avoided CO2 emissions or MWh of electricity savings or electricity generation, 

as described above in Section VI.E.3., incorporating RE and demand-side EE measures under a rate-based approach. 

Depending on a state’s circumstances and its plan approach, these tradable credits might represent a new instrument 

created for use under a state plan, or a state might use an existing instrument, such as RECs. 

114 Credits could be issued on a program or project basis. The types of measures for which credits could be issued 

and the basis for issuing credits would be an enforceable element of a state plan. 
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EGU marginal emissions abatement costs. In effect, accounting of interstate effects would be 

allocated among states based on prices in the credit market. 

This approach is applicable if multiple states are implementing rate-based state plans. 

Where states were implementing a mix of rate-based and mass-based state plans in a shared grid 

region, this approach would lead to double counting of emission effects among plans, unless this 

market-based EE/RE credit approach was also coupled with a cooperative accounting agreement 

among states. In this latter instance, for states implementing a mass-based approach, where 

credits for avoided CO2 emissions are transferred to affected EGUs located in another state for 

compliance purposes, the state from which credits were transferred would adjust its reported CO2

mass emission from affected EGUs when demonstrating achievement of the required CO2

emission performance level by affected EGUs identified in the state plan.115

5. Regional Demonstration by States of Emission Performance 

Under this approach, multiple states would demonstrate CO2 emission performance by 

affected EGUs on a regional basis.116 This could allow states in a contiguous grid region to 

implement a portfolio of RE and demand-side EE measures without the need for state-by-state 

attribution of avoided CO2 emissions. Instead, states would assess the impact of state measures in 

avoiding CO2 emissions from the fleet of affected EGUs in the multi-state region. 

This approach creates incentives for the implementation of system-based approaches that 

collaboratively reduce EGU CO2 emissions on a regional basis, while also avoiding the need to 

attribute interstate emission effects among states. However, regional collaboration will require 

more time for the development of multi-state plans. This approach is also premised on the 

                                                            

115 Note that in this example, state reporting of overall achieved CO2 emission performance by affected EGUs 

under a state plan is distinct from demonstration of compliance by affected EGUs subject to a mass-based CO2

emission limit. For EGU compliance, no adjustment would be made to CO2 emissions reported by affected EGUs 

subject to the mass-based emission limit, even though emissions from these affected EGUs may have been reduced 

as a result of EE/RE regulations and programs implemented in a neighboring state. In this case, the state would 

adjust the overall CO2 emissions from the affected fleet to account for the “export” of avoided CO2 emission credits, 

in order to demonstrate the overall level of CO2 emission performance that is assumed to have been achieved by the 

affected EGU fleet under the plan. 

116 This approach could be applied for CO2 emission performance on either a rate or mass basis. 
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willingness of all states in a grid region to participate in the development and implementation of 

a multi-state plan. Some states in a grid region may be unwilling to collaborate regionally. 

6. Assessment of Interstate Effects by the EPA in the Course of State Plan Review 

Under this approach, the EPA would evaluate interstate effects on a regional basis during 

the plan review process.117 The EPA would assess the emissions performance of affected EGUs 

on a regional basis, considering the measures contained in the group of state plans for a 

respective grid region. Under this approach, the EPA might conduct an analysis that considers all 

of the state program measures together on a combined basis and evaluates projected emissions 

performance achieved by affected EGUs in the region. 

To the extent that all affected EGUs in a region are projected to achieve the required level 

of performance represented in individual state plans, or are projected to achieve an aggregate 

regional level of performance consistent with the level of required performance included in all 

state plans in the region, instances of double counting of interstate effects among states are less 

important. The EPA could indicate as part of plan approval that it will review actual emission 

performance achieved by affected EGUs during the plan period on a regional basis. 

VIII. Appendix 

Survey of Existing State Policies and Programs that Reduce Power Sector CO2 Emissions 

[This appendix will include the content in the document, Survey of Existing State Policies and 

Programs that Reduce Power Sector CO2 Emissions. This draft document was submitted to OMB 

on April 3, 2014.] 

                                                            

117 For example, this assessment could be for a multi-state region that generally aligns with a contiguous grid 

region.
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DRAFT Deliberative Document – Do Not Cite, Quote or Distribute    June 9, 2014

Appendix for State Plan Considerations

Technical Support Document (TSD)  

Survey of Existing State Policies and Programs that  

Reduce Power Sector CO2 Emissions

DRAFT

April 3, 2014

For Carbon Pollution Guidelines for Existing Power Plants:  

Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary

Sources: Electric Utility Generating Units

Docket ID No. EPA‐HQ‐OAR‐2013‐0602

Style Definition: Caption: Space After: 0 pt
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Contents

I. Overview of state climate and energy policies and programs that reduce power sector CO2.............7

II. Existing state and utility policies, programs, and measures that affect EGU CO2 emissions ...............9

a. Actions that reduce EGU CO2 Directly............................................................................................. 10

i. Market‐based Emission Limits.................................................................................................... 10

ii. CO2 emission performance standards........................................................................................ 18

b. Energy Efficiency Policies, Programs and Measures....................................................................... 23

i. Energy Efficiency Resource Standards........................................................................................ 24

ii. Demand‐side Energy Efficiency Programs.................................................................................. 27

iii. Building energy codes................................................................................................................. 30

iv. Appliance and equipment efficiency standards......................................................................... 34

v. Incentives and Finance Mechanisms for Energy Efficiency ........................................................ 35

c. Renewable Energy Policies and Programs...................................................................................... 37

i. Renewable Portfolio Standards.................................................................................................. 37

ii. Performance‐Based Incentives and Finance Mechanisms for Renewable Energy.....................40

d. Utility Planning Approaches and Requirements............................................................................. 42

    
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List of Figures

Figure 1: Status of Electricity Restructuring by State ................................................................................... 8

Figure 2: States with Active Greenhouse Gas Cap and Trade Programs.................................................... 11

Figure 3: Historical GDP and Greenhouse Gas Emissions in the RGGI Region............................................ 17

Figure 4: States with Greenhouse Gas Performance Standards................................................................. 19

Figure 5: Status of Energy Efficiency Resource Standards by State............................................................ 26

Figure 6: Residential State Energy Code Status.......................................................................................... 33

Figure 7: Commercial State Energy Code Status......................................................................................... 33

Figure 8: States with Renewable Portfolio Standards ................................................................................ 38

Figure 9: States with Integrated Resource Planning or Similar Processes.................................................. 47

List of Tables

Table 1: Comparison of RGGI and California Emissions Budget Trading Programs....................................12

Table 2: Examples of State CO2 Performance Standards............................................................................ 20

Table 3: Examples of Measurement and Verification Requirements for CO2 Performance Standards .....21

Table 4: Examples of Penalties for Noncompliance.................................................................................... 26

   
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List of Acronyms

ACEEE ‐ American Council for an Energy Efficient Economy

ACP ‐ Alternative Compliance Payment

BSER – Best System of Emission Reduction

CACJA ‐ Clean Air Clean Jobs Act

CCR – Cost Containment Reserve

CHP – Combined Heat and Power

CEMS – Continuous Emissions Monitoring System

CO2 – Carbon Dioxide

CO2e – Carbon Dioxide Equivalent

CDPHE – Colorado Department of Public Health and Environment

DOE – Department of Energy

DSIRE ‐ Database of State Incentives for Renewable Energy

EERS – Energy Efficiency Resource Standard

EGU – Electricity Generating Unit

EIA – Energy Information Administration

EM&V – Evaluation, Measurement, and Verification

EPA – Environmental Protection Agency

ERP – Electric Resource Plan

ESCO – Energy Service Company

GDP – Gross Domestic Product

GHG – Greenhouse Gas

GW – Gigawatt (1 GW = 1,000 MW)

GWh – Gigawatt‐hour (1 GWh = 1,000 MWh)

IECC ‐ International Energy Conservation Code

IOU – Investor‐Owned Utility

IRP – Integrated Resource Planning

kWh – Kilowatt‐hour

LBNL – Lawrence Berkeley National Laboratory

LDC – Local Distribution Company

MERP ‐ Metropolitan Reduction Proposal

MMBTU – Million British Thermal Units

MW – Megawatt

MWh – Megawatt‐hour (1 MWh = 1,000 kWh)

NOx – Nitrogen Oxides

PBF – Public Benefit Funds

PBI – Performance‐based Incentives

RGGI – Regional Greenhouse Gas Initiative

REC – Renewable Energy Certificate

RES – Renewable Energy Standard
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RPS – Renewable Portfolio Standard

PUC – Public Utility Commission

SO2 – Sulfur Dioxide

VEIC – Vermont Energy Investment Corporation

WAP – Weatherization Assistance Program
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Survey of Existing State Policies and Programs that  

Reduce Power Sector CO2 Emissions

I. Overview of state climate and energy policies and programs that reduce

power sector CO2

Across the nation, many states and regions have shown strong leadership in creating and

implementing policies, programs and measures that reduce greenhouse gas (GHG) emissions

from the power sector, while achieving other economic, environmental, and energy benefits.

These policies and programs can serve as a strong foundation as states develop plans to meet

goals under the Carbon Pollution Standards for existing sources.  

This document provides a survey of many of these activities. Policies and programs range from

market‐based programs and GHG performance standards that require direct GHG emission

reductions from EGUs, to others, such as renewable portfolio standards (RPS) and energy

efficiency resource standards (EERS), that reduce GHG emissions by altering the mix of energy

supply and reducing energy demand. States have developed their policies and programs with

stakeholder input and tailored them to their own circumstances and priorities. Their leadership

and experiences provided the EPA with important information about best practices to build

upon in this proposed rule.

States vary in their regulatory structures, electricity generation and consumption patterns,

while geography affects factors such as the availability of fuels, transmission networks, and

seasonal energy demand. States have tailored their climate and energy policies and programs

accordingly. For example, in some states, utilities are vertically integrated, meaning that the

one company is responsible for electricity generation, transmission, and distribution over a

given service territory.  The state utility regulators have authority over these utilities.  In other

states, where the electric power industry has been restructured, generation has been

decoupled from distribution, and retail customers have their choice of electricity suppliers. In

states where restructuring is active (see

Figure 1), the utility regulators do not have authority or control over the utilities responsible for

generation, only over the distribution utilities. States rely upon and have access to different fuel

types and house a variety of EGU types within state borders.  States are part of regional

electricity grids that do not align with state boundaries.  Electricity is imported and exported by

utilities across states throughout each regional grid.     

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Comment [A1]: Revised awkward phrasing

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Figure 1: Status of Electricity Restructuring by State

Source: “Status of Restructuring by State”, U.S. Energy Information Administration, accessed March 19, 2014,

http://www.eia.gov/electricity/policies/restructuring/restructure_elect.html.

States also have different economic considerations, drivers and approaches when

implementing climate change, energy efficiency and/or renewable energy policies, programs or

measures.  Actions can be motivated by state environmental, energy and/or economic concerns.  

For example, ten states have passed legislation requiring GHG emission reductions and are

using a combination of carbon limits, performance standards, energy efficiency and/or

renewable energy strategies to achieve these targets.2 Other state policies are motivated by

public utility commission (PUC) requirements to achieve all cost‐effective energy efficiency

improvements and still others by renewable energy generation requirements.  Policies,

programs and measures vary from state to state in their implementation levels and

administration, some run by state agencies and others by utilities with varying mechanisms for

ensuring compliance.  

                                                            

2 States include California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, Minnesota, New Jersey, Oregon,

and Washington. Targets are typically defined on a 1990 base year, aiming to achieve reductions of between 0 and

10 percent by 2020, although Maryland and Minnesota have chosen targets of 25 percent below 2006 levels by

2020, and 15 percent below 2005 levels by 2015 respectively.

Deleted: (September 2010)1

Deleted: DOE,

Deleted: ¶

Deleted: limate change

Comment [A2]: It is unclear what’s meant by

“climate change ... strategies”.   

Comment [A3]: Clarified text.
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This appendix is not exhaustive and is only intended to provide information about strategies

states have used to achieve GHG emission reductions, advance energy efficiency and/or

increase the use of renewable resources.   For example, states may also consider policies states

have used to support other low to zero emitting generation beyond what is addressed here.  

Policies and programs included in this appendix are not necessarily approvable in the context of

a 111(d) state plan.  In order to be approvable, they must meet criteria laid out in this proposed

rule, as described in the earlier part of this Technical Support Document, State Plans

Considerations.

II. Existing state and utility policies, programs, and measures that affect EGU

CO2 emissions

Some state and utility policies, programs and measures directly target CO2 emissions by

creating specific limits or standards for CO2 in the power sector. Other policies and programs,

such as those that advance energy efficiency and renewable energy, are designed to reduce

energy demand or promote an increase of supply from low or non‐GHG emitting sources which

reduces CO2 emissions.  Many states that are aggressively pursuing climate change mitigation

look to energy efficiency and renewable energy first, recognizing the potential for low cost GHG

emissions reductions and the economic, reliability and diversity benefits these resources

provide

For example, according to California, “the integrated nature of the grid means that policies

which displace the need for fossil generation can often cut emissions from covered sources

more deeply, and more cost‐effectively than can engineering changes at the plants alone,

though these source‐level control efforts are a vital starting point.”3 In working to meet its

statewide goal of reducing GHG emissions to 1990 levels by 2020 and 80 percent below 1990

levels by 2050, the state calls their energy efficiency standards “the bedrock upon which

climate policies are built” and uses renewable energy to fill any remaining energy needs.”4

  

Compared to the costs of other climate policies, they find that “energy efficiency provides

substantial emissions reductions and should be an essential element of the BSER CO2 reduction

target.”5

  Another state, Connecticut, has a law that requires the state to reduce GHG emissions

to 10 percent below 1990 emissions levels by 2020 and 80 percent from 2001 levels by 2050.6

  

They consider energy efficiency investments, expanded renewable energy and participation in

                                                            

3 Mary Nichols (Chairman of California Air Resources Board), letter to EPA Administrator Gina McCarthy, December

27, 2013.   4 Ibid.

5 Ibid.

6 Environment Northeast, Connecticut House Bill No. 5600: An Act Concerning Connecticut Global Warming

Solutions, accessed March 19, 2014, http://www.env‐

ne.org/public/resources/pdf/CT_Global_Warming_Bill_Summary.pdf.

Deleted: multiple

Comment [A4]: This document should also

address policies used to further other forms of clean

generation. For example, one that should be added

is Construction Work in Progress for new nuclear

plants.  It is unlikely that the four AP1000s under

construction in the Southeast would have been

pursued in the fashion that they were without this

state policy to offset some of the up‐front

construction risk.

Comment [A5]: Added a sentence to reinforce

that there are other options available but that this is

not exhaustive.

Deleted: While states often enact energy

efficiency and renewable energy programs for

economic or reliability purposes, such as for

reducing demand for electricity rates

Comment [A6]: Can EPA provide an example of

when a state has enacted energy efficiency and

renewable energy programs to reduce electricity

rates? Improving system resilience could be a goal

of an EERS, however.

Comment [A7]: Reworded text to convey

intended message.

Deleted: or avoiding the need to build new

generation, many of the states aggressively pursuing

climate change mitigation look to both energy

efficiency and renewable energy for the bulk of

their reductions.  

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of California Air Resources Board, to EPA

Administrator Gina McCarthy.

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the Regional Greenhouse Gas Initiative among its top ten strategies to reduce GHGs when

considering cost‐effectiveness and GHG‐reduction potential.7

   

Beyond these specific policies and programs, some states implement planning requirements

that can affect emissions both directly and indirectly.  This section describes a range of existing

state actions that fall into all of these categories.

a. Actions that reduce EGU CO2 Directly

Existing state actions that directly reduce EGU CO2 emissions tend to fall in one of two

categories: market‐based emission limits or emission performance standards.   

i. Market‐based Emission Limits  

Description

An emissions budget trading program (or “cap‐and‐trade” program) is a market‐based tool for

reducing pollution. The basic approach, which involves the allocation and trade of

environmental permits, has been used across environmental media, including air pollution

control, clean water regulation, and land‐use applications.   

As shown in Figure 2 below, ten states have implemented emissions budget trading programs

addressing CO2 and other GHG emissions. These include California’s cap and trade program and

                                                            

7 States’ §111(d) Implementation Group Input to EPA on Carbon Pollution Standards for Existing Power Plants,

Joint comments from 15 states on Carbon Pollution Standards for Existing Power Plants sent to USEPA

Administrator McCarthy on December 16, 2013. Signatories include: Mary D. Nichols, Chairman of California Air

Resources Board, Robert B. Weisenmiller, California Energy Commission, Michael R. Peevey, Chair of California

Public Utilities Commission, Larry Wolk, MD, MSPH, Executive Director and Chief Medical Offices of Colorado

Department of Public Health and Environment, Dan Esty, Commissioner of Connecticut Department of

Environmental Protection, Collin O’Mara, Secretary of Delaware Department of Natural Resources and

Environmental Control, Dallas Winslow, Chairman of Delaware Public Service Commission, Douglas Scott, Chair of

Illinois Commerce Commission, David Littell, Commissioner of Maine Public Utilities Commission, Robert M.

Summers, Secretary of Maryland Department of the Environment, Kelly Speakes‐Backman, Commissioner of

Maryland Public Service Commission, Ken Kimmell,  Commissioner of Massachusetts Department of Environmental

Protection, Mark Sylvia, Commissioner of Massachusetts Department of Energy resources, John Linc Stine,

Commissioner of Minnesota Pollution Control Agency, Mike Rothman, Commissioner of Minnesota Department of

Commerce, Thomas S. Burack, Commissioner of New Hampshire Department of Environmental Service, Joseph

Martens, Commissioner of New York State Department of Environmental Conservation, Audrey Zibelman, Chief of

New York State Public Commission, Dick Pederson, Director Oregon department of Environmental Quality, Janet

Coit, Director of Rhode Island Department of Environmental Management, Marion Gold, Commissioner of Rhode

Island Office of Energy resources, Deborah Markowitz, Secretary of Vermont Agency of Natural Resources, James

Volz, Chairman of Vermont Public Service Board, Maia Bellon, Director of Washington State Department of Ecology.

Letter hereafter referred to as “State environmental agency leaders from CA, CO, DE, IL, ME, MD, MA, MN, NH, NY,

OR, RI, VT, WA, Open Letter to the EPA Administrator Gina McCarthy on Emission Standards Under Clean Air Act

Section 111(d), December 16, 2013.”

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the nine northeast and mid‐Atlantic states participating in the Regional Greenhouse Gas

Initiative (RGGI), consisting of Connecticut, Delaware, Maine, Maryland, Massachusetts, New

Hampshire, New York, Rhode Island, and Vermont.89

Figure 2: States with Active Greenhouse Gas Cap and Trade Programs

Policy Mechanics

Design

An emissions budget trading program establishes an aggregate limit on pollution through an

emissions cap that specifies the total allowable emissions over some time period for all of the

sources subject to the program. To comply with the program, each source must acquire

allowances, through purchases or by allocation from the state, equal to their emissions and

surrender them at the end of each compliance period.  

                                                            

8 “RGGI Homepage”, Regional Greenhouse Gas Initiative Incorporated, accessed March 19, 2014

http://www.rggi.org/.   9 “Cap and Trade Program”, California Air Resources Board, accessed March 19, 2014

http://www.arb.ca.gov/cc/capandtrade/capandtrade.htm.

Greenhouse Gas Cap and Trade

Last updated 3/24/2014

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Allowances may be traded among both regulated and non‐regulated parties, creating a market

for emission allowances. In turn, the allowance market establishes a price signal for emissions

(a market price for emitting a unit of pollution), which triggers broad economic incentives for

reducing emissions across the covered sector(s) and encourages innovation in developing

emissions control strategies and new pollution control technologies.

There are several key design elements that can vary from program to program:

 Scope of coverage (e.g., sectors and types of facilities covered)

 Applicability (criteria for inclusion of emitting facilities and units in the program)

 Emissions budget (i.e., “cap”) and emissions reduction schedule

 Flexibility provisions in addition to ability to trade emission allowances, including:  

o Multi‐year compliance periods,  

o Allowance banking,  

o Offsets (e.g., project‐based emissions reductions occurring outside the capped

sector/sources)

 Additional provisions to mitigate price volatility and overall costs  

o Auction reserve price  

o Cost containment reserve of allowances provided for sale at set price thresholds.

Once the allowance price hits a threshold, an extra supply of allowances are made

available

Table 1 summarizes some of the key design elements of the RGGI and California programs.

Table 1: Comparison of RGGI and California Emissions Budget Trading Programs  

Element RGGI California

Applicability  All fossil fuel fired power plants with a

capacity of 25 MW or greater. 10

 All facilities in covered sectors emitting at

least 25,000 metric tons CO2 equivalent

(CO2e) or greater.11

                                                            

10 RGGI, Overview of RGGI CO2 Budget Trading Program (Regional Greenhouse Gas Initiative, 2007), accessed on

March 19, 2014, http://www.rggi.org/docs/program_summary_10_07.pdf.

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Scope  Facilities in electric utility sector.12  Facilities in utility and large industrial sectors

(plus fuel distributors in 2015)13

Emissions

budget  Recently reduced 45 percent to 91

million tons CO2 in 2014.  Beginning in

2014, cap was reduced a further 2.5

percent per year to 2020.14

 Emissions cap was set at 2 percent below

expected 2012 emissions, declining by 2

percent in 2014 and 3 percent annually from

2015 to 2020. 15

Compliance

period  Must demonstrate compliance every

three years and hold at least 50 percent

of their obligations during first two

years of each period. 16

 Facilities must demonstrate full compliance

every three years, but the must surrender

allowances and offsets to cover 30 percent

of their compliance obligations during the

first two years of each three year cycle.17

Allowance

allocation

method

 Each state distributes their share of

allowances in an amount determined by

their applicable statute or regulation.  

Approximately 90 percent of CO2

allowances are auctioned off to EGUs or

the secondary market.18

 Large industrial facilities and electric utilities

are allocated the majority of their

allowances for free, set at 90 percent of the

average efficiency of a given sector. The

remaining allowances are auctioned off. The

share of allowances allocated for free

declines over time.19

Cost

containment

provisions

 An additional five million allowances

became available March 2014 when

market price exceeded $4 per ton.20  

 Trigger price increases until 2020 when

10 million allowances become available

if price per ton exceeds $10.7521

 A strategic reserve is starting at 1 percent of

allowances, rising over the years to 7

percent.  Shares of allowances held in the

reserve will be released at three price trigger

points; $40, $45, and $50 per ton and rise by

5 percent per year including inflation.22

                                                                                                                                                                                                11 CARB, Cap and Trade Regulation Instructional Guidance, Chapter 2: Is My Company Subject to the Cap‐and‐Trade

Regulation (California Air Resources Board, 2012), accessed on March 19, 2014,

http://www.arb.ca.gov/cc/capandtrade/guidance/chapter2.pdf. 12 “Regulated Sources”, Regional Greenhouse Gas Initiative Inc., accessed on May 14, 2014,

http://www.rggi.org/design/overview/regulated_sources. 13 CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf. 14 RGGI Inc. “RGGI States Make Major Cuts to Greenhouse Gas Emissions from Power Plants,” Regional Greenhouse

Gas Initiative Press Release (January 13, 2014), accessed on March 19, 2014,

http://www.rggi.org/docs/PressReleases/PR011314_AuctionNotice23.pdf. 15 CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014,  http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf. 16 “CO2 Offsets” Regional Greenhouse Gas Initiative Inc., accessed on March 19, 2014

http://www.rggi.org/market/offsets. 17 CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014,  http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf. 18 “2013 Allowance Allocation”, Regional Greenhouse Gas Initiative, Inc., accessed on March 19, 2014,

http://rggi.org/design/overview/allowance‐allocation/2013‐allocation.    19 CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014,  http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf. 20 Thomson Reuters Point Carbon, “RGGI, You’ve Broken through the Cost Containment Reserve Price! Now What?”

Thomson Reuters, March 17, 2014, accessed March 19, 2014,

http://www.pointcarbon.com/research/promo/research/1.4523252?&ref=searchlist.

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Allowance Allocation

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Banking  Allows unlimited allowance banking23  Allows unlimited banking of allowances.24

Offsets  Power plants covered under RGGI are

allowed to use offsets within the RGGI

region25 to meet 3.3 percent of their

compliance obligation.26.

 Facilities may use domestic offsets for up to

8 percent of their compliance obligations.27

A framework has been established to include

international offsets but these are currently

not allowed in the program. 28

Authority

State and regional GHG emissions budget trading programs are authorized through individual

state legislation and implemented through state regulations. For example, California

implemented their emissions trading program under the authority of its 2006 Global Warming

Solutions Act, which requires the state to reduce its 2020 GHG emissions to 1990 levels.29   Each

RGGI state has separate authorizing legislation, and in some cases their legislation specifically

directs the use of auction proceeds. For example, Maine authorized its participation in RGGI

through Stature 580‐A. Title 38 Chapter 3B: Regional Greenhouse Gas Initiative. This statute

                                                                                                                                                                                                21 RGGI Inc., Summary of RGGI Model Rule Changes (Regional Greenhouse Gas Initiative, Inc., 2013), accessed on

March 19, 2014,

http://www.rggi.org/docs/ProgramReview/_FinalProgramReviewMaterials/Model_Rule_Summary.pdf. 22 CARB Proposed Regulation to Implement the California Cap‐and‐Trade Program (California Air Resources Board,

2010), accessed on March 19, 2014,  http://www.arb.ca.gov/regact/2010/capandtrade10/capisor.pdf. 23 RGGI, Overview of RGGI CO2 Budget Trading Program (Regional Greenhouse Gas Initiative, 2007), accessed on

March 19, 2014, http://www.rggi.org/docs/program_summary_10_07.pdf. 24 CARB Proposed Regulation to Implement the California Cap‐and‐Trade Program (California Air Resources Board,

2010), accessed on March 19, 2014,  http://www.arb.ca.gov/regact/2010/capandtrade10/capisor.pdf. 25 “CO2 Offsets” Regional Greenhouse Gas Initiative, Inc., accessed on March 19, 2014  

http://www.rggi.org/market/offsets. 26 Ibid.

Eligible offsets under RGGI include: landfill methane capture and destruction, sulfur hexafluoride (SF6) reduction

from power transmission, afforestation, end use energy efficiency, and agricultural manure management. 27 CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014,  http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf.

Offsets are initially limited to forestry, urban forestry, livestock methane capture and destruction, and destruction

of ozone depleting substances. However, rice cultivation and coal mine methane are proposed for inclusion in the

program. See: CARB – Potential New Compliance Offset Projects:

http://www.arb.ca.gov/cc/capandtrade/offsets/offsets.htm for more information. 28 Ibid. 29 Assembly Bill 32, California Global Warming Solutions Act of 2006, Division 25.5 (September 27, 2006), accessed

March 19, 2014, http://www.leginfo.ca.gov/pub/05‐06/bill/asm/ab_0001‐

0050/ab_32_bill_20060927_chaptered.pdf.  

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also requires that 100 percent of auction proceeds to go towards carbon reduction and energy

conservation efforts.30  

The regulatory authority issues individual authorizations to emit a specific quantity of emissions

(“allowances”), which typically represent one ton of a pollutant, in an amount that equals the

emissions cap.

Obligated Parties

Obligated parties in trading programs are generally the covered facilities. It is the facilities that

are responsible for acquiring enough emission allowances to cover their emissions at the end of

each compliance period. For example, as stated above, RGGI covers fossil fuel fired power

plants 25 megawatts or greater in size.31 The California cap and trade program covers electricity

generators, importers of electricity, and industrial facilities whose annual emissions exceed

25,000 metric tons CO2e. Starting in 2015, the California trading program will cover distributors

of transportation, natural gas, and other fuels with emissions greater than 25,000 metric tons

CO2e as well as electricity imports from all sources.32

Measurement and Verification

Emissions budget trading programs include requirements for emissions monitoring and

reporting, holding and transfer of allowances, and surrender of allowances (and offsets) in an

amount equal to reported emissions.  This is often referred to, generally, as the program

“compliance obligation”).   

For example, facilities obligated under the RGGI program must report emissions quarterly to

environmental agencies in their respective state as well as annually to the U.S. EPA Greenhouse

Gas Reporting Program.34 They must provide verification of emissions as well as third party

verification of emission reductions from CO2 offsets. GHG emissions reporting for affected

sources in California is addressed through the California mandatory GHG reporting regulations,

                                                            

30 Maine revised statutes, Title 38, Chapter 3‐B, section 580‐B, the Regional Greenhouse Gas Initiative Act of 2007,

accessed on March 19, 2014, http://www.mainelegislature.org/legis/statutes/38/title38sec580‐B.html. 31 “Regulated Sources”, Regional Greenhouse Gas Initiative Inc., accessed on May 14, 2014,

http://www.rggi.org/design/overview/regulated_sources. 32 CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014,  http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf. 34 RGGI, Overview of RGGI CO2 Budget Trading Program (Regional Greenhouse Gas Initiative, 2007), accessed on

March 19, 2014, http://www.rggi.org/docs/program_summary_10_07.pdf.

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using a modified version of the reporting platform administered through the EPA Greenhouse

Gas Reporting Program.35

Penalties for Non‐compliance

Failure to submit allowances in an amount equal to reported emissions can result in emissions

penalties. Penalties can come in the form of additional allowance submission requirements (e.g.,

three‐to‐one submission requirements to account for any shortfall), as well as other

administrative fines and penalties.  For example, if facilities are found to be short of the

allowances necessary to meet their obligation under the RGGI program, they can be fined for

up to three times the price of the value of the allowances they lack. Additional penalties may

also be applied at the state level.36 If facilities under the California program are unable to meet

their compliance obligations in time, they must surrender four allowances for every one

allowance they lack.37

Implementation Status

The RGGI program was established in 2009. From 2009 through 2012, the nine RGGI states

invested auction proceeds of more than $700 million in programs that lower costs for energy

consumers and reduce CO2 emissions, including approximately $460 million into energy

efficiency programs.38 The participating RGGI states estimate that those investments are

providing benefits of more than $1.8 billion in lifetime energy savings to energy consumers in

the region.39

                                                            

35CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014,  http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf. 36 Bifera, Lucas, Regional Greenhouse Gas Initiative (Center for Climate and Energy Solutions, 2013),,

http://www.c2es.org/docUploads/rggi‐brief‐12‐18‐13‐updated.pdf. 37 C2ES, California Cap‐and‐Trade Program Summary Table, (Center for Climate and Energy Solutions, 2012),

available at http://www.c2es.org/docUploads/California‐Cap‐Trade‐Summary.pdf. 38 RGGI Inc., Regional Investment of RGGI CO2 Allowance Proceeds, 2012 (Regional Greenhouse Gas Initiative Inc.,

2014), accessed on March 19, 2014, http://www.rggi.org/docs/Documents/2012‐Investment‐Report.pdf,  

Programs include residential, commercial, and industrial programs. Of the $707 million in auction proceeds

invested by RGGI participating states through 2012, 65 percent supported end‐use energy efficiency programs. 39 Ibid.

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Figure 3: Historical GDP and Greenhouse Gas Emissions in the RGGI Region

Between 2005, when RGGI was first announced, and 2012, power sector CO2 emissions in the

RGGI participating states fell by more than 40 percent while GDP in the region grew (see Figure

3).

40 The RGGI program was not a primary driver for the reductions in RGGI states but the lower

emissions led participating states to adjust the CO2 emission limits. 43  In January 2014, the

participating states lowered the overall allowable CO2 emission level in 2014 by 45 percent,

                                                            

40 Regional Greenhouse Gas Initiative, Open Letter to Gina McCarthy on Emission Standards Under Clean Air Act

Section 111(d), December 2, 2013, accessed on March 19, 2014,

http://www.rggi.org/docs/RGGI_States_111d_Letter_Comments.pdf.

By contrast, total U.S. power sector CO2 emissions fell by 16 percent during the same period of time. See 2014 U.S.

Greenhouse Gas Inventory for more detail:

U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990‐2012 (U.S. Environmental Protection Agency,

2014), accessed May 14, 2014, http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html. 43 Environment Northeast, RGGI Emissions Trends (Environment Northeast, 2011), accessed on March 19, 2014,

http://www.env‐ne.org/public/resources/pdf/ENE_RGGI_Emissions_Report_110502_FINAL.pdf.

The first three‐year control period under RGGI, establishing CO2 emission limits for EGUs, began on January 1, 2009.

Low gas prices, increased renewables, decreased electric demand and weather are considered four primary drivers

of the reductions through 2010 as reported by Environment Northeast in May 2011.  

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CO2 emissions fell by 16 percent during the same

period of time.42

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setting a multi‐state CO2 emission limit for affected EGUs of 91 million short tons of CO2 in 2014

and 78 million short tons of CO2 in 2020, more than 50 percent below 2008 levels.44  

The California economy‐wide market‐based GHG emissions trading program, which addresses

GHG emissions from multiple sectors, was implemented in 2012 with emissions limitations

beginning in 2013. 45,46 While California’s emission trading program, like its state emission limit,

is multi‐sector in scope, the state projects that the emissions trading program and related

complementary measures will reduce power sector GHG emissions to less than 80 million

metric tons of CO2 equivalent by 2025, a 25 percent reduction from 2005 power sector

emission levels. 47 Prior to the implementation of the emission trading program, California

reports that it reduced CO2 power sector emissions by 16 percent from 2005 to a 2010‐2012

averaging period, a reduction of 16 million metric tons of CO2 equivalent.48    

ii. CO2 Emission Performance Standards

Description

CO2 emission performance standards can apply either directly to EGUs or to the local

distribution company (LDC) that sells electricity to the customers. (For more information about

electricity is generated and distributed, see Chapter 2 of the Regulatory Impact Analysis).

As of March 2014, four states ‐ California, New York, Oregon and Washington ‐ have enacted

mandatory GHG emission standards that impose enforceable emission limits on new and/or

expanded electric generating units. Three states ‐ California, Oregon and Washington ‐ have

enacted mandatory GHG emission performance standards that set an emission rate for

electricity purchased by electric utilities. 49 In addition to these states, Illinois and Montana have

                                                            

44 RGGI Inc. “RGGI States Make Major Cuts to Greenhouse Gas Emissions from Power Plants,” Regional Greenhouse

Gas Initiative Press Release (January 13, 2014), accessed on March 19, 2014,

http://www.rggi.org/docs/PressReleases/PR011314_AuctionNotice23.pdf.

45 “Cap‐and‐Trade Program,” California Air Resources Board, accessed March 19, 2014,

http://www.arb.ca.gov/cc/capandtrade/capandtrade.htm. 46 The California program was developed in coordination with U.S. state and Canadian province WCI partners. 47 State environmental agency leaders from CA, CO, DE, IL, ME, MD, MA, MN, NH, NY, OR, RI, VT, WA, Open Letter

to the EPA Administrator Gina McCarthy on Emission Standards Under Clean Air Act Section 111(d), December 16,

2013, accessed on March 19, 2014,

http://www.georgetownclimate.org/sites/default/files/EPA_Submission_from_States‐FinalCompl.pdf.

Preliminary California Air Resources Board analyses, based in part on CARB 2008 to 2012 Emissions for Mandatory

GHG reporting Summary (2013), cited in this letter. 48Ibid.

49 “Standards and Caps for Electricity GHG Emissions,” Center for Climate and Energy Solutions, accessed on March

19, 2014, http://www.c2es.org/us‐states‐regions/policy‐maps/electricity‐emissions‐caps.

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requirements that new coal plants capture at least 50 percent of their CO2 emissions (see

Figure 4).

50

Figure 4: States with Greenhouse Gas Performance Standards

Policy Mechanics

Design  

States have implemented three different types of CO2 performance standards that affect EGUs

and/or LDCs differently.  The first requires power plant emissions per electricity generated to

be less than or equivalent to an established standard and is directly applicable to EGUs. The

second type places conditions on the emissions attributes of electricity procured by electric

utilities.  It consists of standards that are applicable to LDCs that provide electricity to retail

customers. A third type requires that new coal‐fired power plants must capture and store a

specific percentage of CO2 emissions. Table 2 provides state examples for each of the types of

CO2 performance standards.  

                                                            

50 Ibid.

Greenhouse Gas Performance Standards

Applies to new units and power purchase agreements

Last updated 8/8/2013 Requires carbon capture and storage at new coal-fired power 

Applies only to new units

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Authority  

In some states, programs are regulated through the Public Utilities Commission (California,

Oregon). New York’s program is regulated through the Department of Environmental

Conservation. Washington’s program is regulated through two different sets of entities

depending on the ownership of the utilities.  The Washington Utilities and Transportation

Commission regulate investor owned utilities, and the utility's governing board, Washington

Department of Ecology, and the State Auditor oversees consumer owned utilities.

Obligated Parties

The emission performance standard can apply either directly to EGUs or to the local distribution

company (LDC) that sells electricity to the customer.

Measurement and Verification

Obligated parties must measure and report on electricity generation and CO2 emissions on a

regular basis to verify their compliance with the standard. The reporting requirements and

timing varies from state to state and are typically set by the agency that oversees the program

as described under authority above. Table 2 provides an overview of different CO2 performance

standards, while Table 3 provides examples regarding measurement and verification

requirements across California, New York, Oregon, and Washington

Table 2: Examples of State CO2 Performance Standards

                                                            

51 New York Department of Environmental Conservation, Part 251: CO2 Performance Standards for Major Electric

Generating Facilities, June 12, 2012, accessed March 19, 2014, http://www.dec.ny.gov/regs/83094.html#83097. 52 Oregon Department of Energy, Oregon’s Carbon Dioxide Emission Standards for New Energy Facilities (Oregon

Department of Energy, 2010) http://www.oregon.gov/energy/Siting/docs/Reports/CO2Standard.pdf.

What It Does State Examples

Requires power

plant emissions

per electricity

generated to be

less than or

equivalent to the

established

standard; Applies

to EGUs

 New York (Part 251, 2012) ‐ New or expanded baseload plants (25 MW and larger)

must meet an emission rate of either 925 lb CO2/MWh (output based) or 120 lbs

CO2/MMBTU (input based).  Non‐baseload plants (25 MW and larger) must meet an

emission rate of either 1450 lbs CO2/MWh (output based) or 160 lbs CO2/MMBTU

(input based).51  

 Oregon (HB 3283; 1997, 2007) ‐ New natural gas‐fired power plants (baseload and

non‐baseload) must meet an emission rate of 675 lb CO2/MWh.  Cogeneration and

offsets may be used to comply with the emission standard.52

 Washington (RCW 80‐70‐010; 2004) ‐ New EGUs 25 MW and larger must have an

approved CO2 mitigation plan that results in mitigation of 20 percent of the total CO2

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Table 3: Examples of Measurement and Verification Requirements for CO2 Performance

Standards

State Measurement and Verification Details

California  The California PUC is responsible for approving any long term financial commitment by

an electric utility and must adopt rules to enforce these requirements as well as

verification procedures.60

New York  CO2 emission regulations require recordkeeping, monitoring and reporting consistent

                                                            

53 Washington State Legislature, Chapter 80.70 RCW: Carbon Dioxide Mitigation, Accessed on March 19, 2014,

http://apps.leg.wa.gov/rcw/default.aspx?cite=80.70&full=true. 54 California Energy Commission, California SB 1368, Chapter 598: Emission Performance Standards, September 29,

2006, accessed March 19, 2014, http://www.energy.ca.gov/emission_standards/. 55 U.S. EPA, eGRID 2010 data files (U.S. Environmental Protection Agency, 2014), accessed on March 19, 2014,

http://www.epa.gov/cleanenergy/energy‐resources/egrid/index.html#. 56 Oregon Department of Energy, Oregon’s Carbon Dioxide Emission Standards for New Energy Facilities (Oregon

Department of Energy, 2010) http://www.oregon.gov/energy/Siting/docs/Reports/CO2Standard.pdf. 57 Washington State Legislature, Chapter 80.70 RCW: Carbon Dioxide Mitigation, Accessed on March 19, 2014,

http://apps.leg.wa.gov/rcw/default.aspx?cite=80.70&full=true. 58 Illinois General Assembly, Public Act 095‐1027, SB1987, Clean Coal Portfolio Standard Law, January 12, 2009,

accessed March 19, 2014 http://ilga.gov/legislation/publicacts/95/PDF/095‐1027.pdf. 59 Montana State Legislature, H.B.0025.05, An Act Generally Revising the Electric Utility Industry and Customer

Choice Laws, May 14, 2007, accessed March 19, 2014, http://leg.mt.gov/bills/2007/billpdf/HB0025.pdf. 60 “SB 1368 Emission Performance Standards,” California Energy Commission, Accessed March 19, 2014,

http://www.energy.ca.gov/emission_standards/.

emissions over the life of the facility.  Includes modifications to existing EGUs that

result in an increase in CO2 emissions of 15 percent or more.  The CO2 mitigation plan

may include one or more of a list of eligible measures (includes indirect measures,

such as EE/RE and offsets). 53

Places conditions

on the emissions

attributes of

electricity

procured by

electric utilities;

Applies to LDCs

 California (SB 1368; 2006) ‐ Electric utilities may only enter into long‐term power

purchase agreements for baseload power if the electric generator supplying the power

has a CO2 emission rate that does not exceed that of a natural gas combined cycle

plant.  The California Energy Commission promulgated regulations establishing an

emission rate of 1,100 lb CO2/MWh.54 By comparison, the average emissions rate of

gas plants in the U.S. is 945 lb CO2/MWh, while the average emissions rate of

pulverized coal plants is 2,154 lb CO2/MWh.55

 Oregon (HB 101; 2009) and Washington (SB 6001; 2007) ‐ Electric utilities may only

enter into long‐term power purchase agreements for baseload power if the electric

generator supplying the power has a CO2 emission rate of 1,100 lb CO2/MWh or

less.5657

Requires that new

coal‐fired power

plants must

capture and store

a specific

percentage of CO2

emissions

 Illinois (SB 1987; 2009) Illinois utilities and retailers must purchase at least 5 percent of

their electricity from Clean Coal Facilities in 2015 and beyond. To be designated a

Clean Coal Facility, new coal‐fired power plants must capture and store 50 percent of

carbon emissions from 2009‐2015, 70 percent for 2016‐2017, and 90 percent after

2017.58

 Montana (HB 25; 2007). The Public Service Commission may not approve new plants

constructed after January 2007 that are primarily coal‐fired unless at least 50 percent

of the plant’s CO2 emissions are captured and stored.59 Formatted: Font: 12 pt

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with existing state and federal regulations.

 Each applicable emissions source must install Continuous Emissions Monitoring Systems

(CEMS) subject to Federal CO2 reporting requirements for 40 CFR part 75, successfully

complete certification tests, and record, report, and quality assure the data from the

CEMS.  

 The owner or operator must report the CO2 mass emissions data and heat input data on

a semi‐annual basis to the Department of Environmental Conservation.  

 On a quarterly basis, the owner or operator must report all of the data and information

required in either 40 CFR part 60 or subpart H of 40 CFR part 75.61

Washington  Mitigation projects must be approved by the appropriate council, department, or

authority, and made a condition of the proposed and final site certification agreement

or order of approval.  

 Direct investment projects are approved if they provide reasonable certainty that the

performance requirements of the projects will be achieved and that they were

implemented after July 1, 2004.  

 For facilities under the jurisdiction of a council, the implementation of a carbon dioxide

mitigation project, other than purchase of carbon credits, is monitored by an

independent entity for conformance with the performance requirements of the carbon

dioxide mitigation plan.  The independent entity shares the project monitoring results

with the council.  

 For facilities under jurisdiction of the department or authority, the implementation of a

carbon dioxide mitigation project, other than a purchase of carbon credits, is monitored

by the department or authority issuing the order of approval.62

Oregon  It is up to the Council during the certificate application phase to determine the gross

CO2 emissions over a 30 year lifetime of the proposed facility to determine whether it

meets the CO2 performance standard.  

 During the operation phase of approved facilities, there are CO2 reporting requirements

to the Oregon Department of Environmental Quality and US EPA.  

 New facilities must pass a 100 hour test in their first year of operation to show they

meet the performance standards.64

Penalties for Noncompliance

For policies that affect target new electric generating units, utilities must prove any proposed

units are in compliance at the time of permitting. In Oregon, if facilities do not meet the

performance standard in their first year of operation during a 100 hour test65, they must

purchase offsets to account for any excess emissions.66

                                                            

61 New York Department of Environmental Conservation, Part 251: CO2 Performance Standards for Major Electric

Generating Facilities, June 12, 2012, Accessed March 19, 2014, http://www.dec.ny.gov/regs/83094.html#83097. 62 Washington State Legislature, Chapter 80.70 RCW: Carbon Dioxide Mitigation, Accessed on March 19, 2014,

http://apps.leg.wa.gov/rcw/default.aspx?cite=80.70&full=true

64 Oregon Department of Energy, Oregon’s Carbon Dioxide Emission Standards for New Energy Facilities (Oregon

Department of Energy, 2010) Accessed on March 19, 2014

http://www.oregon.gov/energy/Siting/docs/Reports/CO2Standard.pdf. 65 During the first year of operation new power plants test their equipment to ensure compliance with standards

for commercial equipment. Initial CO2 performance requirements can be validated during this test. 66 Oregon Department of Energy, Oregon’s Carbon Dioxide Emission Standards for New Energy Facilities, Accessed

on March 19, 2014, http://www.oregon.gov/energy/Siting/docs/Reports/CO2Standard.pdf.

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Implementation Status

Since enacting the performance standard, California’s carbon emissions rates have fallen from

approximately 1,245 lbs CO2e/MWh for fossil generation (considering both in‐state and

imported power) and 875 lbs CO2e/MWh for all power in 2005 to an average of approximately

1,090 lbs CO2e/MWh and 775 lbs CO2e/MWh in the three years before 2012.67  

b. Energy Efficiency Policies, Programs and Measures

Demand‐side energy efficiency policies and programs reduce utilization of EGUs and avoid

greenhouse gas emissions associated with electricity generation.  These electricity demand

reductions can be achieved through enabling policies that incentivize investment in demand‐

side energy efficiency improvements by overcoming market barriers that otherwise prevent

these investments, such as lack of information on energy efficient options, high transaction

costs, split‐incentives, lack of product availability, and perceptions of organizational risks.

Reducing electricity demand also reduces the associated transmission and distribution losses

that occur across the grid between the sites of electricity generation and the end use.  

Demand‐side energy efficiency is considered a central part of climate change mitigation in

states that currently have mandatory GHG targets, accounting for roughly 35 percent to 70

percent of expected reductions of state's power sector emissions.68 For example, California

expects to achieve reductions of 21.9 MMTCO2e in 2020 from energy efficiency programs

targeting electricity reductions. Taking into account expected reductions of 21.3 MMTCO2e

expected from California's RPS and 2.1 MMTCO2e from the million solar roofs program, energy

efficiency makes up 48 percent of power sector reductions based on California's Climate

Change Scoping Plan.69 Another state, Washington, expects to reduce 9.7 MMTCO2e from

energy efficiency measures in 2020. Taking into account expected reductions of 4.1 MMTCO2e

from Washington's RPS, energy efficiency makes up 70 percent of expected emission reductions

from stationary energy within the state.70

                                                            

67 State environmental agency leaders from CA, CO, DE, IL, ME, MD, MA, MN, NH, NY, OR, RI, VT, WA, Open Letter

to EPA Administrator Gina McCarthy on Emission Standards Under Clean Air Act Section 111(d), December 16,

2013, accessed on March 19, 2014,

http://www.georgetownclimate.org/sites/default/files/EPA_Submission_from_States‐FinalCompl.pdf. 68 These reduction target ranges are based on a review of state GHG reduction laws in California, Connecticut,

Hawaii, Maine, Maryland, Massachusetts, Minnesota, New Jersey, Oregon, and Washington. 69 California Air Resources Board, Climate Change Scoping Plan, accessed March 19, 2014,

http://www.arb.ca.gov/cc/scopingplan/document/adopted_scoping_plan.pdf. 70 Washington Department of Ecology, Growing Washington’s Economy in a Carbon‐Constrained World, Accessed

March 19, 2014, https://fortress.wa.gov/ecy/publications/publications/0801025.pdf.  

Deleted: enhances system efficiency by reducing

the

Comment [A8]: Any reduction in T&D losses

should be proportional to the demand reduction –

it’s not making the system more efficient; it’s just

minimizing demand.  

Comment [A9]: Revised text accordingly.

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States have employed a variety of strategies to increase investment in demand‐side energy

efficiency technologies and practices, including (1) energy efficiency resource standards, (2)

demand‐side energy efficiency programs, (3) building energy codes, (4) appliance standards and

(5) tax credits.  Each of these strategies are described below.  

i. Energy Efficiency Resource Standards  

Description

Energy Efficiency Resource Standards (EERS) set multiyear targets for energy savings that

utilities or third‐party program administrators typically meet through customer energy

efficiency programs but also through other approaches, such as peak demand reductions,

building codes and combined heat and power (CHP). An EERS can apply to retail distributors of

either electricity or natural gas, or both, depending on the state.  To date, at least 29 states

have some type of EE requirement or goal.71   

Policy Mechanics

Design

EERS design and implementation details vary by state, and may be expressed as a percentage

reduction in annual retail electricity sales, as a percentage reduction in retail electricity sales

growth, or as a specific electricity savings amount over a long‐term period.  A typical EERS sets

multiyear targets for energy savings that drive investment in EE programs implemented by

utilities or third party administrators.  Over the compliance period, an EERS reduces electricity

demand by a target amount that utilities must meet.  As a result, an EERS indirectly affects

utility CO2 emissions by reducing the use of fossil‐fuel‐fired EGUs.

Authority

Most state EERS policies are established through legislation.  However, there are several

instances in which they have been established by PUC orders under broader statutory authority,

such as by setting quantitative targets consistent with the achievement of ‘all cost‐effective

energy efficiency.72   

                                                            

71 Daniel C. Steinberg and Owen Zinaman, State Energy Efficiency Resource Standards: Policy Design, Status, and

Impacts, Technical Report NREL/TP‐6A20‐61023, (National Renewable Energy Laboratory, April 2014), Accessed on

March 19, 2014.

72 Ernest Orlando Lawrence Berkeley National Laboratory, Benefits and Costs of Aggressive Energy Efficiency

Programs and the Impacts of Alternative Sources of Funding: Case Study of Massachusetts, accessed on May 14,

2014, http://emp.lbl.gov/sites/all/files/REPORT%20lbnl‐3833e.pdf.

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Obligated Parties

Retail electricity suppliers, which are utilities that sell electricity to customers for end‐use

purposes, are the obligated parties under an EERS.

Measurement and Verification

PUCs generally oversee EERS.  Retail electricity suppliers comply with EERS requirements by

developing a portfolio of end‐use energy efficiency programs that encourage electric utility

customers to invest in more energy efficient technologies and practices as described below.  

Transmission and distribution infrastructure improvements may also count towards EERS

programs in some states.  PUCs typically rely on independent program evaluators to perform

evaluation, measurement and verification (EM&V) activities that estimate the incremental

annual and cumulative energy savings attributable to the programs.73  These estimates are

typically the basis for compliance reports submitted by retail electricity suppliers. Table 4, for

examples of penalties for program noncompliance. For more information about measurement

and verification of energy efficiency policies or programs, see earlier in the State Plan

Considerations Technical Support Document.

                                                                                                                                                                                               

An important policy driver for EE programs in six states is a statutory requirement for utilities to acquire "all cost‐

effective energy efficiency".  This policy typically requires utilities and other program administrators to pursue

energy efficiency up to the point at which it is no longer cost effective, as defined by cost‐benefit tests and

procedures REQUIRED by state PUCs.  States with all‐cost effective energy efficiency policies include: CA, CT, MA,

RI, VT, WA.  For MA, this goals has translated into achieving annual electric energy savings equivalent to a 2.4%

reduction in retail sales from energy efficiency programs in 2012. 73 Evaluation, measurement, and verification (EM&V) refers to set of techniques and approaches used to estimate

the quantity of energy savings from an EE program or policy.  Since energy savings cannot be directly measured,

efficiency program impacts are estimated by taking the difference between: (a) actual energy consumption after

efficiency measures are installed, and (b) the energy consumption that would have occurred during the same

period had the efficiency measures not been installed (i.e., the baseline).

Comment [A10]: Some state EERS, for example

Ohio, allow transmission and distribution

infrastructure improvements to count towards their

EERS.

http://www.dsireusa.org/incentives/incentive.cfm?I

ncentive_Code=OH16R&re=0&ee=0  

Comment [A11]: Revised text to acknowledge

this.

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Table 4: Examples of Penalties for Noncompliance

   

Penalties for

Noncomplianc

e

If the

obligated

parties do not

demonstrate

compliance

with the EERS,

they may face

financial

penalties. The existence and amount of penalties varies across the states.   Table 3 provides

examples of financial penalties in three states, Pennsylvania, Ohio and Illinois.

Implementation Status

As of January 2014, more than 20 states had an active EERS in place, while at least four have EE

targets or goals that are voluntary, underfunded or uncertain at this time (see Figure 5). In

addition, two states have renewable portfolio standard that allow the option for energy

efficiency to meet requirements.78  

Figure 5: Status of Energy Efficiency Resource Standards by State

                                                            

74 “Energy Efficiency and Conservation Requirements for Utilities: Pennsylvania” Database of State Incentives for

Renewables & Efficiency, accessed on March 19, 2014,

http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=PA14R&re=0&ee=0.   75 RECs represent the non‐energy attributes, including all the environmental attributes, of electricity generation

from renewable energy sources. RECs are typically issued in single MWh increments. See the section on Renewable

Portfolio Standards for more detail. 76 “Energy Efficiency and Conservation Requirements for Utilities: Ohio” Database of State Incentives for

Renewables & Efficiency, accessed on March 19, 2014,

http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=OH16R&re=0&ee=0.

77 “Energy Efficiency and Conservation Requirements for Utilities: Illinois” Database of State Incentives for

Renewables & Efficiency, accessed on March 19, 2014,

http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=IL19R&re=0&ee=0.   78 Daniel C. Steinberg and Owen Zinaman, State Energy Efficiency Resource Standards: Policy Design, Status, and

Impacts, Technical Report NREL/TP‐6A20‐61023, (National Renewable Energy Laboratory, April 2014), Accessed on

March 19, 2014.

State Direct Financial Penalties

Pennsylvania Failure to achieve the requisite reductions in electricity consumption and peak demand

during Phase 1 results in one‐time fines from $1 million to $20 million. Failure to file a

plan with the public utilities commission is also punishable by a fine of $100,000 per day.

Costs associated with any such fines may not be passed on to ratepayers.74

Ohio Failure to comply with energy efficiency or peak demand reduction requirements results

in the state public utilities commission assessing a forfeiture upon the utility, to be

credited to the Advanced Energy Fund. The amount of the forfeiture is either: an

amount, per day per under‐compliance or non‐compliance, not greater than $10,000 per

violation; or an amount equal to the then existing market value of one renewable energy

credit (REC)75 per megawatt hour of under‐compliance or noncompliance. 76

Illinois For both natural gas and electric utilities, failure to submit an energy reduction plan will

result in a fine of $100,000 per day until the plan is filed. This penalty is deposited in the

Energy Efficiency Trust Fund and may not be recovered by rate payers. 77

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Most states are meeting or on track to meet their incremental savings goals, which typically

range from an annual reduction in electricity of about 0.25 ‐ 2.5 percent. 79 In 2011, across the

50 states, incremental savings were equivalent to 0.62 percent of retail electricity sales.80 For

those states with EERS policies in place for more than two years as of 2011, thirteen of twenty

states are achieving 100 percent or more of their goals, three states are achieving over 90

percent of their goals, and only three states are realizing savings below 80 percent of their

goals.81  

ii. Demand‐side Energy Efficiency Programs  

Description  

Demand‐side energy efficiency programs are programs designed to advance energy efficiency

improvements within a state or utility service area.  They are typically implemented to help

meet state policies, standards or objectives, such as energy efficiency resource standards (EERS),

                                                            

79 Ibid. 80 “The 2013 State Energy Efficiency Scorecard”, American Council for an Energy‐Efficient Economy, Accessed on

March 19, 2014, http://www.aceee.org/research‐report/e13k.   81 Michael Sciortino, Seth Nowak, Patti White, Dan York, and Martin Kushler. “Energy Efficiency Resource

Standards: A Progress Report on State Experience.” American Council for an Energy Efficient Economy (2011).

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¶

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¶

¶

¶

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Comment [A12]: Recommend starting the

sentence with “As of 2011,”

Comment [A13]: Edited text.

Deleted: State Policy Table – Internal estimate

drawing on multiple sources

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‘all cost effective’ energy efficiency goals, integrated resource planning, and other demand‐side

management program and budget processes. As of January 2013, utilities in 48 states

implement demand‐side energy efficiency programs.82

Policy Mechanics

Design

Demand‐side energy efficiency programs include financial incentives to use energy efficient

products, make energy efficiency upgrades to improve the performance of residential,

commercial, and industrial buildings, and provide technical assistance and information

programs to address market and information barriers.  Funding for these programs typically

comes from charges added to customer utility bills and from revenues raised through emission

allowance auctions, such as under RGGI. The RGGI auction proceeds go to a variety of sources

with the authority to run demand‐side energy efficiency programs, including those also funded

via independent trusts, DOE’s Weatherization Assistance Program (WAP), and state‐run energy

efficiency grant programs for municipalities.83

States are also funding energy efficiency programs using revenues from “forward capacity

markets” operated by regional electricity operators. Forward capacity markets allow energy

suppliers to bid against each other for the amount of capacity they can supply into the

electricity market in a future year. Demand‐side management programs have been allowed to

bid into these markets as an energy source, demonstrating that energy efficiency programs can

compete with more traditional forms of electricity supply in meeting the needs of the power

grid.  

Authority

Demand‐side programs that are a part of EERS programs are typically established through

legislation or PUC authority. Other demand‐side management programs can arise as a result of

utility planning processes and state and local government efforts to ensure all cost‐effective

energy efficiency and other policy goals are met.  

                                                            

82 “The 2013 State Energy Efficiency Scorecard”, American Council for an Energy‐Efficient Economy, Accessed on

March 19, 2014, http://www.aceee.org/research‐report/e13k. 83 RGGI, “Regional Investment of RGGI CO2 Allowance Proceeds, 2012”, Regional Greenhouse Gas Initiative

(February 2014), accessed on March 19, 2014, http://www.rggi.org/docs/Documents/2012‐Investment‐Report.pdf.

Deleted: and

Comment [A14]: Demand‐side energy efficiency

financial incentives are also used to improve

industrial facilities, not just residential and

commercial buildings.

Comment [A15]: Edited text accordingly.

Deleted: Funding may also come

Comment [A16]: This sentence mischaracterizes

forward capacity markets (FCM) as providing

funding for EE programs. Energy efficiency programs

can be bid into the FCM but the FCM does not

provide funding in the same way that ratepayers

fund efficiency programs.  

Comment [A17]: Edited accordingly.

Deleted: and integrated resource planning

Deleted: Additional

Deleted: and budget processes also

Deleted: exist via

Deleted: , as well as

Comment [A18]: This paragraph incorrectly uses

IRP and utility planning as two different concepts.

Comment [A19]: Revised text to address.

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Obligated Parties

Energy efficiency programs can be administered by investor‐owned, municipal or cooperative

utilities; third party administrators; or state and local government agencies.    

Measurement and Verification

PUCs generally oversee demand‐side energy efficiency programs.  Program administrators

typically rely on independent evaluators to perform evaluation, measurement and verification

(EM&V) activities that estimate the incremental annual and cumulative energy savings

attributable to the programs.  These estimates are typically the basis for annual performance

reports submitted by retail electricity suppliers or third party administrators to the PUCs. In the

case of state and local government agency run programs that are not overseen by the PUC,

energy savings are typically estimated to assure proper use of grants or other funds.  For more

information about the evaluation, measurement and verification of energy efficiency policies

and programs, see earlier in the State Plan Considerations Technical Support Document.

Penalties for Noncompliance

As discussed above, some states with an EERS levy direct fines for missing energy efficiency

targets or failure to submit an energy efficiency plan. For some programs under PUC oversight,

failure to reach certain performance levels may result in an inability to receive an incentive

payment or recover all incurred costs. Demand‐side programs funded by RGGI proceeds or

grants typically do not have penalties for noncompliance. However, state agencies play a role in

evaluating these programs and deciding whether funding should continue to flow to them.

Implementation Status

Well‐established state demand‐side energy efficiency programs have demonstrated their ability

to reduce electricity demand.84  For example, data reported to the U.S. Energy Information

Administration (EIA) show that in 2012 California avoided 35,482 GWh of electricity

consumption through its demand‐side efficiency programs, while Illinois avoided 3,084 GWh

and Maryland avoided 1,528 GWh, .

85 These reductions are equivalent to 13.7 percent, 2.1

percent, and 2.5 percent of total 2012 retail electricity sales in those states, respectively.86  

                                                            

84 “The Future of Utility Customer‐Funded Energy Efficiency Programs in the United States”, Lawrence Berkeley

National Laboratory, Accessed on March 19, 2014, http://emp.lbl.gov/publications/future‐utility‐customer‐funded‐

energy‐efficiency‐programs‐united‐states‐projected‐spend. 85 “Electric Power Sales, Revenue, and Energy Efficiency Form EIA‐861 Detailed Data Files”, Energy Information

Administration, Accessed on March 19, 2014, http://www.eia.gov/electricity/data/eia861/. 86 “Electricity: Detailed State Data”, Energy Information Administration, Accessed on March 19, 2014,

http://www.eia.gov/electricity/data/state/.   Deleted: Form 861:
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According to data and analyses from sources including Lawrence Berkeley National Lab (LBNL),

the U.S. Department of Energy’s Energy Information Administration, and the American Council

for an Energy Efficient Economy (ACEEE), as well as the EPA’s own analysis, 12 leading states

have either achieved – or have established requirements that will lead them to achieve ‐ annual

incremental savings rates of at least 1.5 percent of the electricity consumption that would

otherwise have occurred.87

In 2011, state demand‐side energy efficiency programs are estimated to have reduced CO2

emissions by 75 million metric tons, or 3.5 percent of national power sector emissions.8889

iii. Building Energy Codes

Description

Building energy codes establish minimum efficiency requirements for new and renovated

residential and commercial buildings.  These measures are intended to eliminate inefficient

technologies with minimal impact on up‐front project costs.  This can reduce the need for

energy generation capacity and new infrastructure while reducing energy bills. Energy codes

lock in future energy savings during the building design and construction phase, rather than

through a renovation.  

Policy Mechanics

Design

Codes specify “thermal resistance” improvements to the building shell and windows, minimum

air leakage, and minimum efficiency for heating and cooling equipment.

Mandatory building energy codes establish minimum efficiency requirements for residential

and commercial construction.  The International Energy Conservation Code (IECC) is the

prevailing model code for the residential sector.  ASHRAE 90.1‐2010 is the model commercial

code.   

                                                            

87 See the Greenhouse Gas Abatement Measures TSD for more information. 88 The Edison Foundation: Innovation, Electricity Efficiency, Summary of Customer‐Funded Electric Efficiency

Savings, Expenditures, and Budgets (2011‐2012), March 2013, Accessed on March 19, 2014,

http://www.edisonfoundation.net/iei/Documents/FINAL_IEE%20Whitepaper_2012_US%20Energy%20Efficiency.p

df.

89 EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990‐2011 Executive Summary, (Environment

Protection Agency, 2013), accessed on March 19, 2014.

http://www.epa.gov/climatechange/Downloads/ghgemissions/US‐GHG‐Inventory‐2013‐ES.pdf.

Deleted: ten

Comment [A20]: The preamble says 12 states

have achieved or have established requirements to

achieve savings of 1.5%.

Comment [A21]: Thank you for catching that

error.  Number is now updated.

Deleted: 

Comment [A22]: This section should be titled

Building Energy Codes, not just “building codes”.

Similarly, the first paragraph should begin with

“Building energy codes establish...”

Comment [A23]: Revised text accordingly.

Deleted: E

Deleted: (an Institute of ), (), available at

http://www.edisonfoundation.net/iei/ourwork/Pag

es/issuebriefs.aspx.

Deleted: 2011 National GHG
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By locking in efficiency measures at the time of construction, codes are intended to capture

energy savings that are more cost‐effective than retrofit opportunities available after a building

has been constructed.  Energy code requirements are also intended to overcome market

barriers to efficient construction in both the commercial and residential sectors, such as the

complexity of advanced codes, lack of local‐level implementation resources, and a shortage of

empirical data on the costs and benefits of codes.   

Authority

Model building codes are typically developed at the national or international level, adopted at

the state and/or local level, and implemented and enforced locally.

Obligated Parties

Local parties, such as developers and property owners requiring building permits, are the most

common obligated parties.

Measurement and Verification

Program implementation steps, including builder training, compliance assurance, and

enforcement, are typically the responsibility of state and local governments. These steps,

however, are often not fully or uniformly implemented for numerous reasons, including an

emphasis on health and safety issues over the proper functioning of mechanical equipment, a

lack of trained staff to review building plans and conduct onsite inspections, and limited

funding to carry out key implementation activities. As a result, most jurisdictions do not have

the capacity to analyze code compliance and to identify the measures and strategies that

should be targeted for improved implementation. For more information about measurement

and verification of energy efficiency, see earlier in the State Plan Considerations Technical

Support Document.

Penalties for Noncompliance

In order to get building permits approved, the relevant developer or property owners must

show they are in compliance with standards. Since permitting is done at the local level, the use

of penalties and the ability to enforce standards vary significantly by region. DOE has been

working with states and localities to improve compliance practice

Implementation Status

To date, 28 states have adopted IECC 2009 while four states have gone further by adopting the IECC

2012. In the commercial sector, 33 states have adopted ASHRAE 90.1‐2007 and five states have adopted

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ASHRAE 90.1‐2010. Currently, 11 states have outdated or no state‐wide residential energy code, and 9

states have outdated or no state‐wide energy codes for commercial construction.90 The current status of

state residential and commercial energy codes are shown below in Figure 6 and

Figure 7, respectively. The State of Oregon, which has adopted residential and commercial codes based

on the IECC 2009, estimated total savings in 2009 from building energy codes of 1.17 GWh and 2.3 GWh

in the residential and commercial sectors, respectively.91 This was equivalent to more than 7 percent of

total retail electricity sales in Oregon in 2009.92

                                                            

90 “Code Status”, Online Code Environment & Advocacy Network, Accessed on March 19, 2014,

http://energycodesocean.org/code‐status.   91 Oregon Department of Energy, 2011‐2013 State of Oregon Energy Plan, Accessed on March 19, 2014,

http://www.oregon.gov/energy/docs/reports/legislature/2011/energy_plan_2011‐13.pdf.   92 “State Electricity Profiles: Oregon Electricity Profile 2012”, Energy Information Administration, Accessed on

March 19, 2014, http://www.eia.gov/electricity/state/oregon/.

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Figure 6: Residential State Energy Code Status

Figure 7: Commercial State Energy Code Status

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iv. Appliance and Equipment Efficiency Standards

Description

State appliance standards establish minimum energy‐efficiency levels for those appliances and

other energy‐consuming products that are not already covered by the federal government.

These standards typically prohibit the sale of less efficient models within a state. States are

finding that appliance standards offer a cost‐effective strategy for improving energy efficiency

and lowering energy costs for businesses and consumers, though these standards are

superseded when Federal standards are enacted for new product categories,.

While state appliance standards can be useful in testing and exploring the effectiveness of

standards for new products, states cannot preempt or supersede existing Federal standards.

States may apply to DOE for a waiver to implement more stringent standards.  This is

sometimes granted if a certain period of time has passed since the federal standard has been

updated.

Policy Mechanics

Design

When states implement appliance and equipment standards, they are establishing a minimum

efficiency for products, such as refrigerators or air conditioners, thereby reducing the energy

associated with using the product. Standards prohibit the production and sale of products less

efficient than the minimum requirements, encouraging manufacturers to focus on how to

incorporate energy‐efficient technologies into their products at the least cost and hastening the

development of innovations that bring improved performance.

Authority

State energy offices, which typically administer the federal state energy program funds, have

generally acted as the administrative lead for standards implementation. In contrast, inspection

and enforcement of appliance standards regulations has typically involved self‐policing.

Industry competition is such that competitive manufacturers usually report violations.

Obligated Parties

Manufacturers of products being sold in a given state are typically obligated to ensure their

appliances meet the appropriate energy efficiency standards.

Deleted: ¶

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Measurement and Verification

Evaluating the benefits and costs of the standards is important during the standards‐setting

process. Once enacted, however, little field evaluation is performed. For more information

about measurement and verification of energy efficiency, see earlier in the State Plan

Considerations Technical Support Document.

Penalties for Noncompliance

Appliances and equipment found in violation of the minimum energy performance standards

are not allowed to be sold or manufactured in the state.

Implementation Status

Currently, fifteen states and the District of Columbia have enacted appliance efficiency

standards. However, most of these standards have been superseded by federal standards. Still,

nine states (AZ, CA, CT, MD, NV, NY, OR, RI, WA) and the District of Columbia have either

enacted standards for equipment not covered federally or obtained waivers to enact tougher

appliance standards where the federal regulations have become outdated. California currently

leads all states in active state standards, covering 13 products, including consumer audio and

video products, pool pumps and hot tubs, vending machines, televisions, battery chargers, and

various lighting applications.93

v. Incentives and Finance Mechanisms for Energy Efficiency  

Description

States offer a diverse portfolio of financing and incentive approaches that are designed to

address specific financing challenges and barriers and incentivize specific markets and customer

groups to invest in energy efficiency. These programs include revolving loan funds, energy

performance contracting, tax incentives, rebates, grants, and other incentives.   

Policy Mechanics  

Design

Revolving loan funds provide low‐interest loans for energy efficiency improvements.  The funds

are designed to be self‐supporting. States create a pool of capital that “revolves” over a multi‐

                                                            

93 “Appliance Efficiency Regulations: California”, Database of State Incentives for Renewables & Efficiency,

Accessed on March 19, 2014,

http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=CA48R&re=0&ee=0.

Deleted: help

Comment [A24]: Suggest “encourage” or

“incentivize”. Instead

Comment [A25]: Edited text accordingly.

Deleted: 

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year period, as payments from borrowers are returned to the capital pool and are subsequently

lent to other borrowers. Revolving loan funds can be created from several sources, including

public benefits funds (PBFs),94 utility program funds, general state revenues, or federal funding

sources. Revolving funds can grow in size over time, depending on repayment interest rates and

program administrative costs.  

Energy performance contracting allows the public sector to contract with private energy service

companies (ESCOs) to provide building owners with energy‐related efficiency improvements

that are guaranteed to save more than they cost over the course of the contracting period.

ESCOs provide energy auditing, engineering design, general contracting, and installation

services, and help arrange project financing.95 The contracts are privately funded and do not

involve state funding or financial incentives.

State tax incentives for energy efficiency are available as personal or corporate income tax

credits, tax exemptions (e.g., sales tax exemptions on energy‐efficient appliances), and tax

deductions (e.g., for construction programs). Tax incentives aim to spur private sector

innovation to develop more energy efficient technologies and practices and increase consumer

choice of energy‐efficient products.96  

Rebates (also known as “buy‐downs”) are used to promote demand‐side energy efficiency

reductions by providing direct incentives to customers who purchase or make upgrades to

approved efficient appliances or retrofit their homes (e.g., a utility may refund part of the cost

for a homeowner to improve attic insulation or purchase a high‐efficiency furnace). Funding for

rebates may come from PBFs, direct grants, or utility program funds.  

Grants from the federal government, state government, regional agency, or private source may

be used to start or finance energy efficiency programs. A grant may be used to provide funding

for a specific construction project (e.g., retrofit of a school), finance a rebate program, initiate a

revolving fund, conduct a behavior change campaign (e.g., educate public about the benefits of

off‐peak energy use), or any other type of program that meets the specific grant requirements.  

                                                            

94 Public benefit funds (PBFs) are dedicated funds used for supporting research and development of energy

efficiency and renewable energy projects. Funds are normally collected either through a small charge for every

electric customer or through specified contributions from utilities.   95 EPA, Integrating State and Local Environmental and Energy Goals: Energy Performance Contracting. Fact Sheet

(EPA, September 2004). 96 Elizabeth Brown, Harvey Sachs, Patrick Quinlan, and Daniel Williams. “Tax Credits for Energy Efficiency and

Green Buildings: Opportunities for State Action.” American Council for an Energy Efficient Economy (2002).

Deleted: generation incentives

Comment [A26]: Recommend changing

“generation incentive” to “program funds” for

consistency with the rest of the document.

Comment [A27]: Edited text accordingly.

Deleted: .

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Authority

Financial mechanisms and incentives for energy efficiency are run by utilities and state and local

governments. Utilities primarily offer rebates, grants, and loans. Personal, corporate, sales, and

property tax incentives are mainly offered by state and local governments.97  

Implementation Status

Financial mechanisms and incentives for energy efficiency exist in all 50 states, with the most

prevalent financial mechanisms and incentives for energy efficiency are rebates and loan

programs. There are 43 tax incentives and over one‐thousand rebate, grant, and loan

programs.98 In the first 3 years of Alaska’s Home Energy Rebate Program, the State provided an

estimated $110 million to help finance energy efficiency retrofits for 16,500 homeowners.

Retrofitted housed are currently saving an estimated 1.6 trillion BTUs of energy annually, or 5

percent of the Alaska’s total annual energy demand for residential space heating.99

c. Renewable Energy Policies and Programs

States have adopted a range of requirements and programs to advance the deployment of

renewable energy technologies, including renewable portfolio standards, performance‐based

incentives and public benefit funds.100 These renewable energy policies and programs reduce

GHG emissions by increasing the use of renewable energy and altering the mix of energy supply.

i. Renewable Portfolio Standards

Description

A renewable portfolio standard (RPS), also known as a renewable electricity standard (RES), is a

mandatory requirement for retail electricity suppliers to supply a minimum percentage or

amount of their retail electricity load with electricity generated from eligible sources of

renewable energy.  An RPS indirectly affects EGU CO2 emissions by reducing the utilization of

                                                            

97 “Financial Incentives for Energy Efficiency” Database of State Incentives for Renewables & Efficiency, accessed

on March 19, 2014, http://www.dsireusa.org/summarytables/finee.cfm. 98 Ibid.   99 Scott Goldsmith, Sohrab Pathan, and Nathan Wiltse, Snapshot: The Home Energy Rebate Program, (Cold Climate

Housing Research Center, May 2012), Accessed on March 19, 2014,

http://cchrc.org/docs/snapshots/HERP_snapshot.pdf. 100 Feed‐in tariffs, a performance‐based incentive, offer long‐term purchase agreements to renewable energy

electricity generators. Public benefit funds are typically created by levying a small fee as a part of retail electricity

rates and are used to support rebate, loan, and other programs that support renewable energy deployment. For

more information, see Database of State Incentives for Renewables and Efficiency, available at

http://www.dsireusa.org/.

Comment [A28]: 

Recommend changing the opening sentence for this

footnote to “Feed‐in tariffs, a performance‐based

incentive, offer...”  This footnote is before the

reader is told that feed‐in tariffs are a performance‐

based incentive.  

Comment [A29]: Made edit requested.

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fossil‐fuel‐fired EGUs. As of June 2013, 29 states and Washington, DC have adopted a

mandatory RPS (see Figure 8), although designs vary (e.g., applicability, targets and timetables,

geographic and resource eligibility, alternative compliance payments) and an additional nine

have voluntary renewable goals.101

Figure 8: States with Renewable Portfolio Standards

Policy Mechanics

Design  

RPS requirements typically start at modest levels and ramp up over a period of several years.

An RPS relies on market mechanisms to increase electricity generation from eligible sources of

renewable energy.

                                                            

101 Ibid.

Renewable Portfolio Standards

Mandatory -

Voluntary -

29

9

Last updated 8/8/2013

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Retail electricity suppliers can comply with RPS requirements through several mechanisms,

which vary by state, including:

 Ownership of a qualifying renewable energy facility and its electric generation output,  

 Purchasing electricity bundled with renewable energy certificates (RECs)102 from a

qualifying renewable energy facility, and  

 Purchasing RECs separately from electricity generators.  Unlike bundled renewable

energy, which is dependent on physical delivery via the power grid, renewable energy

certificates (RECs) can be traded between any two parties, regardless of their location.

However, state RPS rules typically condition the use of RECs based on either location of

the associated generation facility or whether it sells power into the state or to the

regional grid.

Authority

Most state RPS are established through legislation and administered by state PUCs.  

Obligated Parties

RPS applicability varies by state. All state RPS apply to investor‐owned utilities, while some

state RPS obligate municipal utilities, rural cooperatives, and/or other retail providers, often

depending on a minimum number of customers served.   

Measurement and Verification

Some state RPS include an alternative compliance payment (ACP) option, where a retail

electricity supplier may purchase compliance credits from the state at a known price, which

acts as a de facto price cap, if it has not procured sufficient electricity from renewable energy

sources or RECs to meet the RPS compliance requirement.  State PUCs typically require annual

compliance reports from retail electricity suppliers subject to a RPS. Most states use regional

tracking systems (e.g., Western Renewable Energy Generation Information System, PJM

Generation Attribute Tracking System) to issue, track, and retire RECs for RPS compliance

purposes.103  For more information about measurement and verification of renewable energy,

see earlier in the State Plan Considerations Technical Support Document.

                                                            

102 RECs represent the non‐energy attributes, including all the environmental attributes, of electricity generation

from renewable energy sources. RECs are typically issued in single MWh increments.   103 For a summary of REC tracking systems, see: U.S. Department of Energy Renewable Energy Certificates (RECs) 

National REC Tracking Systems. http://apps3.eere.energy.gov/greenpower/markets/certificates.shtml?page=3  

(

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Market: Renewable Certificate Tracking

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2010); available at http://www.ferc.gov/market‐

oversight/othr‐mkts/renew/othr‐rnw‐rec‐trk.pdf.
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Penalties for Noncompliance

States have developed a range of compliance enforcement and flexibility mechanisms. As of

2007, despite the fact that several states had not achieved the RPS targets, only Connecticut

and Texas had levied fines. A $5.6 million penalty was incurred in Connecticut in 2006. In 2003

and 2005, two competitive electricity service providers in Texas were penalized a total of

$4,000 and $28,000 respectively. Flexible enforcement and opportunities to “make‐up”

shortfalls in subsequent years or ACPs that are recycled to support other renewable and

efficiency measures have helped other states avoid penalties for noncompliance.104      

Implementation Status

States with RPS policies have demonstrated higher levels of renewable energy capacity

development. From 1998‐2012, 67 percent (46 GW) of all non‐hydro renewable capacity

additions occurred in states with active or impending RPS requirements, although other factors

may contribute to the growth in renewable capacity.105

ii. Performance‐Based Incentives and Finance Mechanisms for Renewable

Energy  

Description

States offer a diverse portfolio of financing, performance based incentive and state utility

ratemaking approaches that are designed to address specific financial challenges and barriers

and help specific markets and customer groups produce clean energy.  

Policy Mechanics

Design

States support the advancement of clean generation technologies through performance‐based

incentives, including feed‐in tariffs and other payments, or tax incentives.  Performance‐based

incentives are paid based on the actual energy production of a system. Feed‐in tariffs establish

temporarily elevated price per kWh in order to encourage renewable energy innovation using

high cost technologies. Tax incentives are used to lower financial barriers to renewable energy

production.  

                                                            

104 Ryan Wiser and Galen Barbose, Renewables Portfolio Standards in the United States – A Status Report with Data

Through 2007, (Lawrence Berkeley National Laboratory, 2008), Accessed March 19, 2014

http://emp.lbl.gov/sites/all/files/REPORT%20lbnl‐154e‐revised.pdf. 105 Ibid.

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Portfolio Standards in the United States: A Status

Update. Lawrence Berkeley National Laboratory.

http://www.renewableenergymarkets.com/docs/pr

esentations/2013/REM_Sept2013_Barbose.pdf
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A major source of funding for renewable energy activities comes from PBFs, but states also

fund these activities through alternative sources including direct grants, rebates and generation

incentives provided by utilities.  

State tax incentives for renewable energy and Combined Heat and Power (CHP) take the form

of personal or corporate income tax credits and tax exemptions. State tax incentives for

renewable energy are a common policy tool, mainly using credits on personal or corporate

income tax and exemptions from sales tax, excise tax, and property tax.  

Authority

Financial mechanisms and incentives for renewables are run by utilities, non‐profits, and state

and local government. Personal, corporate, sales, and property tax incentives are mainly

offered by state and local government.106  

Implementation Status

Financial mechanisms and incentives for renewable energy of some form exist in most states.

According to the Database of State Incentives for Renewable Energy (DSIRE), there are over 200

tax incentives. In addition, nearly a hundred performance based incentives are offered from

state and local governments, as well as utilities and non‐profits.107  

There are currently 18 states that have state‐wide performance‐based policies, and in several

other states utilities have adopted programs based on performance‐based incentives, including

feed‐in tariffs, standard offer payments, and payments in exchange for RECs.108 In many cases,

however, PBI is limited to customer‐sited projects or limited by size eligibility.

Financial incentives, working in concert with a strong RPS and net metering policies, have

contributed to the rapid growth in solar power deployment in New Jersey. The state’s RPS

includes a minimum carve‐out for solar sources, and allows solar energy generators to earn

Solar Renewable Energy Certificates (SRECs) that can then be sold to electricity suppliers trying

to meet the minimum solar production and/or purchase requirement. As a result of these

interdependent policies, solar photovoltaic facilities are increasing, with installations more than

                                                            

106 “Financial Incentives for Renewable Energy”, Database of State Incentives for Renewable Energy, accessed

March 19, 2014, http://www.dsireusa.org/summarytables/finre.cfm. 107 Ibid.    108 Ibid.  

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doubling from 2010 through 2011.109 New Jersey ranks second only to California in terms of

total installed capacity.110

d. Utility Planning Approaches and Requirements

Description

Some public utility commissions require utilities to conduct portfolio management or

integrated resource planning (IRP) to ensure the supply of least cost and stable electric service

to customers over the long term.  Portfolio management refers to energy resource planning

that incorporates a variety of energy resources, including supply‐side (e.g., traditional and

renewable energy sources) and demand‐side (e.g., energy efficiency) options. The term

"portfolio management" typically describes resource planning and procurement in states that

have restructured their electric industry and may be required for default service providers (the

backup electric service provider in areas open to competition).  IRP is generally used by

vertically integrated utilities and is a long‐range planning process to meet forecasted demand

for energy within a defined geographic area through a combination of supply‐side resources

and demand‐side resources and considering a broad range of perspectives. The goal of an IRP is

to identify the mix of resources that will minimize future energy system costs while ensuring

safe and reliable operation of the system.  

In addition to energy resource planning, two states have policies or requirements for utilities to

specifically factor pollution reduction requirements into their planning.  In Colorado, the Clean

Air Clean Jobs Act (CACJA), signed into law on April 19, 2010, required utilities to submit a plan

to the PUC showing how they would meet EPA standards for a variety of pollutants. 111 The law

was passed because the state was out of compliance with the national Ambient Air Quality

Standard for Ozone, and the EPA threatened to propose more stringent standards for the state.  

                                                            

109 Solar‐New Jersey.org, “Why has New Jersey become a Leader in Solar in the U.S.?”, Solar‐New Jersey.org,

August 15, 2012, Accessed March 19, 2014, http://www.solar‐new‐jersey.org/2012/08/15/why‐has‐new‐jersey‐

become‐a‐leader‐in‐solar‐in‐the‐us/. 110“Open PV State Rankings”, National Renewable Energy Laboratory, Accessed March 19, 2014,

https://openpv.nrel.gov/rankings. 111 RAP, Addressing the Effects of Environmental Regulations: Market Factors, Integrated Analyses, and

Administrative Processes (Regulatory Assistance Project, 2013), Accessed March 19, 2014,

www.raponline.org/document/download/id/6455.

Comment [A30]: This section (section d)

confuses portfolio management with IRP.

Recommend EPA review the SEE Action Using

Integrated Resource Planning to Encourage

Investment in Cost‐Effective Energy Efficiency

Measures Guide

[http://www1.eere.energy.gov/seeaction/pdfs/rate

payer_efficiency_irpportfoliomanagement.pdf].

Comment [A31]: Thank you for connecting us

with the resource.

Deleted: provide

Comment [A32]: This sentence equates

portfolio management with resource planning for all

energy resources. Portfolio management is used

generally for default service only, so it is not the

same thing as IRP. This should be clarified. For

example, this sentence could read: “The term

‘portfolio management’ typically describes resource

planning and procurement in states that have

restructured their electric industry and applies to

default services.”

Comment [A33]: Clarified text

Deleted: However, it can also include the more

traditional integrated resource planning (IRP)

approaches applied to vertically integrated utilities.

Comment [A34]: Commenters recommend

changing this sentence to “Related is integrated

resource planning (IRP) which is generally used by

vertically‐integrated utilities and is a long‐range

utility plan for meeting the forecasted demand for

energy within a defined geographic area through a

combination of supply side resources and demand

side resources.” (SEE Action IRP Guide)

They also recommend adding a sentence that

describes the goal of IRP. For example: “Generally

speaking, the goal of an IRP is to identify the mix of

resources that will minimize future energy system... [1]

Comment [A35]: Updated text based on

comments.

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In 2001, Minnesota enacted Minnesota Statute 216B.1692, which encourages utilities to make

voluntary emission reductions and provides them with a mechanism to recover the costs

through customer rate increases outside of the normal rate review cycle.112  

Policy Mechanics

Design

 Portfolio Management and IRP ‐ Portfolio management emphasizes diversity in fuels,

technologies, and power supply contract durations. Portfolio management includes

energy efficiency and renewable generation as key strategic components. Portfolio

management typically involves a multi‐step process of forecasting, resource

identification, scenario analysis, and resource procurement.

Several states and vertically integrated utilities rely on an IRP process for long‐term

planning. Since these utilities own generation assets, they use their IRPs to evaluate a

broad range of options for meeting electricity demand over a 20‐ or 30‐year time frame.  

The IRP considers new supply‐side options (including renewable resources) and

demand‐side options, and purchased power (including transmission considerations).  A

broad range of plans are considered, reflecting a range of objectives and capturing key

uncertainties. Plans are evaluated against established criteria (e.g., costs, rate impacts,

emissions, diversity, etc.) and are ranked. The IRPs detail fuel and electricity price

information, customer demand forecasts, existing plant performance, other plant

additions in the region, and legislative decisions. The following examples show how

various states have designed their programs:

o Montana is a deregulated state that has established least cost planning rules and

policy guidelines for default electricity suppliers. These rules and guidelines

target long‐term electricity supply and are slightly different for vertically

integrated utilities and restructured utilities. Vertically integrated utilities are

required to submit electric supply resource plans every two years with the aim of

providing a balanced, environmentally responsible electricity portfolio.

Meanwhile, restructured utilities must file updates to their portfolio action plans

every three years.113 These plans must include supply‐side and demand‐side

resources, and they must address the need to supply power in a way that

                                                            

112 Minnesota PUC, Report To The Legislature On Emissions Reduction Projects Under Minnesota Statutes

216B.1692 (Minnesota Public Utilities Commission, 2008), Accessed March 19, 2014,

http://www.puc.state.mn.us/portal/groups/public/documents/pdf_files/000661.pdf. 113 Wilson, Rachel and Bruce Biewald, Best Practices in Electric Utility Integrated Resource Planning (Regulatory

Assistance Project, 2013), Accessed March 19, 2014, http://www.raponline.org/document/download/id/6608.

Comment [A36]: This bullet should also include

IRP.

Comment [A37]: Edited accordingly.

Deleted: involves deliberately choosing among

a variety of electricity products and contracts.

The approach emphasizes diversity—fuels,

technologies, and power supply contract

durations. In its fullest form,

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or numbering

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management.

Deleted: their own

Deleted: weigh the benefits of building their

own generation plants against procuring energy

from other entities. The plans also evaluate how

best to balance peak versus off‐peak electric load

requirements. In addition, they compare various

supply‐ and demand‐side options and contract

and financial hedging options. Companies

achieve these goals simultaneously by analyzing

different scenarios.

Comment [A38]: This set of sentences isn’t

necessary and isn’t entirely accurate as written,

since IRP and PM are not the same thing. Also,

deciding between building generation or buying

from others isn’t the primary focus of IRP. Rather,

the purpose is to analyze resource options over a

20‐30 yr timeframe, including supply and demand

side resources. This paragraph should be adjusted

or deleted, since it is not essential.

Comment [A39]: Modified text per comment.

Comment [A40]: Is this the most current

information on Montana? See the Synapse report ... [2]

Comment [A41]: Modified language to clarify;

used reference provided.

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minimizes the environmental cost by estimating the cost to the environment of

alternatives. In addition, utilities must account for the costs of complying with

existing and future environmental regulations. When considering various

resource options, Montana requires a competitive solicitation process, allowing

resource operators and developers to submit their proposals to the default

electricity supplier for consideration. Montana also requires the portfolio

management plans to be subject to an advisory committee review and a public

review.114

o Oregon electric utilities submit IRPs every two years, covering a 20‐year

timeframe. The goal of these plans is to consider the acquisition of resources at

least cost while keeping the public interest in mind. Potential risk factors must be

considered, including price volatility, weather, and the cost of meeting existing

and future federal environmental regulations. Quantifiable environmental

externalities are included, as are less quantifiable developments such as changes

in market structure and the establishment of a renewable portfolio standard. As

for energy efficiency requirements during the planning process, Oregon

determines these on a utility‐by‐utility basis. 115

 Multi‐Pollutant Utility Planning – Two states, Minnesota and Colorado, have worked

collaboratively with their investor‐owned utilities to develop multi‐pollutant emission

reduction plans on a utility‐wide basis. This multi‐pollutant, collaborative approach

enables utilities to determine the least cost way to meet long‐term and comprehensive

energy and environmental goals.

o The Colorado CACJA requires investor‐owned utilities (IOUs) with coal plants to

submit a multi‐pollutant plan to the PUC to meet the EPA standards for NOx, SO2,

particulates, mercury, and CO2. Utilities were not required to adopt a specific

plan set by the state, but had to meet with Colorado Department of Public

Health and Environment (CDPHE) and PUC approval. Xcel Energy’s plan was

submitted and approved in 2010.116

                                                            

114 U.S. EPA, Clean Energy‐Environment Guide to Action (U.S. Environmental Protection Agency, 2006), Accessed

March 19, 2014, http://epa.gov/statelocalclimate/resources/action‐guide.html. 115 Ibid. 116 RAP, Addressing the Effects of Environmental Regulations: Market Factors, Integrated Analyses, and

Administrative Processes (Regulatory Assistance Project, 2013), Accessed March 19, 2014,

www.raponline.org/document/download/id/6455.

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o The Minnesota Emission Reductions Rider allows utilities to submit plans for

projects that reduce emissions and go beyond federal requirements outside of a

general rate case.  It allows them to recover the costs of those actions as an

incentive.117  The specific design and process of the projects vary by utility, but

typically involve installing additional pollution control equipment at coal‐fired

power plants, or repowering them with natural gas.  

Authority

State utility commissioners oversee utilities’ and default service providers’ procurement

practices in their states. Typically, the commissions solicit comments and input as they develop

portfolio management practices from a wide variety of stakeholders. The utility regulator may

also play a role in reviewing and approving utilities’ planning procedures, selection criteria,

and/or their competition solicitation processes.

Obligated Parties

Vertically integrated utilities are often obligated under integrated resource planning, while in

restructured markets, the default utility service provider may be obligated to conduct portfolio

management.

For multi‐pollutant planning, Colorado IOUs, Xcel Energy and Black Hills Energy were required

to file plans with the Department of Public Health and Environment and the PUC in order to be

compliant with the CACJA. Plans needed to meet the National Ambient Air Quality Standards

for a number of air pollutants.

As the Minnesota multi‐pollutant legislation is voluntary for state utilities, there is neither

compliance nor reporting requirements.

Measurement and Verification

Regulatory oversight aims to ensure utilities are following through with their plans. Regulators

often require utilities to submit portfolio management plans and progress reports at regular

intervals. These plans and reports describe in detail the assumptions used, the opportunities

assessed, and the decisions made when developing resource portfolios. Regulators then

carefully review these plans and either approve them or reject them and recommend changes

needed for approval. California, for example, requires utilities to submit biennial IRPs and

quarterly reports on their plans.

                                                            

117 Minnesota Office of Revisor of Statutes, 2013 Minnesota Statutes, §216B,1692 Emissions Reduction Rider , 2013,

Accessed March 19, 2014, https://www.revisor.mn.gov/statutes/?id=216B.1692.

Deleted: is

Comment [A42]: Recommend changing this to

read “Vertically integrated utilities are often

obligated to conduct resource planning, while in

restructured markets the default utility service

provider may be obligated to conduct portfolio

management for default service.”

Comment [A43]: Edited text per comment.

Deleted: ,

Deleted: sometimes

Comment [A44]: Recommend changing to

“Regulators often require utilities to submit

resource plans and progress reports at regular

intervals.”

Comment [A45]: Updated with recommended

text.

Deleted: (

Deleted: )
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Penalties for Noncompliance

There are no penalties for noncompliance, however there is usually significant interaction with

the regulator during the planning and implementation process as is described above.  

Implementation Status

Currently more than half of the states have integrated resource or other long‐term planning

requirements,118 while Minnesota and Colorado have multi‐pollutant planning policies or

requirements (see Figure 9).

In Montana, for example, the 2011 Electric Supply Resource Plan for NorthWestern Energy calls

for:

 Shortening the length of power supply contracts from seven years to a more

competitive, staged process of between three to five years.

 Diversifying Montana’s resource mix with the recent addition of a 150 MW gas‐fired

power plant.

 Improving the integration of intermittent power sources into the power supply as new

wind turbines play a larger role in the state’s resource mix.

 Meet state RPS requirements.

 Acquire cost‐effective demand side management resources, targeting 6 MW of

additional energy conservation per year.

 Monitor market, regulatory, and technology changes to better manage risks and

opportunities.119

                                                            

118 Wilson, Rachel and Bruce Biewald, Best Practices in Electric Utility Integrated Resource Planning (Regulatory

Assistance Project, 2013), Accessed March 19, 2014, http://www.raponline.org/document/download/id/6608. 119 NorthWestern Energy, Electric Supply Resource Plan (NorthWestern Energy, 2011), accessed March 19, 2014,

https://www.northwesternenergy.com/our‐company/tariffs‐and‐rates/montana‐tariffs‐and‐rates/electric‐supply‐

resource‐procurement‐plan.

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Figure 9: States with Integrated Resource Planning or Similar Processes

In Oregon, PacifiCorp has filed its 2013 integrated resource plan. Key highlights from the report

include:

 Demand‐side energy efficiency efforts are expected to meet 67 percent of electricity

load growth from 2013 to 2022

 Market analyses for integrating wind resources into the grid, and pursuing opportunities

for combined heat and power resources.

 Goals to obtain 1,425‐1,876 GWh of energy efficiency resources by 2015 and 2,034‐

3,180 GWh by 2017.

 Permitting and development efforts to convert a unit of the Naughton power plant from

coal to gas.121

To meet Colorado’s multi‐pollutant planning requirement, Xcel Energy submitted a plan that

was approved by the Colorado PUC on December 9, 2010. Implementation of the plan will

reduce NOx levels 88% and CO2 levels 28% relative to 2008 levels by 2018.122   Black Hills Energy

has also filed its electric resource plan (ERP). This plan includes the retirement of a coal‐fired

                                                            

121 PacifiCorp, Integrated Resource Plan (PacifiCorp, 2013), Accessed March 19, 2014,

http://www.pacificorp.com/content/dam/pacificorp/doc/Energy_Sources/Integrated_Resource_Plan/2013IRP/Pac

ifiCorp‐2013IRP_Vol1‐Main_4‐30‐13.pdf. 122 Xcel Energy, Xcel Energy‐Emissions Reduction Plan, (Xcel Energy, 2011), accessed March 19, 2014,

https://www.xcelenergy.com/staticfiles/xe/Corporate/Environment/10‐12‐303_CACJ‐6E_FS.pdf.

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power plant and two older natural gas‐fired gas units, as well as a proposal to build a 40 MW

natural gas turbine. It plans to add 100 MW of capacity by 2017, Black Hills Energy will use

competitive bidding to meet the remaining 60 MW.123

In Minnesota, projects currently implemented under the multi‐pollutant legislation include the

Minnesota Power’s Arrowhead Regional Emissions Abatement (AREA) Project, Minnesota

Power’s Boswell 3 Emissions Reduction Plan, Xcel Energy’s Mercury Reduction Plan, and Xcel

Energy’s Metropolitan Emissions Reduction Proposal (MERP). MERP, authorized in 2002, has

shown a 93% reduction in SO2, 91% reduction in NOx, 81% reduction in mercury, 55% reduction

in particulates, and 21% reduction in CO2 from 2007‐2009.124

                                                            

123 Black Hills Energy, “Black Hills Energy Files Plan for Ongoing Reliable, Cost‐effective Energy for Years to Come in

Colorado” accessed on March 19, 2014, http://www.blackhillsenergy.com/node/34671#.UzHkuIXYhIt. 124 Xcel Energy, “Minnesota Metro Emissions Reduction Project – Environmental Benefits,” accessed March 19,

2014, https://www.xcelenergy.com/Environment/Doing_Our_Part/Clean_Air_Projects/MN_MERP.

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Page 42: [1] Comment [A34] Author 

Commenters recommend changing this sentence to “Related is integrated resource planning (IRP) which

is generally used by vertically‐integrated utilities and is a long‐range utility plan for meeting the

forecasted demand for energy within a defined geographic area through a combination of supply side

resources and demand side resources.” (SEE Action IRP Guide)

They also recommend adding a sentence that describes the goal of IRP. For example: “Generally

speaking, the goal of an IRP is to identify the mix of resources that will minimize future energy system

costs while ensuring safe and reliable operation of the system.” (SEE Action IRP Guide)

Page 43: [2] Comment [A40] Author 

Is this the most current information on Montana?  See the Synapse report  

[http://www.cleanskies.org/wp‐content/uploads/2011/05/ACSF_IRP‐Survey_Final_2011‐04‐28.pdf]  

for more nuances on how Montana IRP is accomplished, including that Montana is unique in that it has

separate rules for vertically‐integrated utilities and restructured utilities. For vertically‐integrated

utilities, the focus is on “long‐term” planning, which some consider to mean 20‐25 years. For

restructured utilities, the planning horizon is “the longer of: 1) the longest remaining contract term in a

utility’s supply resource portfolio; 2) the period of the longest lived electricity supply resource being

considered for acquisition; or 3) ten years.”  
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1

Technical Memorandum

Consideration of Heat Rate Improvement (HRI) Potential at Existing Oil/Gas‐fired Steam,

Natural Gas Combined Cycle, and Combustion Turbine EGUs for Inclusion in Building Block 1  

As described in the GHG Abatement Measures TSD, the EPA identified four categories 

of demonstrated measures, or “building blocks,” that are technically viable and broadly 

applicable, and can provide cost-effective reductions in CO2 emissions from individual existing 

EGUs. These building blocks include: 

Building Block 1 - Reducing the carbon intensity of generation at individual affected 

EGUs through heat rate improvements; 

Building Block 2 - Reducing emissions of the most carbon-intensive affected EGUs in 

the amount that results from substituting generation at those EGUs with generation from 

less carbon-intensive affected EGUs (including NGCC units under construction); 

Building Block 3 - Reducing emissions from affected EGUs in the amount that results 

from substituting generation at those EGUs with expanded low- or zero-carbon 

generation; and, 

Building Block 4 - Reducing emissions from affected EGUs in the amount that results 

from the use of demand-side energy efficiency that reduces the amount of generation 

required. 

Coal-fired Steam EGUs 

For Building Block 1, the EPA evaluated the fleet-wide potential for lowering the carbon 

intensity of generation at individual affected coal-fired steam EGUs by improving heat rates at 

these EGUs (see the GHG Abatement Measures TSD). The EPA analyzed 11 years of historical 

heat rate data and the literature on HRI methods to estimate that the U.S. coal-steam EGU fleet 

might reasonably be expected to reduce its annual average gross heat rate by about 6%. 

Furthermore, the EPA understood that any HRI method that reduces gross heat rate will also 

reduce net heat rate, and that some HRI methods reduce net heat rate without reducing gross heat 

rate. As such, the EPA expects that the HRI potential on a net output basis is somewhat greater 
DRAFT     May 16, 2014

2

than on a gross output basis, primarily through upgrades that result in reductions in auxiliary 

loads. Therefore, the EPA conservatively assumed that the coal-steam fleet average net heat rate 

can be reduced by 6% and included this finding in its Building Block 1. 

As discussed in the preamble, for purposes of developing the alternate set of goals on 

which we are taking comment, the EPA used an estimate of a 4% HRI from affected coal-fired 

steam EGUs on average. The EPA views the 4% estimate as a reasonable minimum estimate of 

the technical potential for HRI on average across affected coal-fired EGUs. 

Oil/Gas Steam EGUs 

As summarized above, the EPA made a detailed assessment of the fleet-wide potential for 

HRI at existing affected coal-fired steam EGUs in Building Block 1. However, we decided to not 

make a detailed assessment of this potential for existing affected oil and gas steam units for the 

three main reasons described below. 

First, oil and gas contain significantly less carbon per unit of heating value than coal. Oil 

and gas therefore produce significantly less CO2 than coal for the same amount of heat. (This is 

discussed further under NGCCs, below.) 

Second, coal-fired steam EGUs are utilized at much higher levels compared to oil/gas 

steam EGUs. Therefore the amount of CO2 reduction that can be achieved via HRI at oil/gas 

EGUs is significantly smaller. For example, EPA modeling1

 projects that in 2020 coal-steam 

units will provide 59% of all fossil-fired electrical generation, while oil/gas steam units will 

provide only 2%. Even if CO2 emissions from all oil/gas steam units could be reduced by 6% on 

average using HRI methods (as assumed on coal-steam units) that reduction would amount to 

only a fraction of 1% of the HRI reduction that might be obtained from coal-steam units. 2

Third, oil/gas steam EGUs employ less extensive systems and equipment compared to 

coal steam EGUs and therefore, in general, have a lesser range of opportunities for implementing 

HRI. For example, oil/gas steam units do not typically use flue gas SO2 scrubbers, particulate 

                                                            

1

 IPM Base Case v5.13 modeling results as presented in RIA Chapter 3. 

2

 The EPA is not suggesting that relatively small CO2 reductions from fossil-fired sources other than coal-steam 

EGUs are not important. Such reduction might be significant in a few situations, and states are free to make use of 

these reductions in meeting their goals. 
DRAFT     May 16, 2014

3

collection devices, coal mills, coal conveyors, ash handling systems, sootblowers, etc. 

Consequently, some of the HRI methods discussed in the GHG Abatement Measures TSD are 

not applicable for oil/gas steam EGUs. 

The above factors taken together explain why the potential for CO2 reduction achieved 

via HRI at oil and gas steam EGUs would be quite small compared to that from the existing fleet 

of coal-fired EGUs. Therefore the EPA conservatively decided to not separately itemize and 

include this potential in Building Block 1. 

Natural Gas Combined Cycle (NGCC) EGUs 

EPA modeling also projects that in 2020 natural gas-fired NGCCs will provide about 

39% of the U.S. electrical generation from fossil fuels, compared to 59% from coal-steam EGUs. 

Also, as explained below, NGCCs in 2020 would emit only about 20% of the total CO2

emissions from fossil fuels used in electrical generation. 

The significantly lower amount of CO2 produced by combustion of natural gas compared 

to coal (about 40% less for the same amount of heat input) is due primarily to the higher 

hydrogen content and lower carbon content in natural gas compared to coal. Also, because a 

NGCC is typically more efficient than a coal-steam EGU, thus using less heat input from fuel to 

make an equal electrical output, a very efficient NGCC can further reduce the CO2 emission rate 

per MWh to about 60% less than that from coal-steam EGUs. Thus, natural gas, particularly as 

used in NGCCs, inherently reduces CO2 emissions by more than one-half. Existing NGCC EGUs 

are therefore already significantly reducing CO2 emissions compared to existing coal EGUs, per 

MWh of output, before considering whether NGCCs might be able to further reduce their CO2 

emissions via HRI methods. 

The EPA has preliminarily considered that there may be some potential for a further 

reduction in the CO2 emissions of NGCC EGUs via HRI. However, as with coal-steam EGUs, 

we do not have the unit-specific detailed design information on existing individual NGCCs that 

would be needed to make a detailed assessment of the HRI potential via best practices and 

upgrades for each NGCC unit. While it would be possible for EPA to make a “variability 

analysis” of NGCC historical hourly heat rate data (as was done for coal-steam EGUs), we are 

aware that the various NGCC configurations in use and the historically lower capacity factors of 
DRAFT     May 16, 2014

4

the NGCC fleet (less run time per start, and more part load operation) would require a NGCC 

analysis that includes more complexity and likely more uncertainty than in the coal-steam 

analysis. In addition, the analysis would be limited by the fact that only one-third of the NGCC 

fleet has historically reported complete (combustion turbine and steam turbine generator) load 

data to EPA. 

To preliminarily gauge the HRI potential for NGCCs, EPA engineering staff familiar 

with NGCC design and operation informally discussed the NGCC HRI potential with power 

sector engineering firms and NGCC suppliers. Our preliminary conclusion is that the fleet-wide 

HRI potential for existing NGCC EGUs may be only about 2-3% at most, on a sustained basis, 

for the following two reasons. 

First, as a “combined” combustion turbine and steam turbine power cycle, some of the 

available HRI methods would be applicable only to the steam turbine portion of the power cycle: 

the HRSG (heat recovery steam generator), the steam turbine-generator, and the heat rejection 

system (water or air-cooled condenser systems). The HRI potential associated with the steam 

portion of the NGCC is significantly less than in a coal-steam unit because the NGCC steam 

system is much simpler (gaseous fuel, no back-end scrubbers, less parasitic power, no air heater 

leakage, no feedwater heaters, etc) and its flue gas exit temperature is typically already much 

lower than in a coal-steam unit. 

Second, the HRI methods applicable to the combustion turbine portion of the NGCC 

relate primarily to critical components in the hot expansion side of the unit - components that are 

exposed to the products of combustion of fuel and air that contain small amounts of 

corrosive/erosive contaminants at very high temperatures. These critical components 

(combustors, nozzles/vanes, seals, rotating blades) therefore require regular periodic removal and 

refurbishment or replacement to maintain high NGCC efficiency levels, and indeed to avoid 

potentially catastrophic mechanical failures. The greatest loss in the performance (increased heat 

rate) of a NGCC is this physical degradation that occurs in proportion to its hours of operation 

and number of starts. Consequently, it has long been an accepted practice by NGCC owners to 

closely follow the NGCC manufacturer’s maintenance recommendations, a practice that 

regularly restores the NGCCs efficiency and reliability. This close adherence to manufacturer 

recommendations is financially motivated in part by the fact that many NGCC owners have long-
DRAFT     May 16, 2014

5

term maintenance contracts with the manufacturers, wherein the manufacturer guarantees the 

service life and replacement costs of expensive critical components - provided that the regular 

preventive/restorative maintenance schedule is followed. Regularly scheduled maintenance 

practices are the most effective HRI methods that can be applied on NGCCs, and the EPA 

concludes that they are likely already being applied across most of the NGCC fleet. 

With NGCCs projected to produce 20% of fossil CO2 emissions in 2020, and with a max 

sustained HRI potential for existing NGCCs of 2-3%, as mentioned earlier, the CO2 reduction 

potential for NGCCs would amount to only a fraction of 1% of total fossil emissions in 2020, 

which would be only about 10% of the potential CO2 reductions expected from coal-steam EGUs 

via HRI. Because of this limited potential and the uncertainty associated with it, EPA 

conservatively decided to not separately itemize and include this NGCC potential in Building 

Block 1. 

Simple-cycle Combustion Turbine (CT) EGUs 

Natural gas-fired CTs provide peaking generation, typically operating at very low 

capacity factors. This is primarily because of their relatively low efficiency, which is 

economically only partially offset by their relatively low capital cost. As peaking capacity, any 

CT may have many starts/shutdowns in the course of a year. It may also “load follow,” with an 

average electric power output that may be well below its most efficient load point. CTs have an 

operational flexibility well suited to their role as peakers, but this role requires them to be 

inherently less efficient than they could be if it were economic to operate them at higher capacity 

factors. 

EPA modeling projects that the power sector CT capacity in 2020 (Base Case) will be 

about 21% of total fossil-fired capacity (GW), and that it will provide only about 1% of total 

fossil-fired electrical generation (GWh). Whether gas or oil-fired, CT capacity can therefore only 

contribute CO2 emissions amounting about 1% of total fossil CO2 emissions, or perhaps 2% of 

total coal-steam CO2 emissions. Any single-digit percentage reduction in CT heat rates, can 

therefore only provide much less than a 1% reduction in total fossil-fired CO2 emissions. 

Most CTs likely benefit from the same regular preventive/restorative maintenance as the 

combustion turbine portion of a NGCC, as discussed above, and for the same reasons, Thus, the 
DRAFT     May 16, 2014

6

heat rates of most CTs are already periodically (even if not regularly, depending on their 

irregular operating hours and starts) restored to a level that allows them to be both reliable and as 

efficient as reasonably possible. Therefore the EPA decided to not include HRI for CTs as an 

additional potential in Building Block 1. 

Conclusion 

This technical memorandum outlines the EPA’s reasons for not including CO2 reduction 

potentials via HRI on oil/gas steam, NGCC, and CT EGUs as part of the CO2 reduction target of 

Building Block 1. For each non-coal technology the EPA concludes that the total additional 

potential reduction is small compared to the potential coal-steam CO2 reduction. Furthermore, 

we do not have the detailed site-specific information that would be needed to make a more 

precise engineering evaluation of the HRI potential for any individual EGU, including coalsteam units; only the owners/operators of these EGUs would have that information. 

The EPA notes, however, that although we did not include an HRI potential for these 

non-coal classes of existing fossil-fired EGUs in Building Block 1, we do expect that some 

amount of CO2 reduction via HRI is available from these EGUs. States and sources would be 

free to use HRI at these EGUs to help reach the state CO2 reduction goals.
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Chapter 5: Demand-side Energy Efficiency (EE) 

Introduction 

This chapter provides information on demand-side energy efficiency (EE) as an 

abatement measure for reducing carbon dioxide (CO2) emissions from fossil fuel-fired electric 

generating units (EGUs). Specifically, this chapter addresses EE as a component of both the 

“best system of emission reduction” (BSER) and state goals, and the inclusion of EE within the 

impacts assessment. Support is provided in this chapter for the discussion of the EE abatement 

measure throughout the preamble (most extensively in these sections: Building Blocks for 

Setting State Goals and Considerations, State Goals, State Plans, and Impacts of the Proposed 

Rule) and its representation within the RIA. Results from this chapter feed into the technical 

support document (TSD) on Goal Computation. EE is also addressed in TSDs on state plan 

considerations and projecting emissions performance. 

This chapter is organized as follows: 

 Background 

– EE Technologies and Practices 

– Barriers to EE Investment 

– EE Policies 

– EE Programs 

 The EE Opportunity 

– Rapid Growth in EE 

– EE Program Impacts 

– EE Potential 

– Costs and Cost-Effectiveness of State EE Policies 

– EE as an Abatement Measure 

 State Goal Setting 

– Approach 

– Inputs 

– Calculations 

– Results 
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 Impacts Assessment 

– Approach 

– Inputs 

– Calculations 

– Results 

 Analysis Considerations 

 Appendices 

 References 

Background 

As discussed in the State Plan Considerations TSD (Appendix: “Survey of Existing State 

Policies and Programs that Reduce Power Sector CO2 Emissions”), demand-side energy 

efficiency policies and programmatic efforts have existed for decades and are now used in all 50 

states. These strategies are intended to help states achieve energy savings goals, reduce the 

environmental impacts (including carbon dioxide (CO2) emissions) of meeting energy service 

needs, save energy and money for consumers, and provide a significant resource for meeting 

power system capacity requirements. EE policies currently in place are considered by states to be 

cost-effective strategies for contributing to these policy objectives1

. Moreover, states – through 

their utilities, primarily – have been rapidly increasing their funding of EE programs in recent 

years, more than tripling budgets in the five years from 2006 to 2011, from $1.6 billion to $5.9 

billioni

. In 2012, the cumulative impacts of these programs represented a 3.7% reduction in 

national electricity demandii. And, EE spending is projected to continue to grow at a substantial 

rate. A recent study by Lawrence Berkeley National Laboratory (LBNL) projects EE program 

spending to reach $8.1 billion to $12.2 billion (“Medium Case” and “High Case,” respectively) 

in 2025 even “without considering possible major new policy developments,” such as 

requirements under Clean Air Act, Section 111(d)2iii. 

                                                            

1

 See below for discussion of cost-effectiveness and related cost tests used by states to evaluate EE programs. 2 Specifically, the LBNL study states: “By virtue of limiting the analysis to current energy efficiency policies, we do 

not consider the potential impact of major new federal (or state) policy initiatives (e.g., a national energy efficiency 

resource standard, clean energy standard, or carbon policy) that could result in customer-funded energy efficiency 

program spending and savings that exceed the values in our High Case.” 

Comment [A1]: Suggest including citation.

Comment [A2]: EPA RESPONSE: Citation is at

end of the sentence (iii).

Comment [A3]: Calls into question the need for

BB 4??

Comment [A4]: EPA RESPONSE: This building

block will provide incentive to reach these forecast

levels and beyond. State EE policies are being

reconsidered in many states and sometimes rolled

back (e.g., OH). At the same time, we clearly are

building upon the ongoing work of leading states,

integrating their best ideas and successes into the

basis of this building block.
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This section provides relevant background for the subsequent sections that address the EE 

opportunity, EE as a component of BSER, EE within state goal setting, and the integration of EE 

within the benefit, cost, and impacts assessments as reported in the RIA and elsewhere. This 

section begins with a discussion of EE technologies and practices, and then describes the market 

failures that limit cost-effective EE investments. We then summarize EE policy objectives and 

discuss policy types, their relative impacts, and discuss in more detail the key strategy of 

employing EE programs. 

EE Technologies and Practices 

Energy efficiency is using less energy to provide the same or greater level of service. 

Demand-side energy efficiency refers to an extensive array of technologies, practices and 

measures that are applied throughout all sectors of the economy to reduce energy demand while 

providing the same, and sometimes better, level and quality of service. Utilities employ a large 

array of strategies in implementing energy efficiency programs, these include financial 

incentives such as rebates and loans, technical services such as audits and retrofits, and 

educational campaigns about the benefits of energy efficiency improvements. The purpose of 

these EE programs is to induce EE investments and practices that would not otherwise occur in 

the presence of market failures and behavioral impediments. In the residential sector, examples 

of EE activities include the purchase of more efficient products and equipment (e.g., ENERGY 

STAR labeled), the upgrading of insulation in attics and walls, sealing of air leaks, and 

undertaking home energy audits leading to customized whole home retrofits. Opportunities for 

cost-effective EE in commercial buildings include optimization of heating, ventilation, and air 

conditioning (HVAC) systems, upgrades of windows, and use of more efficient office equipment 

at replacement. In the industrial sector key EE strategies include motor upgrades and 

maintenance programs, recovery of waste heat streams, and optimization of processes through 

modern instrumentation and controls systems. 

The opportunity presented for economic investment in EE is dynamic, growing over time 

as technologies and practices advance, as populations grow, and as investment occurs in the 

construction of new homes, buildings, and industrial facilities. As new policies are enacted, 

leading to the acceleration of investment in EE, an additional portion of the expanding 

Deleted: ’

Comment [A5]: Can EPA explain why the EE

intervention is needed?

Comment [A6]: EPA RESPONSE: As discussed in

the following section “Barriers to EE Investment,” a

number of market failures and behavioral

impediments hinder the realization of energy

efficiency level that is socially optimal. The diversity

of EE programs is designed and devised to address

these market failures and behavioral impediments.

Also, made edits to the text to clarify.      

Comment [A7]: Although Figure 5.1 some pages

ahead does some of this, this would be a good

opportunity to bulletize real world examples of EE

actions, like windows, light bulbs, etc., with their

cost and efficiency and gains, even maybe a graph

showing how EE are the lowest cost GHG actions.  

Otherwise, the next few pages read like a

philosophical discussion of an abstract concept.  

Comment [A8]: EPA REPONSE: Thanks for this

observation. Real world examples are provided in

this paragraph. As you point out, Fig. 5.1 also

provides a sense of the varied nature of the

opportunities and strategies.  
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opportunity is realized. After decades of experience implementing policies to accelerate 

investment in cost-effective energy efficiency, states are finding renewed opportunities as they 

develop more sophisticated and effective strategies, evolving from a focus on individual enduses and products to whole-building and systems-based strategies that account for the 

interactions between the many energy end-uses in buildings and industryiv. As will be discussed, 

the experience in the U.S. has been that on balance, a persistent and large potential for 

achievable and cost-effective EE has remained even as the impact of past and ongoing efforts 

have accumulated. 

Barriers to EE Investment 

Despite the persistent and large potential for electricity savings through investment in EE 

technologies and practices, market failures, as well as non-market failures, limit the realization of 

the many benefits of these investments. Several market failures that lead to inefficiencies in 

energy use are well recognized by analysts and practitioners, and are discussed extensively in the 

economic literature.v

 Some of the most common examples of these market failures include: 

 Pollution externalities. Energy consumption is associated with negative externalities, such as 

emissions of CO2, SO2, and NOx that cause human health and environmental damages. 

Energy prices that do not correctly reflect these externalities lead to investments in energy 

efficiency below the socially optimal levels. 

 Imperfect information. Energy users often lack accurate information about energy savings 

and other attributes of energy efficient products or practices to understand the costs and 

benefits of EE investments. Market failure due to information imperfection leads to 

underinvestment in energy efficiency by consumers. 

 Split incentives (or the “principal-agent problem”). Incentives of individuals who make EE 

investment decisions are not always aligned with incentives of those who use and pay for 

energy. Examples include misalignment between landlords and tenants, and between builders 

and homeowners. Split incentives also persist within organizations and institutions that lead 

to underinvestment in EE in both public and the private entities.vi
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 Credit constraints. Limited access to credit may prevent some consumers, especially lowincome consumers, from making cost-effective EE improvement decisions due to the higher 

upfront cost of energy efficient products or practices. 

 Under-provision of research and development (R&D). Because of the public good nature of 

knowledge, technology innovation invested by one firm likely spills over to other firms. As a 

result, firms involved in technology development may be less willing to invest in R&D, 

leading to sub-optimal levels of EE investments from a social perspective.vii 

 Supply market imperfections. Market for energy efficient products is incomplete. 

Manufacturers do not have perfect information about consumer preferences and may supply 

limited menu of products to consumers. High start-up costs and the existence of patents may 

create barriers to entry in markets and result in oligopolistic or monopolistic behavior. 

Supply chains of EE products is fragmented, leading to underinvestment in innovation and 

energy efficiency by suppliers. In addition, supply chain fragmentation may also add 

complexity to the purchase and installation of otherwise economically rational investments, 

thereby slowing the adoption of EE technologies. 

 Behavioral impediments. Behavioral economics and psychology have identified potential 

behavioral phenomena that lead to consumers to deviate from the standard theory of welfare 

maximizing in consumption and other decisions, including energy efficiency investments. 

Behavioral economics posits possible explanations, including bounded rationality, heuristic 

decision-making, and non-standard preference and belief.viii 

In the presence of market failures, users of electricity, or those making energy efficiency 

investments, face prices or incentives that prevent them from weighing the social benefits and 

costs of their investments and thus under-invest in approaches to reduce electricity consumption. 

The behavioral impediments discussed above explain why individuals do not always make 

energy efficiency investments that are seemingly in their own best interest to reduce their total 

expenditure, given prevailing electricity prices. 

In addition to market failures and behavioral impediments, other factors, such as hidden 

costs, risk and uncertainty experienced by both consumers and suppliers of energy efficient 
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products, and heterogeneity among consumers, producers and markets, also influence EE 

investment decisions3

. Examples of such factors include: 

 Risk and uncertainty. Adopting an unfamiliar, typically more expensive EE technology can 

be an uncertain undertaking given the lack of credible information on product performance 

and future energy prices, and the irreversibility of the investment. Imperfect or asymmetric 

information can exacerbate the perceived risk of energy efficiency investments and help 

explain why consumers and firms do not always invest in EE measures. Suppliers also face 

risk and uncertainty, without perfect information of consumer preferences for energy 

efficiency. In the presence of risk and uncertainties, consumers and suppliers alike will 

underinvest in EE. 

 Transaction costs. Consumers face transaction costs in searching, assessing and acquiring 

energy efficient technologies and services. It can be time-consuming and difficult for 

consumers to estimate lifetime operating costs of a product. The complexity of the search 

process puts many efficient products at a disadvantage relative to less-efficient products with 

lower upfront costs. 

 Capital market barriers. Consumers sometimes face higher interest rates to finance EE 

investments compared to other investments. Lenders can be reluctant to invest in EE loan 

portfolios in part because energy efficiency loans may lack standardization and financial 

markets have difficulty ascertaining the likely payoff from such investments. 

EE policies and programs can play an important role in correcting market failures and 

addressing the barriers to the investment and adoption of socially beneficial energy efficiency 

opportunities. Examples of effective EE policies and programs include public funding of R&D, 

information programs (such as energy labeling, the voluntary ENERGY STAR Program, and 

                                                            

3 It has been recognized that there is a difference between cost-effective energy efficiency investment levels, based 

on cost-minimizing consideration, and observed levels of energy efficiency. This phenomenon, also termed ‘energy 

paradox,’ or ‘energy efficiency gap,’ has been studied extensively in the literature. See, for example, Jaffe, AB, and 

RN Stavins. 1994. “The Energy Paradox and the Diffusion of Conservation Technology.” Resource and Energy 

Economics 16(2): 91–122; Sanstad, A. H. and R. B. Howarth. 1994. ‘Normal’ markets, market imperfections and 

energy efficiency, Energy Policy, 22: 811-818; DeCanio 1998; DeCanio, SJ and WE Watkins. 2008. Investment in 

Energy Efficiency: Do the Characteristics of Firms Matter? The Review of Economics and Statistics, 80: 95-107;

Allcott, H, and M. Greenstone. 2012. Is There an Energy Efficiency Gap? Journal of Economic Perspectives 26 

(1):3-28. 
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consumer education), rebates for high-efficiency products, product energy performance 

standards, financing and loan programs, and technical assistance. 

EE Policies4

Objectives and Role in Reducing CO2 Emissions from the Power Sector 

EE policies are implemented by states to meet a number of closely related policy goalsix

including: 

- Reducing costs to electricity customers,

- Providing a significant resource for meeting power system capacity needs,

- Meeting energy savings goals,

- Stimulating local economic development and new jobs, and

- Reducing the environmental impacts of meeting electricity service needs.

EE policies currently in place are considered by states to be cost-effective strategies for 

contributing to each of these policy objectivesx

. While each of these objectives, and others, 

contribute to the motivation of state policymakers to pursue EE policies, reducing energy costs 

over the long term is the leading objective in pursuing these policies. In addition, EE policies are 

central to meeting state objectives for reducing CO2 emissions from the power sector. As noted 

in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO2

reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in 

ten states5

 with statutory requirements for greenhouse gas reductions. 

Economy-wide studies of climate mitigation scenarios confirm that energy efficiency plays a 

critical role in reducing the costs and enhancing the flexibility of meeting long-term climate 

stabilization targetsxi. Analysis by the International Energy Agency (IEA) suggested that in order 

to stabilize carbon concentration in the atmosphere at 450 ppm, as much as 44% of the estimated 

global abatement potential in 2035 derives from greater energy efficiency in the world 

economyxii. Several recent Energy Modeling Forum (EMF) studies have investigated the role of 

technology in achieving climate policy objectives in the U.S. (“EMF 24” and “EMF 25” studies) 

                                                            

4

 Existing state EE policies are described extensively in the State Plan Considerations TSD. 

5 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, 

Minnesota, New Jersey, Oregon, and Washington. 

Comment [A10]: EPA RESPONSE: The rebound

effect is referenced in “Cost of Saved Energy”

section, below and a fuller discussion has been

added to that section.

Comment [A9]: Rebound effect??

Formatted: Font: (Default) Times New Roman,

Complex Script Font: Times New Roman
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and globally (“EMF 27” study).6

 These studies concluded that compared to business-as-usual 

energy efficiency, improvements in energy efficiency in various economic sectors would slow 

the increases of GHG emissions in the short run, substantially reduce the costs of GHG 

mitigation (on average, by about 50%7

), and ease the technology transformation pathways to 

achieve long-term carbon reduction goalsxiii,xiv. 

Several economic studies (including EMF25 studies) examined the role of energy efficiency 

policies (such as energy efficiency standards and subsidies) in relation to other climate policy 

instruments (such as carbon taxes). These studies found that when energy efficiency policies 

address market failures, they are welfare improving and can complement climate policy.xv In 

addition, EE policies are recognized to be an appropriate response to demonstrated market 

failures and behavioral impediments, particularly in contexts where these failures have broader 

societal implications such as environmental externalities.xvi

In addition to providing cost-effective opportunities for reducing GHG emissions, energy 

efficiency is recognized to provide other co-benefits, including air quality and public health 

benefits, waste reduction from energy generation, energy security, energy system reliability, 

community economic and social development, and consumer amenities.xvii Energy efficiency 

investments and policies are also found to spur productivity growth, technology learning and 

innovation.xviiixix Recently, more attention has been paid to developing methods for recognizing 

these co-benefits and integrating them into the cost-benefit analysis framework used by state 

utility commissions and administrators of EE programsxx. These co-benefits have not been fully 

accounted for in the EPA analysis. 

Policy Types 

                                                            

6

 Energy Modeling Forum (EMF) is a consortium of energy economists and energy economic modeling teams that 

was established in 1976. Through ad hoc working groups, the EMF has focused on a series of energy and 

environmental topics that are of interest to policy decisions. In recent years, the EMF is recognized for its 

contribution to the advancement of economics of climate change and the reports of the Intergovernmental Panel on 

Climate Change (IPCC).   7 It should be noted that these energy-economy modeling studies do not typically include the costs of implementing 

energy efficiency measures or would treat such costs as exogenous.  

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EE policies come in many forms. The most prominent and impactful EE policies in most 

states include those that drive development and funding of EE programs8

, and building energy 

codes. Other policies that are leading to significant impacts include state appliance and 

equipment standards, building energy disclosure requirements, innovative financing strategies 

(e.g., Property Assessed Clean Energy or “PACE”), state tax policies, and “lead by example” 

strategies targeting energy use in state operations. Comparing the relative impact (achieved or 

potential) of the different policy types is challenging, particularly to do so comprehensively, 

across all states, and at the national level. EE programs are the only state EE approach that has 

comprehensive and detailed reporting of impacts, costs, and other characteristics from all 50 

states9

. This information is generally based upon measurement and verification studies submitted 

annually, most commonly to state utility commissions, and reported to the Energy Information 

Administration (EIA) for all program administrator types (all utility types, third-parties, and 

government agencies). EE program data reported to EIA includes incremental and cumulative 

energy and peak demand savings, program costs broken down by component, and composition 

by end-use sector (residential, commercial, industrial). In 2012, utilities and other program 

administrators in 48 states reported savings from EE programs to EIA through form EIA-861. At 

a national level, the EPA is not aware of a comprehensive dataset reported by states of the 

achieved impacts of strategies other than those that lead to investment in EE programs. However, 

state and regional-level information does exist. For example, the Northwest Power and 

Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility 

and third-party EE programs, state building energy codes, and federal appliance standards) 

across their member states (ID, MT, OR, WA) for more than three decades. For the past decade, 

EE programs have accounted for more than 75% of the cumulative energy savings from state EE 

policies for NPCC, with building energy codes accounting for the remaining savingsxxi. 

                                                            

8

 EE programs are described in more detail in the following section of this chapter and in the State Plan 

Considerations TSD. 

9

 This is a reference to data collected by the Energy Information Administration (EIA) through Form EIA-861. In 

2011, EIA began collecting data from third-party administrators of programs. Prior to 2011, this was a significant

shortcoming in the breadth of the data collected. The breadth and quality of information collected through Form 

EIA-861 has improved over time, however, outside entities (e.g., ACEEE) have found that the data can be improved 

through expert review and supplementation with other data sources. While now fairly comprehensive, the EIA data 

can be improved further with regards to data quality and consistency. See “Analysis Limitations” section for further 

discussion.
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Another representation of the relative opportunity provided by different state EE 

strategies is presented by evaluations of EE achievable potential or projections of the impacts of 

EE policies. The results from two recent evaluations at a national level are presented in Table 5-

1. EE programs account for 77% and 82% of achievable savings in ACEEExxii and Georgia 

Techxxiii studies, respectively. These studies indicate that the substantial majority of potential 

savings from state EE efforts are available through EE programs, and that state and local 

building energy codes can make a significant additional contribution. Massachusetts provides a 

state example of the impacts of EE programs relative to other state EE policies. The 

Massachusetts Global Warming Solutions Act of 2008 established statewide limits on 

greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this 

target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE 

policies are expected to provide the largest contribution to meeting the 25 percent target with 

utility sponsored EE programs and state building energy codes accounting for 76% and 17%, 

respectively, of those policiesxxiv. In their 2013 progress report, Massachusetts indicates that they 

are generally on track for meeting or exceeding these projections.xxv

TABLE 5-1 

Relative Opportunities Provided by Key EE Programs and Building Codes 

Study Year EE Programs Building Codes Other 

ACEEE 2030 77% 13% 10% 

Georgia Tech 2035 82% 18% 0% 

The full range of EE policies are addressed in greater detail (including designs, authority, 

obligated parties, measurement and verification (M&V), penalties for non-compliance, and 

implementation status) in the State Plan Considerations TSD. Because EE programs have 

provided the majority of state EE-policy electricity savings to-date and offer the majority of 

potential savings going forward, we next summarize key characteristics of this strategy. 

EE Programs 

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EE programs (actually portfolios of programs) are comprised of numerous measures and 

measure types that are applied across all sectors of electricity end-users. Figure 5-1xxvi illustrates 

the multi-level composition and breadth of EE program portfolios. The diversity represented by a 

typical portfolio of EE programs implemented by a utility (or other program administrator) is an 

important characteristic relevant to analysis of EE policies. Every detailed program type (as 

illustrated in the lower half of the figure) represents a unique set of characteristics including 

costs of energy saved, ratio of program to participant costs, investment life, scale, M&V 

approach, etc.10

 

                                                            

10 See following sections for discussion of these factors. 
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FIGURE 5-111

Energy Efficiency Program Portfolio 

Administrators 

EE programs are administered by a variety of entities (“program administrators”) 

including utilities of all ownership types (investor-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (e.g., Vermont Energy Investment Corporation), and state and 

local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned 

utilities which account for more than 75% of reported savingsxxvii) are overseen by state utility 

commissions, which review and approve program plans, projected impacts, and associated 

budgets; and establish annual reporting and M&V requirements. 

 

                                                            

11 The “EM&V” box is not comparable to the other program types and is not relevant to this discussion.  It was

included in the referenced source to indicate that EM&V is a key activity within a program portfolio.

Comment [A11]: The placement of EM&V in

Figure 5‐1 as separate from the actual programs is

misleading. Each program has EM&V associated

with it, so showing it as a separate activity is

inaccurate and possibly confusing.

Comment [A12]: EPA RESPONSE: See added

footnote 11. We cannot easily edit this graphic.
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Policy Drivers 

EE programs result from a number of different policy approaches or “drivers.”12 These 

include energy efficiency resource standards (EERS) (26 states)xxviii, system benefit charges (14 

states), integrated resource planning (IRP) requirements (34 states), demand-side management 

plan or multi-year energy efficiency budget (28 states), and statutory requirement to acquire “allcost-effective EE” (6 states)xxix13. EERS is a more recently developed strategy and has quickly 

become the leading driver of the rapid growth in EE programs due to their clear goals and proven 

success as a policy toolxxx. These policy drivers lead to the evaluation, planning, and adoption of 

EE programs and associated budgets, which are supported through different funding 

mechanisms. 

Funding Sources 

Funding sources for EE programs are varied but for most states are dominated by revenues 

collected from ratepayers through electricity surcharges, typically ranging from $1 to $4 per 

megawatt-hourxxxi. More recently adopted funding sources include proceeds from the auction of 

allowances in the Regional Greenhouse Gas Initiative (RGGI) states and from EE resources bid 

into the forward capacity market operated by the New England Independent System Operator 

(NE-ISO). Ratepayer-funding accounts for more than 90% of total EE program support 

nationally. 

The EE Opportunity 

As discussed, states are employing a number of EE strategies with EE programs yielding the 

most significant impacts both historically as well as in terms of future potential. Furthermore, EE 

programs are unique among state EE strategies in the comprehensiveness and transparency of 

their reported impacts, funding, and other characteristics. In this section we address the rapid 

                                                            

12 These policies are discussed in depth in State Plan Considerations TSD. 

13 The number of EERS states is from ACEEE (see endnote) and includes states with explicit EERS, those with 

long-term energy savings targets for individual program administrators, and those with EE incorporated as an 

eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL 

(see endnote). 
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growth in EE programs, estimated impacts of EE programs to-date and projections of the impacts 

of existing EE programs and trends, and the electricity savings potential achievable through 

expanded use of EE policies and programs. Finally, we will discuss the costs and costeffectiveness of EE programs, specifically. 

Rapid Growth in EE 

Funding for EE programs has increased rapidly in recent years driven by recent policy 

innovations and increasing evidence of the effectiveness of these new strategies. Table 5-2 

presents levels of EE program funding in the U.S. since 2006xxxii. In the previous five years, 

funding increased by more than 200%, from $1.6 billion in 2006 to $5.9 billion in 2011. 

TABLE 5-2 

U.S. Electric Utility EE Program Funding (2006-2011) 

Year

2006 2007 2008 2009 2010 2011 2012 

Electric Efficiency 

Program Budgets 

(billions of $s, nominal)

1.6 2.2 2.6 3.4 4.6 5.9 5.9 

 Key new state policies that have helped to drive these rapid increases in EE program 

funding include EERS, electricity savings goals, and “all cost-effective energy efficiency” 

requirements. The adoption of EERS, in particular, increased through this period and clearly has 

been the primary driving force behind the increasing success of and investment in EE programs. 

Table 5-3 shows the number of states adopting EERS by yearxxxiii. 

TABLE 5-3 

U.S. State Adoption of Energy Efficiency Resource Standards 

Year States Adopting an EERS Total 

1997-2004 California, Hawaii, Texas, Vermont 4 

Comment [A13]: Please update this table to

include the most recent numbers

Comment [A14]: EPA RESPONSE: Done.

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1.6
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2005 Nevada, Pennsylvania 2 

2006 Rhode Island, Washington 2 

2007 Colorado, Connecticut, Illinois, Minnesota, North Carolina 5 

2008 Maryland, Michigan, New Mexico, New Year, Ohio 5 

2009 Arizona, Indiana, Iowa, Maine, Massachusetts 5 

2010 Arkansas, Oregon 2 

2011 Wisconsin 1 

1997-2011 26 

EE Program Impacts 

Impacts to-date 

The primary sources for EE program information (including costs and impacts) are 

annual EE program reports required by utility commissions, or cooperative or municipal utility 

boards of directors. These reports are based on M&V studies of individual EE programs within 

the program portfolio. The Energy Information Administration (EIA) has been collecting data on 

EE programs through Form 861, “Annual Electric Power Industry Report,” for more than three 

decades.

14 The data collection reflects an increasing degree of breadth and detail over time. For 

example, third-party-administered programs were not initially required to report but were added 

beginning in 2011. Data fields have been added over the years to reflect industry trends (e.g., EE 

programs are now reported separately from load management programs). Outside organizations 

have taken the EIA data, supplemented it with additional sources including surveys of utility 

commissions and program administrators, and published their own annual reports that capture 

EE program impactsxxxiv,xxxv. 

The EPA has relied on the EIA Form 861 dataset for identifying historic impacts of EE 

programs by state. Specifically, the reported sales data, and incremental and cumulative 

electricity savings in the 2012 EIA 861 dataset are used to estimate electricity EE impacts by 

                                                            

14 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/. 

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Comment [A15]: As noted elsewhere in the

comments, this dataset can be problematic.

Comment [A16]: EPA RESPONSE: EPA is aware

of issues associated with consistency and quality of

reported DSM program data in Form EIA‐861. Our

goal setting analysis utilizes two variables in the EIA‐

861 data. Specifically, we use the 2012 electricity

sales data and reported incremental annual

electricity savings of demand‐side management

programs to estimate electric EE program impacts

for each state. We recognize that different utilities

and states may have different approaches to

estimate incremental savings, but over time there

has been increased standardization in data

reporting. The instructions for the form direct the

filer to report net energy savings and most

states/utilities estimate net savings. We rely on this

dataset because it remains the most comprehensive

one publicly available that we are aware of. In the

preamble, we are soliciting comments on better or

more appropriate dataset that can be used for our

analysis. In addition, a FN is added to discuss the

data and caveats and the dataset is addressed

further in Analysis Limitations section. We would

welcome support from DOE on improving this

dataset prior to Final.

Deleted: nergy 
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state.15 EIA data is reported by program administrator (e.g., utility, third-party, or state agency) 

and requires the disaggregation of reported data by state for administrators with programs in 

multiple states (e.g., multi-state investor-owned utilities). Program administrators in 48 states 

reported savings in 2012. The EPA has compiled this information and aggregated key data to the 

state level. Table 5-4 provides a summary of this data by state for the 2012 reporting year, the 

most recent available. At the national level, incremental electricity savings16 in 2012 was 0.58% 

of retail sales with individual state values ranging from 0.00% to 2.19%. Cumulative electricity 

savings17 (representing the remaining impacts of programs from all prior years) reported at the 

national level for 2012 represent 3.74% of retail sales with individual state values ranging from 

0.0% to 15.44%. 

TABLE 5-4 

 2012 Reported Electricity Savings by State 

State 

Incremental Savings as a % 

of Retail Sales (2012) 

Cumulative Savings as a % 

of Retail Sales (2012) 

Alabama 0.07% 0.78% 

Arizona 1.61% 5.39% 

Arkansas 0.11% 0.39% 

California 1.24% 13.67% 

Colorado 0.84% 4.67% 

Connecticut 1.05% 13.37% 

Delaware 0.00% 0.00% 

District of Columbia 0.00% 0.57% 

                                                            

15 EPA recognizes concerns associated with consistency and quality of 861 data that different reporting entities may 

have used different methodologies to estimate savings and the EIA 861 data are self-reported. Over time, there has 

been increased standardization in data reporting. We believe his dataset remains to be the most comprehensive 

publically available dataset. See “Analysis Limitations” section below for further discussion. 16 Incremental savings (also known as first-year savings) represent the reduction in electricity use in a given year 

associated with new EE activities in that same year, either new participants in DSM programs that already existed in 

the previous years, or new DSM programs that existed for the first time in the current year. 17 Cumulative savings (also known as annual savings) represent the reduction in electricity use in a given year from 

EE activities in that year and all preceding years, taking into account the lifetimes of installed measures. 

Comment [A17]: EPA needs to provide much

more detail about how this data was derived.

Comment [A18]: EPA RESPONSE: See response

to immediately prior and following comments. The

data is from 861 as is now noted. And discussion has

been added addressing limitations of the data.

Comment [A19]: EPA used EIA 861 data

Comment [A20]: EPA RESPONSE: That is correct.

The estimates are based on the 2012 EIA 861 data.

A note is added at the end of the table. The text in

the previous paragraph is also modified to provide

more explanation of the use of EIA data.

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Florida 0.27% 3.60% 

Georgia 0.18% 0.67% 

Idaho 0.79% 6.20% 

Iowa 1.05% 7.80% 

Illinois 0.93% 2.15% 

Indiana 0.58% 1.72% 

Kansas 0.02% 0.24% 

Kentucky 0.23% 1.04% 

Louisiana 0.00% 0.00% 

Maine 1.96% 5.42% 

Maryland 0.89% 2.47% 

Massachusetts 0.94% 6.27% 

Michigan 1.01% 2.77% 

Minnesota 1.12% 13.10% 

Mississippi 0.08% 0.50% 

Missouri 0.12% 0.55% 

Montana 0.66% 5.85% 

Nebraska 0.30% 0.99% 

Nevada 0.54% 6.19% 

New Hampshire 0.48% 4.90% 

New Jersey 0.03% 1.04% 

New Mexico 0.60% 1.86% 

New York 0.93% 6.89% 

North Carolina 0.37% 1.26% 

North Dakota 0.07% 0.22% 

Ohio 0.87% 3.20% 

Oklahoma 0.21% 0.70% 

Oregon 1.09% 7.72% 

Pennsylvania 1.06% 3.08% 
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Rhode Island 0.78% 11.22% 

South Carolina 0.35% 1.12% 

South Dakota 0.13% 0.33% 

Tennessee 0.31% 1.76% 

Texas 0.19% 1.54% 

Utah 0.74% 6.59% 

Vermont 2.19% 15.44% 

Virginia 0.03% 0.30% 

Washington 0.93% 7.37% 

West Virginia 0.18% 0.20% 

Wisconsin 1.05% 6.61% 

Wyoming 0.14% 0.71% 

Continental U.S. Total 0.58% 3.75% 

Alaska 0.02% 0.10% 

Hawaii 0.04% 0.25% 

U.S. Total 0.58% 3.74% 

Source: EPA calculation based on 2012 EIA Form 861 data.

Projected Spending and Savings from EE Programs 

In 2013, Lawrence Berkeley National Laboratory (LBNL) published an update to a 2009 

analysis and projected future spending levels and savings through 2025 from energy efficiency 

programs funded by electric and gas utility customers in the United States under three scenarios 

(high, medium, and low cases)xxxvi. The scenarios represent “a range of potential outcomes under 

the current policy environment” and were based on detailed, bottom-up analysis of existing state 

energy efficiency policies. Significantly, the study presumes no new major policy developments 

such as a “national energy efficiency standard, clean energy standard, or carbon policy” and 

specifies that such policy changes could “result in customer-funded energy efficiency program 

spending and savings that exceed the values in our High Case.” 

The study concludes that efficiency programs are “poised for dramatic growth over the 

course of the next 10 to 15 years” with the most significant increases occurring in regions with 

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lower levels of program spending, historically, including the Midwest and South. For example, 

under the medium scenario total U.S. spending on electric efficiency programs increase by 40% 

to $8.1 billion in 2025 from 2012 levels. Under the high scenario, spending more than doubles 

from 2012 levels to $12.2 billion in 2025. Incremental savings levels grow commensurately, to 

0.8% and 1.1% of sales under the medium and high scenarios, respectively. The study results 

indicate that under the high scenario 20 states would be achieving 1.5% or higher levels of 

incremental savings, with 11 of those reaching or exceeding 2.0%18. Table 5-5 summarizes the 

results of the LBNL analysis. 

 

                                                            

18 LBNL provided these unpublished results from their analysis.

Comment [A21]: So what does this imply about

BB #4? Why does EPA put into the baseline?

Comment [A22]: EPA RESPONSE: This building

block will provide incentive to reach these forecast

levels and beyond. State EE policies are being

reconsidered in many states and sometimes rolled

back (e.g., OH). At the same time, EPA is clearly

building upon the ongoing work of leading states,

integrating their best ideas and successes into the

basis of this building block.
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Table 5-5 

Summary of Impacts: 

Scenarios of Future Utility Customer-Funded Energy Efficiency Programs 

Case

2025 

Incremental Savings 

(% of Sales) 

Program Costs 

(billions of $, nominal) 

Programs Costs 

(% of Revenues) 

Low 0.5% 5.5 1.1% 

Medium 0.8% 8.1 1.7% 

High 1.1% 12.2 2.7% 

EE Potential

Evaluations of EE Potential 

Energy efficiency potential studies are a common tool for informing the development of EE 

program plans and budgets, as well as supporting the development of electricity savings targets, 

required savings levels under an EERS, or “all cost-effective” EE requirement. In conducting 

these studies, states and utilities have developed a standardized methodology that is often 

described as a “bottom-up, engineering-based” approachxxxvii. EE potential studies are conducted 

at various geographic scopes (national, regional, state, and utility service territory level) and at 

different degrees of aggregation (e.g., economy-wide, sectoral, and program), and can be broadly 

grouped into a few types: technical, economic, market, and program.19

 Technical potential represents the theoretical maximum amount of energy use that could 

be displaced by efficiency, without regard to non-engineering constraints such as costs 

and the willingness of energy consumers to adopt the efficiency measures. It often 

assumes immediate implementation of all technologically feasible energy saving 

measures, with additional efficiency opportunities assumed as they arise. 

                                                            

19 The definitions discussed below largely follow that outlined in the Guide for Conducting Energy Efficiency 

Potential Studies (NAPEE 2007) but the variations in definition are also discussed (e.g., Sathaye and Murtishaw 

2004; Huntington 2011). 
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 Economic potential refers to the subset of the technical potential that is economically 

cost-effective. Definition of “economic potential” can vary to some degree by study. 

Some estimate economic potential by evaluating technology upfront cost, operating costs 

that considers energy prices, product lifetime and discount rate, compared to a 

conventional alternative or the supply-side energy resources. Others incorporate 

consideration of consumer preferences in addition to consumers’ out-of-pocket 

expenditure when evaluating the economic potential. Both technical and economic 

potential estimates assume immediate implementation of efficiency measures without 

regard to technology adoption process or real-life program implementation. In addition, 

these estimates do not always reflect market failures or barriers that impede energy 

efficiency and often fail to capture transaction costs (e.g., administration, marketing, 

analysis, etc.) beyond the costs of efficiency measures. 

 Market potential (or “achievable” potential) refers to the subset of economic potential 

that reflects the estimated amount of energy savings that can realistically be achieved, 

taking into account factors such as technology adoption process, market failures or 

barriers that inhibit technology adoption, transaction costs, consumer preferences, social 

and institutional constraints, and possibly the capability of programs and administrators 

to ramp up program activity over time. 

 Program potential refers to the subset of market potential that can be realized given 

specific program funding levels and designs. Program potential studies can consider 

scenarios ranging from a single program to a full portfolio of programs20. 

As mentioned, the EE industry standard for potential studies is the bottom-up, 

engineering evaluation of energy efficiency potential of individual end-use technologies and 

measuresxxxviii. Bottom-up analyses all employ a similar methodology but can vary significantly 

in key assumptions (e.g., breadth of sectors and end-uses considered, study period, discount rate, 

pattern of technology penetration, whether economically justified early replacement of 

                                                            

20 Each subsequent potential estimate described above is a subset of the previous potential estimate, e.g., the market 

potential is a subset of the economic potential, and the economic potential is a subset of the technical potential. 
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technologies is allowed for, and whether continued improvement in efficiency of technology is 

provided for). As a result, estimated efficiency potential can vary significantly among studies21. 

Overview of Results 

Studies of energy efficiency potential are numerous. In recent years, dozens of studies 

have been conducted at regional, state, and utility levels. This section reviews recent studies and 

presents a summary of findings. We first address meta-analyses that summarize results from 

multiple utility, state, and regional studies, and then we address the few national studies that have 

been conducted. To normalize results of analyses addressing different study periods, we present 

average annual achievable potential by dividing cumulative percentage savings in the last year of 

the study by the duration (in years) of the study period. This is a common method of 

normalization for energy efficiency potential studies. 

At the regional and state level, two meta-analyses, Sreedharan (2013)xxxix and Eldridge et 

al. (2008)xl, captured numerous studies conducted between 2001 and 2009. The meta-analysis 

conducted by Sreedharan (2013) presents average annual values of 4.1% per year in technical 

potential, 2.7% per year in economic potential, and 1.2% per year in maximum achievable 

potential. In comparison, Eldridge et al. (2008) estimated average annual values of 2.3% per year 

in technical potential, 1.8% per year in economic potential, and 1.5% per year in achievable 

potential. To supplement these studies with more recent data, the EPA has conducted a metaanalysis of twelve studies conducted between 2010 and 2014 at the utility, state or regional level 

(see Appendix 5-1). The EPA review indicates an average annual achievable potential of 1.5% 

per year across the reviewed studies. See Appendix 5-2 (Summary of Recent (2010-2014) 

Electric Energy Efficiency Potential Studies) for complete results from the EPA research. 

 

                                                            

21 Because of the complex consumer behavior, energy market and macroeconomic drivers of energy use and energy 

efficiency, and in some cases due to the lack of consistent data, quantifying energy efficiency potential and energy 

savings from policies and programs remains a challenging analytical task. Assumptions about consumer technology 

adoption behavior, market barriers and failures, and how technology diffusion occurs can also affect estimated 

potential. 

Comment [A23]: Font is different in footnote

Comment [A24]: EPA RESPONSE: FN font

corrected.

Comment [A25]: Assuming these studies are

correct, what, if any, policies are needed to realized

the achievable potential of 1.2%‐1.5%?

Comment [A26]: EPA RESPONSE: See p. 49,

section “Results in Context,” and Table 5‐22. We

compare our best practice scenario and alternative

(options 1 & 2) results with results from these

studies of achievable potential and find that our

scenarios are either within or below ranges that

result from these scenarios (below the

utility/state/regional results and within the national

results). As discussed, EERS is one policy type that

could be used to realize these levels of impacts,

however, other policies could be used instead of or

in addition to EERS.

Comment [A27]: Make sure to check this

carefully!!

Comment [A28]: EPA RESPONSE: We have re‐

checked.

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Table 5-6 presents a summary of these three meta-analyses of EE potential. 

TABLE 5-6 

Summary of Meta-Analyses of EE Potential at Utility, State, and Regional Levels 

Study Dates of Studies Number of 

Studies 

Average Annual Achievable 

Potential 

Sreedharan (2013) 2001-2009 10 1.2%/year 

Eldridge (2008) 2001-2007 20 1.5%/year 

EPA (2014) 20010-2014 12 1.5%/year 

In addition to the numerous studies conducted at the utility, state, or regional levels since 

2001, a number of studies have evaluated efficiency potential at the national level, applying a 

generally consistent methodology and employing a common data set, across all regions of the 

country. Sreedharan (2013) evaluated four major energy efficiency potential studies at the 

national level, namely, McKinsey and Co. (2007), McKinsey and Co. (2009), EPRI (2009), and 

AEO (2008) Energy Efficiency Side Case. All four studies used the AEO 2008 reference case as 

the baseline but differed in other key respects (e.g., breadth of end-uses, assumed technology 

improvement over time, and definition of cost test for economic potential screening). These 

studies suggest technical electricity savings potential in the range of 25-40% and economic 

potential in the range of 10-25%, as a percentage of total demand in 2020. Of these studies, only 

EPRI provided an estimate of achievable potential. On a normalized basis, the EPRI 2009 study 

provides an achievable annualized potential range of 0.2-0.4% per year (realistically achievable 

and maximum achievable potential, respectively) through 2030 at the national level. 

Two more recent studies also provide national estimates of achievable EE potential: 

EPRI (2014)xli updates their 2009 analysis, using a conventional bottom-up engineering 

approach, and ACEEE (2014)xlii, using a top-down, policy-based approach derived from state 

experience and their evaluated results. EPRI (2014) results show an average annual achievable 

potential range of 0.5% to 0.6% per year (achievable and high achievable potential, 

respectively). ACEEE found average annual achievable potential of 1.5% per year. The results of 

the EPRI and ACEEE studies are summarized in Table 5-7. 

Comment [A29]: Seems like too few studies to

do a valid meta‐analysis.

Comment [A30]: EPA RESPONSE: EPA is

interested in any supplementary references that are

available and relevant to the evaluation of EE

potential at a regional or sub‐regional level.  
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TABLE 5-7 

Summary of National EE Potential Studies 

Study Study Type Average Annual Achievable 

Potential 

EPRI (2009) Bottom-up, engineering 0.2%-0.4%/year 

 (realistic to maximum achievable) 

EPRI (2014) Bottom-up, engineering 0.5%-0.6%/year 

(achievable to high achievable) 

ACEEE (2014) Top-down, policy-based 1.6%/year 

 Notably, each of these national potential studies show significant potential in every 

region of the country including regions with lower electricity prices like the southeast, regions 

with historically high levels of EE program budgets like the northeast and west coast, and across 

regions with varied sectoral composition (e.g., higher manufacturing regions like the midwest 

and south, as well as higher service industry regions like the northeast and California). Both 

EPRI studies illustrate the substantial and similar scale opportunity across all regions. For 

instance, EPRI (2014) shows achievable potential ranging from 8% to 14% relative to baseline in 

2035 across the thirteen regions of their analysis as well as significant opportunity in the 

residential, commercial and industrial sectors in every region. The ACEEE (2014) study also 

shows consistently large potential across all states and regions through 2030, with an average 

potential of 24% and a range of 20% to 36% across 50 states. 

Costs and Cost-Effectiveness of State EE Policies

EE Cost-Effectiveness 

States enact EE policies to meet multiple policy objectives including reduction of customer 

electricity bills, lower costs of meeting electricity supply needs, energy reduction, environment 

Comment [A31]: This raises the question of peer

review.  Have these been independently peer

reviewed? If so, state it.  If not, is EPA planning to do

so?   

Comment [A34]: EPA RESPONSE: Note that this

metric is not the same as the 1.5% incremental

savings as a % of sales that is used as a basis for the

best practices scenario.  As presented on p. 49,

section “Results in Context,” and Table 5‐22, the

best practices scenarios result in average annual

pace of <1%, significantly lower than this value from

one study. Also, note that this particular study is

developed based upon the use of multiple EE policy

types rather than the single type (EE programs) that

serves as the basis for EPA’s BB4 and, thus, results in

higher levels of savings. If EPA had included, for

example, the potential additional impacts of

building codes, appliance standards, and other state

policies, our BB4 savings levels would be higher.

ACEEE presents a credible case indicating that these

levels are achievable.

Comment [A33]: If these are the average

achievables, is it really feasible to go for 1.5% for all

states in each year?

Comment [A32]: EPA RESPONSE: EPA is not

aware of a formal peer review of these studies. EPA

is not planning to conduct a peer review of these

studies prior to proposal.
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and health benefits, and local economic development benefitsxliii. Most states evaluate their EE 

policy options through the application of cost tests, weighing the projected benefits with the 

costs of the energy efficiency technologies and practicesxliv xlv. Each state determines their own 

policies for the specific costs and benefits to include in these tests. The costs and benefits are 

compared on an equal footing by using present value analysis. This is necessary because EE 

typically requires primarily upfront expenditures (e.g., a whole home retrofit) while the 

economic benefits (e.g., electricity bill savings) accrue over the life of the investment (“measure 

life”) which can range from a few to twenty or more years. As such, the choice of discount rate 

and the estimation of measure life are significant determinants of the cost-effectiveness results. 

Most states employ multiple tests, adjusting cost and benefit categories depending upon the 

economic perspective of interest (e.g., utility, ratepayer, program participant, society), and 

consider the results from each one, usually with an identified primary test typexlvi. Policies that 

are selected are those that are found to be cost-effective, with benefits greater than costs, as 

determined by the utility applying methods defined by their state utility commission. 

There are five primary cost-effective tests used in the U.S.: 

(1) Participant cost test from the perspective of the customer installing the measure. Costs 

may include incremental equipment and installation costs; benefits include incentive payments, 

bill savings, and applicable tax credits or incentives. 

(2) Utility/program administrator cost test from the perspective of utility, government 

agency or third-party implementing the program. Costs may include program incentive, 

installation, and overhead costs; benefits may include avoided energy and capacity costs - 

including generation, transmission and distribution - by the utility. 

(3) Ratepayer impact measure test from the perspective of utility ratepayers not participating 

in available energy efficiency programs. This text includes the costs and benefits that will affect 

utility rates, including program and administration costs, as well as “lost revenues” to the utility; 

benefits include avoided energy and capacity costs, and additional resource savings. 

(4) Total resource cost test from the perspective of all utility customers in the service area. 

Costs may include the full incremental cost of the measure, program installation and overhead 

costs; benefits may include avoided energy and capacity costs, and additional resource savings. 
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(5) Societal cost test from the social perspective. In addition to benefits considered in total 

resource cost test, may also include non-monetized benefits such as environmental and health 

benefits. 

While many states consider more than one cost test in evaluating EE programs, the most 

commonly used (29 states) primary test is the total resources cost test. This test is considered to 

be the best measure of the interests of all utility customers. The utility and societal cost tests are 

the next most commonly used primary tests, used by five states each. The utility cost test is 

considered to be the most comparable metric to compare with supply-side resource investments 

from a utility resource planning perspective. 

Economic and modeling analyses of climate change policy suggests that energy 

efficiency presents a large potential in reducing greenhouse gas emissions and plays a critical 

role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction 

targetsxlvii. Consistently, evaluations of the economic potential for carbon dioxide reductions 

from the United States’ power sector identify demand-side energy efficiency as the lowest cost 

strategy (typically, as noted above, with positive net present value) as well as the strategy having 

the greatest reduction potentialxlviii. For example, McKinsey (2007)xlix found that EE accounted 

for more than 60% of their mid-range potential for greenhouse gas reductions from the U.S. 

power sector and that it was available at positive net present value if “persistent barriers to 

market efficiency” could be addressed. 

Costs of Saved Energy 

A common metric for comparing alternative electricity resource options within utility 

resource plans is the levelized cost of energy (LCOE) or, for EE resources, the levelized cost of 

saved energy (LCSE)l

. LCSE EE is often compared favorably with LCOE of alternative new 

generation sources such as fossil-fueled or nuclear power plants, or renewable energy resources 

like wind or solar-power generation. In these comparisons, typically only utility (or program) 

costs are considered, not the total costs of saved energy that are discussed later in this chapter. 

The energy efficiency analysis literature reports average LCSE in the range of 1-6 cents/kWh 

Comment [A35]: These LCSE numbers by most

metrics would be considered very competitive and

sometimes even lowest‐cost.  The methodology

presented later, based on program costs, ends up

providing numbers that seem high. Please consider

revisiting the methodology if appropriate.

Comment [A36]: EPA RESPONSE: Comment is

unclear to EPA. The LCSE values here do represent

program/utility costs. The later cost discussion

considers both program/utility and participant

costs. The later values are estimates of the total

costs of realizing EE savings at BB4 levels and are

include several conservative judgments leading to

relatively high values.
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 27 

based on program administrator cost.22 A recent review by ACEEE (2014) examined studies 

across 20 states between 2009 and 2012, and estimated LCSE for electricity energy efficiency 

programs in the range of 1.3-5.6 cents/kWh, with a mean value of 2.8 cents/kWh.li Earlier 

reviews of utility EE programs identified a similar range of LCSE. Friedrich et al. (2009) 

reviewed 14 utility studies of LCSE and found a range from 1.6 to 3.3 cents/kWh, with a mean 

value of 2.5 cents/kWh.lii An earlier ACEEE study (2004) reviewed cost-effectiveness analysis 

results in nine states and suggested that reported utility LCSE ranged between 2.3-4.4 

cents/kWh, with a mean value of 3 cents/kWh.liii

The economic literature also evaluates the LCSE from EE measures using other

techniques (e.g., econometrics, top-down modeling), although this body of studies is much 

smaller compared to the bottom-up, engineering-based analysis. The economic literature has 

varying treatment of the free ridership, EE program endogeneity, and the rebound effect. The 

different assumptions used in these analyses make direct comparison challenging, but overall 

these empirical analyses present a wider range of estimates of cost of saved energy. For example, 

a recent study by Auffhammer et al. (2008) examining utility DSM programs estimated the 

average utility cost of saved energy in the range of 5.1 to 14.6 cents per kWh.23 Some other 

studies in the economic literature suggest estimated LCSE in a similar range as from the bottomup analyses. Gillingham et al. (2004) estimated an average cost of 3.4 cents per kWh saved from 

utility EE programs.liv In a recent econometric analysis of utility rate-payer funded demand-side 

management and energy efficiency programs between 1992 and 2006, Arimura et al. (2009) 

found that the estimated energy savings in electricity consumption were achieved at an expected 

average cost to utilities of approximately 5 cents/kWh.lvi Using a top-down approach that 

evaluates the savings potential of EE investments using state- and region-specific price elasticity, 

Paul et al. (2011) estimated that electricity savings of 1 to 3 percent were available at a marginal 

cost of 5 cents/kWh and a corresponding average cost of 2.5-3.5 cents/kWh.lvii 

                                                            

22 Unless otherwise noted, estimates of LCSE discussed in this section refer to program administrator cost (also 

known as utility cost). The discount rates, average measure lives, and other assumptions affecting the calculation of 

LCSE were not always consistent or reported in all studies. 23 Auffhammer M., C. Blumstein, M. Fowlie. 2008. Demand Side Management and Energy Efficiency Revisited. 

Energy Journal 29(3): 91-104. It is noted that the econometric analyses in the literature addresses 

Deleted: ¶

Deleted: various

Deleted: empirical 

Deleted: al

Deleted: ,

Deleted: and

Deleted: s

Deleted: foun

Deleted: $

Deleted: $

Deleted: recent analyses 

Deleted: in 

Deleted: energy efficiency 

Deleted: i

Deleted: literature

Moved down [1]: Using a top-down approach 

that evaluates the savings potential of EE 

investments using state- and region-specific price 

elasticity, Paul et al. (2011) estimated that electricity 

savings were available at an average utility LCSE of 

2.5-3.5 cents/kWh.lv 

Comment [A37]: “Under the scenarios modeled, 

electricity savings of between 1 and 3 percent are 

achievable at a marginal cost of $50 per megawatt 

hour (MWh) and a corresponding average cost of 

$25–$35/MWh.”

  

Comment [A38]: EPA RESPONSE: See revised

text below including  

The marginal cost estimate from the paper.

Deleted: n

Comment [A40]: EPA RESPONSE: Your

calculation omits the large and critical cost savings

from avoided power system operations, etc. (fuel, ... [1]

Comment [A39]: Implies $50/MWh and coal

emits appox. 1 metric ton/MWh which is valued at

an SCC of approximately $40. Am I missing

something? I guess the co‐benefits make it

economically viable?   

Deleted: , using a 5% discount rate

Moved (insertion) [1]

Deleted: an 

Deleted: utility LCSE

Comment [A41]: “Under the scenarios modeled, ... [2]

Comment [A42]: EPA RESPONSE: See revised... [3]

Deleted: not 

Formatted ... [4]

Formatted ... [5]
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A number of analytical and data considerations related to LCSE estimation are also 

discussed in the literature, including the issue of “free riders” in EE programslviii, and the 

accuracy of utility reported costs and energy savings.

lix Energy efficiency practitioners also 

recognize the need to consider “free rider” and “spillover” effects in program evaluation. A 

slight majority of states adjust for free ridership in energy savings estimates, leading to higher 

LCSE values than otherwise would be the case. A smaller number of states adjust for spillover 

effects which reduce LCSE values when addressed.lx

Another consideration related to LCSE estimation is the rebound effect. The economic 

literature has extensive discussion of the potential rebound effect, market interactions and 

economy-wide response of energy efficiency policies and investments. An improvement in 

energy efficiency would effectively reduce the cost of a service or production input, potentially 

boosting its demand or production output thus increasing energy use (“direct” rebound). In 

addition, money saved from energy efficiency can be used for consumption or investment that 

can increase energy consumption in other markets of the economy and lower energy prices as a 

result of energy efficiency improvement may increase energy consumption (two forms of 

“indirect” rebound). Reviews suggest that both direct and indirect rebound effects exist and the 

size of such effects varies among different studies, technologies, sectors and income groups.lxi 

Overall, however, rebound effects are found to be relatively modest compared to the importance 

of energy efficiency as an effective way of reducing energy consumption and carbon emissions 

(Greening et al. 2000lxii; Sorrell 2007lxiii; Davis 2008lxiv; Gillingham et al. 2013lxv).

EE as an Abatement Measure 

Demand-side energy efficiency is a technically viable and broadly applicable measure for 

achieving significant reductions in the amount of generation required and associated emissions 

from affected EGUs. Moreover, this measure has been adopted by every state and most utilities 

across the country, typically through multiple policy approaches. Increased use of, and impacts 

from, state energy efficiency policies is a leading industry trend over recent years and the trend 

of increasing investment in EE programs is projected to continue for the next decade, at least. 

These findings support the inclusion of demand-side energy efficiency as an abatement measure 

for reducing carbon dioxide (CO2) emissions from fossil fuel-fired electric generating units 

Deleted: .

Deleted: ¶

Deleted: ; 

Deleted: ¶

Comment [A43]: Suggest making a table

comparing the estimated LCSE for these studies.

Comment [A44]: EPA RESPONSE: EPA believes

that the discussion presents this information well.

We will consider alternative presentation

approaches for Final.

Deleted: ,

Deleted: , and generally cost-effective

Deleted: utilization of 

Comment [A45]: EPA needs to provide data to

show how DSM is cost‐effective relative to other

abatement techniques (e.g. increased NGCC

capacity and generation)

Comment [A46]: EPA RESPONSE: This section

summarizes the appropriateness of EE as an

abatement measure. In later sections we discuss

cost analysis of the EE best practices scenario,

specifically, and the reasonableness of those costs

including a conservative estimation of costs per ton

of CO2 reduced.

Deleted: nearly 

Comment [A47]: 1) If it is projected to

increase then why is it not in the baseline? And 2)

If it does increase then the cost of achieving

incremental energy savings will be increasing.

Comment [A48]: EPA RESPONSE: The issue of

baseline is discussed in Analysis Limitations section

below. In short, the EE‐based state goals are not

additive to existing EE policies, but inclusive of those

policies. The EE goals generally represent both

continuation and expansion of existing policies. The

full costs and benefits of the goals are accounted for

without attempting to forecast BAU EE

policies/impacts through 2030.
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 29 

(EGUs). In the next section, we address the setting of state-specific goals for electricity savings 

levels resulting from state demand-side energy efficiency efforts. In the final section, the 

integration of these goals into the impacts assessment is presented and we consider the 

reasonableness of the costs of this building block.

State Goal Setting 

Approach 

To estimate the potential CO2 reductions at affected EGUs that could be achieved 

through implementation of demand-side energy efficiency policies as a part of state goals, the 

EPA developed a “best practices” demand-side energy efficiency scenario. This scenario 

provides an estimate of the potential for states to implement policies that increase investment in 

cost-effective demand-side energy efficiency technologies and practices, and projects the annual 

impacts of the scenario for each state. The scenario does not distinguish between policies that are 

currently in place and additional policies that in most states would be required to be implemented 

to realize the goals established. It does not represent an EPA forecast of business-as-usual 

impacts of state energy efficiency policies or an EPA estimate of the full potential of end-use 

energy efficiency available to the power system, but rather is intended to represent a feasible 

policy scenario showing the reductions of CO2 emissions from fossil fuel-fired EGUs resulting 

from accelerated use of energy efficiency policies in all states, generally consistent with ongoing 

industry trends. The scenario uses: 1) a level of performance that has already been demonstrated 

or required by policies (e.g., energy efficiency resource standards) of many leading states; 2) 

considers each state’s unique existing level of performance; and 3) allows appropriate time for 

each state to increase from their current level of performance to the identified best practices 

level. 

The best practices scenario is derived from state experience with, and reliance on, 

policies that drive investment in energy efficiency programs, and the energy savings that result 

from those efforts. We focus on energy efficiency programs for several reasons: 

 EE programs have achieved significant levels of savings and are being used in almost 

every state, 

Deleted: cost

Deleted: -effectiveness

Deleted: within the context of the broader 

proposal

Comment [A49]: Does it include, though, BAU

for states that should be in the baseline?   

Comment [A50]: EPA RESPONSE: See added

sentence.  Also see Analysis Limitations section

below for additional discussion.

Deleted: ,

Deleted: , 
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 30 

 EE program spending and savings levels are reported by utility or other program 

administrator, by state, and compiled nationally, using standardized elements and 

definitions, and 

 EE program savings are projected and evaluated under requirements established and 

overseen by state utility commissions, and by municipal and cooperative utility boards of 

directors. 

While the approach is derived from information about energy efficiency programs 

overseen by state utility commissions, other state energy efficiency policies are available to 

realize a state’s goals24 such as building energy codes, appliance standards, and building energy 

benchmarking requirements. All policies included in a state plan will need to meet established 

requirements or guidance for EM&V25. 

The following steps were taken to establish the inputs for development of the best 

practices scenario for each state: 

 Step 1: Determine current level of performance 

 Step 2: Determine best practices level of performance 

 Step 3: Determine start year for state efforts 

 Step 4: Determine start year level of performance 

 Step 5: Determine pace at which states improve from start year to best practices level of 

performance 

 Step 6: Determine average portfolio measure life and distribution of measure lives 

 Step 7: Determine sustainability of best practices level of performance 

Inputs 

Step 1: Determine Current Level of Performance 

A fundamental indicator of the level of energy efficiency program performance is 

incremental annual savings as a percent of retail sales. This is a common metric defining savings 

levels for energy efficiency resource standards and is readily calculated from EIA Form 861 data 

                                                            

24 See State Plan Considerations TSD. 

25 See State Plan Considerations TSD. 
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for each state. Incremental annual savings are also more directly estimated and evaluated than 

are cumulative savings26. For the best practices scenario, we aggregated the most recent year of 

EIA Form 861 data to the state level to establish each state’s current level of performance. These 

results were presented previously in Table 5-4. 

Step 2: Determine Best Practices Level of Performance

As discussed previously, achievable demand-side energy efficiency potential exists at 

significant and comparable levels (on the basis of total cumulative potential over a period of ten 

to twenty years) in all regions of the country. While varied regional characteristics (e.g., avoided 

power system costs, economic growth, sectoral mix, climate, and level of past energy efficiency 

efforts) affect estimates of achievable potential, ongoing improvements in energy-efficient 

technologies and practices, economic growth, population increases, and continually improving 

strategies for program delivery have resulted in persistent and substantial levels of achievable 

potential regardless of specific regional characteristics. 

A direct indicator of the achievable incremental levels of energy savings performance is 

provided by past performance at the state and utility levels, and by requirements states have put 

in place for levels of savings to be achieved by 2020. As discussed, these requirements are 

typically in the form of energy efficiency resource standards or similar savings goals that are 

applied to utilities in the state27. 

Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA 

Form 861 (2012) data, aggregated to the state level, and categorized into four ranges of savings 

levels (< 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and >= 1.5%). As shown, three states achieved 

the highest level of performance (> 1.5%) and an additional eight states achieved the second 

highest level of performance (1.0% to 1.49%). 

TABLE 5-8 

2012 Reported State Levels of Incremental Annual Savings 

                                                            

26 Estimates of cumulative savings impacts in a given year are derived from incremental savings values and 

information on measure lives. Information on measure lives is less consistently gathered than is information on 

incremental savings values. 

27 See State Plan Considerations TSD for more information. 

Deleted: ¶

Formatted: Space After: 0 pt, Line spacing: 

single

Deleted: Page Break

¶

Deleted: lxvi

Comment [A51]: What and where is this

superscript?  

Comment [A52]: EPA RESPONSE: This is an

endnote that provides reference to the EIA 861

data. It is deleted to avoid confusion and the data is

referenced elsewhere in the chapter.

Deleted: >

Comment [A53]: Should this be < 0.5?  

Comment [A54]: EPA RESPONSE: Thanks for

pointing out the typo. It is corrected in the text.  

Comment [A55]: What evidence does EPA have

that 1) these states achieved this level of EE due to

the DSM policies they have in place; 2)  these

policies will continue to have this level of effect in

the future; and 3) that demonstrates that these

DSM will be able to push the remaining states to

achieve the same level of success with these

policies?  

Comment [A56]: EPA RESPONSE: 1)  This is

discussed throughout this TSD. These are the values

that administrators of EE programs report to EIA as

representing the impacts due to their EE programs.

The large majority of savings reported are also

reported to state utility commissions using

methodologies required by the commissions. The

commissions statutory role is to ensure that rates

are “just and reasonable” and EE program costs are

a component of rates. 2) In this section we are not

addressing are asserting this point. 3) the ability of

other states to meet these levels is supported in

several sections of this chapter including the

following table, the discussion of studies of EE

achievable potential, and the results in context

section below.
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 32 

Incremental Savings as % of 

Retail Sales 

# of States States 

>= 1.5% 3 AZ, ME, VT 

1.0% to 1.49% 8 CA, CT, IA, MI, MN, OR, PA, WI 

0.5% to 0.99% 14 

< 0.5% 25 

Table 5-9 summarizes incremental savings levels required by state policy on or before 

2020 and categorized into the same four rangeslxvii. Eleven states are required to achieve the 

highest level of performance (> 1.5%) and an additional five states are required to achieve the 

next highest level of performance (1.0% to 1.49%). 

TABLE 5-9 

Levels of Incremental Savings Required by State Policy on or before 2020 

Incremental Savings as % 

of Retail Sales 

# of States States 

>= 1.5% 11 AZ, CO, IL, IN, 

 MA, MN, NY, OH, RI, VT, WA 

1.0% to 1.49% 5 HI, IA, ME, MI, OR 

0.5% to 0.99% 3 AR, CA, WI 

< 0.5% 1 TX 

 For the best practices level of performance for Option 128, the EPA has chosen 1.5% 

incremental savings as a percentage of retail sales. This level was achieved by three states (AZ, 

ME, and VT) in 2012 and an additional nine states (CO, IL, IN, MA, MN, NY, OH, RI, and 

WA), accounting for overlap, are expected to achieve this level by 2020. Thus, twelve states 

have either achieved or are required to achieve this level of performance by 2020. 

                                                            

28 See Preamble and Goal Computation TSD for description of Option 1. 

Comment [A57]: Indicates that all these states

should be in the baseline and not the policy

scenariors.

Comment [A58]: EPA RESPONSE: See sentence

added above in Approach section of this, the Goal

setting section. Also see discussion below in Analysis

Limitations. EPA establishes these goals to be

inclusive of, not in addition to, existing state efforts

including statutory/regulatory requirements for

future years.
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For Option 229, the EPA has chosen 1.0% incremental savings as a percentage of retail 

sales as the best practices level of performance for this alternate approach. This level was 

achieved by eleven states in 2012 and an additional twelve states are expected to achieve this 

level by 2020. In total, twenty states (accounting for duplication between the two sets of states) 

have either achieved or are required to achieve this level of performance by 2020. 

Step 3: Determine Start Year for State Efforts 

For construction of the best practices scenario, the EPA has used 2017, the year following 

the required state plan submittal30, as the first year for state efforts. 

Step 4: Determine Start Year Level of Performance 

For construction of the best practices scenario, the EPA has set each state’s level of 

performance (incremental savings) in the start year (2017) to its current level of performance 

(aggregated to the state-level from reported EIA Form 861 data). This approach reflects neither 

improvement nor decline in performance between 2012 and 2017. Any improvement in EE 

savings performance between 2012 and 2017 will benefit a state in meeting its state EE goals for 

the 2020-2029 interim compliance period31. 

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of 

Performance 

To determine a trajectory of incremental savings levels from the 2017 level to the best 

practices level, the EPA considered past performance of individual program administrators32 as 

well as requirements of existing state energy efficiency resource standards. For the past 

performance of individual program administrators, we first screened the data and divided them 

into moderate and high performing sub-groups. The moderate group (47 entities) was defined as 

programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high 

group (26 entities) was defined as programs that achieved greater than 1.5% maximum 

                                                            

29 See Preamble and Goal Computation TSD for description of Option 2. 

30 See Preamble and State Plan Considerations TSD for descriptions of the schedule for state plan submittals. 

31 See Preamble for description of interim and final compliance periods. 

32 EIA 861 was the primary data source; however, we supplemented EIA 861 data with data for third-party program 

administrators because prior to 2011 EIA did not collect data from third-party program administrators. 

Comment [A59]: In the preamble, it is noted

that states can have an additional year to submit a

final state plan, or two additional years if submitting

a multi‐state plan.  How is this reflected in terms of

determining the start year for state efforts?

Comment [A60]: EPA RESPONSE: The start year

used for this analysis is 2017. This is the year

following state plan submittal if not requesting

delay for one or two years. However, that does not

preclude a state from taking steps on EE programs

consistent with this goal setting approach and they

will benefit in EE contributing to their compliance by

doing so.
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 34 

incremental savings levels. We then calculated the rate at which each entity had increased 

savings over time and calculated average values for each sub-group. For the moderate group, the 

average rate of improvement of incremental annual savings rate was 0.30% per year. For the 

high group, the average rate of improvement of incremental annual savings rate was 0.38% per 

year. See Appendix 5-3 for supporting data and analysis. 

 The EPA also considered requirements of existing state EERS and evaluated the rate at 

which their incremental savings levels increase over time. For several EERS, we were unable to 

clearly identify ramp-up schedules. We identified ten states with clear schedules and calculated 

the average rate of improvement for each. The average rate of improvement of incremental 

annual savings rate required for these ten states is 0.21% per year. See Appendix 5-3 for 

supporting data and analysis. 

Based on these results, for the best practices rate of improvement the EPA has chosen 

0.2% per year and 0.15% per year for Options 1 and 2, respectively. These values are 

conservative by comparison with our analysis of past state performance and future state 

requirements. 

Step 6: Determine Average Portfolio Measure Life and Distribution of Measure Lives 

The next step in defining the best practices scenario requires projecting the cumulative 

future impacts of the annual incremental savings levels for each state. The incremental savings 

impacts reflect the savings from EE measures put in place in that year, driven by EE program 

activities in that year. The cumulative annual savings represent the total impacts of all EE 

measures put in place in that year and all prior years, due to EE program activities. The 

cumulative savings account for the continuing impacts of energy efficiency measures that remain 

in place for a period of time (the “measure life”) before being replaced. For example, the 

purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being 

replaced with a new model. To estimate cumulative impacts of a series of years of incremental 

savings, the industry uses the concept of an average measure life for the entire portfolio of EE 

programs. Rather than use a single, average measure life to represent a diverse portfolio of 

programs, that range in measure lives from as little as a few years (e.g., certain lighting 

technologies and applications) to as long as fifteen or twenty years (e.g., adding insulation to an 

Deleted: s

Comment [A61]: Again if the improvements are

not driven by 111(d) then they should be in the

baseline.

Comment [A62]: EPA RESPONSE: See other

responses to this comment, above. This sentence is

simply defining the term incremental savings.
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attic), the EPA is assuming a distribution of measure lives around the average to account for 

future impacts of incremental savings levelslxviii. 

 In 2014, ACEEE updated their 2004 and 2009 national reviews of EE program costs and 

related program characteristics, including measure lives. They reviewed electricity EE program 

data from 20 states and summarized average measure lifetimes by state and customer class. 

Table 5-10 summarizes the results from the ACEEE study and shows an average across all 

sectors for these states of 10.6 years.

TABLE 5-10 

Average Electricity Measure lifetimes by state and customer class 

Sector 

Residential Commercial/Business Industrial All Sectors 

Average 8.1 12.5 9.5 10.6 

Other studies have found slightly higher values for average measure life for EE portfolios, 

ranging from 10 to 13 yearslxix. Our assumption of 10 years is conservative by comparison and 

leads to lower cumulative impacts over time and correspondingly lower state goals. 

To approximate a distribution of measure lives across an EE portfolio, consistent with an 

average measure life of ten years, we have assumed an even distribution from one year in length 

to two times the average measure life (twenty years) in length. Our approach is generally 

supported by the substantial range in measure lives reviewed and summarized in a 2014 study by 

LBNL which shows an interquartile range from five to 25 years across twelve program 

categories (e.g., low income, residential new construction, commercial/industrial custom, etc.). 

Our approach represents a first-order approximation of the distribution of measure lives across a 

diverse portfolio of programs. The more common approach in other studies is to assume a 

portfolio with no diversity of measure lives whatsoever, with the entirety of incremental savings 

being realized in each year from the first through the full average measure life and then dropping 

to zero in the following year. Our approach is a conservative one, leading to the same quantity of 

total energy savings, but with a greater portion of the savings occurring in later years than occurs 

Comment [A63]: What are the costs?

Comment [A64]: EPA RESPONSE: EE program

costs include costs associated with all activities of

operating a comprehensive EE program portfolio.

These include costs such as: customer incentives for

investing in more efficient technologies and

practices, development and implementation of

information programs, subsidized energy audits,

etc., as well as the administrative costs associated

with planning, managing and implementing the full

portfolio.

Deleted: ¶

Deleted: ¶

Page Break

Comment [A65]: Consider referencing other

sources in addition to ACEEE data, including a

forthcoming DOE technical memo and Northwest

Regional Forum documents.

Comment [A66]: EPA RESPONSE: EPA is

interested in reviewing the DOE technical memo

cited. We do believe the values cited in this table

are a reasonable basis for the measure life

assumption for this analysis, particularly since we

are erring on the conservative side of lower average

measure life which leads to lower EE goals and

higher LCSE.

Comment [A67]: The distribution of measure

lives is not uniform, but if you do want to use a

uniform distribution, it should be longer, since LBNL

has shown that some measures last 25 years. (Based

on data, the best estimate of the top end of a

uniform distribution is the maximum of the

observations.) If you use a uniform distribution from

0 to 25 years, and take the midpoint as your

estimate, moving from 10 to 12.5 years would have

a significant impact.   

Comment [A68]: EPA RESPONSE: EPA recognizes

that there are measures that have longer lives than

20 years and we are familiar with the LBNL analysis

referred to. We believe our approach is a

reasonable first order approximation that is an

improvement over the industry standard approach

of assuming no variation whatsoever across the

portfolio. If we took the approach suggested, we

would increase our average measure life to 12.5

solely on the basis that some measures have a life

longer than 20 years and we do not agree with this

approach as the 12.5 avg measure life is not

supported by known data sources.
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with the more common, and simpler, approach. This results in lower cumulative impacts in 

earlier years and correspondingly lower state goals through 2030. 

 

Step 7: Determine Sustainability of Best Practices Level of Performance 

For construction of the best practices scenario, once a state achieves the best practices 

level of performance, the EPA has kept the level of performance constant through 2030. For 

states with lower levels of current performance (and, hence, later achievement of the best 

practices level of performance – as late as 2025 in some instances), this requires sustaining the 

target level for as little as five years. For states currently at or above the best practices level of 

performance, this reflects an ability to sustain the target level for thirteen years (2017 through 

2030). 

 Limited empirical data suggests the reasonableness of this approach; however, 

comprehensive data, across all regions and states, does not exist because these levels of 

performance have not been achieved and sustained nationwide previously. The Northwest Power 

Conservation Council (NPCC) provides one such example. NPCC has been conducting the most 

consistent and long-running series of evaluations of achievable cost-effective potential in the 

country, updated every five years, as part of their five-state33 regional energy resource planslxx. 

These analyses have become more detailed, reliable, and purposeful over time. Since 1998, 

NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity 

savings from energy efficiency programs have increased rapidly, more than quadrupling between 

1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and 

exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our 

research shows that when programs invest in higher levels of efficiency, this helps drive 

measurement improvements and technical innovation, resulting in larger and more reliable 

conservation supply estimates.”lxxi Table 5-11 summarizes the NPCC’s achievable potential 

estimates and evaluated savings since 1998lxxii. 

TABLE 5-11 

                                                            

33 NPCC’s resource plans cover Idaho, Oregon, and Washington in their entirety, and western regions of Montana 

and Wyoming. 

Comment [A69]: Does this mean that they no

longer make any improvements after the reach the

best practices level of performance  

Comment [A70]: EPA RESPONSE: This means

that a state continues to achieve 1.5% (or 1.0%)

incremental savings each year. This requires

continuing implementation of effective EE programs

(or other policies). Without this ongoing effort, the

effects of EE programs wane over time. This ongoing

effort is a significant aspect of the state EE goal

setting approach.

Comment [A71]: EPA could reference the 12‐13

year average measure life. This matches very well

with the sustained period from 2017 to 2030.

Comment [A72]: EPA RESPONSE: The measure

life represents the ongoing impact of a single year’s

portfolio of EE investments. It does not support the

ability of a state to sustain a best practice level of

incremental/first‐year performance for 10 or more

years.

Comment [A73]: Not peer‐reviewed. It would

be good for EPA to provide peer‐reviewed evidence

on the efficacy of these types of DSM programs.

Comment [A74]: EPA Response: EPA is

interested in reviewing any relevant peer‐reviewed

analysis on this issue that we have not referenced.

EPA believes that NPCC’s experience and analysis is

very significant and relevant. They are one of a small

number of leading entities that has been developing

resource plans that include EE policies, tracking the

impacts, and revising plans for several decades.

Their work is reviewed through extensive public

processes and provides a critical basis for energy

planning in the northwest. As such, their study has

significant relevance. In the next paragraph we

point to further support for the assumption being

addressed in this section.  
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NPCC Achievable EE Potential and Achieved Incremental Savings (1998-2010) 

Year 

1998 2005 2010 

Achievable Potential over 20 Years 

(GWh) 

13,447 24,651 51,684 

Achieved Incremental Savings from 

EE Programs (GWh) 

547 1,184 2,248 

 Additional substantiation of this approach is provided by average annual achievable rates 

from reviewed studies, as discussed previously, and comparison of those with the rates resulting 

from the best practices scenario. We address this in a later section, Results in Context, after 

presenting those results. 

Summary of Best Practices Scenarios Construction 

 Table 5-12 provides a summary of inputs for the best practices scenarios for Options 1 

and 2. The pace of improvements, average measure life, and distribution of measure lives are 

each conservative and, therefore, contribute lower state goals than would otherwise result. 

Similarly, the best practices level of performance, being based solely on results from and 

requirements of EE programs, is less stringent than a level would be that accounted for potential 

impacts of other state EE policies such as building energy codes, building energy benchmarking 

requirements, and state appliance standards. The use of 2012 level of performance for the 2017 

start year, allows states that increase their use of effective EE policies prior to submitting their 

implementation plan to benefit. 

Table 5-12 

Summary of EE Best Practices Scenario Inputs 

Input Option 1 Option 2 

Current Level of Performance 

(incremental savings as % of 

Data from 2012 EIA 861 

(2012) 

Data from 2012 EIA 861 

(2012) 

Formatted: Not Highlight
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retail sales) 

Best Practices Level of 

Performance 

(incremental savings as % of 

retails sales) 

1.5% 1.0% 

Start Year 2017 2017 

Start Year Level of 

Performance 

Data from 2012 EIA 861 

(2012) 

Data from 2012 EIA 861 

(2012) 

Pace of Improvement 

(increase in incremental 

savings rate per year) 

0.20% per year 0.15% per year 

Average Measure Life and 

Distribution of Measure Lives 

10 years; evenly distributed 

across 20 years 

10 years; evenly distributed 

across 20 years 

Continued Performance 

Once achieved, best 

practices level sustained 

through 2030 

Once achieved, best practices 

level sustained through 2025 

Calculations 

This section addresses the calculations for determining cumulative savings levels 

(cumulative savings as a percentage of baseline sales) for each state, for each year of the interim 

and final compliance periods for Options 1 and 2. The cumulative savings levels are derived 

based upon the key inputs summarized in Table 5-12. These levels represent the demand-side EE 

component of the state goals for each state. See the Goal Computation TSD for a detailed 

description of how the demand-side EE component is used as one of several inputs to the 

calculation of interim and final state emission rate goals. 

Calculating the net cumulative savings as a percent of electricity sales for each state involves 

six steps. For each state, for each year (2017-2030 for Option 1 and 2017-2025 for Option 2) the 

following steps are taken: 

1. Determine annual business as usual (BAU) sales 
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2. Determine annual incremental EE savings as a percentage of sales 

3. Determine annual incremental EE savings (GWh) and sales after net EE (GWh) 

4. Determine annual expiring EE savings (GWh) 

5. Determine net cumulative EE savings (GWh) 

6. Determine net cumulative EE savings as a percentage of BAU sales 

To illustrate these calculations, each step is described and results provided for one 

state (using South Carolina as an example) for 2017 through 2025 for Option 1. We 

truncate the results at 2025 for simplicity, but full results are presented for all states in the 

section. 

Step 1: Determine the Annual Business as Usual (BAU) Sales 

BAU sales are derived by taking 2012 sales from EIA Form 861 data for the state 

and increasing them for each subsequent year by the average annual growth rate from the 

AEO 2013 Reference Case for the region corresponding to the state. For South Carolina 

the corresponding region is SERC and the average annual growth rate from 2012 to 2040 

is 1.10% per year. The resulting values are summarized in Table 5-13 for South Carolina. 

TABLE 5-13 

BAU Sales for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

BAU Sales 

(GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005 

Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales

As discussed, the 2017 value for annual incremental EE savings as a percentage 

of sales is set at the 2012 value based upon EIA-861 reported data. For South Carolina 

that value is 0.34%. This value is then increased by the pace of improvement of 0.2% per 

year (for Option 1) until the goal level of 1.50% (for Option 1) is reached and then held 

constant. The resulting values are summarized in Table 5-14 for South Carolina. 

Deleted: ¶

Deleted: Page Break

¶

Formatted: After: 0", Line spacing: single
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TABLE 5-14 

Annual Incremental EE Savings as a Percentage of Sales for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annual 

Incremental 

EE Savings 

(% sales) 

0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50% 

Step 3: Determine Annual Incremental EE Savings (GWh) and Sales after net EE

Annual incremental EE savings are calculated by multiplying the annual 

incremental savings as a percentage of sales times the prior year sales after net EE. Sales 

after net EE are calculated by subtracting net cumulative savings from BAU sales. The 

resulting values are summarized in Table 5-15 for South Carolina. 

TABLE 5-15 

Annual Incremental EE Savings and Sales after Net EE Savings for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annual 

Incremental 

EE Savings 

(GWh) 

274 440 608 777 945 1,113 1,250 1,249 1,249 

Sales after 

Net EE 

(GWh) 

82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346 

Step 4: Determine Annual Expiring EE Savings (GWh)
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Expiring EE savings are calculated as the sum of all expired savings in a given 

year from all prior years’ incremental (first-year) savings based upon an average measure 

life of 10 years and a linear decline in first-year savings over twenty years. As an 

example, Figure 2 illustrates the decline in first-year savings from EE measures installed 

in 2017. The resulting values for expiring EE savings are summarized in Table 5-16 for 

South Carolina. 

FIGURE 1 

Generalized Distribution of First-Year Savings over Time. 

TABLE 5-16 

Expiring EE Savings for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Expiring 

EE Savings 

(GWh) 

0 14 38 70 110 160 219 285 350 

Step 5: Determine the Net Cumulative EE Savings (GWh)
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Net cumulative EE savings in a given year are equal to annual incremental 

savings for that year minus total expiring savings for that year plus net cumulative 

savings for the prior year. The resulting values are summarized for South Carolina in 

Table 5-17. 

 

Formatted: Indent: First line: 0.5"

Deleted: ¶
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TABLE 5-17 

Net Cumulative EE Savings for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Net 

Cumulative 

EE Savings 

(GWh) 

274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659 

Step 6: Determine the Net Cumulative EE Savings as a Percentage of BAU Sales

Net cumulative EE savings as a percentage of BAU sales are equal to net 

cumulative savings divided by BAU sales. The resulting values are summarized for South 

Carolina in Table 5-18. 

TABLE 5-18 

Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Net 

Cumulative 

EE Savings 

as % of 

BAU Sales 

0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40% 

Summary of General Formulas and Results by Step for South Carolina 

 Tables 5-19 and 5-20 provide summaries of the generic formulas and results for 

South Carolina for each step. 
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TABLE 5-19 

Summary of Calculation Formulas by Step 

Step Result Formula 

1 BAU Sales (GWh) BAU Sales year i = BAU Sale year i-1 * Annual Average Sales Growth Rate 

2 

Annual Incremental 

EE Savings (% of 

Sales) 

Annual Incremental EE Savings 2017 = Annual Incremental EE Savings 2012; 

Annual Incremental EE Savings year i = Annual Incremental EE Savings year 

i-1 + annual pace of improvement (until goal level is reached) 

3 

Annual Incremental 

EE Savings (GWh) 

Annual Incremental Savings year i = Annual Incremental Savings as a 

Percent of Sales year i * Sales After Net EE year i-1

3 

Sales after Net EE 

(GWh) 

Sales After Net EE year i = BAU Sales year i – Net Cumulative Savings year i

4 Expiring EE 

Savings (GWh) 

Expiring Savings year i = Σ Expiring measures from all prior program years 

(10-year average measure life with linearly decline over 20 years) 

5 

Net Cumulative 

Savings (GWh) 

Net Cumulative Savings year i = Σ Annual Incremental Savings YTD – 

Expiring Savings year i

6 

Net Cumulative 

Savings (% of Sales) 

Net Cumulative Savings year i = Net Cumulative Savings year i / BAU Sales

year i

TABLE 5-20 

Summary of Results by Step for South Carolina. 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

BAU Sales 

(GWh) 

82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005 

Annual 

Incremental 

0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50% 
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EE Savings 

(% sales) 

Annual 

Incremental 

EE Savings 

(GWh) 

274 440 608 777 945 1,113 1,250 1,249 1,249 

Sales after 

Net EE 

(GWh) 

82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346 

Expiring EE 

Savings 

(GWh) 

0 14 38 70 110 160 219 285 350 

Net 

Cumulative 

EE Savings 

(GWh) 

274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659 

Net 

Cumulative 

EE Savings 

as % of BAU 

Sales 

0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40% 

Results 

Summary of Results 

As discussed, the EE goals for each state are represented as cumulative savings as a 

percentage of retail sales by year for each option. Table 5-21 summarizes these values for the 

first and last year of the interim compliance period for Options 1 and 2. See Appendix 5-4 for 

comprehensive results by state, for years 2017 through 2030, including both annual incremental 

and cumulative savings as a percentage of retail sales, for each options. 
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TABLE 5-21 

Summary of State EE Goals for Options 1 and 2 

State 

EE State Goal 

Cumulative Savings as a % of Retail Sales 

Option 1 Option 2 

2020 2029 2020 2024 

Alabama 1.36% 9.48% 1.07% 3.86%

Arizona 1.52% 9.71% 1.24% 4.10%

Arkansas 5.24% 11.42% 3.52% 5.98%

California 4.95% 11.56% 3.55% 6.08%

Colorado 3.92% 11.01% 3.32% 5.87%

Connecticut 4.71% 11.88% 3.61% 6.25%

Delaware 1.14% 9.47% 0.86% 3.59%

District of Columbia 1.14% 9.47% 0.86% 3.59%

Florida 2.03% 9.98% 1.75% 4.65%

Georgia 1.76% 9.83% 1.48% 4.36%

Idaho 4.36% 11.63% 3.52% 6.15%

Iowa 3.80% 11.10% 3.28% 5.88%

Illinois 3.20% 11.11% 2.89% 5.70%

Indiana 4.65% 11.66% 3.58% 6.17%

Kansas 1.22% 9.52% 0.94% 3.70%

Kentucky 1.91% 10.02% 1.63% 4.55%

Louisiana 1.14% 9.33% 0.85% 3.56%

Maine 4.43% 11.77% 3.55% 6.21%

Maryland 4.21% 11.51% 3.47% 6.10%

Massachusetts 5.37% 12.13% 3.61% 6.25%

Michigan 4.59% 11.77% 3.59% 6.22%
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Minnesota 4.80% 11.72% 3.58% 6.17%

Mississippi 1.58% 9.92% 1.29% 4.20%

Missouri 1.40% 9.59% 1.12% 3.93%

Montana 3.36% 10.90% 3.01% 5.69%

Nebraska 2.84% 11.00% 2.56% 5.49%

Nevada 2.37% 10.26% 2.09% 4.98%

New Hampshire 1.25% 9.58% 0.96% 3.74%

New Jersey 3.10% 10.60% 2.81% 5.50%

New Mexico 4.42% 11.76% 3.54% 6.20%

New York 1.39% 9.71% 1.11% 3.95%

North Carolina 2.20% 10.40% 1.91% 4.89%

North Dakota 2.95% 10.69% 2.67% 5.45%

Ohio 4.17% 11.56% 3.47% 6.12%

Oklahoma 1.86% 9.97% 1.57% 4.49%

Oregon 4.66% 11.41% 3.55% 6.06%

Pennsylvania 4.67% 11.69% 3.58% 6.18%

Rhode Island 3.90% 11.56% 3.35% 6.06%

South Carolina 2.32% 10.23% 2.04% 4.94%

South Dakota 1.60% 9.91% 1.32% 4.22%

Tennessee 2.21% 10.26% 1.93% 4.86%

Texas 1.78% 9.91% 1.50% 4.40%

Utah 3.62% 11.03% 3.19% 5.82%

Vermont 1.23% 9.33% 0.95% 3.67%

Virginia 5.37% 12.13% 3.61% 6.25%

Washington 4.24% 11.26% 3.45% 6.00%

West Virginia 4.68% 11.79% 3.60% 6.22%

Wisconsin 1.77% 10.11% 1.49% 4.44%

Wyoming 1.61% 9.73% 1.33% 4.19%

Continental U.S. 3.05% 10.66% 2.44% 5.18%
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Alaska 1.22% 9.45% 0.94% 3.69%

Hawaii 1.29% 9.52% 1.01% 3.79%

U.S. Total 3.04% 10.65% 2.43% 5.17%

Results in Context 

 To provide context for state cumulative savings results presented in Table 5-21 and 

Appendix 5-4, the average annual savings were calculated for each state through 2025 and 2030, 

starting from 2017. Table 5-22 summarizes the results. 

TABLE 5-22 

Summary of Average Annual Savings Rates of Best Practices Scenario 

Option Years 

Number 

of Years 

Range of Cumulative 

Savings (% of Sales) 

across States in Last 

Year (2025/2030) 

Range of Average 

Annual Savings 

Rates across States 

National 

Average Annual 

Savings Rate 

1 

2017-

2030 

13 9.9% to 12.5% 0.76%/year to 

0.96%/year 0.86% per year 

2 

2017-

2025 

8 4.3% to 6.8% 0.54%/year to 

0.85%/year 0.72% per year 

 The state range and national values for the average annual savings rate represented in the 

EE best practices scenario are below the range of values found in recent utility, state, and 

regional studies (1.2% to 1.5% per year) as summarized in Table 5-6, and within the range of 

values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year) 

as summarized in Table 5-7. These results provide additional support for the feasibility of the EE 

best practices scenario and associated state-specific EE goals. 

Impacts Assessment 

Approach 
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In the Goal Computation TSD, state-specific EE goals from the previous section are 

integrated with the other building blocks and used to set state-specific emission rate goals for the 

interim and final compliance periods. These state emission rate goals are then represented as 

requirements within the power sector modeling for the Regulatory Impacts Analysis (RIA). In 

addition, the EE state goals, resulting from the EE best practices scenario, are used to adjust 

electricity demand levels used as exogenous inputs to power sector modeling for the illustrative 

compliance scenarios. In other words, the degree to which EE is employed as an abatement 

resource is not determined endogenously within the power sector modeling based upon 

optimization of costs but, rather, “hard wired” into the illustrative compliance scenarios. This 

approach is taken because the EPA has determined, as discussed previously, that EE is costeffective at the established EE goal levels. The EE goal levels were constrained by practical 

considerations of state EE policy implementation, specifically, the current levels of EE 

performance and the pace at which states can feasibly improve their levels of performance over 

time. 

The EE goals represented in the illustrative compliance scenarios lead to substantial 

reductions in power system costs due to the reductions in specified electricity demand. Since EE 

is not represented endogenously as an abatement measure within the power sector modeling, the 

costs associated with the EE best practice scenario must be estimated outside of the power sector 

modeling and integrated with the results from that modeling. These EE cost estimates, their 

basis, and calculations are addressed in the following sections. 

Inputs 

The following steps were taken to establish the inputs for development of the EE cost 

estimates for each state. 

 Step 1: Determine state-specific electricity savings by year 

 Step 2: Determine first-year program costs of saved energy 

 Step 3: Determine the ratio of program to participant costs 

 Step 4: Determine the escalation rate of EE costs 

Step 1: Determine State-Specific Electricity Savings by Year

Comment [A75]: This section creates inputs that

feed directly through to end costs. These costs are

external to the IPM modeling and are simply added

to the “rest of system” costs produced by IPM. The

ultimate program costs are therefore sensitive to

these input parameters. Highlighted below are

several places where it seems EPA may have been

overly conservative in its choice of parameters.

Using other numbers that are cited in this report it

is possible to deduce input costs that are only half

as high as what EPA uses. Even a more moderate

calculation reduces input costs by roughly 20‐30%.

Since this rule BSER relies on high EE, even a few

percentage points of reduction in these EE inputs

may lead to significant changes in the RIA cost

calculations. Please consider revisiting EE cost

inputs.

Comment [A76]: EPA RESPONSE: EPA

acknowledges that our determination of these

inputs have generally resulted in conservative inputs

(leading to higher costs) and, in combination, these

have a cumulative impact leading to costs that are

higher than would have resulted from less

conservative determinations.
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 Results from the previous section, State Goal Setting, provide the starting point for 

estimation of EE costs. From those results, state-specific annual incremental savings (MWh) and 

yearly distribution of associated continuing savings (MWh) in future years are used as inputs to 

the cost estimation calculations. 

Step 2: Determine First-Year Program Costs of Saved Energy

 First-year program costs refer to the full costs (e.g., administration, incentive payments, 

marketing, information to consumers, etc.) incurred by a utility or other administrator of EE 

programs in a given year that lead to EE measures (technologies and practices) put in place in 

that year and resulting in reductions in electricity demand in that and future years (driven by the 

mix of measure lives across the portfolio of EE programs employed). Unlike participant costs, 

program costs are readily known by the administrator of EE programs and are, therefore, an 

appropriate starting point for EE program cost analysis. In 2009, ACEEE conducted a national 

review of data on EE program costs from program annual reports, evaluation reports, and 

information compiled from contacts at program administers in 14 states. Compiled data was 

sourced from multiple EE program administrators in each state and over multiple years of data 

for each administrator. ACEEE found average first-year net34 costs of $275/MWh (2011$). The 

EPA has used this value for our analysis. 

 Two recent national analyses have found lower program costs than the 2009 ACEEE 

study. In 2014, ACEEE updated their analysis from 2009, expanding the number of states to 20, 

and including a greater number of program administrators and years. In this analysis ACEEE 

found average first-year net costs of $230/MWh (2011$). In 2014, an LBNL study presented 

results from a uniquely comprehensive study of EE program costs. The LBNL analysis reviewed 

program-level data from over 100 program administrators in 31 states. Data were collected from 

over 1,700 individual programs for up to three years (2009-2011), covering more than 4,000 

individual program years of data points. Because of the broad scope of their study and the lack of 

net savings information for many programs, LBNL focused on gross35, rather than net, savings 

                                                            

34 “Net costs” refers to costs per electricity saved after accounting for effects of free-ridership on those savings. 

Depending upon the state, spillover effects may also be accounted for in net costs. 

35 “Gross savings” refers to electricity savings before any accounting for effects of free-ridership or spillover. 

Comment [A77]: Some commenters

recommend using either the most recent ACEEE

analysis, with a first‐year net cost of $230/MWh or

the recent LBNL study, with a calculated first‐year

net cost of $175/MWh.  If used, these numbers

would lead to a cost of saved energy more in line

with the available literature than what EPA is

currently proposing.

Comment [A78]: EPA Response: EPA agrees that

use of those sources would lower our cost values.

The LBNL value cited, however, is not directly

applicable as it represents costs per GROSS MWh

saved and we are seeking to represent costs per

NET MWh saved. LBNL study does not offer any data

to suggest how to derive costs per NET Mwh from

their study. EPA believes that our inputs are in line

with reasonable ranges from available literature but

our conservatism does lead to cost estimates

towards the higher end of available bottom‐up

engineering based estimates.

Deleted: slightly substantially (16-36%) 

Comment [A82]: EPA RESPONSE: Already

responded to. See above.

Comment [A81]: Suggest EPA analysis use these

more recent and more robust numbers.  

Comment [A79]: The RFF discussion paper

“Supply Curves for Conserved Electricity” at

http://www.rff.org/documents/RFF‐DP‐11‐11.pdf

has cost estimates that could be included: “Under

the scenarios modeled, electricity savings of

between 1 and 3 percent are achievable at a

marginal cost of $50 per megawatt hour (MWh) and

a corresponding average cost of $25–$35/MWh.”  

Comment [A80]: EPA RESPONSE: This RFF paper

is referenced in the earlier discussion on “costs of

saved energy,” where estimates of levelized cost of

saved energy in the literature are discussed. This

section discusses the first‐year program costs, thus

is not the most appropriate place to reference this

paper

Deleted: as
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values. LBNL found national average first-year cost of gross savings of $162/MWh (2012$). 

Applying an average net-to-gross ratio of 0.9 and deflating costs at 3%, results in an estimated 

national average first-year cost of net savings of $175 (2011$). The up-to-date, more 

comprehensive results from the ACEEE and LBNL studies, indicate that the value of $275/MWh 

used for this analysis is conservative, resulting in comparatively higher total costs than would be 

the case based upon the newer studies. 

Step 3: Determine the Ratio of Program to Participant Costs

 As noted above, while program costs are readily known and consistently reported by the 

program administrator, participant costs require significant effort to estimate, and are less 

consistently estimated and reported. The ratio between program and participant costs will vary 

significantly from one program to the next within a utility’s portfolio. The EPA has used a 

generic approach to estimate the ratio of program to participant costs across an entire portfolio, 

thus providing for the estimation of total costs once program costs are determined. To derive the 

ratio, the EPA reviewed 2012 EE annual reports from program administrators in 22 states 

identified as leaders in EE programs36 based upon their magnitude of savings or their savings as 

a percentage of retail sales. Complete information on full portfolio participant costs were 

available for nine of the 22 states. Across the nine states, the average program and participant 

costs as a percentage of total costs were 53% and 47%, respectively. See Appendix 5-3 for the 

data and analysis documenting this review. Based on this review, the EPA has taken a slightly 

conservative approach37 and used a ratio of 1:1 between program and participant costs. We use 

this ratio to derive participant and total costs based upon program costs. Starting from program 

CSE of $275/MWh and applying the 1:1 ratio, we estimate participant CSE of $275/MWh and 

total CSE of $550/MWh (all values 2011$). 

Step 4: Determine the escalation rate of EE costs

                                                            

36 Leaders were identified using results from the 2013 ACEEE State Energy Efficiency Scorecard based on energy 

savings as a percentage of retail sales or total savings. 

37 If we had used the 53% and 47% values, starting from program costs, total costs would have been slightly lower 

than calculated with the 1:1 split used. 

Comment [A83]: It may not be necessary to

take the most conservative assumption particularly

when there have been subsequent refinements to

the study that may create more reliable numbers. In

addition, it may not be appropriate to use this old

number as a single point estimate to calculate EE

program costs for this rule. Please consider

revisiting if appropriate.

Comment [A84]: EPA RESPONSE: EPA

acknowledges repeatedly the conservatism of the

cost estimates but also believe they are supported

by the sources provided with appropriate expert

judgement and recognition of context in which

estimates are being made.

Comment [A85]: A commenters suggest DOE

may have updated participant cost data.

Comment [A86]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis DOE has

to provide.

Comment [A87]: If possible, please provide

further support for using a number different than

EPA’s calculations. If the derived number is used

(53/47), the total program costs would drop by

about 6%. This again inflates EE costs relative to

what the empirical evidence suggests they would

be.

Comment [A88]: EPA RESPONSE: Given the

survey on which these values are based, EPA finds it

reasonable to err towards the higher value for our

analysis. At higher levels of savings, national

breadth, etc., it is reasonable to anticipate the

historic results might underestimate portion of costs

borne by participants in future expanded program

efforts.

Comment [A89]: EPA seems to have taken a

conservative approach to costs that may not be

justified by the empirical literature. Please consider

revisiting if appropriate.

Comment [A90]: EPA RESPONSE:  EPA

acknowledges repeatedly the conservatism of the

cost estimates but also believe they are supported

by the sources provided with appropriate expert

judgement and recognition of context in which

estimates are being made.
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 The level of EE program impacts represented in the state EE goals are substantial and 

represent a scenario that has not previously been achieved and sustained at a national level in the 

U.S. Thus, even though the EPA has taken a conservative approach (i.e., leading to higher 

estimates of costs) to the development of the EE state goals as well as to other factors that affect 

the EE cost estimates, we have also chosen to take a cautious approach to the escalation of EE 

costs at higher levels of performance (i.e., as states improve from their historic levels of 

incremental savings to the best practices level of 1.5% of retails sales). Economic theory 

suggests two mechanisms that would change EE costs as higher levels of performance are 

achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE 

programs, and learning and expertise gained over time from the continued implementation of 

programs, are two factors that would lower costs as programs scale up and expand to realize 

higher levels of performance. However, the limited supply of EE abatement measures and the 

need to employ higher cost measures, over time, to reach higher levels of performance, and to 

sustain high levels of performance, are factors that would increase costs as higher levels of 

performance are achieved. Analysis based upon limited empirical data does provide support for 

significant economies of scale and/or cost reductions over time as learning and expertise are 

gainedlxxiii. “Supply curves” of EE as an energy resource, as well as EE as a measure represented 

within a GHG abatement curve, provide support for escalating costs as higher levels of savings 

are realizedlxxiv. In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted 

an approach that generically represented both effects discussed abovelxxv. LBNL changed EE 

costs (first-year program costs) as a function of EE savings levels, decreasing costs at savings 

levels up to 0.5%, leaving costs constant at the base level at savings levels from 0.5% to 1.5%, 

and increasing costs at savings levels above 1.5%. Another recent analysis, by ACEEE, provides 

weak statistical support for a cost increase of 20% when going from 0.5% to 1.0% savings rate 

and an additional cost increase of 20% when going from 1.0% to 1.5% savings ratelxxvi. 

In consideration of the above discussion, the EPA has chosen to escalate EE costs over 

three steps as a function of incremental savings (as a percentage of electricity sales) at the state 

level. Until a state reaches a 0.5% savings level, their costs are set at the base level; for savings 

levels between 0.5% and 1.0%, state costs are escalated to 120% of the base level; and for 

savings levels over 1.0%, state costs are escalated to 140% of the base level. This approach leads 

Comment [A91]: On the timescales of this rule

(16 years) we can expect a reasonable amount of

technological change to be occurring over that

period as well, which would offset some of the cost

escalation from extracting new supply. Please

consider incorporating or accounting for this.

Comment [A92]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis on this

topic that may be relevant to this discussion.

Comment [A93]: Please consider incorporating

literature on learning and cost reductions over time

across many technologies, and particularly in many

energy technologies.   

Comment [A94]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis on this

topic that may be relevant to this discussion.

Comment [A95]: EPA methodology seems to

select only the supply curve dimension and not the

cost improvement dimension. DOE now uses cost

improvements as part of their efficiency rulemaking

so it seems appropriate to apply that principle here.

Please consider revisiting if appropriate.

Comment [A96]: EPA RESPONSE: EPA is

interested in reviewing any relevant data or analysis

on cost improvements and would like to incorporate

them into the final rule.

Comment [A97]: This is quite different from the

approach EPA chose. The LBNL study takes into

consideration more programs and more driving

factors and seems to be a robust study. Please

consider revisiting EPA’s approach if appropriate.

Comment [A98]: EPA RESPONSE: EPA agrees

this is a significantly different approach, however,

LBNL was applying expert judgement in a context of

EE investments and savings at a substantially lower

level than EPA’s Best Practices scenario.  

Comment [A99]: Also came in lower than the

approach EPA chose.  Please consider revisiting

approach.

Comment [A100]: EPA RESPONSE: EPA agrees

this is a conservative assumption but we believe it is

reasonable and appropriate.

Comment [A101]: Here is a third example of

possibly overly conservative assumptions.

Reasonable alternates are to use 100% to base level

and then an adder, say 120%, above 1.5. LBNL

actually discounts from 0 to 0.5 level. If a cost

escalation to 140% is justified, please consider doing

it after 1.25 of base.

Comment [A102]: EPA RESPONSE: EPA agrees

this is a conservative assumption but we believe it is

reasonable and appropriate.
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to higher costs relative to the one used by LBNL when applied to EPA’s EE best practices 

scenario. 

Summary of Inputs for EE Cost Analysis 

 Table 5-23 provides a summary of inputs for the EE cost analysis including first-year 

costs of saved energy, ratio of program to participant costs, and escalation of costs as a function 

of the rate of incremental savings. Each of these factors incorporates some level of conservatism, 

leading to higher costs than would otherwise result. 

Table 5-23 

Summary of EE Cost Analysis Inputs 

Input Source or Assumption 

State-Specific Electricity Savings by Year Results from state goal setting 

First-Year Program Cost of Saved Energy $275/MWh (2011$) 

Ratio of Program to Participant Costs 1:1 

First-Year Participant Cost of Saved Energy $275/MWh (2011$) 

First-Year Total Cost of Saved Energy $550/MWh (2011$) 

Escalation of Costs 

Incremental savings rate 

0.5% - 1.0% > 1.0% 

120% of base costs: 

$660/MWh (2011$) 

140% of base costs: 

$770/MWh (2011$) 

Calculations 

This section addresses the calculations for estimating the costs associated with the statespecific EE goals discussed above. The results of these calculations are then used within the RIA 

and preamble. Specifically, three values are calculated (annual first-year costs, levelized cost of 

saved energy (LCSE), and annualized costs); for each, program and participant components are 

then calculated using the 1:1 ratio (i.e., 50% of total for each) derived above. Specific results 

from prior sections on state goal setting and impacts assessment inputs are used as inputs for 

Comment [A103]: Shown below that the EE cost

inputs can, under defensible and reasonable

alternate assumptions, range from  34% to 50%

lower; and are very likely to be at least 10‐15%

lower, than the numbers in this original Table 5‐23.

Suggest that EPA revisit the set of assumptions and

modify accordingly.

Comment [A104]: EPA RESPONSE: EPA

understands your alternative approach and

justification. We believe our approach is defensible,

reasonable, and more conservative. Given the

unique nature of the attaining the EE Best Practices

Scenario at a national level and the aggregate

impacts, EPA believes that this conservatism is

appropriate.

Deleted: ¶

Summary of Alternate EE Cost Analysis Inputs 

based on ACEEE 2014¶ ... [6]

Comment [A113]: To clarify, we are not

suggesting these are redline additions, but instead ... [7]

Comment [A106]: EPA RESPONSE: OK

Comment [A105]: Using only information

provided in the preceding paragraphs about cost

inputs, derived below are two alternate price inputs,

one based on the LBNL study and one based on the

updated ACEEE numbers.  

Comment [A107]: As stated above this

escalation can be treated in a couple of ways based

on the cited literature. Using the following:

0 To 1 = 100%

1 To 1.5 = 120%

Alternate suggestion (or some variation thereon):

0 to 0.5 = 80%

0.5 to 1 = 100%

1 to 1.25 = 120%

1.25to 1.5 = 140%

Comment [A108]: EPA RESPONSE: OK

Comment [A109]: This uses LBNL seed data plus

100/120 escalation.

Comment [A110]: EPA RESPONSE: OK

Comment [A112]: EPA RESPONSE: OK

Comment [A111]: Please compare these final

numbers to the original calculation of 660/770

$/MWh and consider revisiting approach.

Comment [A113]: To clarify, we are not

suggesting these are redline additions, but instead

are providing the information for illustrative

purposes.

Comment [A112]: EPA RESPONSE: OK

Comment [A115]: 

Comment [A114]: EPA RESPONSE: OK

Deleted: ¶
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these calculations. For each state, the following steps are taken for each year (2017-2030) and for 

each option. Calculations for steps 2 and 3 are done using real discount rates of 3% and 7%. 

 The steps are: 

1. Calculate annual first-year costs 

2. Calculate levelized cost of saved energy (LCSE) 

3. Calculate annualized costs 

To illustrate these calculations, each step is described and results are provided for one 

state (using South Carolina as an example) for 2017 through 2025 for Option 1. The 

results are truncated at 2025 for simplicity, but full national results (through 2030) are 

presented below. 

Step 1: Calculate Annual First-Year Costs

Annual total first-year costs are calculated by multiplying annual total incremental 

savings (MWh) (from Table 5-15) by the first-year total CSE (from Table 5-23 with 

escalation based upon results from Table 5-14). Program and participant portions of the 

first-year costs are then calculated as 50% of total first-year costs for each per Table 5-23. 

The resulting values are summarized for South Carolina in Table 5-24. 

TABLE 5-24 

Calculation of Annual First-Year Costs for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annual 

Incremental 

Savings 

(GWh) 

274 440 608 777 945 1,113 1,250 1,249 1,249 

First-Year 

Total Cost of 

Saved Energy 

(2011$/MWh) 

$550 $660 $660 $660 $770 $770 $770 $770 $770 

First-Year 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

Deleted: ¶

Formatted: After: 0", Line spacing: single

Deleted: Page Break

¶
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Total Cost 

(millions 

2011$) 

First-Year 

Program 

(millions of 

2011$) 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 

First-Year 

Participant 

(millions of 

2011$) 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 

Step 2: Calculate Levelized Cost of Saved Energy

Levelized costs of saved energy (LCSE) are based on levelization of all savings 

(first and future years) resulting from EE activities in a given year. The levelization 

algorithm is based on the 2002 California Standard Practice Manuallxxvii. The net present 

value of all savings from a single year’s EE activities (i.e., over the entire distribution of 

program lifetimes) is calculated using the real discount rate. The levelized cost of saved 

energy is then calculated by dividing the annual first-year costs (from Table 5-24) by the 

levelized savings. The resulting values are summarized for South Carolina in Table 5-25. 

TABLE 5-25 

Levelized Cost of Saved Energy for South Carolina (at 3% discount rate) 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Levelized 

Savings 

(GWh) 

2,313 3,720 5,139 6,563 7,987 9,405 10,562 10,553 10,554 

First-Year 

Total Cost 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

Comment [A116]: There seems to be an error in

the calculation of LCSE in Table 5‐25 below (factor

of 10 difference). Please check.

Comment [A117]: EPA RESPONSE: Calculation

has been re‐checked.

Formatted Table
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(millions 

2011$) 

Total LCSE

(cents/kWh)

6.51 7.81 7.81 7.81 9.11 9.11 9.11 9.11 9.11 

Program 

LCSE

(cents/kWh)

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

Participant 

LCSE

(cents/kWh)

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

Step 3: Calculate Annualized Costs

The costs of the EE program can also be represented as annualized costs in a 

given year. Annualized costs are calculated by multiplying the LCSE for each year by the 

estimated savings in each year through the full distribution of measure lifetimes. For each 

year in the analysis, the annualized costs resulting from all current and past investments 

are summed to calculate the total annualized costs in that year. The resulting values are 

summarized for South Carolina in Table 5-26. 

TABLE 5-26 

Annualized Costs for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annualized 

Total Costs 

(millions 

2011$) 

17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2 

Annualized 

Program 

Costs 

8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1 

Comment [A118]: Might want to check

calculation – seems there is a factor of 10

difference?

Comment [A119]: EPA RESPONSE: Calculation

has been checked again.

Comment [A121]: EPA RESPONSE: Thanks.

Cents/kWh, added to the table.

Comment [A120]: Unit?.

Deleted: remaining 
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(millions 

2011$) 

Annualized 

Participant 

Costs 

(millions of 

2011$) 

8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1 

Summary of General Formulas and Results by Step for South Carolina 

 Tables 5-27 and 5-28 provide summaries of the generic formulas and results for 

South Carolina for each step. 
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TABLE 5-27 

Summary of Calculation Formulas by Step 

Step Result Formula 

1 

Annual FirstYear Costs 

(2011$) 

Annual First-Year Costs year i = Annual Incremental Savings year i x First-Year CSE year i 

First-Year CSE year i = f(incremental savings rate) per Table 5-23 

2 

Levelized 

Savings 

(GWh) 

Levelized Savings year i = ∑ ஺௡௡௨௔௟ ூ௡௖௥௘௠௘௡௧௔௟ ௌ௔௩௜௡௚௦ ೤೐ೌೝ	೔

ሺଵା௥ሻ೔

்

௜ୀ଴ , 

where T = measure life, r = discount rate.

2 

LCSE 

(2011$/MWh) 

Levelized Cost of Saved Energy year i = Annual First-Year Cost year i / Levelized 

Savings year i

3 

Annualized 

Costs (2011$) 

Annualized Cost of Saved Energy year i = 

௡ ௧ሻି௘௔௥	௜௬ ݅ ݎ݁ܽݕ ݅݊ ݏ݅݊݃ݒܽݏ ݈ܽݐ݁݉݁݊ݎ݅݊ܿ ݈ܽݑܽ݊݊ ൈ ௧ି௘௔௥	௜௬ܧܵܥܮሺ∑ 

௧ୀ଴ , 

where LCSE year i-t is the LCSE of EE programs implemented in year i-t, 

 estimated represents ௧ି௘௔௥	௜௬	݅	ݎ݁ܽݕ	݅݊	ݏ݅݊݃ݒܽݏ	݈ܽݐ݁݉݁݊ݎ݅݊ܿ	݈ܽݑܽ݊݊ 

incremental savings in year i from EE programs implemented in year i-t. 

TABLE 5-28 

Summary of Results by Step for South Carolina. 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Total FirstYear Costs 

(millions 2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

Total LCSE 

(2011$/MWh) 

65.1 78.1 78.1 78.1 91.1 91.1 91.1 91.1 91.1 

Annualized 

Total Costs 

(millions 2011$) 

17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2 

Formatted Table

Formatted: Subscript

Deleted: Levelized Savings i = NPV [Lifetime 

Savings from EE Investments year i at Discount Rate]

Deleted: Incremental 

Deleted: Annualized Cost of Saved Energy year i = 

(LCSE year i x Remaining Savings year i)¶

+ (Levelized Cost of Saved Energy year i-1 x 

Remaining Savings year i-1) + ...¶

+ (Levelized Cost of Saved Energy year i-n x 

Remaining Savings year i-n)¶

Annualized Cost of Saved Energy year i = ∑
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Results 

Summary of National Results 

Tables 5-29 and 5-31 summarize the national first-year and annualized EE costs for 

Option 1 for 2018, 2020, 2025, and 2030. Table 5-30 summarizes national LCSE for Option 1 

for the same years. Each of the three tables includes values for program, participant, and total 

costs. 

TABLE 5-29 

First-Year EE Costs (3% discount rate, billions 2011$) 

(Continental U.S.) 

Year 

2018 2020 2025 2030 

Program 10.2 15.4 21.8 21.8 

Participant 10.2 15.4 21.8 21.8 

Total 20.5 30.7 43.6 43.5 

TABLE 5-30 

Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) 

(Continental U.S.) 

Year 

2018 2020 2025 2030 

Program 42 43 45 45 

Participant 42 43 45 45 

Total 83 85 89 90 

 

Deleted: ¶

¶

Page Break

¶

Formatted: Line spacing: single
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TABLE 5-31 

Annualized EE Costs (3% discount rate, billions 2011$) 

(Continental U.S.) 

Year 

2018 2020 2025 2030 

Program 2.0 5.1 14.4 21.4 

Participant 2.0 5.1 14.4 21.4 

Total 4.1 10.2 28.9 42.7 

See Appendix 5-4 for comprehensive data sheets of EE cost results at the national level 

by year for Options 1 and 2, and at discount rates of 3% and 7%. These data sheets provide 

results of LCSE (total, program and participant), first-year costs (total, program and participant), 

and annualized costs (total, program and participant). 

Results in Context

 To provide context for the pace of increase in EE program spending levels represented by 

Option 1, we consider the compound annual growth rate (CAGR) of the recent rapid increase in 

historic investment (2006 to 2011) and the CAGR from 2011 through 2018, 2020, and 2025 

represented by Option 1 program costs. Historic data is from Table 5-2 and Option 1 data is from 

Table 5-29. Table 5-32 provides a summary of the results. The CAGRs represented by Option 1 

through 2018, 2020, and 2025 vary from 8% to 11%. The historic growth rate reflecting the rapid 

recent growth in EE program spending is 30%, roughly three times the Option 1 values. 

TABLE 5-32 

Comparison of Historic and Projected (Option 1) Annual Growth Rates 

Time Period (Years) Compound Average Growth Rate 

Historic (2006-2011) 29.8% 

Option 1 (2011-2018) 8.1% 

Option 1 (2011-2020) 11.3% 

Option 1 (2011-2025) 9.8% 
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Costs per Tonne CO2 Reduced

 To estimate the reductions in power system costs and CO2 emissions associated with this 

building block, EPA analyzed a scenario incorporating the resulting reduction in electricity 

demand (the “energy efficiency scenario”) and compared the results with the base case scenario. 

Both analyses were conducted using the Integrated Planning Model (IPM) described in earlier 

chapters. Combining the resulting power system cost reductions with the energy efficiency cost 

estimates associated with the energy efficiency scenario, EPA derived net cost impacts for 2020, 

2025, and 2030. Dividing these net cost impacts by the associated CO2 reductions for each year, 

EPA found that the average cost of the CO2 reductions achieved ranged from $16 to $24 per 

metric tonne of CO2. Although EPA considers this estimated range of average $/tonne to be 

reasonable, we expect the $/tonne would be lower in combination with the other building blocks 

because, in that context, power system costs would be somewhat higher and, thus, avoided power 

system costs due to this building block would be higher as well, leading to lower $/tonne CO2 

avoided. 

Analysis Considerations 

 Two considerations are worth noting in regards to the analysis described in the previous 

two sections, “Goal Setting” and “Impacts Assessment:” 1) state energy efficiency policies 

implicitly represented in the baseline electricity demand and 2) Form EIA-861 as a data source. 

State Energy Efficiency Policies in the Baseline Electricity Demand

 The baseline electricity demand forecast used for the state goal setting approach 

represented in this chapter, as well as for the power sector modeling discussed in the Regulatory 

Impacts Assessment, is based upon the AEO 2013 reference case scenario. AEO 2013 does not 

explicitly represent existing utility energy efficiency programs or future requirements (e.g., 

EERS) to achieve savings goals through such programs. For example, existing state EERS are 

not evaluated and represented in the AEO 2013 reference case. However, to some degree, AEO 

2013 does implicitly reflect a continuation of the effects of existing state energy efficiency 

programs in the electricity demand projections represented in the reference case. This implicit 

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representation is captured in part through a calibration process that is affected by several historic 

factors including reported electricity sales and sectoral energy consumption surveys. 

 As noted, EPA’s state goal setting approach for demand-side energy efficiency is built 

upon the AEO 2013 forecast of electricity demand. However, because the goal setting approach 

uses percentage incremental savings by year to derive percentage cumulative savings by year 

(for each state), the resulting state goals (expressed in percentage cumulative savings by year, by 

state) are not affected by the underlying electricity demand forecast. The impacts assessment of 

the demand-side energy efficiency building block is affected, to some degree, by the implicit 

representation of a continuation of existing energy efficiency programs because the assessment is 

built partly from absolute energy savings values that are partly derived from the business-asusual (BAU) demand forecast. If the BAU forecast did not implicitly represent a continuation of 

existing energy efficiency programs, the forecast would indicate higher electricity demand, at 

least in the near term. However, the direction (higher or lower) of the net cost impacts (energy 

efficiency program costs as well as power system cost reductions) is not clear as it is possible 

that program costs could increase while avoided power system costs also increase. 

Energy Information Administration Form EIA-861 as Data Source 

Comprehensive data on energy efficiency programs’ spending and energy savings are 

limited for evaluating and comparing energy efficiency programs and their effectiveness at the 

utility, state, and national scale. Issues related to the lack of standardized definitions and 

reporting, and data quality are noted to limit evaluation of energy efficiency programs.lxxviii The 

Energy Information Administration (EIA) Form 861, “Annual Electric Power Industry Report,” 

remains the most comprehensive effort that collects data annually on utility demand-side 

management (DSM) programs, including their spending and energy savings impacts, 

nationally.lxxix The form is requested for electric utilities, electric power producers, energy 

service providers, wholesale power marketers, and all DSM program managers and entities 

responsible to estimate the DSM activity for the reporting year using their best available data, 

including costs and incremental and cumulative energy savings from energy efficiency programs 

and load management programs. 

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This analysis uses only two EIA-861 data variables. Specifically, we use the 2012 sales 

data and reported incremental annual energy savings of energy efficiency programs to estimate 

the current performance of energy efficiency programs to inform setting best practices 

performance level for the state EE goal setting. 

EPA notes potential concerns associated with consistency and quality of reported DSM 

program data in Form EIA-861. Specifically, the data are self-reported by utilities and DSM 

program administrators. The definition and data categories may not be consistently applied 

across utility, state, and data year. Over time, however, the data quality has improved 

significantly and there is increased standardization in data reporting and more detailed data 

categories are being reported. For instance, in 2011, EIA began collecting data from third-party 

administrators of programs. While now comprehensive, outside entities have found that the EIA861 data can be improve through supplementation with publicly available annual energy 

efficiency program reports.lxxx

 

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Appendices 

Appendix 5-1: Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies 

Appendix 5-2: Incremental Electricity Savings Pace of Improvement Analysis 

Appendix 5-3: Review of the Ratio of Program to Participant Costs 

Appendix 5-4: Comprehensive Results: State Goal Setting and Impacts Assessment 

 

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Appendix 5-1

Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies 

The following table summarizes estimates of economic and achievable energy efficiency 

potential from a number of recent studies (2010-2014) for states, utilities, and other agencies 

across the U.S. Study periods ranged from five to twenty-one years in length. As Table 1 shows, 

across the eleven studies that reported achievable potential, results for average annual achievable 

potential range from 0.8% per year to 2.9% per year (of baseline sales) with an average of 1.5% 

per year. 

TABLE 1 

Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies 

State Client Analyst 

Study 

Year 

Study 

Period 

End-year Projected 

Potential as % of 

Baseline Sales 

Average Annual Projected 

Potential as % of Baseline 

Sales 

Economic Achievable Economic Achievable 

Arizona 

Salt River 

Project Cadmus Group 2010 

2012-

2020 

29% 20% 3.2% 2.2% 

California 

California 

Energy 

Commission 

California 

Energy 

Commission 

2013 

2014-

2024 

Not 

reported 

9.6% N/A 0.9% 

Colorado 

Xcel Energy Kema, Inc. 2010 

2010-

2020 

20% 15% 1.8% 1.4% 

Delaware 

Delaware 

DNR/DEC 

Optimal Energy, 

Inc. 

2013 

2014-

2025 

26.3% Not reported 2.2% N/A 

Illinois 

ComEd ICF International 2013 

2013-

2018 

32% 10% 5.3% 1.7% 

Michigan 

Michigan PSC GDS Associates 2013 

2013-

2023 

33.8% 15% 3.1% 1.4% 
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New Jersey 

Rutgers 

University 

EnerNOC Utility 

Solutions 

2012 

2010-

2016 

12.8% 5.90% 1.8% 0.8% 

New Mexico 

State of New 

Mexico 

Global Energy 

Partners 

2011 

2012-

2025 

14.7% 11.1% 1.1% 0.8% 

New York 

ConEd Global Energy 

Partners 

2010 

2010-

2018 

26% 15% 2.9% 1.7% 

Pacific 

Northwest 

(Idaho, 

Montana, 

Oregon, 

Washington) 

US 

Department of 

Energy 

Lawrence 

Berkeley 

National 

Laboratory 

2014 

2011-

2021 

11% Not reported 1.9% Not reported 

Pennsylvania 

Pennsylvania 

PUC 

GDS Associates 

and Nexant 

2012 

2013-

2018 

27.2% 17.3% 4.5% 2.9% 

Tennessee 

Tennessee 

Valley 

Authority 

Global Energy 

Partners 

2011 

2009-

2030 

24.8% 19.8% 1.1% 0.9% 

Range 

0.8% - 2.9% 

per year 

Average 

1.5% 

Per year 

References 

Arizona: The Cadmus Group, Inc. 2010. Salt River Project 2012-2017 Energy Efficiency Plan – 

Final Report. Prepared for Salt River Project, April 16. 

Colorado: Kema, Inc. 2010. Colorado DSM Market Potential Assessment – Final Report. 

Prepared for Xcel Energy, March 12. 
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Delaware: Optimal Energy, Inc. 2013. Delaware Economic Energy Efficiency Potential. 

Prepared for the Delaware Department of Natural Resources and Environmental Control, May 

24. 

Illinois: ICF International. 2013. ComEd Energy Efficiency Potential Study Report, 2013-2018. 

August 20. 

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Natural Gas Energy Efficiency 

Potential Study – Final Report. Prepared for the Michigan Public Service Commission, 

November 1. 

New Jersey: EnerNOC Utility Solutions Consulting. 2012. New Jersey Energy Efficiency 

Market Potential Assessment, Volume 1: Executive Summary. Report Number 1401, Prepared 

for Rutgers, The State University of New Jersey, October 17, 2012. 

New Mexico: Global Energy Partners. 2011. Energy Efficiency Potential Study for the State of 

New Mexico, Volume 2: Electric Energy Efficiency Analysis. June 30. 

New York: Global Energy Partners, LLC. 2010. Energy Efficiency Potential Study for 

Consolidated Edison Company of New York, Inc., Volume 2: Electric Potential Report. March. 

Pacific Northwest states (combined): Barbose, Galen, and Alan Sanstad, Charles Goldman, 

Stuart McMenamin, Andy Sukenik. 2014. Incorporating Energy Efficiency into Western 

Interconnection Transmission Planning. Draft report, Lawrence Berkeley National Laboratory, 

January. 

Pennsylvania: GDS Associates, Inc., and Nexant. 2012. Electric Energy Efficiency Potential for 

Pennsylvania – Final Report. Prepared for the Pennsylvania Public Utilities Commission, May 

10. 
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Tennessee: Global Energy Partners. 2011. Tennessee Valley Authority Potential Study, Final 

Report, Volume 1: Executive Summary. Report Number 1360, December 21.
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Appendix 5-2 

Incremental Electricity Savings Pace of Improvement Analysis 

This appendix summarizes and analyzes data to characterize the pace of improvement of 

incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency 

(EE) programs. We considered two different perspectives: 1) historical data reflecting achieved 

savings of EE programs and 2) requirements of existing state energy efficiency resource 

standards (EERS). For the historical perspective, we reviewed data from the Energy Information 

Administration’s Form EIA-861 on EE program electricity savings (supplemented as needed 

with program administrator reports) and identified the pace at which entities reaching higher 

savings levels have historically increased energy savings over time.38 Specifically, we reviewed 

the historical savings data in the following two groups of energy efficiency program 

administrators. 

1. Top saver 1% - a group with 47 entities that achieved a maximum first-year savings level 

of 0.8% to 1.5%. 

2. Top saver 2% - a group with 26 entities that achieved a maximum first-year savings level 

of 1.5% to 3.0%.39

For the existing state requirements perspective, we reviewed energy savings ramp-up 

schedules established under EERS for states that provide clear ramp-up schedules. According to 

ACEEE’s 2013 State Energy Efficiency Scorecardlxxxi, there are a total of 26 states that have 

                                                            

38 The EIA 861 was the main data source. However, we have supplemented the EIA 861 with third-party program 

administrator data because the EIA 861 just started to collect third-party administrator data in 2011. The third-party 

entities included in our analysis are Efficiency Vermont, Energy Trust of Oregon, Efficiency Maine Trust, and Cape 

Light Compact. In addition, we supplemented the EIA 861 database with additional data for two utilities that we 

found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These 

entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid). 

39 In addition to these maximum first-year savings thresholds, we screened program administrators for the following 

conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of 

increase in first-year savings are provided to evaluate reasonable ramp-up schedules to gain an incremental 1% firstyear savings; and (c) savings data series are continuous between the years for the minimum and maximum savings 

levels.

Formatted: Not Highlight

Comment [A122]: Some commenters

recommend referencing DSIRE as the data source on

which states have EERSs rather than ACEEE

scorecard.

Comment [A123]: EPA RESPONSE: ACEEE is the

best quality and up‐to‐date source EPA has found.  

All summary sources like ACEEE or DSIRE are based

upon state‐by‐state research. We are soliciting

comment in the preamble on all aspects of the

method. EPA will consider switching to DSIRE as a

source (or EPA or DOE research/analysis) for final

rule.
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mandatory EERS policies.40 Our analysis contains 10 states for which clear ramp-up schedules 

were identifiable. 

Our research findings on historical savings performance are: 

 The “Top Saver 1%” group (savings between 0.8% and 1.5%) exhibits a trend that these 

entities took or would take about 3.4 years on average to increase first-year savings by 

1% (with a range of 1.6 years to 10 years) (see Table 1). The entities in this group have 

increased the level of first-year savings by 0.30% per year on average from their 

minimum to their maximum first-year savings levels (with a range of 0.10% per year to 

0.63% per year).41 

 The “Top Saver 2%” group (savings between 1.5% and 3%) exhibits a trend that took or 

would take about 2.6 years on average to increase savings by 1% (with a range of 0.8 

years to 7.3 years) (see Table 1). The entities in this group have increased the level of 

first-year savings by 0.38% per year on average from the minimum to the maximum firstyear savings levels (with a range of 0.14% per year to 1.28% per year).42 

Table 1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and 

“Top Saver 2%” groups43

 Top Saver 1% Top Saver 2% 

                                                            

40 ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, 41 This is a simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

42 This is the simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

43 Data sources: EIA-861; ISO New England, “Compilation of raw PA data for the 2013 EE Forecast,” Jun 21, 2013; 

energy efficiency program administrators’ 2012 energy efficiency annual reports for the following entities: 

Burlington Electric Department, Efficiency Vermont, Efficiency Maine, Energy Trust of Oregon, Cape light 

Compact, and Wisconsin Focus on Energy. 

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Average 

Annual 

First-Year 

Savings 

Increase 

Estimated 

Years to 

Gain 

Incremental 

1% 

Average 

Annual 

First-Year 

Savings 

Increase 

Estimated 

Years to 

Gain 

Incremental 

1% 

Average 0.30% 3.4 0.38% 2.6 

Median 0.29% 3.4 0.34% 3.0 

Max 0.63% 1.6 1.28% 0.8 

Min 0.10% 10 0.14% 7.3 

# of sample entities 47 26 

Our research findings on incremental electricity savings ramp-up based on existing state 

EERS policies are: 

 The states with EERS policies which exhibit savings ramp-up schedules are requiring 

increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and 

Oregon) to 0.40% (Rhode Island) as shown in Table 2. 

 The first-year savings pace of increase averages 0.21% per year across the 10 states. This 

savings level translates to about 4.7 years to achieve an incremental 1% first-year savings 

increase. 

Table 2. First-Year Energy Savings Ramp-up Review of State EERS Policies44

State 

Minimum 

Target 

Maximum 

Target 

Climb 

Time 

(years) 

Annual 

Average % 

Increase 

Years to 

Achieve 1% 

Min Year Max Year Increase 

 a b c d e=d-b f=(c-a)/e g=1/f 

Arizona 1.25% 2011 2.5% 2016 5 0.25% 4.0 

                                                            

44 Data sources: ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, Arkansas Public 

Service Commission, Docket Nos. 13-002-U. Order No. 7, September 9, 2013. 
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Arkansas 0.25% 2011 0.9% 2015 4 0.16% 6.2 

Colorado 0.80% 2011 1.7% 2019 8 0.11% 9.3 

Illinois 0.20% 2008 2.0% 2015 7 0.26% 3.9 

Indiana 0.30% 2010 2.0% 2019 9 0.19% 5.3 

Massachusetts 1.4% 2010 2.6% 2015 5 0.24% 4.2 

Michigan 0.3% 2009 1.0% 2012 3 0.23% 4.3 

Ohio 0.3% 2009 1.2% 2019 10 0.17% 5.9 

Oregon 0.8% 2010 1.0% 2013 3 0.07% 15.0 

Rhode Island 1.7% 2011 2.5% 2013 2 0.40% 2.5 

Average 0.21% 4.8 

References 

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual 

Report to the Iowa Utilities Board," May 1, 2013, available at: 

http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 

2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012-

Annual-Report.pdf

National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas 

Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at: 

http://www.env.state.ma.us/dpu/docs/electric/13-120/8113ngptapb.xlsx

PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, 

for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, 

November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf

Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: 

http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf 

 
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Appendix 5-3 

Review of the Ratio of Program to Participant Costs 

Introduction and Summary 

This appendix summarizes and analyzes data on EE costs (program and participant) to 

develop a ratio to enable the estimation of participant costs from known program costs. We 

reviewed cost data from leading EE program administrators in 22 states. Our research findings 

are as follows: 

 A 1:1 ratio between program and participant costs is a reasonable and slightly 

conservative (i.e., slightly higher total costs) basis for estimating participant costs from 

known program costs. 

 Reported data was reviewed from 22 states; however, program administrator reports from 

only nine states contained sufficient information (participant costs across entire portfolio 

of EE programs) to inform the analysis. 

 Participant cost data from ten program administrators in nine states indicate that the 

weighted average and simple average participant costs were 47 percent of total costs. 

Participant Cost Analysis 

We first identified states having high incremental electricity savings rates or high 

absolute savings levels based upon 2013 ACEEE State Energy Efficiency Scorecard.45 These 

states represent a large portion of total EE savings in the U.S. We identified 22 states meeting 

these criteria and collected publicly available EE program reports for major program 

administrators within each state. From these program reports we identified 10 program 

administrators across nine states where we were able to identify both program administrator and 

participant costs across their full portfolio of EE programs. The table below provides the 2012 

portfolio-level program administrator and participant costs for the nine states. Program 

                                                            

45 For the purpose of this research, we have defined leading or high impact states as the top 15 states in the 2013 

ACEEE State Energy Efficiency Scorecard in terms of incremental savings as a percentage of retail sales or absolute 

annual energy savings in terms of total annual MWh savings. These criteria resulted in a total of 22 states which 

include Arizona, California, Connecticut, Florida, Hawaii, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, 

Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, Texas, Vermont, 

and Washington. 
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administrator costs represent the program administrator’s program development and 

implementation costs, and the participant costs represent the customer costs to partake in the 

program. Total program costs are the sum of both costs. Each state’s program administrator and 

participant costs are presented as a percentage of total program costs in Table 3. The weighted 

and simple average program and participant costs across all nine states are presented as a 

percentage of total program costs. The weighted average cost shares were based on each 

program’s spending by administrator and participants. 

Table 1 

2012 Participant and Program Cost Information from Reported Entities 

2012 Portfolio Costs

State

Program

Administrator

Program  

Costs

(Million $s)

Participant

Costs

(Million $s)

Total Costs

(Million $s)

Program

Costs as

Percent of

Total Cost

(%)

Participant

Costs as

Percent of

Total Cost

(%)

A b c = a + b a / c b / c

California

Southern

California

Edison $         316 $    269 $        585 54.0% 46.0%

Hawaii Hawaii Energy $           31 $      37 $          68 45.6% 54.4%

Iowa

MidAmerican

Energy

Company $           50 $      70 $        120 41.5% 58.5%

Maine Efficiency

Maine Trust $           24 $      36 $          60 39.8% 60.2%

Massachusetts National Grid $         173 $      54 $        227 76.3% 23.7%

Minnesota Xcel Energy $           53 $      98 $        151 35.1% 64.9%

Pennsylvania PECO $           68 $    109 $        178 38.5% 61.5%

Rhode Island National Grid $           63 $      13 $          75 83.2% 16.8%

Vermont

Efficiency

Vermont;

Burlington

Electric

Department $           34 $      23 $          57 59.3% 40.7%

Weighted Average 53.4% 46.6%

Simple Average 52.6% 47.4%
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In our analysis, the weighted average program and participant costs are 53.4% and 

46.6%, respectively, of total costs. On a simple average basis, program and participant costs are 

52.6% and 47.4%, respectively, of total costs. Participant cost results range from a low of 17% of 

total costs (National Grid in Rhode Island) to a high of 65% (Xcel Energy in Minnesota). When 

deriving participant costs from program costs, using a ratio of 1:1 is consistent with these results 

and slightly conservative, leading to slightly higher total costs than the precise average values 

would provide. 

References 

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual 

Report to the Iowa Utilities Board," May 1, 2013, available at: 

http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 

2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012-

Annual-Report.pdf

National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas 

Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at: 

http://www.env.state.ma.us/dpu/docs/electric/13-120/8113ngptapb.xlsx

PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, 

for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, 

November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf

Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: 

http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf 

Burlington Electric Department, "2012 Energy Efficiency Annual Report," 2012, available at: 

https://www.burlingtonelectric.com/ELBO/assets/2012%20DSM%20Annual%20Report%20Mas

ter.pdf

Hawaii Energy, “Annual Report Program Year 2012 July 1, 2012 - June 30, 2013,” 2013, 

available at http://www.hawaiienergy.com/information-reports 
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Xcel Energy, "2012 Status Report & Associated Compliance Filings Minnesota Electric and 

Natural Gas Conservation Improvement Program Docket No. E,G002/CIP-09-198, " April 1, 

2013, available at 

http://www.xcelenergy.com/staticfiles/xe/Regulatory/Regulatory%20PDFs/MN-DSM-CIP-2012-

Status-Report.pdf 

National Grid, "2012 Energy Efficiency Year-End Report," May 31, 2013, available at 

http://www.nationalgridus.com/non_html/eer/ri/4295-YearEnd%20Rept%20(PUC%205-31-

13).pdf 

Southern California Edison, "2013 Energy Efficiency Annual Report," June 2013, available at 

http://eega.cpuc.ca.gov/Documents.aspx http://eega.cpuc.ca.gov/Documents.aspx 

 
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Appendix 5-4 

Comprehensive Results: State Goal Setting and Impacts Assessment 

See attached file, “Abatement Measures TSD Appendix 5-5.xlsx,” containing the 

following: 

Goal Setting Sheets 

 Option 1 – Incremental Savings as % of Sales by State (2017-2030) 

 Option 1 – Cumulative Savings as % of Sales by State (2017-2030) 

 Option 2 – Incremental Savings as % of Sales by State (2017-2030) 

 Option 2 – Cumulative Savings as % of Sales by State (2017-2030) 

Impacts Assessment Sheets 

 Option 1 – National Costs at 3% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

 Option 1 – National Costs at 7% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

 Option 2 – National Costs at 3% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

 Option 2 – National Costs at 7% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

Formatted: Not Highlight
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References

                                                            

i

 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy Efficiency 

Scorecard. Available at http://www.aceee.org/state-policy/scorecard. ii U.S. Energy Information Administration Form EIA-861 data files. 2012. Available at 

http://www.eia.gov/electricity/data/eia861/. iii Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. iv Seth Nowak, Martin Kushler, Patti Witte, and Dan York. Leaders of the Pack: ACEEE’s Third National Review of 

Exemplary Energy Efficiency Programs. American Council for an Energy-Efficient Economy (ACEEE). Research 

Report U132. Available at http://www.aceee.org/research-report/u132. v

 See reviews of market failures and barriers related to energy efficiency in Gillingham, K, R Newell, and K Palmer. 

2009. Energy Efficiency Economics and Policy. Annual Review of Resource Economics. Annual Review of 

Resource Economics 1: 597-619 and Gillingham and Palmer (2013). “Bridging the Energy Efficiency Gap: insights 

for policy from economic theory and empirical analysis,” Resources for the Future DP 13-02. 

vi For example, see DeCanio, S. 1998. The efficiency paradox: bureaucratic and organizational barriers to profitable 

energy-saving investments. Energy Policy 26(5): 441-458; McKinsey & Co and The Conference Board. 2007. 

Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? pp. (52-53). 

vii See discussion in Jaffe, A.B., R.G. Newell, and R.N. Stavins. 2003. Technological Change and the Environment. 

Chapter 11 in the Handbook of Environmental Economics. Volume 1, Edited by K.-G. Maler and J.R. Vincent. 

Elsevier Science B.V. 461-516. 

viii See discussion in Gillingham, K and K Palmer. 2014. Bridging the Energy Efficiency Gap: Policy Insights from 

Economic Theory and Empirical Analysis. Review of Environmental Economics & Policy, 8(1): 18-38. ix U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf. x

 U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf. xi Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving climate policy 

objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change. 

January 2014; Clarke, L, A Fawcett, J Weyant et al. Technology and U.S. Emissions Reductions Goals: Results of 

the EMF 24 Modeling Exercise. [forthcoming]

xii International Energy Agency (IEA). 2012. World Energy Outlook 2012. Paris. 

xiii Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving climate policy 

objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change. 

January 2014.

xiv Kyle P., L. Clarke, S. Smith et al. 2011. The Value of Advanced End-Use Energy Technologies in Meeting U.S. 

Climate Policy Goals. The Energy Journal, 32: 61-87.

xv See, for example, Comstock, O, and E Boedecker. 2011. Energy and Emissions in the Building Sector: A 

Comparison of Three Policies and Their Combinations. The Energy Journal, 32: 23-41; Fischer, C. (2005) “On the 

importance of the supply side in demand side management." in Energy Economics, 27: 165-180; Fischer, C. 2010. 

Imperfect Competition, Consumer Behavior, and the Provision of Fuel Efficiency in Light-Duty Vehicles. 

Resources for the Future DP 10-60. Washington, DC. 

xvi E.g., Gillingham, K, R Newell, and K Palmer. 2009. Energy Efficiency Economics and Policy. Annual Review of 

Resource Economics. Annual Review of Resource Economics 1: 597-619. xvii Woolf, T. W. Steinhurst, E. Malone, K. Takahashi. 2012. “Energy Efficiency Cost-Effectiveness Screening: How 

to Properly Account for ‘Other Program Impacts’ and Environmental Compliance Costs,” Report prepared by RAP 

and Synapse Energy Economics. 

xviii Boyd, GA and JX Pang (2000). “Estimating the linkage between energy efficiency and productivity,” Energy 

Policy, 28: 289-296; Worrell, E. (2011). “Productivity benefits of industrial energy efficiency measures.” Lawrence 

Berkeley National Laboratory Paper LBNL-52727. 
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                                                                                                                                                                                                xix Van Buskirk, R, C. Kantner, B. Gerke et al. The benefits of energy efficiency standards and how policies may 

accelerate declines in appliance costs. Proceeding of National Academy of Sciences. [forthcoming] xx Woolf, T. W. Steinhurst, E. Malone, K. Takahashi. 2012. “Energy Efficiency Cost-Effectiveness Screening: How 

to Properly Account for ‘Other Program Impacts’ and Environmental Compliance Costs,” Report prepared by RAP 

and Synapse Energy Economics. Available at http://www.raponline.org/event/the-importance-of-effective-energyefficiency-cost-effectiveness. xxi Sixth Northwest Electric Power and Conservation Plan, Northwest Power and Conservation Council. February 

2010. Council Document 2010-09. http://www.nwcouncil.org/media/6284/SixthPowerPlan.pdf xxii American Council for an Energy-Efficient Economy (ACEEE). April 2004. Select State-Level Energy Efficiency 

Policy Opportunities 2016-2030. [forthcoming] 

xxiii Yu Wang and Marilyn A. Brown. February 2013. Policy Drivers for Improving Electricity End-Use Efficiency 

in the U.S.: An Economic-Engineering Analysis. Energy Efficiency. xxiv Ian A. Bowles. December 29, 2010. Massachusetts Clean Energy and Climate Plan for 2020. Available at 

http://www.mass.gov/eea/waste-mgnt-recycling/air-quality/green-house-gas-and-climate-change/climate-changeadaptation/mass-clean-energy-and-climate-plan.html. xxv Commonwealth of Massachusetts. Global Warming Solutions Act: 5-Year Progress Report. December 2013. 

Available at http://www.mass.gov/eea/docs/eea/gwsa/ma-gwsa-5yr-progress-report-1-6-14.pdf. xxvi Ian M. Hoffman, Megan A. Billingsley, Steven R. Schiller, Charles A. Goldman and Elizabeth Stuart. Lawrence 

Berkeley National Laboratory. August 28, 2013. Energy Efficiency Program Typology and Data Metrics: Enabling 

Multi-State Analyses Through the Use of Common Terminology. LBNL-6370E. Available at 

http://eetd.lbl.gov/news/article/56865/new-policy-brief-energy-efficie.

xxvii U.S. Energy Information Administration Form EIA-861 data files (2012). Available at 

http://www.eia.gov/electricity/data/eia861/. xxviii American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. xxix Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. 

xxx Sciortino, M., et. al. American Council for an Energy-Efficient Economy (ACEEE). June 2011. Energy 

Efficiency Resources Standards: A Progress Report on State Experience. Report Number U112. Available at 

http://www.aceee.org/research-report/u112. xxxi American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. xxxii American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. xxxiii American Council for an Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency 

Resource Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/policy-brief/eers-02-

2014.pdf. xxxiv Consortium for Energy Efficiency. March 28, 2013. 2012 State of the Efficiency Program Industry. Available at 

http://library.cee1.org/content/2012-state-efficiency-program-industry-report/. xxxv American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. xxxvi Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. xxxvii U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a 

Resource of the National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf. 
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                                                                                                                                                                                                xxxviii U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a 

Resource of the National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf. xxxix Sreedharan, P. 2013. Recent estimates of energy efficiency potential in the USA. Energy Efficiency. xl Eldridge et. al. 2008. State-Level Energy Efficiency Analysis: Goals, Methods, and Lessons Learned. 2008 

ACEEE Summer Study on Energy Efficiency in Buildings. Available at http:// 

xli Electric Power Research Institute (EPRI). April 2014. U.S. Energy Efficiency Potential Analysis through 2035. 

[forthcoming] 

xlii American Council for an Energy-Efficient Economy (ACEEE). April 2014. Select State-Level Energy Efficiency 

Policy Opportunities 2016-2035. [forthcoming] 

xliii U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf. xliv U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action 

Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf. xlv Woolf, T., et. al. November 2012. Regulatory Assistance Project, “Energy Efficiency Cost-Effectiveness 

Screening. Available at http://ww.raponline.org/document/download/id/6149. xlvi U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action 

Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf. xlvii See, for instance, Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving 

climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. 

Climatic Change, January 2014; Kyle P., L. Clarke, S. Smith et al. 2011. The Value of Advanced End-Use Energy 

Technologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87. 

xlviii U.S. EPA and U.S. DOE. September 2009. Energy Efficiency as a Low-Cost Resource for Achieving Carbon 

Emissions Reductions: a Resource of the National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/ee_and_carbon.pdf. xlix McKinsey & Company. December 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? 

Available at 

http://www.mckinsey.com/client_service/sustainability/latest_thinking/reducing_us_greenhouse_gas_emissions. l

 U.S. EPA and U.S. DOE. November 2007. Guide for Resource Planning with Energy Efficiency: a Resource of the 

National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/resource_planning.pdf. li Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy 

Efficiency Programs. ACEEE Report No. U1402. Washington, DC. Available at http://www.aceee.org/researchreport/u1402. lii Friedrich, K., M. Eldridge, D. York et al. 2009. Saving Energy Cost-Effectively: A National Review of the Cost of 

Energy Saved Through Utility-Sector Energy Efficiency Programs,” ACEEE Report No. U092. Available at 

http://www.aceee.org/research-report/u092. liii Kushler, M., D. York, and P. Witte. American Council for an Energy-Efficient Economy (ACEEE). 2004. Five 

Years In: An Examination of the First Half-Decade of Public Benefits Energy Efficiency Policies. liv Gillingham, K., R. Newell, K. Palmer. 2004. Retrospective Examination of Demand-Side Energy Efficiency 

Policies. Resources for the Future Working Paper DP 04-19 REV. Washington, DC. 

lvii Paul, A., K. Palmer and M. Woerman. 2011. Supply Curves for Conserved Electricity. Resources for the Future 

Discussion Paper 11-11. Washington, DC. 

lviii Trains, K. 1994. Estimation of Net Savings from Energy-Conservation Programs. Energy 19 (4):423-441. lix Joskow, P., and D. Marron. 1992. What Does a Negawatt Really Cost? Evidence from Utility Conservation 

Programs. The Energy Journal 13 (4):41-74. 

lx U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action 

Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf. 
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                                                                                                                                                                                                lxi Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy‐wide energy savings from

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy

Assessment function of the UK Energy Research Centre. ISBN 1‐903144‐0‐35. lxii Greening, LA, DL Greene, and C Difiglio. 2000. Energy efficiency and consumption — the rebound effect — a

survey, Energy Policy, 28: 389‐401. lxiii Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy‐wide energy savings from

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy

Assessment function of the UK Energy Research Centre. ISBN 1‐903144‐0‐35 lxiv Davis, LW 2008. Durable goods and residential demand for energy and water: evidence from a field trial. The

RAND Journal of Economics, 39: 530–546. lxv Gillingham K, MJ Kotchen, DS Rapson, G Wagner. 2013. Energy policy: the rebound effect is overplayed. Nature

493: 475‐476. lxvii American Council Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resource 

Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/policy-brief/eers-02-2014.pdf. lxviii Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence 

Berkeley National Laboratory. March 2014. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf. lxix Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence 

Berkeley National Laboratory. March 2014. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf. 

lxx Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and 

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. lxxi Gordon, Fred, Lakin Garth, Tom Eckman, and Charles Grist, “Beyond Supply Curves,” Proceedings of 2008 

ACEEE Summer Study on Energy Efficiency in Buildings, August 17, 2008 available at: 

http://aceee.org/files/proceedings/2008/data/papers/8_419.pdf. lxxii Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and 

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. lxxiii Kenji Takahashi and David Nichols, Synapse Energy Economics, Inc. 2008. The Sustainability and Costs of 

Increasing Efficiency Impacts: Evidence from Experience to Date. ACEEE Summer Study on Energy Efficiency in 

Buildings. http://www.aceee.org/files/proceedings/2008/data/papers/8_434.pdf. lxxiv For example: Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest 

Conservation and Electric Power Plan,” Council Document 2010-09. Available at 

www.nwcouncil.org/energy/powerplan/6/plan/; and McKinsey & Company. December 2007. Reducing U.S. 

Greenhouse Gas Emissions: How Much at What Cost? Available at 

http://www.mckinsey.com/client_service/sustainability/latest_thinking/reducing_us_greenhouse_gas_emissions. lxxv Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. lxxvi Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility 

Energy Efficiency Programs. ACEEE Report No. U1402. Available at http://www.aceee.org/research-report/u1402. lxxvii State of California Governor’s Office of Planning and Research. July 2002. California Standard Practice 

Manual: Economic Analysis of Demand-Side Programs and Projects. Available at 

http://www.calmac.org/events/SPM_9_20_02.pdf. lxxviii MJ Bradley & Associates, LLC. 2011. Benchmarking Electric Utility Energy Efficiency Portfolios in the U.S. lxxix More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/ lxxx Add citation lxxxi American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 
Page 27: [1] Comment [A40] Author 

EPA RESPONSE: Your calculation omits the large and critical cost savings from avoided power system operations,

etc. (fuel, variable O&M, and avoided/delayed new generation and T&D, etc.).  EPA believes that those savings

need to be included in estimating the net cost per ton CO2 reduced. See added discussion later in this chapter with

results of EPAs EE costs per ton CO2 reduced showing a conservative range of $16‐$24/ton.

Page 27: [2] Comment [A41] Author 

“Under the scenarios modeled, 

electricity savings of between 1 and 3 percent are achievable at a marginal cost of $50 per megawatt hour 

(MWh) and a corresponding average cost of $25–$35/MWh.”  

Page 27: [3] Comment [A42] Author 

EPA RESPONSE: See revised text including  

The marginal cost estimate from the paper.

Page 27: [4] Formatted Author 

Font: (Default) Times New Roman, 10 pt, Complex Script Font: Times New Roman, 10 pt

Page 27: [5] Formatted Author 

Font: (Default) Times New Roman, 10 pt, Complex Script Font: Times New Roman, 10 pt

Page 53: [6] Deleted Author 

Summary of Alternate EE Cost Analysis Inputs based on ACEEE 2014[A1][A2]

Input Source or Assumption 

State-Specific Electricity Savings by Year Results from state goal setting 

First-Year Program Cost of Saved Energy $230/MWh (2011$) 

Ratio of Program to Participant Costs 53:47 

First-Year Participant Cost of Saved Energy $204/MWh (2011$) 

First-Year Total Cost of Saved Energy $434/MWh (2011$) 

Escalation of Costs 

Incremental savings rate 

0.5% - 1.0% > 1.0% 

100% of base costs: 

$434/MWh (2011$) 

120% of base 

costs[A3][A4]: 

$520/MWh (2011$) 

Summary of EE Cost Analysis Inputs based on LBNL[A5][A6]

Input Source or Assumption 

State-Specific Electricity Savings by Year Results from state goal setting 
First-Year Program Cost of Saved Energy $175/MWh (2011$) 

Ratio of Program to Participant Costs 53:47 

First-Year Participant Cost of Saved Energy $155/MWh (2011$) 

First-Year Total Cost of Saved Energy $330/MWh (2011$) 

Escalation of Costs 

Incremental savings rate 

0.5% - 1.0% > 1.0% 

100% of base costs: 

$330/MWh (2011$) 

120% of base costs: 

$396/MWh 

(2011$)[A7][A8]

Page 53: [7] Comment [A113] Author 

To clarify, we are not suggesting these are redline additions, but instead are providing the information for

illustrative purposes.