From: Higgins, Cortney 

Sent: Monday, May 19, 2014 9:02 PM 

To: Wayland, Robertj 

Cc: 

Subject: FW: Comments on TSDs for RIN 2060-AR33 

From: Higgins, Cortney 

Sent: Monday, May 05, 2014 5:35 PM 

To: Wayland, Robertj 

Cc: Elman, Barry 

Subject: RE: Comments on TSDs for RIN 2060-AR33 

Hi Bob, 

Here are interagency comments on the remaining GHG Abatement TSD Chapters (5-7). 

Regards, 

Cortney 

<< GHG Abatement Measures TSD-Chapter 5_04-10-14_InteragencyCommentsUnder 

EO12866.docx >> 

<< GHG Abatement Measures TSD-Chapter 6_04-10-14_InteragencyCommentsUnder 

EO12866.docx >> 

<< GHG Abatement Measures TSD-Chapter 7_04-10-14_InteragencyCommentsUnder 

EO12866.docx >> 
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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 

 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]: Calls into question the need for

BB 4??
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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 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 

opportunity is realized. After decades of experience implementing policies to accelerate 

Comment [A3]: Can EPA explain why the EE

intervention is needed?   

Comment [A4]: 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.  
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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

 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. 
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 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 

products, and heterogeneity among consumers, producers and markets, also influence EE 

investment decisions3

. Examples of such factors include: 

                                                            

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 
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 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 

consumer education), rebates for high-efficiency products, product energy performance 

standards, financing and loan programs, and technical assistance. 

EE Policies4

                                                                                                                                                                                               

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. 

4

 Existing state EE policies are described extensively in the State Plan Considerations TSD. 
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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 

- 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) 

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 

                                                            

5 States with GHG reduction laws include:  California, Connecticut, Hawaii, Maine, Maryland, Massachusetts,

Minnesota, New Jersey, Oregon, and Washington.   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).  

Comment [A5]: Rebound effect??
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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 

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 

                                                            

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.   8

 EE programs are described in more detail in the following section of this chapter and in the State Plan 

Considerations TSD. 
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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. 

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 

                                                            

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. 
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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

 
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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 

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. 

Formatted: Not Highlight
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FIGURE 5-1 

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. 

 

Comment [A6]: 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.
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Policy Drivers 

EE programs result from a number of different policy approaches or “drivers.”11 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)xxix12. 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 

                                                            

11 These policies are discussed in depth in State Plan Considerations TSD. 

12 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 

Electric Efficiency 

Program Budgets 

(billions of $s, nominal)

 1.6 2.2 2.6 3.4 4.6 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 [A7]: Please update this table to

include the most recent numbers
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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 for more than three decades with 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 impactsxxxivxxxv. 

The EPA has relied on the EIA dataset for identifying historic impacts of EE programs by 

state. 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 

Comment [A8]: As noted elsewhere in the

comments, this dataset can be problematic.
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most recent available. At the national level, incremental electricity savings13 in 2012 was 0.58% 

of retail sales with individual state values ranging from 0.00% to 2.19%. Cumulative electricity 

savings14 (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% 

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% 

                                                            

13 Incremental savings (also known as first-year savings) represent the reduction in electricity use in a given year 

associated with EE activities in that same year. 

14 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 [A9]: EPA needs to provide much

more detail about how this data was derived.

Comment [A10]: EPA used EIA 861 data
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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% 

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% 
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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% 

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 

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 
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incremental savings, with 11 of those reaching or exceeding 2.0%15. Table 5-5 summarizes the 

results of the LBNL analysis. 

 

                                                            

15 LBNL provided these unpublished results from their analysis.

Comment [A11]: So what does this imply about

BB #4? Why does EPA put into the baseline?
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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.16

 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. 

                                                            

16 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 programs17. 

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 

                                                            

17 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 studies18. 

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. 

 

                                                            

18 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 [A12]: Font is different in footnote

Comment [A13]: Assuming these studies are

correct, what, if any, policies are needed to realized

the achievable potential of 1.2%‐1.5%?

Comment [A14]: Make sure to check this

carefully!!
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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 [A15]: Seems like too few studies to

do a valid meta‐analysis.
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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 [A16]: 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 [A17]: If these are the average

achievables, is it really feasible to go for 1.5% for all

states in each year?
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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. The energy efficiency analysis literature reports average 

LCSE in the range of 1-6 cents/kWh based on program administrator cost.19 A recent review by 

                                                            

19 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 consistent or not reported in all studies. 

Comment [A18]: 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.
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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 various 

techniques (e.g., empirical econometrical, top-down modeling). Some recent analyses suggest 

estimated LCSE in a similar range as in the energy efficiency analysis literature. Gillingham et 

al. (2004) estimated an average cost of 3.4 cents per kWh saved from utility EE programs.liv 

Using a top-down approach that evaluates the savings potential of EE investments using stateand 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 In an 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

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 programslvii, and the 

accuracy of utility reported costs and energy savingslviii. 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.lix 

EE as an Abatement Measure 

Demand-side energy efficiency is a technically viable, broadly applicable, and generally 

cost-effective measure for achieving significant reductions in utilization of affected EGUs. 

Moreover, this measure has been adopted by nearly every state and most utilities across the 

country, typically through multiple policy approaches. Increased use of, and impacts from, state 

Comment [A19]: “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 [A20]: 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?

Comment [A21]: Suggest making a table

comparing the estimated LCSE for these studies.

Comment [A22]: EPA needs to provide data to

show how DSM is cost‐effective relative to other

abatement techniques (e.g. increased NGCC

capacity and generation)
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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 (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 cost-effectiveness of 

this building block within the context of the broader proposal. 

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. 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: 

Comment [A23]: 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 [A24]: Does it include, though, BAU

for states that should be in the baseline?   
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 EE programs have achieved significant levels of savings and are being used in almost 

every state, 

 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 goals20 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&V21. 

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 

                                                            

20 See State Plan Considerations TSD. 

21 See State Plan Considerations TSD. 
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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 

for each state. Incremental annual savings are also more directly estimated and evaluated than 

are cumulative savings22. 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. 

 

                                                            

22 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. 
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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 state23. 

Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA 

Form 861 (2012) datalx, 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 

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 

                                                            

23 See State Plan Considerations TSD for more information. 

Comment [A25]: What and where is this

superscript?

Comment [A26]: Should this be < 0.5?

Comment [A27]: 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?  
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Table 5-9 summarizes incremental savings levels required by state policy on or before 

2020 and categorized into the same four rangeslxi. 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 124, 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. 

For Option 225, 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 

                                                            

24 See Preamble and Goal Computation TSD for description of Option 1. 

25 See Preamble and Goal Computation TSD for description of Option 2. 

Comment [A28]: Indicates that all these states

should be in the baseline and not the policy

scenariors.
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For construction of the best practices scenario, the EPA has used 2017, the year following 

the required state plan submittal26, 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 period27. 

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 administrators28 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 

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 

                                                            

26 See Preamble and State Plan Considerations TSD for descriptions of the schedule for state plan submittals. 

27 See Preamble for description of interim and final compliance periods. 

28 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 [A29]: 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?
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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 years. 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 

attic), the EPA is assuming a distribution of measure lives around the average to account for 

future impacts of incremental savings levelslxii. 

 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. 

Comment [A30]: Again if the improvements are

not driven by 111(d) then they should be in the

baseline.

Comment [A31]: What are the costs?
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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 yearslxiii. 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 

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 

Comment [A32]: Consider referencing other

sources in addition to ACEEE data, including a

forthcoming DOE technical memo and Northwest

Regional Forum documents.

Comment [A33]: 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 [A34]: Does this mean that they no

longer make any improvements after the reach the

best practices level of performance  
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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-state29 regional energy resource planslxiv. 

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.”lxv Table 5-11 summarizes the NPCC’s achievable potential 

estimates and evaluated savings since 1998lxvi. 

TABLE 5-11 

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 

                                                            

29 NPCC’s resource plans cover Idaho, Oregon, and Washington in their entirety, and western regions of Montana 

and Wyoming. 

Comment [A35]: EPA could reference the 12‐13

year average measure life. This matches very well

with the sustained period from 2017 to 2030.

Comment [A36]: Not peer‐reviewed. It would

be good for EPA to provide peer‐reviewed evidence

on the efficacy of these types of DSM programs.

Formatted: Not Highlight
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 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 

retail sales) 

Data from 2012 EIA 861 

(2012) 

Data from 2012 EIA 861 

(2012) 

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 0.20% per year 0.15% per year 
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(increase in incremental 

savings rate 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 

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 
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truncate the results at 2025 for simplicity, but full results are presented for all states in the 

section. 

 
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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. 

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% 
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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)

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. 
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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)

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. 

 
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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 [A37]: 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.
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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 net30 costs of $275/MWh (2011$). The 

EPA has used this value for our analysis. 

 Two recent national analyses have found slightly substantially (16-36%) 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 wasere 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 

                                                            

30 “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. 

Comment [A38]: 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 [A40]: Suggest EPA analysis use these

more recent and more robust numbers.  

Comment [A39]: 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.”
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focused on gross31, rather than net, savings 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 programs32 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 approach33 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

                                                            

31 “Gross savings” refers to electricity savings before any accounting for effects of free-ridership or spillover. 32 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. 

33 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 [A41]: 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 [A42]: A commenters suggest DOE

may have  updated participant cost data.

Comment [A43]: 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 [A44]: EPA seems to have taken a

conservative approach to costs that may not be

justified by the empirical literature. Please consider

revisiting if appropriate.
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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 

gainedlxvii. “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 realizedlxviii. In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted 

an approach that generically represented both effects discussed abovelxix. 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 ratelxx. 

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 [A45]: 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 [A46]: Please consider incorporating

literature on learning and cost reductions over time

across many technologies, and particularly in many

energy technologies.   

Comment [A47]: 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 [A48]: 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 [A49]: Also came in lower than the

approach EPA chose.  Please consider revisiting

approach.

Comment [A50]: 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.
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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: 

$770/MWh (2011$) 

Summary of Alternate EE Cost Analysis Inputs based on ACEEE 2014 

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 

Comment [A51]: 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 [A56]: To clarify, we are not

suggesting these are redline additions, but instead

are providing the information for illustrative

purposes.

Comment [A52]: 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.  
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0.5% - 1.0% > 1.0% 

100% of base costs: 

$434/MWh (2011$) 

120% of base costs: 

$520/MWh (2011$) 

Summary of EE Cost Analysis Inputs based on LBNL 

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: 

$396/MWh (2011$) 

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 

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 

Comment [A53]: 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 [A54]: This uses LBNL seed data plus

100/120 escalation.

Comment [A55]: Please compare these final

numbers to the original calculation of 660/770

$/MWh and consider revisiting approach.

Comment [A57]: 
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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. 

 
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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 

Total Cost 

(millions 

2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

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 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 
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2011$) 

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 Manuallxxi. 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 

(millions 

2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

Total 

LCSE 

6.51 7.81 7.81 7.81 9.11 9.11 9.11 9.11 9.11 

Program 

LCSE 

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

Participant 

LCSE 

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

Step 3: Calculate Annualized Costs

Comment [A58]: There seems to be an error in

the calculation of LCSE in Table 5‐25 below (factor

of 10 difference). Please check.

Comment [A59]: Might want to check

calculation – seems there is a factor of 10

difference?

Comment [A60]: Unit?
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Annualized costs are calculated by multiplying the LCSE for each year by the 

remaining 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. 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 

(millions 

2011$) 

8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1 

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 x First-Year CSE year i 

First-Year CSE = f(incremental savings rate) per Table 5-23 

2 

Levelized 

Savings 

(GWh) 

Levelized Savings i = NPV [Lifetime Savings from EE Investments year i at Discount 

Rate] 

2 

LCSE 

(2011$/MWh) Levelized Cost of Saved Energy year i = Incremental Cost year i / Levelized Savings year i

3 

Annualized 

Costs (2011$) 

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) 

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 

 
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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 

 
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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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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.34 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%.35

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 Scorecardlxxii, there are a total of 26 states that have 

                                                            

34 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). 

35 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 [A61]: Some commenters

recommend referencing DSIRE as the data source on

which states have EERSs rather than ACEEE

scorecard.
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mandatory EERS policies.36 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).37 

 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).38 

Table 1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and 

“Top Saver 2%” groups39

 Top Saver 1% Top Saver 2% 

                                                            

36 ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, 37 This is a simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

38 This is the simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

39 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 Policies40

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 

                                                            

40 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.41 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 

                                                            

41 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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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. 

lv Paul, A., K. Palmer and M. Woerman. 2011. Supply Curves for Conserved Electricity. Resources for the Future 

Discussion Paper 11-11. Washington, DC. 

lvi Arimura, T., S. Li, R. Newell, K. Palmer. 2012. Cost-Effectiveness of Electricity Energy Efficiency Programs. 

The Energy Journal 33(2): 63-99. lvii Trains, K. 1994. Estimation of Net Savings from Energy-Conservation Programs. Energy 19 (4):423-441. lviii Joskow, P., and D. Marron. 1992. What Does a Negawatt Really Cost? Evidence from Utility Conservation 

Programs. The Energy Journal 13 (4):41-74. 
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                                                                                                                                                                                                lix 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. lx U.S. Energy Information Administration Form EIA-861 data files (2012). Available at 

http://www.eia.gov/electricity/data/eia861/. lxi 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. lxii 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. lxiii 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. 

lxiv 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/. lxv 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. lxvi 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/. lxvii 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. lxviii 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. lxix 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. lxx 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. lxxi 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. lxxii 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. 
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Chapter 6: Fuel Switching 

Coal-to-Natural Gas Switching 

Introduction 

Firing natural gas in a boiler designed for coal-fired generation is one approach to reducing the 

output-based CO2 emissions rate (lbs/MWh) in these boilers. The CO2 emission rate is reduced 

when natural gas is substituted for coal because the gas has a much higher percentage of 

hydrogen and a lower percentage of carbon than the coal it replaces. When quantities of gas and 

coal are burned with oxygen from air to produce the same amounts of heat, the higher hydrogen 

content of natural gas produces more water vapor (H2O) than coal, but far less CO2. 

The discussion below focuses solely on the conversion of an existing coal-fired boiler to burn 

natural gas instead of, or along with, coal. There are other technical options for gas substitution 

in an existing coal-steam EGU that are not examined in any detail here. They include: 

 Repowering an existing coal EGU by providing heat input to the boiler from the exhaust 

of a newly installed gas turbine generator; and, 

 Gasification of coal, producing substitute natural gas (SNG) that provides heat input to 

the existing coal-fired boiler. 

These other options have higher capital cost and thus would not be as economic as the direct 

substitution of natural gas in an existing coal boiler, for reasons that will become apparent in this 

analysis. 

This chapter evaluates the cost-effectiveness of widespread adoption of coal-to-gas switching at 

a national level for the purpose of setting CO2 emissions goals consistently in each state. 

Description of Technology 

 Engineering Considerations 

Most existing coal-fired EGU boilers can be modified to switch to 100% gas input, or to co-fire 

gas with coal in any desired proportion. This transition typically requires at least some plant 

modifications and might have some negative impact on the efficiency of the unit as described 

later in this chapter. 
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A conversion from coal to gas firing first requires either an existing natural gas delivery system 

to the boiler or the installation of a new gas pipeline to serve the boiler. While it is sometimes 

assumed that a need to install a new gas pipeline would render the conversion uneconomic, this 

analysis will show that the cost of a new gas pipeline is not likely to be the determining factor for 

the project’s economic merit, given the significance of the change to the cost of fuel for 

generation from the converted boiler. 

Conversion to natural gas firing in a coal-fired boiler typically involves installation of new gas 

burners and supply piping, modifications to combustion air ducts and control dampers, and 

possibly modifications to the boiler’s steam superheater, reheater, and economizer heating 

surfaces that transfer heat from the hot flue gas exiting the boiler furnace. The conversion may 

also involve some modification of downstream air pollution emission control equipment. 

Engineering studies are performed to assess changes in furnace heat absorption and exit gas 

temperature; material changes affecting heat transfer surfaces; the need for sizing of flue gas 

recirculation fans; and operational changes to sootblowers, spray flows, air heaters, and emission 

controls. 

Whether co-firing with coal or switching completely to natural gas, boilers will become less 

efficient due to the high hydrogen content of natural gas. When combusted, the additional 

hydrogen yields increased moisture content (water vapor) in the flue gas. The increased 

moisture content, in turn, results in additional heat lost up the stack instead of being directed 

towards electricity generation. Additionally, depending on the design of the boiler and extent of 

modifications, some boilers may incur some derate (reduction in generating capacity) in order to 

maintain steam temperatures at or within design limits, or for other technical reasons. 

Even with a decrease in boiler efficiency, the overall net output efficiency of a coal-steam boiler 

EGU that switches from coal to natural gas firing may change only slightly, depending on how 

much auxiliary load is converted to net output by avoiding the need to run coal pulverizers, 

conveyors, ash sluice pumps, and relevant air pollution control equipment (e.g., PM and SO2

controls). 

Fuel Considerations 

Delivery of natural gas via pipeline is critical for conversion of a coal-fired boiler to a gas-fired 

boiler. Some coal boilers are connected to the natural gas pipeline network for purposes of using 

Comment [A1]: Would there also be a reduction

in emissions equipment (and possible removal of

baghouses, electrostatic precipitators, and

scrubbers.).If so, consider making this point.
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gas as a startup fuel, or are located at facilities with onsite gas-fired generators. These boilers are 

likely able to co-fire to some degree with gas (at least 10% total output1

) without constructing 

additional gas pipeline capacity. For purposes of this analysis, the EPA conservatively assumed 

that gas use of 10% or greater at these boilers, or any gas use at boilers without an existing gas 

pipeline, would require construction of additional pipeline capacity. 

Unlike coal, natural gas cannot be stored in quantities sufficient for sustained utilization on site. 

To the extent that firm (uninterruptible) gas supply is contractually unavailable or costprohibitive, any potential interruption in gas supply could impact the ability of the unit to 

continue operating without increasing its CO2 emissions rate (since it would likely need to 

substitute more CO2-intensive fuel for the unavailable natural gas). Additionally, for boilers that 

switch to 100% gas, interruption in gas impacts the ability of the unit to continue generating at 

all if gas is unavailable. For these reasons, an EGU switching to a large percentage of gas use 

may elect to install more than one new gas supply pipeline from separate sources. Although the 

EPA assumes the addition of one gas pipeline in the simplified cost analysis presented below, it 

will be seen that pipeline cost will generally not be the main driver of economic feasibility. 

Cost and Performance Impacts of Coal-to-Gas Switching 

The analysis described in this section presents a hypothetical conversion of a boiler from burning 

100% coal to burning varying proportions of gas (10%, 50%, and 100%). The capital cost of 

modifying a coal boiler to switch to natural gas includes the new gas burners and piping, 

combustion air ductwork and control damper modifications, air heater upgrades, gas 

recirculating fans, control systems modifications, and other site-specific modifications, as well as 

any pipeline installation costs that would be necessary to supply the unit’s assumed level of gas 

combustion following the conversion. 

For this analysis, the EPA assumes capital costs for pulverized coal (PC) and cyclone boiler 

modifications are as follows2

: 

$/kW = 267*(75/MW)0.35 (pulverized coal) 

                                                            

1 Based on assumed use of Class 1 igniters (10% of burner capacity) as defined in NFPA 85 Boiler and Combustion

Systems Hazards Code. 2 EPA assumptions on costing and performance associated with coal‐to‐gas conversion and pipeline additions in

this analysis are generally consistent with assumptions presented and discussed in EPA’s power sector modeling

documentation, Chapter 5.7, at: http://www.epa.gov/airmarkets/progsregs/epa‐ipm/docs/v513/Chapter_5.pdf
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$/kW = 374*(75/MW)0.35 (cyclone) 

Based on the above formula, a 500 MW pulverized coal unit would have a capital cost of 

$137/kW to convert the boiler such that it could burn any proportion of natural gas. For this 

illustrative example, to support 100% gas combustion we assume that a 50-mile gas pipeline3

 at 

$50 million, 4 or $100/kW for a 500 MW unit, is also required, which raises the unit’s total 

capital cost for conversion to $237/kW. Black & Veatch also used a similar cost level in a recent 

case study.5

 At a 14.3% capital charge rate6 and 75% annual capacity factor, the total capital cost 

in this example equates to an annualized capital cost of about $5/MWh. This $/MWh capital cost 

is relatively insignificant compared to the increase in fuel cost discussed later. 

Due to a reduced need for operators, maintenance materials, and maintenance staff, fixed O&M 

costs are reduced by 33% as a result of switching from coal to 100% gas. Similarly, variable 

O&M costs are reduced by 25% due to reduced waste disposal, reduced auxiliary power 

requirement, and miscellaneous other costs. 

EPA also assumed for this analysis that there would be no derate in the net EGU output, and 

estimated that the impact on net heat rate for an average unit would be a 3% increase for a switch 

from coal to 100% natural gas firing. The assumed 3% increase in net heat rate is conservative 

compared to the 2% assumption used by Black & Veatch in their previously mentioned case 

study. 

Cost of Fuel 

For this analysis, the EPA uses base case projections for delivered gas prices that are about 

double projected delivered coal prices on average ($2.62/MMBTU for coal, $5.36/MMBTU for 

                                                            

3 Based on EPA analysis, the majority of existing coal units would require less than 50 miles of new gas pipelines to

switch fuels from coal to 100% natural gas.  See Chapter 5 and Table 5‐22 of Documentation for EPA Base Case

v.5.13 Using the Integrated Planning Model, available at: http://www.epa.gov/airmarkets/progsregs/epa‐

ipm/BaseCasev513.html

4 For those plants that require additional pipeline capacity, the average capital cost of constructing new pipelines is

assumed to be approximately $1 million per mile of pipeline built, which is consistent with assumptions used in

EPA’s IPM modeling. 5 A Case Study on Coal to Natural Gas Fuel Switch, Black & Veatch, Power‐Gen International, December 2012,

available at http://bv.com/redirects . 6 Capital charge rate at 14.3% is the average of the regulated utility and unregulated merchant rates as used in

IPMv5.13 for environmental retrofits having a 15 year book life.  

Comment [A2]: Are there any references to cite

for fixed O&M and variable O&M cost reductions?

Comment [A3]: Suggest providing further

support for this choice. What is the sensitivity to

conclusions from this decision?
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gas).7 As a result, the fuel cost for a typical converted boiler burning 100% gas is expected to be 

at least double its prior fuel cost on an output basis as well ($27/MWh for coal, $57/MWh for 

gas).8,9 Compared to the estimated $5/MWh capital cost impact presented above, a $30/MWh 

increase in fuel cost would make the difference in fuel costs the most significant driver of project 

economics when switching from coal to gas in a coal boiler.10 This difference would increase 

with higher gas prices, which would be projected to result from an increase in overall gas 

demand caused by widespread adoption of gas co-firing. 

Emission Reduction Potential 

The CO2 reduction potential is directly related to the amount of gas co-fired, and is due largely to 

the different carbon intensities of each fuel. More reductions in CO2 rate are achieved at higher 

levels of gas co-firing as shown in Table 6-1. At 10% gas co-firing, the net emissions rate 

(lbs/MWh net) of a typical unit would decrease by approximately 4%. At 100% gas co-firing, 

the net emissions rate (lbs/MWh net) of a typical unit would decrease by approximately 40%. 

Table 6-1. CO2 Rates at Various Levels of Natural Gas Co-Firing 

Case Heat Rate

(Btu/kWh)

CO2 Rate

(lbs/MWh net)

Reduction in CO2 Rate from 100% Coal

(lbs/MWh net)

100% Coal 10,340 2,108 N/A

10% Gas 10,370 2,021 4%

50% Gas 10,490 1,673 21%

100% Gas 10,640 1,239 41%

In addition to reducing CO2 emissions, natural gas co-firing at a coal-fired steam EGU will 

generally also reduce criteria air pollution. Reducing CO2 and criteria air pollution will result in 

                                                            

7 EPA Base Case 5.13, projections for 2020    8 This estimated fuel cost also accounts for the decrease in efficiency that results from switching from coal to gas in

a boiler, as well as the decrease in parasitic power consumption. 9 Combusting natural gas using combined‐cycle turbine technology can remain economically attractive

notwithstanding these types of fuel price differentials because combined cycle turbine technology converts a

substantially higher share of the fuel’s heat input into electricity output as compared to boiler technology.  The

$/MWh impact of a higher gas price in that instance is significantly mitigated by higher MWh output produced for

a given amount of heat input from the fuel purchased. 10 This demonstration assumes that the converting boiler in question remains a “price taker” in the fuel

marketplace, such that the projected gas and coal prices would be unaffected by this hypothetical unit’s potential

decision to convert.  However, if enough other units might be expected to make similar conversions, the aggregate

increased demand for natural gas would be likely to further increase the price differential between coal and gas,

making fuel costs an even more influential factor in the evaluation of such a project’s economic merit.

Comment [A4]: Would it be worth mentioning

the potential volatility of natural gas prices (vs. coal

prices)?  

1.http://www.eia.gov/naturalgas/

http://www.eia.gov/coal/

Comment [A5]: Suggest inclusion of heat rate

units.
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climate benefits and human health co-benefits. The impacts of these pollutants on the 

environment and health are discussed in detail in Chapter 5 of the RIA for this proposed rule. 

For this analysis, EPA estimated the PM2.5-related human health co-benefits of SO2, NOX, and 

direct-PM2.5 emission reductions attributable to a range of natural gas co-firing levels at an 

illustrative coal steam unit burning bituminous coal in 2020.11 The estimated monetized cobenefits do not include climate benefits or health effects from direct exposure to NO2, SO2, and 

HAP; ecosystem effects; or visibility impairment. Only the unit-level emissions of SO2, NOX and 

direct-PM2.5 are considered in this illustrative exercise. Additionally, emissions from the 

extraction and transport of the fuels used by these technologies are not considered. Furthermore, 

there may be differences in upstream greenhouse gas emissions (in particular, methane) from 

different technologies but those were not quantified for this assessment. The estimated avoided 

emissions under 10% gas co-firing and a 100% switch to gas are presented Table 6-2. 

Table 6-2. Avoided Emissions at Various Levels of Co-Firing, based on Illustrative Unit 

(lbs/MWh net) 

 

10% 

Gas 

100% 

Gas 

SO2 0.3 3.1 

NOX 0.2 2.04 

PM2.5 0.02 0.2 

To estimate human health co-benefits for this illustrative coal steam unit, the EPA used PM2.5-

related benefit-per-ton estimates for SO2, NOX, and direct-PM2.5 emission reductions described 

in detail in Chapter 5 of the RIA for this proposal. To estimate the benefits associated with cofiring, we determine the emission reductions for co-firing in Table 6-2 and apply the 2020 social 

benefit values discussed in Chapter 5 of the RIA for this proposal. Specifically, we multiply the 

reduction in SO2, NOX, and direct-PM2.5 emissions by the PM2.5-related benefit per-ton estimates, 

and add those values to get a measure of 2020 benefits. Table 6-3 shows the PM2.5-related 

benefits expected based on the estimated emission reductions that would occur in this illustrative 

example. These estimates are purely illustrative as the EPA does not assert a specific location for 

                                                            

11 The illustrative unit in this analysis was assumed to be a 500 MW coal‐steam unit burning bituminous coal with a

heat rate of 10,339 btu/kWh (net) operating at 75% capacity factor.  Furthermore, this unit was assumed to

operate a wet scrubber, cold‐side ESP, and SNCR.
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the illustrative electricity generation technologies and is therefore unable to specifically 

determine the population that would be affected by their emissions. Therefore, the benefits for 

any specific unit can be different than the estimates shown here. 

Table 6-3. Rounded PM2.5-related Co-benefits ($/MWh net) of Gas Co-firing (2011$) 

Health Co‐benefit Discount Rate

   3% Discount Rate 7% Discount Rate

Gas Co‐firing 10% $6.5 to $15 $5.9 to $13

Gas Co‐firing 100% $67 to $150 $61 to $140

Note: All estimates are rounded to two significant figures. Co‐benefits are based on national benefit‐per‐ton estimates for directly emitted

PM2.5 and PM2.5 precursors, SO2 and NOX. It is important to note that the monetized health co‐benefits do not include reduced health effects

from ozone or direct exposure to NO2, SO2, and HAP; ecosystem effects; or visibility impairment. Emissions of directly emitted particles are

disaggregated into EC+OC or crustal components using the method discussed in Appendix.12 5A of the RIA for this proposal. The health co‐

benefits reflect the sum of the PM2.5 co‐benefits and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) to

Lepeule et al. (2012)).

The precise incremental health co-benefits associated with lower emissions would depend 

primarily on the location of the co-firing unit, the specific types of coals that natural gas would 

replace, and the pollution controls installed on that unit. This illustrative assessment is unable to 

account for these characteristics. However, these factors will not change the qualitative 

conclusion. There will always be incremental human health co-benefits associated with co-firing 

natural gas in an existing coal steam boiler, independent of the location, coal type, and operating 

pollution controls. 

A related beneficial use of natural gas in existing coal boilers can be via gas reburning, a NOx 

reduction technology.13 Gas reburning involves firing natural gas (between 10 and 25% of total 

heat input) above the primary combustion zone in the boiler furnace. This upper-level firing 

creates a slightly fuel-rich zone. NOx produced in the primary zone of the furnace is "reburned" 

in this zone and converted to molecular nitrogen and other reduced nitrogenous species. Overfire 

                                                            

12 Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes; Y. Shi, et al. 2009. Extended Follow‐Up and Spatial Analysis

of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140,

Health Effects Institute, Boston, MA.

Lepeule, J.; F. Laden; D. Dockery; J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended

Follow‐Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives, July, 120(7):965‐

70.

13 DOE/NETL 2001, Evaluation of Gas Reburning and Low‐NOx Burners on a Wall‐Fired Boiler, DOE/NETL‐

2001/1143, February 2001, available at

http://www.netl.doe.gov/File%20Library/Research/Coal/major%20demonstrations/cctdp/Round3/GRLNBPPA.pdf
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air is injected downstream of the reburn zone to burn out the remaining combustibles and convert 

the reduced nitrogenous species to molecular nitrogen. The heat input from gas would 

approximately substitute for a similar heat input from coal, thus reducing CO2 and other coal 

emissions in a manner similar to gas co-firing as discussed above. 

Cost of Reductions and Cost Effectiveness 

This analysis examines the average $/tonne14 cost of avoided CO2 that results from applying a 

range of natural gas co-firing levels to a typical baseload coal boiler. We capture the capital 

costs of boiler modifications and new pipeline construction (assuming 50 miles of new 

pipeline),15 decreased FOM and VOM costs, and incremental fuel costs (based on IPMv5.13 

Base Case average delivered fuel price projections for coal and gas in 2020). For a typical coal 

boiler at current base case fuel prices, the average cost of avoided CO2 ranges from $83/tonne for 

100% gas switch to $150/tonne for co-firing at 10% (see Table 6-4). 

Table 6-4. Average Cost of Avoided CO2 and CO2 Emission Rate Reductions from 100% Coal at 

Various Levels of Natural Gas Co-Firing at Base Case Projected Gas Price ($5.36/MMBtu) 

Case Average Cost of Avoided CO2

($/tonne)

Change in CO2 Rate from 100% Coal

(lbs/MWh net)

100% Coal N/A N/A

10% Gas 150 4%

50% Gas 91 21%

100% Gas 83 41%

Note: Based on a typical 500 MW bituminous coal steam unit operating at 75% capacity factor.  Assumes

construction of new 50‐mile pipeline. EPA estimated reduced total capital costs for the 50% and 10% gas cases; for

example, total capital cost for 10% gas was estimated to be about one‐half of the capital costs for the 100% gas

case.

However, widespread adoption of gas co-firing would increase overall gas demand and place 

upward pressure on the natural gas price, which would consequently increase the average cost of 

avoided CO2 of a potential boiler conversion. 

   

                                                            

14 This document uses “tonne” to refer to a metric tonne.  All control costs in this analysis are presented in dollars

per metric tonne, or “$/tonne.” 15 Based on EPA analysis, the majority of existing coal units would require less than 50 miles of new gas pipelines to

switch fuels from coal to 100% natural gas.  See Chapter 5 and Table 5‐22 of Documentation for EPA Base Case

v.5.13 Using the Integrated Planning Model, available at: http://www.epa.gov/airmarkets/progsregs/epa‐

ipm/BaseCasev513.html

Comment [A6]: What is the sensitivity to your

gas price assumptions?  
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Conclusion 

Switching from coal to gas is a relatively costly approach to CO2 reductions at existing coal 

steam boilers when compared to other measures such as heat rate improvements and re-dispatch 

of generation supply to other existing capacity with lower CO2 emission rates. Moreover, this 

analysis shows that coal-to-gas conversion of an existing boiler is less efficient than constructing 

a new natural gas combined cycle (NGCC) turbine in its place. For example, replacing the coal 

steam plant discussed above with a new NGCC facility would reduce the net CO2 emission rate 

of the generating capacity by 62% at a cost of about $50/tonne of avoided CO2 under the base 

case projected gas price and about $81/tonne of avoided CO2 at a future gas price 50% higher 

than the base case projection. [INSERT CROSS-REFERENCE TO LOCATION IN RULE 

MATERIALS WHERE WE EXPLAIN WHY WE ARE NOT CONSIDERING NEW NGCC 

POTENTIAL TO REPLACE EXISTING COAL AS A REDUCTION MEASURE FOR 

CRAFTING STATE GOALS.] 

The EPA is considering cost-effectiveness at a national level for the purpose of setting emissions 

goals consistently in each state. While this analysis suggests that cost-effective reductions of 

CO2 are not available on a national basis from widespread adoption of natural gas co-firing, it 

does not preclude the potential for individual EGUs to utilize co-firing as a way to reduce CO2

and other emissions, nor does it preclude states from factoring in that unit-level potential into the 

design of state plans for compliance with the 111(d) standard. EPA notes that there are utilities 

that see merit in converting some existing coal units to burn 100% gas, and several are currently 

doing so.16,17

   

                                                            

16 Reuters 2014, “Southern to repower three Alabama coal power plants with natgas,” Reuters U.S. Edition,

January 16, 2014  , available at http://www.reuters.com/article/2014/01/16/utilities‐southern‐alabama‐

idUSL2N0KP1WA20140116

17 Dominion 2012, “Dominion Virginia Power Proposes To Convert Bremo Power Station From Coal To Natural

Gas,” Dominion News, September 5, 2012, available at http://dom.mediaroom.com/2012‐09‐05‐Dominion‐

Virginia‐Power‐Proposes‐To‐Convert‐Bremo‐Power‐Station‐From‐Coal‐To‐Natural‐Gas

Comment [A7]: Is there any reference to cite for

these numbers?

Comment [A8]: please fix.
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Biomass Co-firing 

Introduction 

Co-firing biomass in existing boilers designed for coal-fired generation is another approach to 

potentially reduce the output-based CO2 emissions rate (lbs/MWh) of these boilers. In the 

analysis presented in this technical support document, the physical CO2 emissions rate at the 

boiler stack could increase or decrease, depending on the amount of coal energy replaced by 

biomass energy and differences in the properties of a selected biomass and the coal it replaces.18 

There are many possible combinations of coals and biomass types that could be co-fired. Sitespecific economics and accessibility would determine which combinations might actually be 

feasible. This TSD analysis does not attempt to estimate an economically feasible national 

average increase or decrease in CO2 emission rate via biomass co-firing. Instead, this analysis 

simply employs one reasonably representative case to evaluate the cost effectiveness of biomass 

energy substitution in reducing the physical CO2 emission rate based only on the CO2 coming 

from coal. This analysis indicates that while the co-firing of biomass with coal is technically 

feasible as a means of reducing the coal-based CO2 emission rate due to the substitution of 

biomass for coal, it has limited economic feasibility due to the generally higher cost of energy 

from biomass as compared to coal. This general finding largely explains the very limited amount 

of biomass co-firing currently practiced in the U.S. It is also consistent with findings by others, 

including a study by the State of Maryland19 that concluded as follows: 

“Due to the higher cost of biomass fuels when compared to coal, cofiring with biomass 

will lead to an increase in fuel costs. Without consideration for any environmental 

benefits, it is unlikely that any Maryland coal-fired facility would make the investments 

required to cofire with a more expensive and less efficient fuel.” 

Based on the basic analysis of the cost effectiveness of biomass energy substitution in reducing 

the physical CO2 emission rate based only on the CO2 coming from coal presented below, the 

                                                            

18 Fuel properties particularly affecting relative CO2 emission rates are: higher heating value, carbon and hydrogen

contents, and as‐fired moisture content.    19 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006, (pg 53)

http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf

Comment [A9]: You might also mention coal

plants that have been retired (i.e. decommissioned)

or smaller facilities that have been converted

entirely to biomass (i.e. Schiller, NH and others).

Comment [A10]: Maryland is not representative

of biomass co‐firing across the United States,

neither is a 2006 study. For example, limiting to this

case ignores coal plants that have been retired or

facilities that have been converted entirely to

biomass (i.e. Schiller, NH and others).

Comment [A11]: : Biomass will be more

economically feasible with an implicit carbon tax on

fossil emissions, as well as increased recognition of

the other air quality benefits (e.g. lowered toxic

emissions, etc.) that could be realized by cofiring.

Indeed the co‐firing of biomass is much more

prevalent than is CCS, which EPA has described in

more generous terms in the 111(b) rulemaking. It

also ignores one of the major economic barriers to

biomass co‐firing which has been the difficulty/lack

of desire/cost to open up Clean Air Act permits to  

allow biomass co‐firing where that was not part of

the original permitting proposal.
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EPA concludes in this TSD that biomass co-firing would not be a cost-effective measure on 

which to base state goals. 20 

This TSD’s analysis indicates that while the co-firing of biomass with coal is technically 

feasible, and in some cases it can yield increases in the stack emission rate of CO2 (lb/MWh). In 

the absence of this rulemaking or a program that puts an implicit price on fossil carbon biomass 

co-firing ) and has limited economic feasibility given the delivered cost of available biomass 

versus the cost of coal. However, the use of co-firing biomass to help meet the 111(d) state goals 

may be economically feasible if the cost of reducing fossil carbon emissions is high. For 

example, Europe is currently co-firing substantial amounts of biomass to help lower carbon 

emissions of their fossil fuel fired electricity sector (Ehrig and Behrendt 2013). That study notes 

that co-firing biomass is one of the lower cost options to reach the EU-2020 targets.

Description of Technology 

Engineering/Economic Considerations 

The technical feasibility of biomass co-firing in existing coal-fired boilers has been thoroughly 

investigated in many research and engineering studies, as well as in test burns at coal power 

plants in the U.S. and globally.21 It has been demonstrated that the boiler and related systems of 

almost any existing coal-fired EGU can accept or be modified to support co-firing of at least 

some small percentage of biomass. In some cases, major modifications can be made to support a 

switch to 100% biomass. 

A decision to actually modify an existing coal-fired boiler for biomass co-firing at any 

percentage level depends on numerous technical and economic factors, including reliable 

                                                            

20 This analysis does not include evaluation of stack biogenic CO2 emissions relative to the net landscape and

process‐related carbon fluxes associated with the production and use of the biogenic feedstocks combusted. Issues

related to methods for assessing biogenic CO 2 emissions from stationary sources are currently being evaluated by

the EPA. In general, the overall net atmospheric contribution of CO 2 resulting from the use of a biogenic feedstock

by a stationary source, such as an EGU, will ultimately depend on the stationary source process and the type of

feedstock used, as well as the conditions under which that feedstock is grown and harvested. In September 2011,

the EPA submitted a draft Accounting Framework to the Science Advisory Board (SAB) Biogenic Carbon Emissions

(BCE) Panel for peer review. The SAB BCE Panel delivered its Peer Review Advisory to the EPA on September 28,

2012. In its Advisory, the SAB recommended revisions to the EPA's proposed accounting approach, and also noted

that biomass cannot be considered carbon neutral a priori, without an evaluation of the carbon cycle effects

related to the use of the type of biomass being considered.   21 See Partial Bibliography – Biomass Co‐firing at end of this section.

Comment [A12]: For example, Schiller, NH

facility.
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availability of suitable biomass at an economic cost; adequate onsite space for biomass receiving, 

storage, preparation, and handling systems; potential corrosive effects of biomass ash in the 

boiler furnace; potential impacts of co-firing on boiler efficiency even at low biomass 

percentages, and the likely reduction (derate) in unit generating output at very high biomass 

percentages. 

There are considerable physical differences between coal and biomass that will generally limit 

the extent to which biomass can be reasonably used to replace coal in a boiler. For example, 

compared to most coals, many solid biomass fuels have both a significantly higher as-fired 

moisture content and a significantly lower heating value per unit of weight. Most solid biomass 

fuels are also significantly less dense than most coals. For example, a typical biomass might have 

twice the moisture, half the heating value, and less than half the density of coal.22 Important 

consequences of these physical differences are that the weight of biomass needed to provide a 

given amount of heat energy could be twice the weight of the coal it replaces, and the volume 

(cubic feet) of biomass needed could be four-to-eight times the volume of coal replaced. 

Biomass requires space for storage after delivery to a facility, and the length of time that the 

biogenic material would remain on site prior to use can differ. For example, wood chips could 

be delivered year-round while crop residue delivery would follow specific seasons in which the 

crop was grown. As noted above, the four-fold or greater increase in volume occupied by 

biomass relative to coal means that the necessary additional storage space could be large.

However, if pelletized or torrefied biomass is used, these concerns may not apply, recognizing 

that such pre-preperations of the feedstock could entail additional costs. Stored biomass can be at 

even greater risk of spontaneous combustion than stored coal; this may limit the safe height of 

biomass piles and further increase storage area requirements.23

The volumetric differences alone can have other unexpected consequences. For this analysis, 

experienced EPA engineering staff estimated that a 500 MW baseload coal plant co-firing 10% 

biomass and receiving biomass deliveries 10 hours per day and 5 days per week would require a 

20-ton truck delivery to the plant every 10 minutes, in addition to the ongoing coal deliveries. 

                                                            

22 Biomass Energy Data Book‐ Edition 4, October 2012, DOE‐EERE‐ORNL,  http://cta.ornl.gov/bedb/index.shtml 23 Properties of Wood Waste Stored for Energy Production, Purdue University, 2011,

http://www.extension.purdue.edu/extmedia/ID/ID‐421‐W.pdf  

Comment [A13]: A commenter suggest that EPA

could do the following here:

elaborate on the challenges of preparing biomass

fuel for cofiring in pulverized systems (i.e. in

reducing biomass fuel particle size to close to

pulverized coal size).  

consider the influence of biomass on the potential

markets for flyash in concrete.  

mention the need for fuel drying systems for many

types of biomass.

Comment [A14]: There is extensive effort and

additional research being done to mitigate these

concerns through pre‐preparation of the feedstock.

Wood pellets and low rank coal may have

approximately the same energy density, and

depending on the cost structure of the feedstock

supply chain, the reduced transportation and

storage benefits could outweigh the preparation

costs, particularly for cases like torrefaction that

may produce economic co‐products.  
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Limiting traffic issues may arise in some situations. Also, because of the low energy density of 

biomass and its relatively higher transportation cost per unit of delivered energy, it may only be 

economically viable to transport biomass a limited distance from where it is grown. This could 

limit the both the percentage of biomass co-firing in a single boiler and the maximum MW 

output from biomass at a single site. New technologies under development, such as torrefaction 

of biomass, but could mitigate some of these transportation, storage and energy content concerns, 

but are not yet commercially available.

The relatively higher moisture content and lower heat content of biomass reduces boiler 

efficiency, and typically requires a derating in unit generation at very high co-firing percentages 

as furnace volume and boiler fan capacities become inadequate. 

For all of the above reasons, the EPA assumed for this analysis that coal-steam EGU boilers will 

generally only co-fire with biomass to a limited degree. While the actual level at which any 

plant can co-fire with biomass is highly site-specific, this analysis adopts the assumption used in 

EPA’s fleet wide IPM modeling of the electric power sector: a reasonable average limit on 

biomass co-firing is up to 10% on any single boiler, not to exceed 50 MW total biomass powered 

output at an individual plant site. This amount of co-firing has been used as representative 

practical limit in other studies as well.24 But EPA notes that higher percentages of co-firing and 

at higher levels may be available in other cases as have been noted in many other studies. 

For example, Europe’s 2nd biggest coal-fired power plant is being converted to burn wood 

pellets imported from the United States and Canada to power half of the plants 4 gigawatt 

capacity. The 700 million Euro project will “transform Drax from the largest carbon emitter by 

site in UK to one of the largest clean-energy plants in the world.” (Financial Times, August 14, 

2013). Similarly, in Canada the potential to reduce coal generation through co-firing is assumed 

to be 20 to 30%, much higher than IPM’s assumptions (See Canadian Clean Power Coalition 

http://www.canadiancleanpowercoalition.com/files/7813/2621/8340/Append ix%20C.pdf). As 

such EPA specifically requests for comment on data to support higher levels of biomass co-firing 

in the EPA’s IPM model and for this rulemaking.

Costs and Performance Impacts of Retrofitted Biomass Co-firing 

                                                            

24 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006,

http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf

Comment [A15]: The use of sawmill residues,

when avaialabe, could provide a high quality

feedstock at potentially lower moisture content (i.e.

planer shavings). A high quality feedstock could

justify greater transportation distances (perhaps

75+ miles)

Comment [A16]: : Might mention, for

perspective, that 50MW is in line with some of the

largest standalone biomass powered electrical

facilities in the US. Agreed that 50 MW upper limit is

realistic. However if EU cofiring practices were

adopted in the US, this could exceed 100MW at an

individual site.
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For this analysis the EPA adopted capital and O&M costs, and performance impacts for 

retrofitted biomass co-firing capability that are approximately representative of EPA assumptions 

used in its IPM modeling and discussed in the documentation for IPM v.5.13.25

EPA estimated that the capital cost to install 50 MW of biomass co-firing capability would be at 

least $10 million.26 As applied to a 500 MW coal unit, the minimum cost of this 10% co-firing 

capability would then be $20/kW. Fixed O&M cost was estimated by EPA engineering staff to 

be 10% greater than with coal alone, and variable O&M cost was estimated to remain 

unchanged. 

The heat rate impact (Btu/kWh) of 10% biomass co-firing as estimated by EPA engineering staff 

for this analysis was an increase of slightly more than 1% compared to coal alone. At low 

biomass cofiring rates, this factor slightly affects calculated biomass fuel consumption and any 

associated CO2 emission from biomass. 

Cost of Fuel 

For this analysis, the EPA uses a delivered biomass cost of $4/MMBtu, representative of 

delivered woody crops grown specifically for energy-generating combustion,27 and roughly 50% 

greater than IPM projected 2020 average delivered coal costs.28 This analysis also considers a 

sensitivity scenario assuming a higher $6/MMBtu biomass price. 

The EPA recognizes that the cost of biomass is highly site-specific, and in some cases could be 

largely comprised of collection and transportation cost (as is the case for opportunity fuels with 

little to no other market value). The transportation component depends primarily on the distance 

that biomass needs to be transported. For example, the EPA engineering staff estimate that for a 

one-way distance of 50 miles with a 20-ton semi-trailer truck, transportation costs could be $10-

20/ton. For biomass at a total delivered price of $4/MMBtu with an indicative heating value of 

5,000 Btu/lb (higher heating value (HHV) basis), transportation cost in this example case could 

                                                            

25 See Sec 5.3, pg 5‐19 at: http://www.epa.gov/airmarkets/progsregs/epa‐ipm/docs/v513/Chapter_5.pdf 26 Generally consistent with EPA assumptions in IPM modeling; also see the following source using the same

retrofit capital cost assumption: Cofiring Biomass and Coal for Fossil Fuel Reduction and Other Benefits – Status of

North American Facilities in 2010, USDA, August 2012, http://www.fs.fed.us/pnw/pubs/pnw_gtr867.pdf 27 Average biomass price as projected by EPA modeling in IPMv5.13 Base Case 28 EIA, Electric Power Annual 2012 – Electricity (Table 7.4)

http://www.eia.gov/electricity/annual/html/epa_07_04.html

Comment [A17]: Should this number be

$200/kW? ($200/kW) x (1,000 kW/MW) x (50 MW)

=  $10 million

Comment [A18]: There are some assumptions in

this section that state “estimated by EPA staff”.

Suggest providing further support if possible.

Comment [A19]: This is intriguing, and it might

be worthwhile providing more detail about this

(perhaps in a separate Table)‐ especially given the

emphasis on heat rates in chapter 2.

Comment [A20]: It might be worth

distinguishing transportation costs for plantation

grown biomass vs. forest harvesting residues vs.

wood products manufacturing residues.
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account for 25-50 percent of the total delivered biomass cost. In any case, it is the total delivered 

price of biomass on a $/MMBtu basis that will primarily determine the economic feasibility of 

biomass co-firing. 

Emission Reduction Potential 

The CO2 reduction potential of biomass co-firing is directly related to the amount and type of 

biomass co-fired and is due to the difference in heating value, moisture content and 

hydrogen/carbon ratios29 for a selected biomass fuel compared to the particular coal it replaces. 

The types of biomass typically available to EGUs in the United States include woody-based 

feedstocks such as wood chips, forest industry byproducts, and to a lesser degree agricultural 

crop residues, as well as emerging dedicated energy crops such as switchgrass and short-rotation 

woody crops.30 In general, when comparing coal-only versus co-firing coal with biomass, cofiring may result in either an increase or decrease in the stack CO2 emission rate. The extent to 

which biogenic emissions can reduce fossil emissions is being considered in EPA’s current study 

on the framework for accounting for biogenic emissions. European studies show that biomass 

can significantly displace fossil emissions in a co-fired plant. EPA specifically requests for 

comment on the degree to which biomass can be co-fired to reduce fossil emissions in the United 

States.

Cost of Reductions and Cost Effectiveness 

In order to evaluate cost-effectiveness of potential reductions, the EPA first estimated the cost of 

avoided coal CO2 emissions in a hypothetical scenario where biomass CO2 emissions are not 

included in total stack CO2 emissions (in effect, biogenic CO2 emissions are subtracted from 

total CO2 emissions measured at the stack). The estimated results presented below are based on 

a reasonably representative case using a baseload bituminous coal-fired boiler with a net heat 

rate of 10,340 btu/kWh that shifts from 100% bituminous coal to 90% coal and 10% biomass 

(assuming fuel prices of $2.62/MMBtu for coal in 2020 as projected in IPMv5.13 Base Case and 

$4/MMBtu for biomass as explained above). When biogenic stack emissions are not counted as 

                                                            

29 IFRF Combustion Handbook, Combustion File No. 23, What is Biomass? (Van Krevelen Diagram),

http://www.handbook.ifrf.net/handbook/cf.html?id=23 30 Biomass Combined Heat and Power Catalog of Technologies, U.S. EPA, September 2007,  

http://www.epa.gov/chp/documents/biomass_chp_catalog.pdf

Comment [A22]: Shouldn’t this at least include a

discussion of the potential net/lifecycle emissions

benefits of utilizing biomass.

Comment [A21]: However, isn’t it safe to say

that most studies indicate a decrease in CO2

emissions with increasing biomass amounts.  

Example‐ Mann and Spath 2001.

http://link.springer.com/article/10.1007%2Fs10098

0100109
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part of total emissions, the cost of avoided CO2 for a “typical” baseload coal boiler co-firing 10% 

biomass is $30/tonne. This estimated cost of avoided coal CO2 emissions would increase if some 

portion of the biogenic CO2 emissions from the co-fired biomass were included. At higher 

delivered fuel price differentials, the cost of avoided coal CO2 emissions would increase (for 

example, at a biomass price of $6/MMBtu, cost of avoided CO2 is $80/tonne if CO2 emissions 

from biomass are not counted). Similarly, the costs of avoided coal CO2 emissions would 

decrease at lower delivered biomass prices associated with waste and residue materials, at higher 

coal prices than assumed here, or if other means of GHG reductions were less available. 

Conclusion 

Replacing some coal with low levels of biomass co-firing may can result in stack CO2 emission 

increases and the degree to which combined heat and power are accounted for in energy 

demand.

31 Even if biogenic CO2 emissions are not counted as part of stack emissions, 

bBiomassbiomass co-firing can be is a relatively costly cost-effective approach to CO2

reductions at existing coal steam boilers in certain circumstances, such as availability of 

inexpensive biomass supplies, costly alternatives to improve heat rates, and inability to use redispatch of generation supply. In all cases, though, an efficient policy would seek to equalize 

marginal costs of abatement and not average costs of abatement. As such some level of biomass 

co-firing will likely be efficient where feedstock supply costs are below the average stated 

herewhen compared to other measures such as heat rate improvements and re-dispatch of 

generation supply to other existing capacity with lower CO2 emission rates.

The EPA is considering cost-effectiveness at a national level for the purpose of setting emissions 

goals consistently in each state. While this analysis concludes that cost-effective reductions of 

CO2 are not available on a national basis from widespread adoption of biomass co-firing, it does 

not preclude the potential for individual EGUs to utilize co-firing as a way to reduce overall CO2

                                                            

31 Depending on biogenic feedstocks used and whether or not an assessment system is applied that evaluates

biogenic CO2 emissions from the stack in relation to the terrestrial carbon cycling associated with the production

and use of that biogenic feedstock.
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emissions, nor does it preclude states from factoring in that unit-level potential into the design of 

state plans for compliance with the 111(d) standard.32 

Partial Bibliography for Technical Feasibility of Biomass Co-firing 

(a) Briggs, J. and J. M. Adams, Biomass Combustion Options for Steam Generation, Presented at 

Power-Gen 97, Dallas, TX, December 9 – 11, 1997. 

(b) Grusha, J and S. Woldehanna, K. McCarthy, and G. Heinz, Long Term Results from the First 

US Low NOx Conversion of a Tangential Lignite Fired Unit, presented at 24th International 

Technical Conference on Coal & Fuel Systems, Clearwater, FL., March 8 – 11, 1999. 

(c) EPRI, Biomass Co-firing: Field Test Results: Summary of Results of the Bailly and Seward 

Demonstrations, Palo Alto, CA, supported by U.S. Department of Energy Division of Energy 

Efficiency and Renewable Energy, Washington D.C.; U.S. Department of Energy Division 

Federal Energy Technology Center, Pittsburgh PA; Northern Indiana Public Service Company, 

Merrillville, IN; and GPU Generation, Inc., Johnstown, PA: 1999. TR-113903. 

(d) Laux S., J. Grusha, and D. Tillman, Co-firing of Biomass and Opportunity Fuels in Low NOx 

Burners, PowerGen 2000 - Orlando, FL 

(e) Tillman, D. A., Co-firing Biomass for Greenhouse Gas Mitigation, presented at Power-Gen 

99, New Orleans, LA, November 30 – December 1, 1999. 

(f) Tillman, D. A. and P. Hus, Blending Opportunity Fuels with Coal for Efficiency and 

Environmental Benefit, presented at 25th International Technical Conference on Coal Utilization 

& Fuel Systems, Clearwater, FL., March 6 – 9, 2000 

(g) Tillman D A, Harding N S (2004) Fuels of opportunity: characteristics and uses in 

combustion systems. Elsevier, Oxford, UK 

(k) http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf 

(h) Tillman D, Conn R, Duong D (2010) Coal characteristics and biomass cofiring in pulverized 

coal boilers. In: Electric Power, Baltimore, MD, USA, 18 - 20 May, 2010 

                                                            

32 EPA notes that states will need to consider any future EPA finding regarding an assessment framework that

considers carbon fluxes on the biogenic feedstock production landscape applied when evaluating net stack CO2

emissions from biomass co‐firing.

Comment [A23]: Suggest the following 2

references: Spath, P.L.; Mann, M.K.  

Life Cycle Assessment of a Natural Gas Combined‐

Cycle Power Generation System  

http://www.nrel.gov/docs/fy00osti/27715.pdf

Mann, M.K.; Spath, P.L.  

A Life Cycle Assessment of biomass cofiring in a coal

fired power plant.  

https://www.bioenergykdf.net/sites/default/files/z

pdfzholderz/NREL_Dat a/KC_091102094512.pdf  
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(i) Renewable and Alternative Energy Fact Sheet – Co-firing Biomass with Coal, Pennsylvania 

State University, 2010, http://pubs.cas.psu.edu/FreePubs/PDFs/ub044.pdf 

(j) Fernando R, Cofiring High Ratios of Biomass with Coal, IEA Clean Coal Centre (CCC/194), 

January 2012 

(k) DOE-EERE-ORNL, Biomass Energy Data Book- Edition 4, October 2012, 

http://cta.ornl.gov/bedb/index.shtml 
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Chapter 7: Carbon Capture & Storage 

Introduction 

Another possible approach for reducing CO2 emissions from existing fossil fuel-fired 

EGUs is through the application of carbon capture and storage technology (CCS; sometimes also 

referred to as carbon capture and sequestration). In the recently proposed standards of 

performance for new fossil fuel-fired EGUs (79 FR 1430), the EPA proposed to find that the best 

system of emission reduction for new fossil fuel-fired boilers and IGCC units is partial 

application of CCS. In that proposal, the EPA found that, for new units, partial CCS has been 

adequately demonstrated; it is technically feasible; it can be implemented at reasonable costs; it 

provides meaningful emission reductions; and its implementation will serve to promote further 

development and deployment of the technology. This chapter examines the potential for 

implementation of CCS technology at existing fossil fuel-fired utility boilers and IGCC units. 

Carbon Capture Options for Existing Fossil Fuel-fired EGUs 

In general, CO2 capture technologies applicable to existing fossil fuel-fired power 

generation can be categorized into three approaches – (1) post-combustion capture; (2) precombustion capture; and (3) oxy-combustion. Each of these is described and discussed in more 

detail below. 

Post-combustion Capture 

Post-combustion CO2 capture refers to removal of CO2 from a combustion flue gas prior 

to discharging to the atmosphere. Separating CO2 from such a gas stream can be challenging for 

a number of reasons. Because CO2 is a dilute fraction of the combustion flue gas – typically 13-

15 % in coal-fired systems and 3-4 % in natural gas-fired systems – a large volume of flue gas 

must be treated. The flue gas from typical combustion systems is usually at near atmospheric 

pressure. Therefore, most of the available capture systems rely on chemical absorption 

(chemisorption) options (e.g., amines) that require added energy to release the captured CO2 and 

regenerate the solvent. Many of the chemical solvents require a flue gas stream that is free of or 

has very low quantities of components – such as SO2, NOX, and HCl – that can degrade the 

solvent. The captured CO2 must then be compressed from near atmospheric pressure to much 

higher pipeline pressures (about 2,000 psia). 

 
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Pre-combustion Capture 

Pre-combustion capture systems are applicable to fossil fuel gasification power plants 

(i.e., IGCC units) where coal or other solid fossil fuel (e.g., pet coke) is converted into a 

synthesis gas (or “syngas”) by applying heat under pressure in the presence of steam and limited 

O2. The product syngas contains primarily H2 and CO – and, depending on the fuel and 

gasification system – some lesser amount of CO2. The amount of CO2 in the resulting syngas 

stream can be increased by “shifting” the composition via the catalytic water-gas shift (WGS) 

reaction. This process involves the catalytic reaction of steam (“water”) with CO (“gas”) to form 

H2 and CO2. The resulting CO2 contained in the syngas is then captured before combustion of 

the H2-enriched syngas for power generation in a combined cycle system. Contrary to the postcombustion capture flue gas, the IGCC syngas can contain a high volume of CO2 and is 

pressurized. This allows the use of physical absorbents (e.g., SelexolTM, Rectisol®) that require 

much less added energy to release the captured CO2 and require less compression to get to 

pipeline standards. 

Oxy-combustion 

Oxy-combustion systems for CO2 capture rely on combusting coal or other fuels with 

relatively pure O2 diluted with recycled CO2 or CO2/steam mixtures. Under these conditions, the 

primary products of combustion are water and CO2, with the CO2 purified by condensing the 

water. Challenges associated with oxy-combustion include the capital cost and energy 

consumption for a cryogenic air separation unit (ASU) to produce oxygen, introduction of N2 via 

boiler air infiltration, and excess O2 in the CO2 product stream. 

CO2 Transportation and Storage 

CO2 Pipeline Infrastructure 

Carbon dioxide has been transported via pipelines in the U.S. for nearly 40 years. 

Approximately 50 million metric tons of CO2 are transported each year through 3,600 miles of 

pipelines. Moreover, a review of the 500 largest CO2 point sources in the U.S. shows that 95 

percent are within 50 miles of a possible geologic sequestration site, which would lower 

transportation costs. There are multiple factors that contribute to the cost of CO2 transportation 

via pipelines including but not limited to: availability and acquisition of rights-of-way for new 

pipelines, capital costs, operating costs, length and diameter of pipeline, terrain, flow rate of 

CO2, and the number of sources utilizing the pipeline. 
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Geologic Storage 

 Existing project and regulatory experience, research, and analogs (e.g. naturally existing 

CO2 sinks, natural gas storage, and acid gas injection), indicate that geologic sequestration is a 

viable long term CO2 storage option. The viability of geologic sequestration of CO2 is based on a 

demonstrated understanding of the fate of CO2 in the subsurface. Geologic storage potential for 

CO2 is widespread and available throughout the U.S. and Canada. Nearly every state in the U.S. 

has or is in close proximity to formations with carbon storage potential including vast areas 

offshore. Estimates based on DOE studies indicate that areas of the U.S. with appropriate 

geology have a storage potential of 2,300 billion to more than 20,000 billion metric tons of CO2

in deep saline formations, oil and gas reservoirs and un-mineable coal seams.1

 Other types of 

geologic formations such as organic rich shale and basalt may also have the ability to store CO2; 

and the DOE is currently evaluating their potential storage capacity. 

 Further evidence of the widespread availability of CO2 storage reserves in the U.S. 

comes from the Department of Interior’s U.S. Geological Survey (USGS) which has recently 

completed a comprehensive evaluation of the technically accessible storage resource for carbon 

storage for 36 sedimentary basins in the onshore areas and State waters of the United 

States.2

 The USGS assessment estimates a mean of 3,000 billion metric tons of subsurface CO2

storage potential across the United States. For comparison, this amount is 500 times the 2011 

annual U.S. energy-related CO2 emissions of 5.5 Gigatons (Gt).3

Enhanced Oil Recovery (EOR) 

Geologic storage options also include use of CO2 in EOR, which is the injection of fluids 

into a reservoir to increase oil production efficiency. EOR is typically conducted at a reservoir 

after production yields have decreased from primary production. EOR using CO2, sometimes 

referred to as ’CO2 flooding’ or CO2-EOR, involves injecting CO2 into an oil reservoir to help 

mobilize the remaining oil and make it available for recovery. The crude oil and CO2 mixture is 

produced, and sent to a separator where the crude oil is separated from the gaseous hydrocarbons 

                                                            

1

 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S Department of Energy, Office of 

Fossil Energy, National Energy Technology Laboratory (NETL). 2

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Results: U.S. Geological Survey Circular 1386, 41 p., 

http://pubs.usgs.gov/fs/2013/1386/. 3

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Summary: U.S. Geological Survey Factsheet 2013-3020, 

6p.http://pubs.usgs.gov/fs/2013/3020/.
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and CO2. The gaseous CO2-rich stream then is typically dehydrated, purified to remove 

hydrocarbons, recompressed, and re-injected into the oil or natural gas reservoir to further 

enhance recovery. 

CO2-EOR has been successfully used at many production fields throughout the U.S. to 

increase oil recovery. The oil and natural gas industry in the United States has over 40 years of 

experience of injection and monitoring of CO2 in the deep subsurface for the purposes of 

enhancing oil and natural gas production. This experience provides a strong foundation for the 

injection and monitoring technologies that will be needed for successful deployment of CCS.

Evaluation of Retrofit CCS as BSER for Existing Fossil Fuel-fired EGUs 

Technical Feasibility 

In evaluating partial CCS as the BSER for new fossil fuel-fired boilers and IGCC units, 

the EPA determined that the technology is feasible and adequately demonstrated for new units 

because the major components of CCS – the capture, the transportation, and the storage – are all 

proven technologies that have been demonstrated at large scale. While the EPA found that partial 

CCS is technically feasible for new fossil fuel-fired boilers and IGCC units, it is much more 

difficult to make that determination for the entire fleet of existing fossil fuel-fired EGUs. 

Developers of new generating facilities can select a physical location that is more amenable to 

CCS – such as a site that is near an existing CO2 pipeline or an existing oil field. Existing sources 

do not have the advantage of pre-selecting an appropriate location. Some existing facilities are 

located in areas where CO2 storage is not geologically favorable and are not near an existing CO2

pipeline. Developers of new facilities also have the advantage of integrating the partial CCS 

system into the original design of the new facility. Integrating a retrofit CCS system into an 

existing facility is much more challenging. Some existing sources have a limited footprint and 

may not have the land available to add partial CCS system. Integration of the existing steam 

system with a retrofit CCS system can be particularly challenging. 

Partial CCS has been demonstrated at existing EGUs. It has been demonstrated at a pilotscale at Southern Company’s Plant Barry; it is being installed for large-scale demonstration at 

NRG’s WA Parish facility; and it will very soon be applied at commercial-scale as a retrofit at 

SaskPower’s Boundary Dam coal-fired EGU in Canada. However, all of these facilities are 

located in areas that are either near an existing oil field or in an area that is geologically 

favorable for CO2 storage. Thus, at some existing facilities, the implementation of partial CCS 
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may be a viable GHG mitigation option and some utilities may choose to pursue that option. 

However, the EPA does not believe that it can serve as the best system of emission reduction for 

a broadly applicable GHG mitigation program. Therefore, the EPA does not propose to find that 

CCS is a component of the best system of emission reduction for CO2 emissions from existing 

fossil fuel-fired EGUs.

Reasonableness of Cost 

In the proposed standard of performance for new fossil fuel-fired EGUs (79 FR 1430), 

the EPA found that the costs to implement partial CCS (to a level to meet the proposed emission 

standard of 1,100 lb/MWh-gross) were consistent with costs for other non-natural gas-fired 

generating technologies – such as nuclear, biomass and geothermal – that utilities are considering 

for new intermediate and base load generating capacity. 

The EPA also noted in the proposal, that most of the relatively few new projects that are 

in the development phase are already planning to implement CCS; and, as a result, the standard 

would not have a significant impact on nationwide energy prices. In contrast, the EPA noted that 

identifying CCS as BSER for new natural gas-fired stationary combustion turbines, would likely 

result in higher nationwide electricity prices and could adversely affect the supply of electricity 

since the majority of new fossil-fuel fired power generation is projected to be natural gas 

combined cycle (NGCC). An emission standard for existing units based on CCS (or even partial 

CCS) would most certainly have an even more significant effect on nationwide electricity prices 

and could affect the reliability of the supply of electricity. Therefore, we do not find that the cost 

to implement existing source emission standards to be reasonable, which further supports the 

determination that CCS is not an appropriate component of the best system of emission reduction 

for CO2 emissions from existing fossil fuel-fired EGUs.

Emission Reductions and Promotion of Advanced Technology 

An emission standard for existing units based on CCS (or even partial CCS) would 

clearly result in significant emission reductions and would certainly serve to promote further 

deployment, development and improvement in the most advanced technology. However, the 

EPA has determined that such an emission standard is not technically feasible in all cases and 

cannot be done at a reasonable cost at this time. 

Comment [A1]: This comment is quite

problematic and incorrect, and not consistent with

the language agreed to in the NSPS discussions.  

Recommend EPA cut and paste the NSPS language

here related to NG CCS not currently being the best

system of emission reduction at present, but not

this other text regarding nationwide energy prices

and grid reliability.